Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
sequencelengths 1
91
| keywords
sequencelengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
Inspiratory breathing movements depend on pre-Bötzinger complex ( preBötC ) interneurons that express calcium ( Ca2+ ) -activated nonselective cationic current ( ICAN ) to generate robust neural bursts . Hypothesized to be rhythmogenic , reducing ICAN is predicted to slow down or stop breathing; its contributions to motor pattern would be reflected in the magnitude of movements ( output ) . We tested the role ( s ) of ICAN using reverse genetic techniques to diminish its putative ion channels Trpm4 or Trpc3 in preBötC neurons in vivo . Adult mice transduced with Trpm4-targeted short hairpin RNA ( shRNA ) progressively decreased the tidal volume of breaths yet surprisingly increased breathing frequency , often followed by gasping and fatal respiratory failure . Mice transduced with Trpc3-targeted shRNA survived with no changes in breathing . Patch-clamp and field recordings from the preBötC in mouse slices also showed an increase in the frequency and a decrease in the magnitude of preBötC neural bursts in the presence of Trpm4 antagonist 9-phenanthrol , whereas the Trpc3 antagonist pyrazole-3 ( pyr-3 ) showed inconsistent effects on magnitude and no effect on frequency . These data suggest that Trpm4 mediates ICAN , whose influence on frequency contradicts a direct role in rhythm generation . We conclude that Trpm4-mediated ICAN is indispensable for motor output but not the rhythmogenic core mechanism of the breathing central pattern generator .
Inspiratory breathing movements in mammals emanate from neural rhythms of the pre-Bötzinger complex ( preBötC ) in the lower brainstem [1–4] . If preBötC excitability is sufficiently high , then its constituent interneurons burst synchronously and drive motor output [5 , 6] . Calcium ( Ca2+ ) -activated nonselective cation current ( ICAN ) may play a substantial role by generating the depolarization underlying inspiratory bursts , i . e . , the inspiratory drive potential [7 , 8] . ICAN-mediated inspiratory drive potentials are evoked by excitatory synaptic inputs and intrinsic Ca2+ signaling in the context of rhythmic network activity [7–12] , but whether inherently part of rhythmogenesis or effectuating motor pattern , the role ( s ) of ICAN remain incompletely understood . The ion channels that give rise to ICAN have not been identified , but transient receptor potential ( Trp ) channels are likely candidates . There are 28 different Trp subtypes , subdivided into seven subfamilies , which broadly share structural similarity but vary in their permeability to mono- and divalent cations as well as their activation mechanisms . Widely expressed in the nervous system , Trp channels mediate phototransduction , thermosensation , gustation , nociception , and a range of homeostatic functions [13–16] . A limited subset of Trps from the Trpm ( M for melastatin ) and Trpc ( C for canonical ) subfamilies are implicated in respiration . We originally posited that either Trpm4 or Trpm5 ( or both ) gave rise to ICAN in the preBötC [8 , 17] because they form flufenamic acid ( FFA ) -sensitive 24-picosiemens ( pS ) conductance monovalent cation channels , which are gated by intracellular Ca2+ transients and modulated by phosphoinositides [18–20] . However , 24-pS ion channel activity recorded in sync with fictive inspiration in preBötC neurons is sensitive to ATP−4 [21 , 22] . Trpm4 , but not Trpm5 , is sensitive to blockade by highly charged polyatomic anions like ATP−4 [23–26] , which is only consistent with Trpm4 in the preBötC . As further confirmation that Trpm4 , rather than Trpm5 , is involved with respiratory rhythm and pattern , we recently showed that Trpm4 , but not Trpm5 , transcripts are expressed in excitatory preBötC neurons [27] . Trpc3 forms 23-pS conductance ion channels permeable to mono- and divalent cations [28 , 29] , which seems inconsistent with the Ca2+-activated monovalent intrinsic current ICAN . Nevertheless , the heteromeric association of Trpc3 with Trpc1 markedly reduces Ca2+ permeability [30] , and Trpc1 transcripts are expressed at approximately the same level as Trpm4 in the preBötC [27] , so a heteromeric Trpc3-mediated monovalent ICAN is feasible . Trpc3 has already been implicated in regulation of respiratory rhythm [31] . Furthermore , RNA sequencing ( RNA-seq ) revealed Trpc3 to be the single most abundantly expressed Trp gene in rhythmogenic and nonrhythmogenic preBötC neurons [27] , so it rises to the top of candidates from the Trpc family as an ion channel subunit that could mediate or contribute to ICAN . Excitatory interneurons that form the inspiratory core oscillator in the preBötC are derived from precursors expressing the transcription factor developing brain homeobox 1 ( Dbx1 ) [32–37] . A subset of Dbx1-derived neurons also shapes motor output pattern [38 , 39] . Whether for rhythm or pattern generation , Dbx1-derived neurons express ICAN [33 , 40] . Neighboring non-Dbx1–derived interneurons , presumably inhibitory , influence breathing motor pattern and inspiratory–expiratory phase transition [41–44] , so ICAN , which is ubiquitous in the preBötC [8] , is relevant to their role ( s ) as well . Therefore , we evaluated the breathing-related role ( s ) of Trpm4 and Trpc3 as two likely candidates underlying ICAN in preBötC neurons . Attenuating the activity of Trpm4 , but much less so Trpc3 , affects breathing behavior by attenuating motor output functions of the preBötC but not rhythm generation per se . Koizumi and colleagues reach a similar conclusion using an in vitro approach [45] . We propose that Trpm4 largely mediates ICAN , which forms a part of the pattern generator microcircuit rather than the core oscillator for breathing .
For reasons outlined in the Introduction , we considered Trpm4 or Trpc3 ( or both ) to be likely candidates that mediate ICAN . Having sequenced the transcriptome of Dbx1 and non-Dbx1 preBötC neurons [27] and released those data in the public domain ( National Center for Biotechnology Information [NCBI] Gene Expression Omnibus database , accession no . GSE100356 ) , here , we rank-ordered Trp transcripts according to expression level . Sixteen out of 28 Trp genes showed nonzero expression in the preBötC ( Fig 1 ) . Trpc3 had the highest level of expression ( reads per kilobase of transcript per million mapped reads [RPKM] 18 . 5 in Dbx1 neurons , 11 . 9 in non-Dbx1 neurons ) . Trpm4 expression ( RPKM 0 . 9 in Dbx1 neurons , 1 . 0 in non-Dbx1 neurons ) was below the median RPKM for all expressed genes ( 1 . 7 , [27] ) , but Trpm4 expression was in the top half of all Trp channel genes and , for reasons outlined in the Introduction , was a strong candidate a priori for ICAN . Other Trp channels among the 16 we detected are unlikely to give rise to ICAN for the following reasons . Trpm2 , Trpm3 , Trpm8 , and Trpv2 are thermosensitive; Trpc1 , Trpc4 , Trpc5 , and Trpc7 form store-operated channels ( SOC ) ; and Trpm7 and Trpv6 are primarily divalent ion channels [14 , 16] . Finally , the single-channel conductance of Trpp2 and Trpp5 exceed 100 pS [46] , which is incompatible with 24-pS channel activity in sync with inspiration in preBötC neurons [21] . Having quantified Trpm4 and Trpc3 transcripts in Dbx1 ( excitatory ) and non-Dbx1 ( presumably inhibitory ) preBötC neurons through RNA-seq , we examined expression of these genes at the protein level via immunohistochemical labeling . In adult and neonatal mice , we detected Trpm4 ( Figs 2 and S1 ) and Trpc3 ( Figs 3 and S2 ) in both Dbx1-derived and non-Dbx1–derived preBötC interneurons , which suggests that these channels could comprise ICAN and thus be relevant to breathing in adult as well as perinatal mice . Dbx1-derived cells in the preBötC include astrocytes [32 , 38 , 47] , which did not express Trpm4 ( Fig 2D ) or Trpc3 ( Fig 3C ) . We used short hairpin RNA ( shRNA ) to knock down Trp channels in adult mice . Adeno-associated viruses ( AAVs ) encoding either a Trpm4- or Trpc3-targeted shRNA ( hereafter referred to as Trpm4 or Trpc3 shRNA ) as well as enhanced green fluorescent protein ( eGFP ) were injected in the preBötC . First , we injected shRNA unilaterally into the left preBötC in conjunction with contralateral ( right side ) injections of a nontargeting control sequence ( n = 6 mice total ) . Five weeks later , Trpm4 and Trpc3 expression were 72% ± 3% and 76% ± 4% lower , respectively , in the left preBötC compared to the right preBötC , which shows that shRNAs effectively impede Trp ion channel gene expression in preBötC neurons ( Table 1 ) . We next assessed the time course of Trp channel knockdown by injecting another cohort of mice with shRNA and then measuring Trpm4 and Trpc3 protein expression in the preBötC over time ( n = 12 mice injected with Trpm4 shRNA and n = 12 mice injected with Trpc3 shRNA ) . Western blots quantifying protein expression showed a monotonic decline in Trpm4 and Trpc3 ion channel expression as a function of number of days postinjection . Trpm4 was reduced by 12% , 42% , 62% , and 65% and Trpc3 was reduced by 33% , 38% , 52% , and 68% at days 10 , 20 , 30 , and 40 , respectively ( S3 Fig ) .
Whereas 16 of 28 Trp channel genes are expressed in the preBötC , we favor Trpm4 as one major contributor to ICAN; Trpc3 appears to contribute as well . The other 14 Trps may give rise to ion channels that modulate preBötC activity or underlie cell signaling pathways but , for reasons argued above ( see Results: Trpm4 and Trpc3 ion channels in preBötC neurons ) , are unlikely to mediate ICAN , whose phasic activation coincides with ( probably to amplify ) inspiration and sighs . ICAN was originally attributed to Trpm4 and Trpm5 because of their similar gating mechanisms , 24-pS single-channel conductance , phosphoinositide modulation , and pharmacology [8 , 25] . However , we can now rule out Trpm5 entirely because inspiratory phasic ion channel activity in preBötC neurons is ATP sensitive [21 , 22] , which is a uniquely Trpm4 property [23–26] , and no Trpm5 transcripts are detectable in preBötC neurons ( Fig 1 and [27] ) . Prior reports of Trpm5 in the preBötC [9 , 17] may reflect its expression in laryngeal motoneurons or preganglionic autonomic neurons of the nucleus ambiguus [59] , which adjoin and partially overlap with the preBötC , as well as in non-neural tissues . Trpc3 ion channels may also contribute to preBötC ICAN , given the preeminent expression level of Trpc3 transcripts in Dbx1 and non-Dbx1 neurons ( Fig 1 and [27] ) , 23-pS single-channel conductance [29 , 60] , and phosphoinositide modulation [20 , 61 , 62] . However , Trpc3 Ca2+ permeability exceeds that of monovalent cations , i . e . , PCa/PNa > 1 [14 , 16 , 46 , 63] , which is inconsistent with a current like ICAN that is selective for monovalent cations yet gated by Ca2+ . Nevertheless , heteromeric Trpc channels that include Trpc1 diminish Ca2+ permeability such that PCa/PNa < 1 [30 , 64 , 65] . Trpc3 can form heteromeric channels with Trpc1 [65–68] as well as Trpc6 and Trpc7 [68 , 69] . Trpc1 and Trpc7 are expressed at approximately the same level as Trpm4 in the preBötC , whereas Trpc6 expression is near zero [27] . Therefore , given that Trpc3 and Trpc7 regulate respiratory rhythm [31 , 70] , heteromeric ion channels in which Trpc3 associates with Trpc1 or Trpc7 are feasible and could contribute to ICAN . Trpm4 and Trpc3 can form heteromeric ion channels in HEK293T cells , which modifies gating properties and Ca2+ permeability [71] . Although heteromeric ion channels that cross Trp subfamily boundaries have not been characterized in cells that natively express both subunits like preBötC neurons , the existence of Trpc3/Trpm4-mediated ICAN is conceivable . However , given the disparate results following selective attenuation of Trpm4 and Trpc3 in vitro and in vivo , it seems more likely that homomeric Trpm4 and heteromeric Trpc3 channels contribute to separate ion channel populations whose aggregate represents whole-cell ICAN . Among these two channels , Trpm4 is predominant because the breathing phenotype was far stronger , and often fatal , in Trpm4 shRNA- compared to Trpc3 shRNA-injected mice . In vitro , preBötC field recordings only implicate Trpm4 because its antagonist 9-phenanthrol diminished preBötC inspiratory and sigh bursts , whereas the Trpc3 antagonist pyr-3 did not . Nevertheless , in Dbx1 preBötC neurons , both Trpm4 and Trpc3 contributed to inspiratory and sigh bursts; pyr-3 and 9-phenanthrol nearly equivalently diminished inspiratory bursts , and pyr-3 reduced sigh bursts more than 9-phenanthrol . At face value , those data suggest that Trpc3 and Trpm4 contribute commensurately to burst generation in Dbx1 preBötC neurons , but we argue that because pyr-3 had to be bath-applied during whole-cell recordings , its effects were magnified by impacting all constituent Dbx1 neurons whose collective synaptic drive would have been diminished . 9-phenanthrol , by contrast , acted only through the patch pipette of the neuron being recorded , so network synaptic drive would not have been affected . Therefore , one cannot compare the relative pharmacological attenuation of drive potentials recorded in whole-cell conditions to reach a conclusion about which ion channel predominantly comprises ICAN; instead , we rely on the field recordings in vitro and shRNA experiments in vivo to evaluate their relative contributions . We conclude that Trpm4 is the predominant ion channel underlying ICAN , a ubiquitous current in preBötC neurons . In Dbx1-derived preBötC neurons specifically , Trpm4 and Trpc3 may constitute ICAN , probably via separate ion channel populations , but the contribution of Trpm4 exceeds that of Trpc3 . Slices that include the preBötC and hypoglossal nerve root ( XII ) nucleus retain the minimal microcircuit to generate fictive inspiration and sighs and thus are experimentally advantageous for studies of rhythm and pattern generation [72–75] . A rhythmogenesis model we promoted [58] posits that recurrent excitation triggers ICAN to produce robust inspiratory bursts . That model predicts that ICAN attenuation should either slow down the rhythm—because it would take longer for recurrent excitation to build up the inward currents needed to generate inspiratory bursts—or stop the rhythm entirely if recurrent excitation is insufficient to culminate the respiratory cycle . However , rhythms in vitro sped up after blocking Trpm4 ( and did not change after blocking Trpc3 ) . We conclude that ICAN is not rhythmogenic , but what can explain the counterintuitive frequency increase ? Inspiratory cycles in vitro are unconstrained by phasic synaptic inhibition from pulmonary stretch receptors in the periphery that ordinarily truncate inspiration and thus cut down on cycle time and increase frequency [42 , 43] . Removing that sensory feedback amplifies inspiratory bursts , which are followed by a pronounced refractory period that depresses frequency [42 , 76] . ( Temperature differences also influence frequencies in vivo versus in vitro , but we refer the reader to [77] and instead focus on intrinsic and synaptic factors . ) Here , pharmacological attenuation of Trpm4-dominated ICAN also truncates inspiratory bursts , which produces the same net effect as sensorimotor feedback inhibition in vivo , namely increasing frequency . Regarding pattern , ICAN attenuation reduced drive potentials in Dbx1 preBötC neurons ( which reflects Trpm4 and Trpc3 contributions ) . Trpm4-dominated ICAN attenuation reduced preBötC field potentials for inspiration and sighs . Therefore , ICAN is relevant because it governs preBötC burst magnitude and thus the ability to propagate inspiratory phase activity ( and sighs ) to premotor neurons and drive motor output . Our data and conclusions regarding inspiratory burst magnitude complement a recent report by Koizumi and colleagues [45] , but we uniquely report the role of ICAN in sighs in vitro . We additionally knocked down Trpm4 and Trpc3 expression in the adult mouse preBötC . Attenuation of Trpm4-dominated ICAN progressively decreased breath size ( VT and TI ) —which is consistent with its role governing motor pattern identified in vitro—but also increased breathing frequency . That frequency effect in vivo may be partly attributable to decreasing the refractory period following inspiration in core preBötC neurons , as explained above . A more important factor may be that sentient mice with intact chemosensory feedback increased their breathing rate , either via increased respiratory drive or volitionally , because smaller breaths induced by channel knockdown—at the original frequency—were insufficient to meet oxygen demand . Recall that MV ( the product of VT and frequency ) did not change prior to gasping and/or death , so the Trpm4 shRNA-injected mice were able to sustain ventilation via this compensatory mechanism for approximately 22 days . Whereas inspiratory frequency increased following Trpm4 shRNA , we note that sigh rate decreased , and that may have further contributed to respiratory insufficiency by gradually diminishing the gas exchange surface area of the alveoli [1 , 74] . An increase in fictive ( inspiratory ) breathing frequency was also observed in brainstem-spinal cord preparations in situ following bath application of the Trpm4 antagonist 9-phenanthrol [45] . That frequency effect in situ is attributable ( at least in part ) to a different mechanism involving inhibitory microcircuits unrelated to sensorimotor integration , and extrinsic to the preBötC , that interact with preBötC excitatory microcircuits [42 , 78] . Those inhibitory circuits are excluded from slices but retained in in situ preparations . Trpm4 channels are expressed throughout the medulla and bath-applied 9-phenanthrol acts on the medulla in its entirety . Therefore , as 9-phenanthrol reduces postinspiration ( i . e . , the inspiratory–expiratory phase transition ) , it increases the inspiratory frequency in situ partly because of effects on inhibitory interneurons distributed throughout the medulla [45] . Whether Trpm4 channels are expressed specifically in the postinspiratory complex ( PiCo , [79] ) is not yet known , so PiCo-related contributions to frequency control in the Koizumi and colleagues [45] pharmacological experiments in situ cannot yet be evaluated . Here , we limited shRNA injection to the preBötC so the inhibitory microcircuits that govern postinspiration and inspiratory–expiratory phase transition , predominantly in the rostral medulla [42 , 79–81] , would not be affected in our experiments in vivo . Regarding motor output , inspiratory rhythm propagates caudally from the preBötC to phrenic premotor neurons of the rostral ventral respiratory group ( rVRG ) [39 , 82–84] and then to diaphragmatic phrenic motoneurons in the cervical spinal cord . If either Trpm4 or Trpc3 shRNA affected rVRG premotor neurons , then that could conceivably diminish breath size . However , posthoc histology showed a dearth of eGFP expression in the rVRG , a site that extends notably for approximately 2 mm in the anterior–posterior axis and is not limited to the immediate caudal vicinity of the preBötC . We conclude that is very unlikely that Trpm4 shRNA directly impacted diaphragmatic premotor neurons . It is similarly unlikely that Trpm4 shRNA affected respiratory interneurons of the rostrally sited Bötzinger complex based on the low eGFP expression observed in that region . Nevertheless , because Bötzinger neurons are inhibitory [85–89] , if Trpm4 shRNA had expressed there , it would disinhibit phrenic premotor neurons and phrenic motor neurons and thus probably augment the drive to the breathing pump rather than diminish it . We observed eGFP expression dorsal to the preBötC , where airway premotor neurons and laryngeal motoneurons are located [38 , 90–94] . Therefore , Trpm4 knockdown could have affected airway patency but those airway effects would not impact tidal volume or inspiratory time , which are instead controlled by inspiratory pump musculature ( e . g . , diaphragm , external intercostals ) . Attenuating Trpc3 ( minimally ) reduced breath size in vivo and in inspiratory drive potentials in Dbx1 preBötC neurons but did not affect preBötC field recordings . Only 35% of inhibitory ( non-Dbx1 ) preBötC neurons express Trpc3 [45] . Furthermore , we measured no attenuation of inspiratory or sigh bursts in glycinergic preBötC neurons . Therefore , we conclude that inhibitory preBötC interneurons maintain inspiratory and sigh burst discharge behavior in pyr-3 , which sustains the overall magnitude of preBötC field potentials even as drive potentials decrease in the Dbx1 preBötC subpopulation . While the activity of inhibitory interneurons affects phasic activity of respiratory microcircuits and sensorimotor integration [41 , 42 , 78 , 95] , uniquely Dbx1-derived excitatory neurons of the preBötC and rVRG generate rhythm and drive phrenic premotor neurons [39] and thus directly impact inspiratory breathing movements , which includes sighs . Koizumi and colleagues [45] reported a decrease in XII motor output and an increase in frequency ( in some preparations , e . g . , their Fig 4B , left ) . However , they applied pyr-3 at 50 μM , which exceeds the EC50 by 10-fold , so off-target effects on preBötC and XII motoneurons might be partly responsible . We also found that pyr-3 concentrations exceeding 10 μM diminished XII output and increased frequency ( see S5 Fig ) , whereas there was no change in the XII output or frequency in response to 10 μM pyr-3 , which still exceeds the EC50 [51 , 52] . A factor we cannot quantify is the extent to which the preBötC and ventral respiratory microcircuits compensate during the observation period to recover inspiratory and sigh burst-generating function in the face of Trpm4 ( or Trpc3 ) knockdown [96] . Considering Trpm4 shRNA , which exerted the strongest effect , the cumulative effect of any compensatory changes were insufficient to rescue breathing: the effects on breath magnitude and frequency were comparable to acute effects of 9-phenanthrol in vitro from our data and those of Koizumi and colleagues [45] . The preBötC contains roughly half excitatory neurons ( Dbx1-derived ) and half inhibitory neurons [41 , 54 , 97 , 98] , but the shRNA does not discriminate neuron types . Dbx1 preBötC neurons are central for respiratory rhythm and motor output pattern in adult [34 , 35 , 37 , 99] and neonatal mice [32 , 33 , 36] , so attenuating their activity would be expected to impact breathing . However , could Trpm4 knockdown in non-Dbx1 neurons explain , at least in part , the breathing phenotype here ? If Trpm4 shRNA reduced inspiratory burst magnitude in non-Dbx1 ( presumably ) inhibitory preBötC neurons , then it would increase ( not decrease ) breath size because it has been repeatedly demonstrated that disinhibition of the preBötC enhances breath size [41–43] . If we had observed an increase in breath size following Trpm4 ( or Trpc3 ) shRNA , then it could be attributed to attenuation of ICAN in non-Dbx1 inhibitory neurons of the preBötC , but breath size always decreased after shRNA . Furthermore , only 35% of inhibitory preBötC neurons express Trpm4 [45] , and we show that 9-phenanthrol has no measurable effect on inspiratory and sigh bursts in glycinergic preBötC neurons . While we cannot conclude that shRNA knockdown in non-Dbx1 ( presumably inhibitory ) neurons has no effect on the preBötC , those effects are far less influential than shRNA knockdown in Dbx1 neurons , in which the preeminent role of Trpm4-mediated ICAN is to magnify inspiratory bursts , which directly impacts breath size . Thus , we conclude that Trpm4 knockdown reduces breath size by impeding ICAN-mediated burst generation in Dbx1 preBötC neurons . Breathing is a canonical behavior whose underlying rhythm and motor pattern emanate from a central pattern generator ( CPG ) network [100] . Whereas rhythm and pattern CPG functions are conjoint in lower vertebrates , mammalian motor behaviors incorporate multiple discrete phases ( for locomotion there are multiple limbs and joints ) , which require CPGs with separable constituent parts: rhythmogenic cores that set the frequency and motor–pattern microcircuits that govern the amplitude and phase of the ensemble movements [101] . The breathing CPG shows us that pattern-related function encroaches on the rhythmogenic core . Dbx1 preBötC neurons are inspiratory rhythmogenic [1] , and some project to premotor and motor neurons [36 , 38 , 39 , 102] . Our data can be parsimoniously explained by Trpm4 ( and to a lesser extent Trpc3 ) reduction affecting burst size , hence the number of spikes in Dbx1 preBötC neurons sent to premotor and motor neurons , which ultimately diminishes inspiratory breathing movements . However , a suitable explanation for the breathing changes induced by Trpm4 ( and to a lesser extent Trpc3 ) shRNA should consider the effects across the entire population of Dbx1 preBötC interneurons , not just those projecting to premotor or motor neurons . In that regard , “burstlet” theory may provide a framework for interpretation: it posits that recurrent synaptic excitation is sufficient for rhythm generation but subthreshold for motor output [5 , 6] . Burst generation largely attributable to Trpm4-mediated ICAN plays a crucial role by propagating rhythmic activity from the preBötC core oscillator , i . e . , Dbx1 interneurons that only connect to one another and can generate rhythms without high-amplitude bursts , to pattern-related and premotor microcircuits extrinsic to the preBötC . Thus , we identify discrete sets of ion channels , predominantly Trpm4 but to a lesser extent Trpc3 , whose role in rhythmogenic interneurons pertains specifically to pattern generation . Therefore , rhythm and pattern generating functionality may , to some extent , be shared in canonical classes of CPG interneurons , a principle that may be generally applicable to understanding the neural bases of motor behavior in mammals .
The Institutional Animal Care and Use Committees ( IACUC ) at William & Mary and the Chicago Medical School of Rosalind Franklin University approved these animal protocols , which conform to the policies of the Office of Laboratory Animal Welfare ( National Institutes of Health , Bethesda , MD , United States of America ) and the guidelines of the National Research Council [103] . The specific protocols include IBC-2016-06-06-11257 , IACUC-2018-05-01-12967 , and IACUC-2016-08-02-11305 at William & Mary for CA Del Negro and B18-10 at Rosalind Franklin for K Kam . For neuroanatomy or in vitro physiology experiments , neonatal mice were anesthetized by hypothermia and then killed by thoracic transection . Adult mice were killed via a lethal dose of pentobarbital ( 100 mg/kg body mass , IP ) . For in vivo injections , adult mice were anesthetized via ketamine ( 100 mg/kg , IP ) and xylazine ( 10 mg/kg , IP ) . Mice were housed in colony cages on a 14-hour light/10-hour dark cycle with controlled humidity and temperature at 23°C , and were fed ad libitum on a normal rodent diet ( Teklad Global Diets , Envigo , Madison , WI ) with free access to water . At least three types of environmental enrichment materials were provided in each cage to improve the well-being of the mice . For experiments pertaining to Dbx1 preBötC interneurons , we used knockin mice that express Cre recombinase fused to the tamoxifen-sensitive estrogen receptor Dbx1CreERT2 [104] ( IMSR Cat# JAX:028131 , RRID:IMSR_JAX:028131 ) and floxed reporter mice with inducible expression of the red fluorescent protein variant tdTomato dubbed Ai9 by the Allen Institute for Brain Science [105] ( IMSR Cat# JAX:007905 , RRID:IMSR_JAX:007905 ) . We crossed homozygous Dbx1CreERT2 females with Ai9 males . We refer to their offspring as Dbx1;Ai9 mice . To achieve optimal tdTomato expression in Dbx1-derived neurons , we administered a 22 . 5 mg/kg dose of tamoxifen ( Sigma Aldrich , St . Louis , MO ) dissolved at a concentration of 10 mg/ml in corn oil by oral gavage to pregnant dams at embryonic day 9 . 5 [47] . Newborn Dbx1;Ai9 pups express tdTomato in Dbx1-derived neurons in the preBötC and contiguous regions of the medulla [106] . For experiments pertaining to glycinergic preBötC interneurons , we used transgenic mice that express eGFP under the control of the promoter for the glycine transporter 2 gene ( slc6A5 ) , i . e . , GlyT2-eGFP mice [53] . For anatomy or physiology experiments ( see below ) , we used Dbx1;Ai9 , GlyT2-eGFP , and wild-type CD-1 mice of both sexes at postnatal day 0 to 4 ( P0-4 ) . Dbx1CreERT2 mice have a CD-1 genetic background . Ai9 and GlyT2-eGFP mice have a C57Bl/6 background . The anatomy of respiratory networks is well documented in the brainstem of P0-4 Dbx1;Ai9 ( CD-1 background ) and C57Bl/6 mice [107 , 108] , so the relative position of respiratory nuclei , particularly the preBötC , is stable during postnatal development ( P0-4 ) across mouse strains . Neonatal Dbx1;Ai9 , GlyT2-eGFP , and wild-type CD-1 mice were anesthetized by hypothermia and then killed by thoracic transection . The neuraxis was removed in less than 2 min and further dissected in a dish filled with artificial cerebrospinal fluid ( aCSF ) containing ( in mM ) : 124 NaCl , 3 KCl , 1 . 5 CaCl2 , 1 MgSO4 , 25 NaHCO3 , 0 . 5 NaH2PO4 , and 30 dextrose equilibrated with 95% O2-5% CO2 , pH 7 . 4 . The neuraxes of Dbx1;Ai9 pups were fixed in 4% paraformaldehyde ( PFA ) for 16–24 hours , rinsed in phosphate buffered saline ( PBS ) ( BP399-1 , Fisher Scientific , Hampton , NH ) , then immobilized in 4% agar for sectioning in the transverse plane . preBötC sections ( 50–100 μm thick ) were incubated with 20% normal donkey serum ( 50-413-367 , Fisher Scientific ) overnight at 4°C on an orbital shaker . The following day , sections were washed with PBS 2 x 30 min and were then incubated overnight in PBS with 0 . 4% TritonX-100 at 4°C on an orbital shaker in the presence of primary antibodies . Those included ( 1 ) 1:250 rabbit polyclonal anti-Trpm4 corresponding to amino acids 60–74 ( NH2-TEWNSDEHTTEKPTDC-COOH ) of the amino-terminal tail of rat Trpm4 with an added carboxyl-terminal cysteine [109] whose specificity was tested via western blot of protein extracted from cultured MCF-7 cells [110] as well as western blot and single-cell RT-PCR in mouse brain tissue by the authors ( R . Teruyama ) ; ( 2 ) 1:500 rabbit polyclonal anti-Trpc3 ( ACC-016 , RRID:AB_2040236 , Alomone Labs , Jerusalem ) whose specificity was verified using a Trpc3 knockout mouse [111]; and ( 3 ) 1:500 goat polyclonal anti-GFAP ( RRID:AB_880202 , Abcam Cat #Ab53554 , Abcam , Cambridge , MA ) whose specificity was tested in mouse brain lysate using western blot ( the antibody registry lists 14 different citations in rat and mouse ) . Sections were then washed 4 x 30 min with PBS and placed in a secondary antibody . Those included ( 1 ) 1:200 donkey anti-rabbit Alexa 647 ( 711-605-152 , RRID:AB_2492288 ) ; ( 2 ) 1:200 donkey anti-rabbit Alexa 405 ( 711-475-152 , RRID:AB_2340616 ) ; or ( 3 ) 1:200 donkey anti-goat Alexa 647 ( 705-605-147 , RRID:AB_2340437 ) . All secondaries were obtained from Jackson ImmunoResearch Labs ( West Grove , PA ) . Sections were incubated in secondary antibodies for 1 hour at room temperature before being rinsed 6 x 30 min in PBS . For immunohistochemistry in adults , Dbx1;Ai9 mice of both sexes all within the age range 10–14 weeks were transcardially perfused with PBS followed by 4% PFA . Neuraxes were removed and placed in 4% PFA overnight , rinsed with PBS , then immobilized in 4% agar for sectioning . Transverse preBötC sections ( 50–100 μm thick ) were incubated in 1:250 rabbit anti-Trpm4 , 1:400 rabbit anti-Trpc3 , or 1:500 goat anti-GFAP in PBS with 0 . 4% TritonX-100 overnight at 4°C on an orbital shaker . Sections were then washed 2 x 30 min with PBS and incubated with 20% normal donkey serum with 0 . 4% TritonX-100 for 90 min on an orbital shaker at room temperature . Then , after two 10-min washes , the sections were placed in 1:200 donkey anti-rabbit Alexa 647 , 1:100 donkey anti-rabbit Alexa 405 , or 1:200 donkey anti-goat Alexa 647 for 1 hour at room temperature . Sections were then washed for a minimum of 6 x 30 min in PBS . All sections were mounted on glass slides and cover-slipped , and images were acquired with a 10x air ( NA 0 . 45 ) , 40x water immersion ( NA 1 . 15 ) , and 60x oil immersion ( NA 1 . 49 ) objectives on a confocal microscope ( Nikon , Melville , NY ) and ( Thorlabs , Newton , NJ ) . Specific shRNA sequences designed to knockdown Trp transcripts ( CCTAACTCACTGATCCGAAAT for Trpm4 , NCBI 68667 , RefSeq NM_175130 . 4 , and GAGGTTCAATATTTCACCTATGC for Trpc3 , NCBI 22065 , RefSeq NM_019510 . 2 ) [112] were incorporated into a viral vector , which features a U6 polymerase III promoter to drive shRNA expression and a CMV promoter to drive eGFP expression for identification of transduced neurons ( Cyagen Biosciences , Santa Clara , CA ) . We chose an AAV vector with serotype 9 to package the shRNA because AAV9 achieves high transduction of neurons , but not microglia or oligodendrocytes , in mouse brains [113] . We also used a nontargeting shRNA ( CCTAAGGTTAAGTCGCCCTCG ) , which is a randomized sequence from Trpm4 but has no homology to any known genes in mouse or human as a control . The standard titer of AAV is ≥1 x 1013 GC/ml ( genome copies per ml ) . Our group has been studying respiratory function of Dbx1-derived neurons for nine years using the Dbx1CreERT2 mouse strain with a CD-1 background [37 , 91 , 114–119] . Therefore , we used CD-1 mice for in vivo behavioral experiments . CD-1 mice of both sexes , all within the age range of 10–14 weeks , were anesthetized via ketamine ( 100 mg/kg , IP ) and xylazine ( 10 mg/kg , IP ) and positioned in a stereotaxic frame . We exposed the skull aseptically and drilled two holes with coordinates 6 . 9 to 7 . 8 mm caudal to bregma , 1 . 2 mm lateral to midline on both sides , at a dorsal-ventral depth of 5 . 2 mm from the brain surface . We injected 100 nl of virus into each hemisphere through a 30-gauge 2 . 0 μl Hamilton Neurosyringe ( Hamilton Company , Reno , NV ) at rate of 50 nl/min . The needle was left in place for an additional 5 min to allow for diffusion . The scalp was then stapled , and triple antibiotic cream containing neomycin , bacitracin , and polymyxin antibiotics was applied to the wound . The mice recovered on a heating pad and were closely monitored postoperatively for signs of infection , pain , or distress . Local antibiotics and ketoprofen ( 5 mg/kg ) was administered on an as-needed basis . We periodically measured breathing behavior for 6 weeks before killing the mice to examine the extent of virus transduction and expression . Following transcardial perfusion and fixation , transverse sections of the medulla ( 75 μm thick ) were examined for eGFP expression in the preBötC . The area of virus expression for all mice was quantified by superimposing confocal sections from individual mice onto corresponding sections from a mouse atlas [120] . Pixel intensity in unprocessed 12-bit confocal microscopy sections ( 4 , 096 values ) was equally divided into low ( 0–2 , 048 ) and high ( 2 , 049–4 , 096 ) intensity levels . Low-intensity contours were plotted at 10% transparency; high-intensity contours were plotted at 20% transparency . The darkest area of coloration indicates the center of virus expression , while lightly colored areas indicating peripheral regions with lower expression . In separate experiments to assess the effectiveness of shRNA knockdown of Trp transcripts , adult CD-1 mice of both sexes , all within the age range of 10–14 weeks , were injected with 200 nl of AAV containing either Trpm4-targeted shRNA or Trpc3-targeted shRNA in the left preBötC and a nontargeting control sequence in the right preBötC . Three animals were tested ( i . e . , three biological replicates ) in each group targeting either Trpm4 or Trpc3 ( n = 6 mice total ) . Each animal served as its own control because the left preBötC was injected with either Trpm4 or Trpc3 shRNA , and the right preBötC was injected with control shRNA containing a randomized sequence without a known gene target . After 5 weeks of incubation , the adult mice were rapidly killed , and the bilateral halves of the preBötC region were isolated and separated to extract RNA that was reversed transcribed to cDNA and then processed to quantify gene expression . Briefly , total RNA was extracted using RNAzol RT ( RN 190 , Molecular Research , Cincinnati , OH ) and further purified with 4-bromoanisole ( BAN ) to eliminate contaminating DNA . RNA was normalized to 500 ng across all samples and then reversed transcribed to cDNA using the iScript Reverse Transcription Supermix for RT-qPCR ( 1708840 , Bio-Rad , Hercules , CA ) . RNA and cDNA concentration and quality were measured using NanoDrop One Microvolume UV-VIS Spectrophotometer ( ND-ONE-W , Thermo Fisher Scientific , Waltham , MA ) . We used a final concentration of 2 . 5 ng/μl cDNA to quantify Trpm4 and Trpc3 via droplet digital PCR ( i . e . , ddPCR ) ( QX100 , Bio-Rad ) , which partitions samples into thousands of droplets then amplified by PCR using Taqman Gene Expression Assays ( Trpm4: TCTTGTGAAAGCCTGTGGGAGCTCT , Mm00613173_m1 , RefSeq NM_175130 . 4; or Trpc3: CCTTGTAGCAGGCTGGGGAAGATTC , Mm00444690_m1 , RefSeq NM_019510 . 2; 4331182 , Life Technologies , Carlsbad , CA ) . The hydrolysis primers and probes in the Taqman assays were designed to span exon–exon junctions to avoid amplifying the target from genomic DNA , if present . Following PCR amplification , the Quantasoft software v1 . 7 . 4 ( Bio-Rad ) analyzed the number of positive ( containing at least one copy of the target ) and negative droplets . Absolute copy number of transcripts per μl of cDNA was determined by fitting the fraction of positive droplets to a Poisson distribution . A no-template control and a negative control from the reverse transcription reaction of selected samples were also included in the ddPCR assay , which returned a zero count , as expected . Adult CD-1 mice of both sexes , all within the age range of 10–14 weeks , were injected with 100 nl of AAV containing either Trpm4-targeted shRNA ( n = 12 ) or Trpc3-targeted shRNA ( n = 12 ) in the left preBötC and a nontargeting control sequence in the right preBötC . Three mice from each group were rapidly killed at each time point ( day 10 , 20 , 30 , or 40 postinjection ) , and the bilateral halves of the preBötC region were dissected for protein extraction . Brain tissues containing the preBötC were homogenized and sonicated in 500 μl cold lysis buffer ( Cell Signaling Technology , Danvers , MA ) containing 1 mM protease inhibitor phenylmethylsulfonyl fluoride ( PMSF ) added immediately before use . After centrifugation of brain lysates at 16 , 000 x g for 15 min , crude total protein concentrations of the supernatants were determined using Nanodrop One Spectrophotometer A280 analysis ( Thermo Fisher Scientific ) . Approximately 80 μg of total protein was resolved in 4%–12% gradient gels ( NP0321 , Thermo Fisher Scientific ) and transferred onto polyvinylidene difluoride membrane . After blocking with OneBlock Western ( 20–314 , Genesee Scientific , San Diego , CA ) for 1 hour at room temperature , membranes were probed with primary antibodies against either Trpm4 ( 1:200 ) or Trpc3 ( 1:250 ) and the loading control beta-actin ( 1:1000 , Abcam Cat# ab8224 , RRID:AB 449644 ) , followed by 1:15 , 000 polyclonal secondary antibodies IRDye 680RD goat anti-rabbit ( LI-COR Biosciences Cat# 925–68071 , RRID:AB_2721181 ) or IRDye 800CW goat anti-mouse ( LI-COR Biosciences Cat# P/N 925–32210 , RRID:AB_2687825 ) . Both primary and secondary antibodies were diluted in the blocking buffer . Washes after primary and secondary antibody incubations were done with 1x TBS with 0 . 1% Tween-20 for a minimum of 3 x 10 min . Immunoblot signals were captured and quantified using Odyssey CLx Infrared Imaging System ( Li-Cor Biosciences , Lincoln , NE ) . We measured breathing behavior in unrestrained , awake adult CD-1 mice of both sexes , all within the age range of 10–14 weeks ( n = 26 mice total ) , using a whole-body plethysmograph ( EMKA Technologies , Falls Church , VA ) with a flow rate of 1 l/min in normoxia ( 21% O2 and 79% N2 ) . The airflow traces were analyzed using the spirometry module in LabChart 7 software ( AD Instruments , Colorado Springs , CO ) . We measured VT , TI , and breathing rate ( frequency ) . MV was calculated by multiplying the VT and frequency . The mice were introduced to the plethysmograph 1 week prior to AAV injection via three 30-min sessions inside the chamber , the first with a lightly closed but unsealed chamber , and the final two sessions with a sealed chamber and balanced airflow conditions . Those acclimatization sessions were not analyzed . For analyses , we performed plethysmography before shRNA injection ( day 0 ) , which we define as baseline breathing , and then intermittently for 6 weeks . Mice were recorded in 2 continuous 30-min sessions every 6 days . The day of each recording session is indicated by x-axis in Fig 6 . The mice were placed in the sealed chamber with balanced air flow 10 min prior to data collection during each session for acclimatization . Human experimenters observed the mice firsthand during every session . The mice were alert to their environment , which is consistent with wakefulness . Locomotion , grooming , and sniffing ( with synchronized whisking ) entrain and modify breathing [1 , 121–123] , so we only analyzed epochs of calm breathing absent other orofacial or motor behaviors ( e . g . , Fig 4 ) . Epochs of calm breathing represent eupnea . The sum of the duration of epochs of eupnea always exceeded 2 min for each 30 min session . Sighs , which are periodic large magnitude augmented inspiratory efforts [106] , could be distinguished by inhaled volume exceeding VT by 2–3-fold ( the inspired air during a sigh draws on the “inspiratory reserve volume” of the lungs , and thus exceeds VT by definition ) . We excluded sighs from the analyses whenever they were embedded within an epoch of eupnea . Excluding sighs was important because of their large volume and because of the prolonged pause that follows each sigh; both of which confound measurements of VT , TI , and frequency . Gasps can be distinguished from sighs based on their rise time being approximately half as long and because an active exhale precedes a gasp . We also excluded gasps from analyses of eupnea . We nonetheless counted the number of sighs within the 30-min recording period to compute sigh frequency . However , we could not accurately compute sigh volume or duration because sighs occurred during all behavioral states ( locomotion , grooming , sniffing , as well as eupnea ) and attempting to measure sigh volume and duration in other behavioral states yields inaccuracies due to fluctuations that obscure functional residual capacity , i . e . , equilibrium lung volume and variable respiratory demand . There is no MV pertaining to sighs because they are episodic events interspersed among eupneic breaths , not continuous . Isolated neuraxes from Dbx1;Ai9 , GlyT2-eGFP , and CD-1 wild-type neonatal mice ( 0–4 ) of both sexes were fixed to an agar block and then cut in the transverse plane to obtain a single 550-μm–thick slice that exposes the preBötC at its rostral face [106] . Slices were then perfused with aCSF at 28°C in a recording chamber on a fixed-stage microscope with epifluorescence to visually identify tdTomato- or eGFP-expressing target neurons . Extracellular K+ was increased to 9 mM to elevate preBötC excitability [72] . Inspiratory-related motor output was recorded from the XII , which are captured in transverse slices , using suction electrodes and a differential amplifier . We also simultaneously recorded field potentials from the preBötC . Amplifier gain was set at 2 , 000 , and the band-pass filter was set at 300–1 , 000 Hz . XII and preBötC bursts were full-wave rectified and smoothed for display . We obtained whole-cell patch-clamp recordings under visual control using pipettes with resistance of 4–6 MΩ and a Dagan IX2-700 current-clamp amplifier ( Minneapolis , MN ) , an EPC-10 patch-clamp amplifier in current-clamp mode ( HEKA Instruments , Holliston , MA ) , or a Molecular Devices Multiclamp 700B patch-clamp amplifier in current clamp mode ( San Jose , CA ) . All recordings were digitally acquired at 4 kHz using after 1 kHz low-pass filtering . The patch-pipette solution contained ( in mM ) 140 K-Gluconate , 10 HEPES , 5 NaCl , 1 MgCl2 , 0 . 1 EGTA , 2 Mg-ATP , and 0 . 3 Na ( 3 ) -GTP . We added 50 μM of Alexa 488 hydrazide dye ( A10436 , Life Technologies ) to the patch solution for nonred fluorescent visualization of Dbx1 preBötC interneurons recorded in the whole-cell configuration . We measured the area and amplitude of preBötC field potentials and inspiratory drive potentials , respectively , after digital smoothing . In the case of inspiratory drive potentials , smoothing eliminates spikes but preserves the underlying envelope of depolarization of the inspiratory drive potential [124] . We also measured the amplitude and area of low-frequency sighs ( <0 . 01 Hz ) , i . e . , augmented bursts with two peaks ( doublet ) that typically have a greater amplitude and often double the area of normal inspiratory bursts [73–75] . The Trpm4 antagonist 9-phenanthrol [50] ( 211281 , Sigma ) ( 10 , 50 , 100 , and 200 μM ) and Trpc3 antagonist pyrazole-3 ( pyr-3 ) [51 , 52] ( P0032 , Sigma ) ( 1 , 10 , and 50 µM ) were bath-applied while monitoring field potentials in the preBötC and XII motor output . We applied BAPTA ( 30 mM ) , FFA ( 100 μM ) , and 9-phenanthrol ( 100 μM ) via dialysis through the patch pipette to test their effects on individual Dbx1 preBötC neurons without attendant network effects . Pyr-3 ( 10 μM ) is only effective when applied extracellularly [51 , 52] . We plotted VT , frequency , TI , and MV versus day post-shRNA injection and then performed linear regression analyses to obtain the best-fit slope ( Prism , Graphpad Software , La Jolla , CA ) . Each individual measurement , rather than the mean value for each day post-shRNA , was considered a separate point in the analysis . We report the goodness of fit ( r2 ) along with the 95% confidence interval . We applied an F test to evaluate the slope of the relationships of VT , frequency , TI , and MV versus day post-shRNA . Assuming there is no relationship , i . e . , slope is zero , then the F statistic returns a P value quantifying the likelihood of obtaining a slope deviating from zero . We used orthodox criteria ( alpha of 0 . 05 ) to judge whether a slope was significantly nonzero and thus that a trend exists for VT , frequency , TI , or MV versus day post-shRNA . For the electrophysiology data , we used paired t tests to compare measurements between control and drug . Data are reported as mean ± SD . | Breathing behavior consists of periodic movements of the chest and airways that ventilate the lungs . The brain must generate a rhythm and form a motor output pattern to make breathing movements happen . Here , we address the ion channel–level neural origins of breathing , particularly the role of a class of transient receptor potential ( Trp ) channels hypothesized to be rhythmogenic . Using genetic techniques to knockdown these channels in a specialized brainstem site known for its respiratory rhythmogenic role , we surprisingly did not find the expected changes in breathing frequency . Rather , we measured progressive attenuation of breath magnitude , which in some cases led to fatal breathing pathologies . Therefore , the importance of this ion channel class is not respiratory rhythm generation per se but rather governing the motor output pattern . These results cause us to re-evaluate whether rhythm and pattern are discrete neural processes or instead inextricably linked in microcircuits of the central nervous system that generate and control motor behaviors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"membrane",
"potential",
"vertebrates",
"electrophysiology",
"mice",
"neuroscience",
"animals",
"mammals",
"motor",
"neurons",
"physiological",
"processes",
"receptor",
"potentials",
"ion",
"channels",
"transient",
"receptor",
"potential",
"channels",
"interneurons",
"breathing",
"respiration",
"animal",
"cells",
"proteins",
"biophysics",
"physics",
"biochemistry",
"cellular",
"neuroscience",
"rodents",
"eukaryota",
"cell",
"biology",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"amniotes",
"neurophysiology",
"organisms"
] | 2019 | Trpm4 ion channels in pre-Bötzinger complex interneurons are essential for breathing motor pattern but not rhythm |
Herpes simplex virus ( HSV ) entry into the cells requires glycoproteins gD , gH/gL and gB , activated in a cascade fashion by conformational modifications induced by cognate receptors and intermolecular signaling . The receptors are nectin1 and HVEM ( Herpes virus entry mediator ) for gD , and αvβ6 or αvβ8 integrin for gH . In earlier work , insertion of a single chain antibody ( scFv ) to the cancer receptor HER2 ( human epidermal growth factor receptor 2 ) in gD , or in gH , resulted in HSVs specifically retargeted to the HER2-positive cancer cells , hence in highly specific non-attenuated oncolytic agents . Here , the scFv to HER2 was inserted in gB ( gBHER2 ) . The insertion re-targeted the virus tropism to the HER2-positive cancer cells . This was unexpected since gB is known to be a fusogenic glycoprotein , not a tropism determinant . The gB-retargeted recombinant offered the possibility to investigate how HER2 mediated entry . In contrast to wt-gB , the activation of the chimeric gBHER2 did not require the activation of the gD and of gH/gL by their respective receptors . Furthermore , a soluble form of HER2 could replace the membrane-bound HER2 in mediating virus entry , hinting that HER2 acted by inducing conformational changes to the chimeric gB . This study shows that ( i ) gB can be modified and become the major determinant of HSV tropism; ( ii ) the chimeric gBHER2 bypasses the requirement for receptor-mediated activation of other essential entry glycoproteins .
Herpes simplex virus encodes a multipartite entry apparatus made of four essential glycoproteins , named gD , the heterodimer gH/gL and gB , with distinct functions [1–4] . gD , whose structure includes an Ig-folded core with extensions , serves as a typical receptor-binding glycoprotein , and the major determinant of HSV tropism [5–7] . The heterodimer gH/gL is a multidomain protein , with no structural resemblance to any known protein [8–10] . gB is a trimer with structural features typical of viral fusion glycoproteins [11–13] . gH/gL and gB form the conserved fusion apparatus across the Herpesviridae family . The quartet assembles in complexes [14 , 15 , 16–18] . Contact regions among the glycoproteins were identified [10 , 17–20] . The system of receptors for the quartet of glycoproteins appears to be more and more complex , and affects the process of glycoprotein activation at virus entry . gD interacts with three alternative receptors , nectin1 , HVEM , and modified heparan sulphate [21–24] . gH/gL interact with the αvβ subfamily of integrins [25 , 26] . αvβ6 and αvβ8 are required for entry , in that their depletion , or block with antibodies , results in block to virus infection [26] . Three co-receptors for gB were reported . They are PILRα ( paired immunoglobulin-like type 2 receptor-alpha ) , myelin associated glycoprotein , and isoforms IIA and IIB of non-muscle myosin heavy chain [27–30] . Little is known about the role they play in HSV entry . In particular , there is no evidence that they contribute to define the host range of the virus . PILRα was reported to be expressed , and possibly to play a role in HSV infection of monocytes , a cell type not usually targeted by HSV [27] . The effect , if any , of depleting this receptor in epithelial cells , the targets of wt-HSV in vivo , was not investigated . The lack of contribution of gB receptors to overall viral tropism is highlighted by the finding that abrogation of the gD interaction with one of its receptors abrogates virus entry in virtually any cell . The current model of HSV entry envisions that the four glycoproteins switch from an inactive to a fusion-active conformation [2–4 , 31] . Although numerous steps in this model remain to be elucidated , it is well documented that activation is triggered by the gD binding to one of its alternative receptors , and then propagates to gH/gL , and finally to gB in a cascade fashion . The conformational changes to gH/gL are induced upon transmission of a signal from receptor-bound gD , and upon interaction with one of the two integrins , and was documented as displacement of gL from the heterodimer [26 , 32] . The displacement only occurs if all the viral and cellular components of the entry apparatus are present , and is prevented by a MAb to gH with strong neutralizing activity , supporting the view that it is part of the process of gH activation [32] . The HSV glycoproteins are of interest in the design of oncolytic HSVs . Recently , this field received much attention upon Federal Drug Administration and European Medicines Agency approval of the oncolytic HSV , originally named OncovexGM-CSF , for the treatment of metastatic melanoma [33 , 34] . For this virus , as well as numerous oncolytic viruses , cancer specificity has been achieved at the expense of virulence [35 , 36] . In essence , they carry deletions or mutations in genes involved in contrasting the innate response to the virus , and take advantage of the fact that cancer cells mount a very weak response to them [37–39] . The drawback is that these viruses are attenuated and may replicate poorly [37] . The strategy pursued by other laboratories , including ours , is to develop non-attenuated oncolytic HSVs , by retargeting the HSV tropism to cancer-specific receptors [40–42] . The initial studies identified gD as the glycoprotein to be modified in order to readdress virus tropism to a receptor of choice , and detarget from the natural gD receptors [41] . In our studies , the selected receptor was HER2 ( human epidermal growth factor receptor 2 ) , a member of the EGFR ( epidermal growth factor receptor ) family , overexpressed in about 25–30% of breast and ovary cancers , as well as in stomach , lung and other cancers [43] . Retargeting was achieved by engineering in gD a single chain antibody ( scFv ) to HER2 , derived from trastuzumab , and by appropriate deletions in gD , which remove critical residues for interaction with HVEM and nectin1 [44–47] . Recent studies from our laboratory showed that also gH can be a tool for retargeting [48] . Thus , the insertion of a scFv to HER2 , combined with an appropriate deletion in gD , lead to a HSV fully retargeted to HER2 through gH . Here , we asked whether gB is a suitable glycoprotein for retargeting . We engineered the scFv to HER2 between AA 43 and 44 of gB , thus generating R-903 . By further deletion of AA 6–38 in gD , the recombinant could be detargeted from natural receptors . The retargeting to HER2 via gB was unexpected , since gB is the fusogenic glycoprotein , and was not known to be a determinant of HSV tropism . Inasmuch as the scFv to HER2 mediates entry when engineered in gD , gH , or gB we asked how can a same ligand , engineered in one or the other of the three glycoproteins—gD , gH or gB—enable entry through the HER2 receptor .
The scFv to HER2 was engineered in gB between AA 43–44 , thus generating R-903 ( Fig 1A ) . This position is known to accept the heterologous ligand green fluorescent protein ( GFP ) [49] . The R-909 recombinant was derived from R-903 by deletion of AA 6–38 in gD , for detargeting from the natural gD receptors , HVEM and nectin1 ( Fig 1A ) [45] . Both recombinants carry the Lox-P bracketed BAC sequence and the eGFP ( enhanced green fluorescent protein ) , cloned in the intergenic UL3-UL4 region . The presence of the scFv insert was verified by sequencing the ORF , and by sodium dodecyl sulphate-polyacrylamide gel elecctrophoresis ( SDS-PAGE ) and immunoblotting . As expected , gB from R-909 exhibited a lower electrophoretic mobility than wt-gB present in the R-LM5 recombinant ( Fig 1 B ) . The latter recombinant carries the BAC and eGFP sequences and is otherwise wt ( see Fig 2 A for its tropism ) [45] . The modifications to tropism were assayed in J cells which transgenically express the receptor of choice , HVEM , nectin1 , or HER2 . J cells are negative for HSV gD receptors and cannot be infected by wt-HSV [21] . The cells were infected with R-903 and R-909 recombinants , and scored by fluorescence microscopy . Fig 2A shows that R-903 and R-909 infected J-HER2 cells , implying that they were retargeted to HER2 . R-903 also infected J-HVEM and J-nectin1 cells , as expected , given that it encodes a wt-gD . In contrast , R-909 failed to infect through the gD receptors , in agreement with the AA 6–38 deletion in gD . The tropism of R-909 was essentially similar to that of R-809 ( previously named R-VG809 ) , retargeted to HER2 by insertion of scFv in gH , and detargeted from gD receptors [48] . As expected , the wt R-LM5 infected J-nectin1 and J-HVEM cells and failed to infect J-HER2 cells . For a summary of nomenclature and properties of viruses employed in this study , see Table 1 . R-909 was further assayed for ability to infect HER2-pos ( SK-OV-3 , BT-474 , MDA-MB-453 ) and HER2-neg HeLa , MDA-MB-231 cancer cells , and the keratinocytic cell line HaCaT . The non-detargeted R-903 was included as control . Fig 2B shows that R-909 infected the HER2-pos cells , but failed to infect the HER2-neg cells . By contrast , R-903 infected both sets of cells . The results strengthen the conclusion that R-909 is retargeted to HER2 via gB and detargeted from natural gD receptors . Three main conclusions can be drawn from this set of experiments . First , the insertion of a scFv in gB modifies HSV tropism . This was a surprising result since gB is known for its ability to carry out virion-cell fusion , but not as determinant of HSV tropism . Second , the finding that R-909 infected J-HER2 cells , but not J cells , suggests that the scFv in gB enabled gB activation upon interaction with HER2 , and rules out that gB activation occurred independently of the interaction with HER2 . Lastly , the infection of J-HER2 cells with R-909 occurs in the absence of a gD receptor , or with a deleted gD unable to bind its natural receptor . Essentially , it is independent of receptor-mediated gD activation . These properties imply that the cascade of glycoprotein activation that ultimately leads to gB activation does not occur in a canonical manner in R-909 , i . e . starting from receptor-bound gD and via transmission to gH/gL and then to gB . Fundamental properties for any candidate oncolytic virus are the extent of replication and the ability to kill the infected cells . SK-OV-3 cells were infected with R-909 at 0 . 1 PFU/cell . The gH-retargeted R-809 , the gD-retargeted R-LM113 , and the wt HSV-1 ( F ) were included for comparison . Virus yields were determined at 0 time ( 3 h ) , 24 and 48 h after infection . Fig 3A shows that R-909 grew to titers very similar to those of R-809 , and less than one log lower than those of the gD-retargeted R-LM113 . All retargeted viruses replicated between 1–2 log less than the parental HSV-1 ( F ) . Fig 3B shows that plaques from R-909 were bigger in size relative to R-809 and R-LM113; all were smaller relative to R-LM5 . The killing ability of R-909 for the HER2-pos MDA-MB-453 and SK-OV-3 cancer cells is reported in Fig 3C–3E . Cytotoxicity of R-909 was very similar to that of R-809 and HSV-1 ( F ) , especially at 7 days after infection , and ranged from 70 to 90% at 7 days after infection for the HER2-positive SK-OV-3 and MDA-MB-453 cells . R-909 and R-809 failed to kill the HER2-neg MDA-MB-231 cancer cells ( Fig 3E ) , consistent with failure to infect them , whereas the wt HSV-1 ( F ) killed about 90% at 7 days after infection ( Fig 3E ) . Cumulatively , the results show that retargeting through gB confers very similar properties as retargeting through gH , in terms of virus growth , plaque size and killing ability . To verify that R-909 infection occurs through the interaction of the chimeric gB ( gBHER2 ) with HER2 , we verified whether gBHER2 binds HER2 , and whether R-909 infection was inhibited by trastuzumab , the MAb to HER2 from which the scFv was derived [50] . For the binding assay , we cloned gBHER2 from R-909 , gHHER2 from R-803 . 293T cells were transfected with gBHER2 , gHHER2 , or their wt counterpart , and reacted with a soluble truncated form of HER2 . Fig 4A and 4C show that the chimeric gBHER2 , and gHHER2 reacted with soluble HER2 , whereas the wt-gB and wt-gH did not . This result shows that the binding of gBHER2 , or gHHER2 to HER2 occurs in the absence of the other glycoproteins . For the infection assay , we infected J-HER2 or SK-OV-3 cells in the presence of trastuzumab . A wt HSV was not included as it does not infect J-HER2 cells ( see Fig 2A and references [44] and [45] ) . Trastuzumab blocked entry of R-909 and R-LM113 , but not of R-LM5 ( Fig 4E ) , indicating that R-909 uses HER2 as a portal of entry . This finding and the above implication that R-909 did not require the canonical cascade of glycoprotein activation triggered by the gD interaction with nectin1 or HVEM prompted us to further characterize the entry process of R-909 . We asked whether R-909 infection was inhibited by neutralizing antibodies to gD , gH and gB . R-LM113 , and R-LM5 ( see , Table 1 ) were included for comparison . Virions were preincubated with MAbs to gD ( HD1 ) , to gH ( 52S ) , or to gB ( H126 ) [51–53] . All antibodies inhibited R-909 infection ( Fig 4F and 4G ) ; at low concentrations MAb HD1 failed to inhibit entry of R-909 and R-LM113 , as noted earlier [48] . The results indicate that infection with R-909 requires the essential glycoproteins gD , gH and gB . Altogether , current and previous findings that HER2 can mediate HSV entry once the scFv to HER2 is inserted in gD , in gH , or in gB , raise the question as to the mechanism by which HER2 mediates entry of the three sets of recombinants . Subsequent experiments were finalized to address this question , and were conducted by comparing R-909 to the gH-retargeted R-809 and to the gD-retargeted R-LM113 and R-LM249 . The requirement for gD in infection of R-909 is in apparent contrast with the lack of requirement for receptor-mediated gD activation seen in J-HER2 cells , and may reflect multiple functions of this glycoprotein . Evidence in favour of receptor-independent gD activities in virus infection and cell-cell fusion was provided [42 , 54 , 55] . In particular , in addition to the triggering role exerted by receptor-bound gD , gD may play a “structural” role [48] . Inasmuch as the entry glycoproteins assemble in complexes also in the absence of gD receptors [14–18] , the complete absence of anyone of the glycoproteins , or their binding to antibodies , may affect the stability/structure/stoichiometry/gymnastics of the complexes [48] . A variety of techniques were employed for detection of the complexes . Often , it was unclear whether the complexes under study were or not fusion-competent , and/or whether they reflected functional complexes in the virion envelope . A surrogate functional technique to infection has been the extensively used cell-cell fusion assay . A key problem in interpreting the results of this assay is that it is not known whether mutant glycoproteins , or specific mixtures , which exhibit a low level activity in the cell-cell fusion , would give rise to infectious or non-infectious viruses . Thus , a very low level of cell-cell fusion ( about 4% of the fusion obtained with wt-glycoproteins ) was detected in the absence of gD with a partially deleted form of gH , which was interpreted as a partially activated form of gH [56] . Virions carrying such deleted form of gH were not generated . In contrast to gH , virions carrying hyperactive forms of gB in the presence of gD , exhibited no infection in the absence of gD [42] . All in all , a non-triggering , structural role of gD is plausible , but has not been clearly documented so far . The HER2-retargeted gH ( gHHER2 ) and gB ( gBHER2 ) offered the opportunity to dissect these two functions of gD . We set up a cell-cell fusion assay , whereby the donor cells express the glycoproteins and T7 Polymerase , and the target cells express the receptor—HER2 or nectin1—along with T7 promoter driven luciferase . gBHER2 was cloned from R-909 , and the gHHER2 was cloned from R-803 . To mimic the situation in R-909 and R-809 recombinants , we also cloned gDΔ6–38 . These glycoproteins , or their wt versions , were transfected in appropriate combinations . Fig 5A shows that glycoprotein mixtures which included the gBHER2 or gHHER2 promoted fusion with CHO-nectin1 cells in the presence of wt-gD . These results imply that gBHER2 and gHHER2 maintain the fusogenic , or pro-fusogenic , activity of their wt counterparts , although at somewhat reduced extent , in agreement with the infectivity of R-909 and R-809 . Importantly , in the absence of gD , the fusion activity dropped by at two-three logs and reached the background level ( average value 5 x 10E3 relative luciferase units ) , suggesting that gD can not be omitted . Fig 5B shows that glycoprotein mixtures which included gBHER2 , or gHHER2 exhibited a significant fusion activity with CHO-HER2 cells , again somewhat lower than that of their wt counterparts with CHO-nectin1 cells . wt-gB and wt-gH did not induce fusion with CHO-HER2 cells . The fusion activity with receptor-negative CHO cells was at the background level ( Fig 5C ) . Thus , the cell-cell fusion assay faithfully mirrored the pattern of infection of HER2-positive cells with R-909 or R-809 virions . Of note , wt-gD could be replaced with gDΔ6–38 , again mirroring the situation with R-909 and R-809 . However , no cell-cell fusion activity was detected in the total absence of gD . Hence , although wt-gD , or gDΔ6–38 , are not activated by HER2 , and therefore do not play a triggering activity , gD can not be omitted . The results provide experimental evidence in favour of a role of gD other than triggering , most likely in favour of its structural role . Entry of HSVs into the cell occurs in a cell line dependent fashion by fusion at plasma membrane ( or with neutral endosomes ) , or by endocytosis into acid endosomes [57–59] . αvβ3 and αvβ6 integrins intervene as routing factors , and promote the endocytic pathway of entry [26 , 60] . Thus , infection of J-nectin1 cells with wt-HSV occurs by fusion at plasma membrane , and is not inhibited by bafilomycin A ( BFLA ) [60] . Infection of J-nectin1 cells overexpressing αvβ3 or αvβ6 integrins is via acidic endosomes and inhibited by BFLA . Here , we investigated the pathway of R-909 , R-809 , R-LM113 and R-LM249 infection of J-HER2 and SK-OV-3 cells . The BFLA inhibition curve shows that infection of J-HER2 cells was decreased with all recombinants , except R-LM113 ( Fig 6A ) . Inhibition of SK-OV-3 cell infection was essentially similar ( Fig 6B ) . In these cells , the entry of wt R-LM5 was not inhibited by BFLA . Even the infection of J-nectin1 cells with R-LM5 was not inhibited by BFLA ( Fig 6C ) , in agreement with earlier work [58] . Altogether , HER2 promoted an acidic endosome pathway of entry for all recombinants , but not for R-LM113 . This property is in agreement with promotion of endocytosis by EGFR family members [61] . Why R-LM113 and R-LM249 behave differently is unclear at present . The two viruses differ with respect to the site of scFv insertion , which is N-terminal in R-LM113 , and replaces the gD core in R-LM249 . Previously , our laboratory showed that αvβ6 or αvβ8 integrins serve as receptors for HSV entry , bind gH and contribute to its activation [26 , 62] . Hence , αvβ6 or αvβ8 integrins are part of the mechanism of HSV glycoprotein activation that starts with the receptor-bound gD . Here , we tested whether infection with the retargeted viruses R-909 , R-809 , R-LM113 , and R-LM249 requires αvβ6 or αvβ8-integrins . SK-OV-3 cells ( which express nectin1 , αvβ3 , αvβ6 and αvβ8 integrins ) were simultaneously depleted of β6 and β8 subunits , or mock depleted ( Fig 7A ) , and then infected . As controls , we used the wt R-LM5 . Extent of infection was monitored through eGFP or mCherry . The results summarized in Fig 7B show that infection with R-909 was unaffected by β6 and β8 integrins depletion . By contrast , infection with the wt R-LM5 was almost completely abolished . Infection with the gD-retargeted R-LM113 or R-LM249 was decreased but not abolished in the depleted cells . Interestingly , also the infection with the gH-retargeted R-809 was not decreased by the β-integrins depletion . Thus , the recombinants retargeted through gB , or through gH , exhibited null-to-low requirement for β6 or β8 integrins . In contrast , the wt virus and the gD-retargeted virus exhibited a very high , or high requirement for β6 and β8-integrins . In brief , there appears to be a gradient in terms of integrins-dependence going from wt- and gD-retargeted , to gH-retargeted and to gB-retargeted recombinants . The ligand to αvβ6 or αvβ8 integrins is gH/gL [26] . Earlier , we showed that integrins induce a conformational change to gH/gL , that results in the displacement of gL from the heterodimer [32] . The displacement occurs at virus attachment/entry , and is prevented under conditions that block virus entry . It only occurs if all the components of the entry apparatus are present , in particular it requires receptor-activated gD as well as gB . Several lines of evidence indicated that it is most likely part of the activation process of gH/gL [32] . The gL displacement can be readily monitored in cell overexpressing αvβ6 integrin , by comparing the reactivity of virions to two anti-gH MAbs . MAb 52S recognizes a gL-independent epitope , and reflects the total quantity of virions absorbed to cells . MAb 53S recognizes a gH epitope that is formed only when gH heterodimerizes with gL ( gL-dependent gH epitope ) [52] . Once virions attach to cells , the gL displacement is detected as a decrease in MAb 53S reactivity , relative to MAb 52S reactivity [32] . Here , we asked whether the HER2-mediated entry of R-909 entails gL displacement , hence gH activation . J-HER2 or J-HER2 cells expressing αvβ6 integrin ( J-HER2+αvβ6 ) were exposed to R-909 and , for comparison , to R-LM113 , R-LM249 and R-809 . Fig 8A shows that: In the samples where the 53S reactivity was decreased , there was a concomitant release of gL in the medium ( Fig 8B ) . We conclude the following . In a simplistic view , a receptor can mediate virus entry by promoting juxtaposition of the cell and virion membranes . Alternatively , the receptor induces conformational changes and promotes activation of the cognate glycoprotein . Experimentally , the two mechanisms can be differentiated since , in the latter , but not in the former case , a soluble form of the receptor ( or of the glycoprotein ) , can substitute for the membrane-form of the receptor ( or of the glycoprotein ) [63 , 64] . The results described in the preceding paragraph hint that HER2 is capable to activate the scFv-gB and scFv-gH chimeras of R-909 , and R-809 . To verify this further , we asked whether entry of R-909 , and R-809 , can be mediated by a soluble form of HER2 . The receptor-negative J cells were exposed to R-909 , R-809 , R-LM113 R-LM249 and R-LM5 recombinants in the presence of a soluble form of HER2 , or of BSA or soluble nectin1 , as controls . Twenty-four h later , cultures were scored by fluorescence microscopy , and the number of fluorescent cells was quantified by FACS . The results of a typical experiment are shown in Fig 9A–9O; the quantification is reported as histogram in Fig 9P , in which the lanes are named with the same letters as the corresponding A-O panels . Soluble HER2 promoted entry of all three sets of recombinants , R-909 ( panel N ) , R-809 ( panel K ) , and R-LM113 and R-LM249 ( panels E , H ) . Soluble nectin1 mediated entry of R-LM5 ( panel A ) , as expected . Infection was negligible for all retargeted viruses in the absence of soluble HER2 ( panels D , G , J , M ) , or in the presence of BSA ( panels F , I , L , O ) . The results clearly indicate that the soluble HER2 was able to promote infection of all HER2-retargeted recombinants , and favour the view that HER2 acts by promoting conformational modifications to the respective HER2-retargeted glycoproteins .
R-909 is highly specific for HER2-positive cells . It replicated to similar yields as the gD-retargeted R-LM113 and the gH-retargeted R-809 . It effectively killed HER2-positive cancer cells . gB expands and improves the toolkit for the design of retargeted oncolytic HSVs . It can be envisioned that retargeting via gB could be combined with retargeting via gD , or via gH , so as to generate oncolytic HSVs capable to target cancer cells heterogeneous in receptor display . gB is the fusogenic glycoprotein in HSV and across the Herpesviridae family . The crystal structure of the post-fusion conformation showed features typical of fusion glycoproteins . gB is a trimer , with a central coiled coil , and a crown that carries binding sites for major neutralizing antibodies [11] . Each monomer carries a bipartite fusion loop . A closely similar structure is exhibited by gB from Epstein Barr virus and human cytomegalovirus , hence the structure is conserved across the Herpesviridae family [12 , 65] . The crystals were obtained for gB alone; no co-crystal of gB with cellular proteins was reported . The structure of the prefusion conformation was inferred recently by electron cryotomography [66] . So far , gB was not recognized to be a determinant of HSV tropism . HSV gB interacts with three receptors , PILRα , myelin associated glycoprotein , and non-muscle myosin heavy chain IIA and IIB [27–30] . However , the role played by these receptors in HSV entry , including their contribution to the host range of the virus , is poorly understood . Whether the receptors induce conformational changes to gB , and contribute to gB activation is also unknown . It was noted that the PILRα-mediated entry necessitates of gD [27] . Two series of experiments support this contention . First , R-909 carries the deletion of AA 6–38 in gD , which ablates its ability to interact with , and be activated by nectin1/HVEM . Hence , R-909 infection occurs in the absence of a nectin1/HVEM-mediated gD activation . Second , infection of R-909 , but not of wt-virus , occurred in cells depleted of β6 and β8-integrins [26] . At attachment/entry of R-909 , the displacement of gL from gH/gL did not take place . For wt-HSV , gL displacement appears to be part of the process of gH activation [32] . We conclude that R-909 infection occurs in the absence of integrins-mediated gH activation . Altogether , whereas entry of the wt-virus requires the activation of gD and of gH by their respective receptors in a cascade fashion , entry of the gB-retargeted virus does not . HER2 directly activates the chimeric gB . We conclude that the chimeric gB carries two functional domains: the scFv that enables gB activation upon binding to HER2 , and the fusogenic domain , intrinsic to the glycoprotein . With respect to gD , earlier and current work hinted that gD may encode activities other than triggering of glycoprotein activation upon receptor binding [42 , 48 , 54 , 55] . We reasoned that , since the entry glycoproteins assemble in complexes [14–17] , even in resting virions [17] , the complete absence of anyone of the glycoproteins , or their binding to high amounts of antibodies , may compromise the stability/structure/stoichiometry/gymnastics of the complex , independently of their role in the cascade of activation . We refer to this as a “structural” role of the glycoproteins [48] , and searched evidence for it in a cell-cell fusion assay . The gBHER2 , or the gHHE2 , or their wt counterparts , were transfected in combination with other components of the fusogenic quartet ( gD , gH , gL , gB ) . The transfected cells were allowed to fuse with target cells expressing HER2 or nectin1 . In all combinations the fusion by HER2-retargeted gB , HER2-retargeted gH , or their wt counterparts required gD , be it wt or the non-activable gDΔ6–38 . This provides formal evidence for gD activity other than triggering , most likely for a structural role . To shed further light on the mechanism of HER2-mediated entry , we compared three sets of recombinants: the gD-retargeted R-LM113 and R-LM249 , the gH-retargeted R-809 , and the gB-retargeted R-909 . For all three recombinants the soluble HER2 was able to replace the membrane-bound HER2 in mediating entry . Thus , a common feature was that HER2 did not serve the function of anchoring the viruses to the cell; rather it induced conformational modifications to the respective targeted glycoprotein . Interestingly , HER2 exerted differential effects on the entry apparatus of the three sets of recombinants . While the wt R-LM5 required nectin1/HVEM for gD activation and integrin for gL displacement , the gD-retargeted viruses required HER2 for gD activation , and integrins for gH activation and gL displacement ( Fig 10 ) . In the case of the gH-retargeted R-809 , HER2 brought about both gH activation and gL displacement; gD activation by nectin1/HVEM was not needed . In the case of the gB-retargeted R-909 , neither the gD nor gH activation and gL displacement were needed ( Fig 10 ) . HER2 directly activated gB: because of this direct activation , there was no need for integrins and for gL release . Thus , the direct activation of the chimeric gBHER2 by HER2 bypassed the need for activation by receptor-activated gD and gH . It is possible to engineer a chimeric gB which carries two topologically and functionally distinct domains: the scFv that enables gB activation upon binding to the HER2 , and the fusogenic machine , intrinsic to the glycoprotein . The chimeric gB does not need the activation signaling by receptor-bound gD and gH . Functionally , the entry machine of the recombinant R-909 is a monopartite apparatus . The question arises as to why herpesviruses evolved to have a multipartite entry system . The advantages of a multipartite entry system were in part addressed in a recent review ( see , [62] ) . Briefly , HSV needs specific integrins in order to promote endocytosis , the preferred route of entry for HSV and for other herpesviruses [26 , 60 , 67] . For most of the retargeted HSVs , this role appears to be carried out by HER2 , which promoted the endocytic pathway of entry . In addition , the multipartite system allows the virus to synchronize endocytosis with the cascade of activation of the glycoproteins , so as to avoid premature activation , and exhaustion of the entry apparatus [32 , 62] . Integrins activate a signalosome , which can be usurped to the benefit of the virus . Across the family , the multipartite system may ensure a broad and diversified spectrum of receptors [62] . From an evolutionistic point of view , the monopartite system may not ensure the level of sophistication granted to the family by the multipartite system .
The J cells ( negative for HSV receptors ) and their derivatives expressing HER2 , nectin1 or HVEM were previously described [44 , 68] . J cells were in turn derived from baby hamster kidney tk- ( BHKtk- ) cells ( line B-1 , GM0348A; National Institute of General Medical Sciences Human Genetic Mutant Cell Repository , Bethesda , Md . ) , described in [69] . The R6 cell line is a derivative of rabbit skin ( RS ) cells which expresses HSV gD in inducible manner , and can complement HSV mutants in gD [70] . The RS cells were a generous gift from Prof . Bernard Roizman ( University of Chicago ) , who in turn received them from Dr . J . McLaren ( University of New Mexico ) [71] . The above cells were grown in DMEM ( #31600–083 , Gibco Laboratories ) supplemented with 5% fetal bovine serum ( FBS ) ( #10270–106—E . U . -approved , South America origin , Gibco Laboratories ) . The CHO , MDA-MB-231 , MDA-MB-453 , BT-474 , SK-OV-3 , HaCaT and HeLa cells were purchased from ATCC and cultured as recommended by ATCC . F-12 ( Ham ) nutrient mixture medium ( #BE12615F , Lonza Group Ltd . ) ( CHO cells ) , Dulbecco’s modified Eagle medium ( #31600–083 , Gibco Laboratories ) , RPMI 1640-Glutamax ( #61870010 , Gibco Laboratories ) and FBS ( #10270–106 ) were supplied by Lonza Group Ltd . or GIBCO Laboratories ( Life Technologies , Milano ) as specified . The HSV-1 ( F ) received from Dr . B . Roiman was the prototype wt virus [72] . The recombinant viruses R-LM5 , R-LM113 , R-LM249 , R-803 and R-809 were described elsewhere [45 , 46 , 48] . R8 polyclonal antibody ( PAb ) to gD and BD80 monoclonal antibody ( MAb ) to gD were generously provided by Dr . G . H . Cohen and Dr . R . Eisenberg . MAbs HD1 and H126 were a gift from Dr . L . Pereira . MAb H1817 was purchased from Goodwin Institute . MAbs 52S and 53S were described [52] . A PAb to gH/gL was derived to a soluble form of gH truncated at aa 789/gL produced in insect cells [17] . Recombinant human ErbB2/HER2 Fc chimera corresponding to soluble form of HER2 ( S-HER2 ) was purchased from R&D System . Soluble form nectin1 ( S-Nectin1 ) was described [68] . Bovine serum albumin ( BSA ) was purchased from Sigma-Aldrich . First , we engineered R-903 by insertion of the sequence encoding the trastuzumab scFv between AA 43 and 44 of immature gB , corresponding to AA 13 and 14 of mature gB , after cleavage of the signal sequence , which encompasses AA 1–30 . The starting genome was the BAC LM5 BG , which carries LOX-P-bracketed pBeloBAC11 and eGFP sequences inserted between UL3 and UL4 of HSV-1 genome [45] . The engineering was performed by galK recombineering . Briefly , the galK cassette with homology arms to gB was amplified by means of primers gB43GalKfor GGTGGCGTCGGCGGCTCCGAGTTCCCCCGGCACGCCTGGGGTCGCGGCCGCGCCTGTTGACAATTAATCATCGGCA and gB43GalKrev GGCCAGGGGCGGGCGGCGCCGGAGTGGCAGGTCCCCCGTTCGCCGCCTGGGTTCAGCACTGTCCTGCTCCTT using pGalK as template . This cassette was electroporated in SW102 bacteria carrying LM55 BG BAC . The recombinant clones carrying the galK cassette were selected on plates containing M63 medium ( 15 mM ( NH4 ) 2SO4 , 100 mM KH2PO4 , 1 . 8 μg FeSO4·H2O , adjusted to pH7 ) supplemented with 1 mg/L D-biotin , 0 , 2% galactose , 45 mg/L L-leucine , 1 mM MgSO4·7H2O and 12 μg/ml chloramphenicol . In order to exclude galK false positive bacterial colonies , the positive recombinant clones were streaked also on MacConkey agar base plates supplemented with 1% galactose and 12 μg/ml chloramphenicol and checked by colony PCR with primer galK_129_f ACAATCTCTGTTTGCCAACGCATTTGG and galK_417_r CATTGCCGCTGATCACCATGTCCACGC . Next , the trastuzumab scFv cassette with the downstream Ser-Gly linker and bracketed by homology arms to gB was amplified with primers gB43_sc4D5_for GGTGGCGTCGGCGGCTCCGAGTTCCCCCGGCACGCCTGGGGTCGCGGCCGCGTCCGATATCCAGATGACCCAGTCCCCG and gB43_sc4D5_rev GGCCAGGGGCGGGCGGCGCCGGAGTGGCAGGTCCCCCGTTCGCCGCCTGGGTACCGGATCCACCGGAACCAGAGCC using pSG-scFvHER2-SG plasmid as template [48] . The recombinant genome encodes for the chimeric gB , which carries the scFv to HER2 and one downstream Ser-Gly linker , with sequence SSGGGSGSGGSG , and one linker between VL and VH region with the sequence SDMPMADPNRFRGKNLVFHS . The recombinant clones carrying the excision of the galK cassette and the insertion of the sequence of choice , scFv-HER2 , were selected on plates containing M63 medium ( see above ) supplemented with 1 mg/L D-biotin , 0 . 2% deoxy-2-galactose , 0 . 2% glycerol , 45 mg/L L-leucine , 1 mM MgSO4·7H2O and 12 μg/ml chloramphenicol . Bacterial colonies were also checked for the presence of sequence of choice by means of colony PCR with primers gB_ext_for GAGCGCCCCCGACGGCTGTATCG and gB_431_rev TTGAAGACCACCGCGATGCCCT . The starting material for R-909 was the R-903 BAC genome . To engineer the AA 6–38 deletion in gD , galK cassette flanked by homology arms to gD was amplified with primers gD5_galK_f TTGTCGTCATAGTGGGCCTCCATGGGGTCCGCGGCAAATATGCCTTGGCGCCTGTTGACAATTAATCATCGGCA and gD39_galK_r ATCGGGAGGCTGGGGGGCTGGAACGGGTCCGGTAGGCCCGCCTGGATGTGTCAGCACTGTCCTGCTCCTT . Next , we replaced galK sequence with a synthetic double-stranded oligonucleotide gD_aa5_39f TTGTCGTCATAGTGGGCCTCCATGGGGTCCGCGGCAAATATGCCTTGGCGCACATCCAGGCGGGCCTACCGGACCCGTTCCAGCCCCCCAGCCTCCCGAT . To reconstitute the recombinant virus R-903 , 500 ng of BAC DNA was transfected into SK-OV-3 cells by means of Lipofectamine 2000 ( Life Technologies ) . Alternatively , for R-909 , the corresponding BAC was first transfected in gD-complementing cell line R6 ( a rabbit skin cell derivative expressing glycoprotein D under the control of HSV late promoter UL26 . 5 ) [70] . After one passage , the infected cells were frozen . R-909 was subsequently grown and titrated in SK-OV-3 cells . Virus growth was monitored by green fluorescence . The recombinants were genotyped by sequencing the gB , gD and gH ORFs . To detect the expression of gBHER2 , SK-OV-3 cells were infected at 3 PFU/cell with R-909 and R-LM5 , and harvested 72 h after infection . Cell lysates were subjected to SDS-PAGE , transferred to polyvinylidene difluoride membranes and immunoblotted with MAb H1817 to gB . The indicated J cell derivatives were infected with R-909 , R-903 , R-809 and R-LM5 at an input multiplicity of infection of 3 PFU/cell for 90 min at 37°C and monitored by fluorescence microscopy 24 h post infection . The SK-OV-3 , BT-474 , MDA-MB-453 HER2-pos cancer cells , and theHER2-neg HeLa and MDA-MB-231 cancer cells , and the HER2-neg non-cancer HaCaT cells were infected at an input multiplicity of infection of 3 PFU/cell ( as titrated in SK-OV-3 ) for 90 min at 37°C with R-909 and R-903 . Pictures were taken 24 h after infection by fluorescence microscopy . SK-OV-3 cells were infected at 0 . 1 PFU/cell for 90 min at 37°C . Unabsorbed virus was inactivated by means of an acidic wash ( 40 mM citric acid , 10 mM KCl , 135 mM NaCl , pH 3 ) . Replicate cultures were frozen at the indicated times ( 3 , 24 and 48 h ) after infection and the progeny was titrated in SK-OV-3 . For plaque size determinations , the indicated viruses were plated onto SK-OV-3 cells with agar overlay . Pictures of 10 plaques were taken for each virus . Plaque areas ( pxE2 ) were measured with Nis Elements-Imaging Software ( Nikon ) . Each result represents average areas ± SD . SK-OV-3 , MDA-MB-453 and MDA-MB-231 cells were seeded in 96 well plates 8 x 103 cells/well , and infected with the indicated viruses or mock-infected for 90 min at 37°C . The input multiplicity of infection ( as titrated in the correspondent cell line ) was 2 PFU/cell for the SK-OV-3 and MDA-MB-453 . MDA-MB-231 cells were infected with the recombinant viruses R-909 and R-809 at approximately 0 . 1 PFU/cell ( in these cells the viruses do not form plaques , but only singly infected cells ) . MDA-MB-231 cells were infected with the wt HSV-1 ( F ) at 0 . 05 PFU/cell . AlamarBlue ( Life Technologies ) was added to the culture media ( 10 μl/well ) at the indicated times after virus exposure and incubated for 4 h at 37°C . Plates were read at 560 and 600 nm with GloMax Discover System ( Promega ) . For each time point , cell viability was expressed as the percentage of AlamarBlue reduction in infected versus uninfected cells , after subtraction of the background value ( medium alone ) . Each point represents the average of at least three triplicate samples ± SD . 100 ng of DNA encoding wt gH/gL , gHHER2/gL , wt gB or gBHER2 was transfected into 293T cells by means of Lipofectamine 2000 ( Life Technologies ) . 24 h later cells were fixed with paraformaldehyde , incubated with a soluble truncated form of HER2 tagged with 6x His tag ( 10 μg/ml , recombinant Human ErbB2/Her2 Fc chimera , R&D SYSTEM ) , subsequently incubated with mouse anti-His antibody ( 1 μg/ml , Sigma-Aldrich ) , and with Anti-Mouse IgG ( Fc specific ) -Alkaline Phosphatase ( 1:3000 , Sigma-Aldrich ) in presence of BCIP ( 5-bromo-4-chloro-3-indolyl-phosphate , 166 μg/ml , Sigma-Aldrich ) and NBT ( nitro blue tetrazolium , 333 μg/ml , Sigma-Aldrich ) substrate . Replicate monolayers of J-HER2 cells , or SK-OV-3 cells in 12-well plates were preincubated with trastuzumab , the MAb to HER2 from which the scFv-HER2 was derived or with non–immune mouse IgG ( 28 μg/ml final concentration ) . After 1 h at 37°C , the cells were infected at an input multiplicity of infection of 5 PFU/cell ( as titrated in SK-OV-3 ) with R-909 and R-LM113 or R-LM5 . Alternatively , where indicated , virions were pre-incubated with MAb HD1 to gD ( 1 . 5 μg/ml , or 30 μg/ml ) , MAb H126 to gB ( ascites fluid , 1:2000 ) , MAb 52S to gH ( ascites fluid 1:25 ) for 1 h at 37°C , and then allowed to adsorb to cells for 90 min . In the case of MAb HD1 , the combination of HD1 plus trastuzumab was also tested . Viral inocula were then removed , and cells were overlaid with medium containing the indicated antibodies . Infection was quantified by fluorescent activated cell sorter ( FACS ) . Expression plasmids for wt-gD , gB , gH , and gL were described [73] . EGFR2Δ ( named erb-2 ) carries the extracellular domain and transmembrane ( TM ) sequences of rat HER-2/neu ( nucleotides 25 to 2096 ) ( GenBank accession number NM_017003 ) and is deleted of the tyrosine kinase domain [74] . Plasmid pcagt7 contains the T7 RNA polymerase gene under control of the CAG promoter , and the pt7emcluc plasmid expresses the firefly luciferase under the T7 promoter [75] . Plasmids encoding nectin1 and HER2 were described [21 , 76] . gBHER2 , gHHER2 , and gDΔ6–38 were PCR amplified from R-909 or R-803 , and cloned into pcDNA3 . 1 ( - ) ( Thermo Fisher Scientific ) , as follows . gBHER2 was amplified with primers gB5_XbaIf CCCCGTAGTTCTAGAGGCACGACGGGCCCCCGTAGTCCCGCCATGC and gBB_EcoRI_r ACAACAAACGAATTCGATGCACATGCGGTTTAACACCCGTGG , then digested with XbaI and EcoRI ( New England Laboratories ) . The 3498 bp XbaI/EcoRI fragment was ligated into the XbaI/EcoRI MCS of pcDNA3 . 1 ( - ) . The gHHER2 was amplified with primers gH803_XbaI_f GGGACGGGGTCTAGAATGGGGAATGGTTTATGGTTCG and gH803_NotI_r CCGAAGCCAGCGGCCGCTTATTCGCGTCTCCAAAAAAACGGG , then digested with XbaI and NotI ( New England Laboratories ) . The 3325 bp XbaI/NotI fragment was ligated into XbaI/NotI MCS of pcDNA3 . 1 ( - ) . gDΔ6–38 was amplified with primers gD_XbaI_f GTGGTGCGTTCTAGAATGGGGGGGGCTGCCGCCAGG and gD_NotI_r CCATTAAGGGCGGCCGCCTAGTAAAACAAGGGCTGGTGCG , then digested with XbaI and NotI ( New England Laboratories ) . The 1093 bp XbaI/NotI fragment was ligated into XbaI/NotI MCS of pcDNA3 . 1 ( - ) . Inserts were verified by sequencing and by immunofluorescence of the encoded glycoproteins in transfected cells . The luciferase-based cell-cell fusion assay was performed as described [77 , 78] . Donor CHO cells were transfected with the indicated glycoprotein mixture , and the targeted CHO cells with the indicated receptor , or no receptor . Detection was done by means of a luciferase assay system ( Promega ) . The total amount of transfected plasmid DNA was made equal by the addition of EGFR2Δ 2 plasmid . Each value represents the average of triplicate samples . A 160 mM stock solution of Bafilomycin A ( BFLA ) ( Sigma Aldrich ) was prepared in dimethyl sulfoxide , and further diluted in medium . Cells were exposed to BFLA for 1 h at 37°C and then infected with R-LM5 , R-LM113 , R-LM249 , R-809 and R-909 ( 5 PFU/cell ) for 90 min in the presence of BFLA . The viral inoculum was removed and the cells were overlaid with medium containing BFLA for 18 h . The extent of infection of R-809 and of the GFP-expressing viruses was determined by FACS , or by GloMax Discover System ( Promega ) , respectively . Integrins were silenced in SK-OV-3 cells by means of ON-TARGET plus , smart polls ( Dharmacon ) , as previously described [26] . Control cells were transfected with siRNA to E . coli-poliA_0054 [26] . Extent of silencing was determined by RT-PCR using TaqMan gene expression assay ( Applied Biosystems ) . J cells transfected with indicated receptors were exposed for 30 min at 37°C to R-LM5 , R-LM113 , R-LM249 , R-809 , and R-909 at 10 PFU/cell , fixed with 4% ( wt/vol ) paraformaldehyde and incubated with 53S or 52S MAbs and secondary antibody . At the end of virus absorption , the culture medium was concentrated; devoid of serum IgGs by Protein A-Sepharose and subjected to SDS/PAGE . Immunoblot was performed by means of PAb to gH/gL , MAb H1817 to gB and MAb BD80 to gD . The receptor-negative J cells were exposed to R-LM5 , R-LM113 , R-LM249 , R-809 and R-909 for 3 h at 37°C . Soluble form of HER2 ( 150 nM ) and bovine serum albumin ( 150 nM ) , used as negative control , were added to cell-virions mixture for additional 3 h . As positive control , soluble nectin1 ( 150 nM ) was added to J cells exposed to R-LM5 . 24 h later pictures were taken by fluorescence microscopy and the number of fluorescent cells was determined by flow cytometry . | Herpes simplex virus encodes an entry apparatus made of the glycoproteins gD , gH/gL and gB . gD is the major determinant of HSV tropism . Receptor-induced modifications to gD and gH/gL activate in a cascade fashion gB , the conserved fusogenic glycoprotein across the Herpesviridae family . In herpesviruses other than HSV , but not in HSV , gB also contributes to determine the virus tropism . We took advantage of retargeting studies to investigate the process of HSV glycoprotein activation , and the specific roles played by the glycoproteins . When a heterologous ligand is engineered in gB , the virus tropism is retargeted to the ligand receptor . gB becomes the major determinant of HSV tropism , and does not any longer need the receptor-mediated activation of glycoproteins gD and gH/gL . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"cell",
"physiology",
"herpes",
"simplex",
"virus",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"microbiology",
"viral",
"structure",
"viruses",
"integrins",
"dna",
"viruses",
"glycoproteins",
"antibodies",
"cellular",
"structures",
"and",
"organelles",
"herpesviruses",
"immune",
"system",
"proteins",
"cell",
"adhesion",
"medical",
"microbiology",
"extracellular",
"matrix",
"proteins",
"microbial",
"pathogens",
"virions",
"biochemistry",
"viral",
"tropism",
"cell",
"biology",
"virology",
"virus",
"glycoproteins",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"glycobiology",
"organisms",
"cell",
"fusion"
] | 2017 | Insertion of a ligand to HER2 in gB retargets HSV tropism and obviates the need for activation of the other entry glycoproteins |
Prior experiences can influence future actions . These experiences can not only drive adaptive changes in motor output , but they can also modulate the rate at which these adaptive changes occur . Here we studied anterograde interference in motor adaptation – the ability of a previously learned motor task ( Task A ) to reduce the rate of subsequently learning a different ( and usually opposite ) motor task ( Task B ) . We examined the formation of the motor system's capacity for anterograde interference in the adaptive control of human reaching-arm movements by determining the amount of interference after varying durations of exposure to Task A ( 13 , 41 , 112 , 230 , and 369 trials ) . We found that the amount of anterograde interference observed in the learning of Task B increased with the duration of Task A . However , this increase did not continue indefinitely; instead , the interference reached asymptote after 15–40 trials of Task A . Interestingly , we found that a recently proposed multi-rate model of motor adaptation , composed of two distinct but interacting adaptive processes , predicts several key features of the interference patterns we observed . Specifically , this computational model ( without any free parameters ) predicts the initial growth and leveling off of anterograde interference that we describe , as well as the asymptotic amount of interference that we observe experimentally ( R2 = 0 . 91 ) . Understanding the mechanisms underlying anterograde interference in motor adaptation may enable the development of improved training and rehabilitation paradigms that mitigate unwanted interference .
The history of prior action in the human motor system is known to influence not only future performance through memory , but also the capacity for future learning . Interference and savings are two oppositely-directed phenomena that produce this effect . Interference describes the ability of one task to impair the learning of another , while savings describes the ability of previous learning to enhance future learning . For example , previous work has shown that after initial learning and subsequent washout of a visuomotor rotation task , relearning is faster than the initial learning , even if the performance levels of the learner ( i . e . the motor output ) at the onset of learning and relearning are identical [1]–[2] . Similarly , in a saccadic gain adaptation task , after learning and subsequent opposite-learning such that the motor output returns to pre-learning levels , relearning is also observed to be consistently faster than initial learning [3] . Other studies have demonstrated that previous learning can hinder or interfere with future learning [4]–[10] . An experimental paradigm commonly used to study interference is the A1BA2 paradigm , where a subject is instructed to serially learn Task A , Task B , and then Task A again - often with various time delays inserted between tasks . In this paradigm , Task B is usually made to be the opposite of Task A ( e . g . a clockwise vs . counterclockwise force-field or visuomotor rotation ) . Two types of interference can be studied with this paradigm – ( 1 ) retrograde interference: how Task B interferes with the memory of Task A1 , and ( 2 ) anterograde interference: how the memory of Task A1 interferes with the subsequent learning of Task B ( or how B interferes with A2 ) . Note that both retrograde and anterograde interference can affect performance in Task A2 . Although anterograde interference can often have quite substantial effects [4]–[7] , it has not received as much attention as retrograde interference in the motor adaptation literature . This is surprising because retrograde interference tends to have a relatively small ( 10–20% ) effect on performance in the studies where it is reported [2] , [11]–[13] , whereas anterograde interference often has substantially larger effects [4]–[5] . In fact , several interference studies have been specifically designed to minimize the effects of anterograde interference because they recognized the potential it has for masking retrograde interference [2] , [5] . Acquiring a better understanding of the mechanisms underlying anterograde interference is important not merely to provide greater insight into retrograde interference effects , but because the learning phenomenon is significant in and of itself as the primary cause of interference during motor adaptation . Anterograde interference has been observed in force-field adaptation studies [4]–[5] , [7] and visuomotor rotation studies [6] , and has been shown to weaken as the time between tasks is increased [4] . A recently-proposed computational model for motor adaptation has suggested a possible mechanism for anterograde interference [14] . In this model , one internal adaptive process responds quickly to motor error , but rapidly forgets , while another adaptive process learns slowly from motor error , but has good retention . The contributions of these two processes are combined to generate the net motor output . In the transition from Task A to Task B , the “fast” process will quickly learn the new task , while the “slow” process will be reluctant to follow because of its good retention of the previous task . The multi-rate model predicts that the residual contribution of the slow process would hinder adaptation to Task B , resulting in anterograde interference . The model also predicts that as training in Task A is extended , the amount of interference will also increase , but then level off beyond 15–40 training trials in Task A . Here , using a simple AB paradigm to avoid retrograde interference effects , we examine for the first time how the duration of exposure to Task A influences the amount of anterograde interference observed in Task B in order to determine how the capacity for interference is built up with practice . We then use the predictions of the multi-rate model to determine whether anterograde interference stems from interactions between the different timescales of motor learning .
We studied how exposure to one motor adaptation task ( Task A ) influences the ability to learn a second task ( Task B ) . It has previously been shown that prior exposure to Task A can induce anterograde interference in the learning of Task B when these tasks are opposite [4]–[7] . However , how the capacity for this interference builds up is unclear . Here we focused on how the duration of an initial motor adaptation to velocity-dependent dynamics ( Task A ) influences the amount of interference conferred onto subsequent adaptation to oppositely-directed velocity-dependent dynamics ( Task B ) during reaching arm movements ( Figure 1A ) . We instructed different groups of subjects to learn clockwise [CW] ( Figure 1C ) or counter-clockwise [CCW] velocity-dependent force-fields for varying numbers of trials – either 13 , 41 , 112 , 230 , or 369 trials . After this initial exposure , subjects were switched to the opposite force-field ( Task B ) for about 115 trials ( see Methods ) . Error-clamp trials were interspersed throughout the experiment ( approximately 1 out of every 7 trials ) to probe how the level of adaptation evolved during learning ( Figure 1D; see Methods ) . Baseline-subtracted force patterns measured during these error-clamp trials at various points in training are displayed in Figure 2 . Specifically , this figure shows the data averaged across subjects from the 369-trial group early and late in the training of Task A ( early: red trace , average of first 25 trials; late: green trace , average of trials 259–369 ) , and early in the training of Task B ( blue trace , average of the first 25 trials after force output returned to baseline levels ) . Note that the force pattern produced during late learning of Task A closely matches both the magnitude and shape of the ideal force pattern , which would fully compensate the robot-imposed dynamics . The force pattern produced during early learning is , as might be expected , smaller in magnitude and less specific in shape . Early in training , the force pattern shows an appropriate transient component , but an inappropriate static component at the end of the movement . It has recently been shown that this static component arises because of a pervasive cross-adaptation between position-dependent and velocity-dependent dynamics [15] . Apropos to the current study , the force pattern produced early in Task B appears even smaller , suggesting the presence of anterograde interference from Task A onto Task B . In this study , we define anterograde interference as the reduction in the learning rate for Task B due to previous learning of Task A . This definition is not entirely consistent with all previous work . Numerous studies have characterized anterograde interference by higher initial errors during the learning of Task B when compared to Task A [2] , [6]–[7] , [11]–[12] , [16]–[18] . However , other work has defined anterograde interference in terms of slower learning of Task B instead [14] , [19] . While these two definitions can sometimes be compatible , recent work has shown that this is not necessarily the case – higher initial errors ( often associated with greater interference ) can be coupled with faster learning rates , which indicates reduced interference [15] . Note that the sizes of initial errors have nothing directly to do with the ability to perform subsequent learning , as such . Initial errors in Task B ( especially if these errors are in the feedforward component of motor performance ) should instead reflect the continuity of performance from the end of Task A , in particular when Task B immediately follows Task A [4]–[7] . When a time delay is inserted between these two tasks [4] , initial errors for Task B reflect the retention of Task A . Thus , interference defined this way may say more about performance levels achieved in Task A than the extent to which Task A interferes with the ability to learn Task B . In order to dissociate the interference conferred from Task A onto Task B from the performance level achieved in Task A , we focus on the learning rate observed in Task B once baseline performance has been achieved . Specifically , we compare the opposite-learning curve for Task B ( i . e . the rectified response to Task B starting from when the net adaptation crosses zero; Figure 3B , dashed red line ) to the initial learning of Task A ( Figure 3B , solid red line ) , and use the percent reduction in the Task B learning curve as a metric of interference ( see Methods and Figure 3 ) . If the opposite-learning curves are aligned at the zero-crossing , as illustrated in Figure 4C , then the initial learning and opposite-learning curves will start from the same performance level ( i . e . zero learning ) , and the Task B learning rate can then be directly compared to the Task A learning rate . If the learning curves are compared from task onset rather than zero-crossing , the patterns of performance are similar , regardless of whether anterograde interference occurs ( see Text S1 , Figure S5 and Figure S6 ) . Note that this comparison between initial and subsequent learning curves proceeding from the same performance level is analogous to the comparison between initial learning and relearning rates in the analysis of savings . In the analysis of data from savings experiments , if the unlearning is not complete , initial performance during the second learning period reflects retention of the first adaptation , which is difficult to disambiguate from faster relearning [1] , [3] , [19] . We quantified adaptation levels by regressing the actual force patterns like those displayed in Figure 2 onto the ideal force patterns for each task ( see Methods ) [20] . We refer to the slope of this regression as the adaptation index for a particular trial . Perfect compensation for the force-field would yield an adaptation index of 1 , while no learning would yield an index of 0 . Group-averaged learning curves based on these adaptation indices are shown in Figure 4A ( see Text S1 for an analysis of the R2's for these regressions ( Figure S1 ) , as well as for alternative methods for estimating the regression slopes ( Figure S2 and Figure S3 ) ) . Adaptation can also be assessed by quantifying the amount of force associated with learning-related changes used to counteract the force-field . Since the lateral force required to oppose the force-field is greatest at the peak speed point , which is near the middle of the movement , we used the average mid-movement force as a secondary measure of the progression of adaptation . Here we define mid-movement force as the average force produced during a 250ms window centered at the movement's peak speed . These data are displayed in Figure S4A . We found that both the regression coefficients and mid-movement force metrics revealed learning curves which were essentially identical in shape to one another . In agreement with previous studies [5] , [12] , [14]–[15] , [20]–[25] , we found that the adaptation to the initial velocity-dependent force-field ( Task A ) is at first rapid , and then more gradual . However , upon exposure to Task B , we consistently observed alterations in learning curves that indicated the presence of anterograde interference: after the initial unlearning of Task A brings the learning curves back to zero ( the baseline adaptation level ) , the opposite-learning ( learning of Task B ) appears to proceed more slowly than the initial learning . Figure 4B shows a more direct comparison of Task A and Task B learning curves for the 13-trial and 230-trial groups . The portions of the opposite-learning curves proceeding from the zero-crossings are highlighted with a gray background . Note that this portion of the opposite-learning curve is slower for the 230-trial group than the 13-trial group , consistent with the presence of increased anterograde interference . Analysis of the opposite-learning curves displayed in Figure 4C clearly illustrates the presence of anterograde interference . All of the opposite-learning curves are slower than the initial learning curve , illustrated as the nominal 0-trial group . The opposite-learning curves based on the mid-movement force data also show this effect ( Figure S4B ) . We created a best overall estimate of the initial learning curve by aggregating the data from the initial learning curves from all five groups . The learning curves presented in Figure 4C are smoothed with a three-point moving average ( for non-smoothed versions of these curves based on regression coefficients , see Figure S8 ) . We defined a metric for the amount of anterograde interference caused by initial adaptation to Task A by computing the percent reduction in the opposite-learning curves ( with respect to initial learning ) over the first 25 trials ( Figure 3B ) . We found that the duration of exposure to Task A had a significant effect on the amount of interference ( one-way ANOVA , F ( 5 , 76 ) = 14 . 87 , p = 2 . 7×10−10 ) , indicating that as exposure to Task A is increased from 0 trials , the amount of interference significantly increases . All of the groups experienced significant interference when compared to the aggregated initial-learning curve ( Figure 4B; one-tailed , unpaired student t-tests , p-values between 2 . 8×10−9 and 1 . 4×10−3 ) . Direct comparison of each group's initial-learning and opposite-learning curves reveals that this significant interference is present for all groups , and not just in the comparison with the aggregated initial-learning curve ( i . e . opposite-learning curves are significantly slower than the initial-learning curves within each group; one-sample , one-sided student t-tests , p-values between 2 . 8×10−6 and 0 . 02; because the 13-trial group did not complete 25 trials in Task A , we compared initial and opposite-learning over the first 13 trials in that case ) . However , we found no significant differences between the interference metrics observed for the 41-trial , 112-trial , 230-trial , and 369-trial groups ( one-way ANOVA , F ( 3 , 32 ) = 0 . 38 , p = 0 . 77 ) , but did find a difference when we included the 13-trial group ( one-way ANOVA , F ( 4 , 45 ) = 2 . 71 , p = 0 . 042 ) , indicating that the increase in interference levels off after 15–40 trials ( Figure 4D ) at a value of about 0 . 53 . Interference metrics calculated using the mid-movement force data follow the same pattern as those calculated using the regression coefficients ( Figure S4C ) . What can explain the observation that increasing exposure to Task A leads to greater interference when adapting to Task B , but that this increase in interference then eventually asymptotes ? One possibility is that this pattern of interference results from interactions between different components of the adaptive process . A recent study has suggested that a simple two-process , multi-rate learning model might explain several key features of motor adaptation as a consequence of predictable interactions between these two processes [14] . This learning model is composed of a “fast process , ” which learns very quickly but forgets quickly , and a “slow process , ” which learns slowly but has good retention . The contributions of these two processes are combined to generate the net motor output . The learning curves predicted by this model for the AB adaptation paradigm studied in the current work are displayed in Figure 5A . Note that none of the parameter values we used for this model ( see Methods ) were fit to the current data set; rather , all parameter values were taken from a data set in a previous study ( which looked at spontaneous recovery rather than anterograde interference ) [14] . Ideal performance for Task A is represented as an adaptation index of +1 , while Task B is represented as −1 . Initially , the overall learning ( red curve ) is rapid because the fast process ( green curve ) quickly responds to the motor error . However , this rapid learning results in a quick decrease in the amount of error driving the learning . As a result , the amount of learning decreases and the fast process begins to forget more than it learns , leading to a decline in its level beginning around 10–20 trials after the onset of learning . In parallel , the slow process ( blue curve ) gradually increases in level , and eventually becomes the main contributor to overall learning around 25 trials after exposure to Task A begins . When Task B is presented , the fast process quickly responds because of the increase in error magnitude . However , the slow process follows much more gradually . The external state ( overall learning ) returns to baseline levels ( an adaptation index of zero ) when the fast and slow processes are equal in magnitude but opposite in sign . Note that at this point , although the external state is at baseline levels , the internal states do not match their baseline levels . The residual positive bias of the slow process ( see blue curve in Figure 5A at task transition ) acts to retard the opposite-learning of Task B ( ideal performance = −1 ) . The longer that Task A is learned , the greater the level the slow process achieves , leading to greater anterograde interference , as illustrated in Figure 5B . However , note that if Task A is learned for longer than is required to achieve asymptotic adaptation in the slow process , increasing exposure to Task A should not lead to a corresponding increase in interference . We simulated the multi-rate model's response to an AB learning paradigm for Task A durations of 13 , 41 , 112 , 230 , and 369 trials and a Task B duration of 115 trials ( i . e . the task durations used in the experiment ) . By comparing the predicted opposite-learning curves for these different groups ( Figure 5B ) , it becomes apparent that increasing the duration of Task A exposure leads to slower opposite-learning curves – all of the opposite-learning curves are slower than the 0-trial group , which is identical to the initial-learning curve for Task A . However , the predicted opposite-learning curves for the 41-trial , 112-trial , 230-trial , and 369-trial groups are extremely similar , resulting from similar levels of the slow process during Task A between trials 41 to 369 . When we quantify the amount of interference predicted for each group ( Figure 5C , gray dotted line ) , we found a close match to the experimental data ( Figure 5C , colored squares ) . Note that this match is not the result of model fitting because the parameters of the multi-rate model used to generate these predictions were taken from previous work in which anterograde interference did not occur [14] . The degree to which a model accounts for data is often characterized by a correlation coefficient or , equivalently , the corresponding R2 value derived from a two degrees-of-freedom ( DOF ) linear regression ( slope and offset ) of the relationship between the model output and the data . This regression yields an R2 value of 0 . 93 ( regression slope = 0 . 89 , offset = 0 . 05 ) . However , the idea of an offset parameter implies that anterograde interference will exist even if Task A is not trained . As this is an unreasonable implication , we could restrict the linear regression to just one DOF ( the slope ) . In so doing , we find that the multi-rate model is able to characterize the measured pattern of interference with an R2 value of 0 . 91 ( regression slope = 0 . 992 ) . Note , however , that the multi-rate model should not merely predict the shape of the interference pattern , but the actual levels of interference . Thus , when we abandon the regression altogether and directly compare the model predictions and experimental data , we find that our ability to explain the data remains essentially the same , with an R2 value of 0 . 91 . This suggests that anterograde interference results from interactions between the different timescales of motor learning . Although the data presented so far appear to be consistent with the predictions of the multi-rate learning model , they are also consistent with the idea that the level of motor output at the end of Task A is what actually dictates the amount of anterograde interference . For example , in Figure 4B , the final level of motor output for the 230-trial group is higher than that for the 13-trial group ( one-tailed unpaired student t-test , p<3 . 2×10−6 ) : note that adaptation coefficients of 0 . 86±0 . 03 and 0 . 47±0 . 05 observed in these two groups correspond to lateral force production levels of 3 . 8±0 . 1N and 2 . 2±0 . 3N ( mean±SEM ) , respectively ( see Figures 4A , 4B , and Figure S4A; we operationally define final learning as the last 30% of Task A exposure , see Methods ) . The 1 . 6N increase in motor output displayed by the 230-trial group might explain why this group experiences more interference than the 13-trial group . To evaluate this hypothesis , we instructed an additional group of subjects to learn a 50% reduced Task A ( i . e . the force-field strength was halved to 7 . 5 Ns/m ) for 230 trials , followed by a switch to a full-strength Task B . Given that perfect performance during this reduced Task A would correspond to an adaptation index of 0 . 5 and a mid-movement force level of less than 2 . 5N ( see Methods ) , and that subjects achieve about 85% of perfect learning ( Figure 4A ) , corresponding to an adaptation index of 0 . 43 based on the full-strength force-field , the final learning level would be expected to be less than the final adaptation level of the 13-trial group ( 0 . 47±0 . 05 , mean±SEM ) . The learning curves for the full 13-trial , full 230-trial , and reduced 230-trial groups are shown in Figure 6A . As expected from the experimental design , the final learning level for the reduced 230-trial group ( 0 . 41±0 . 02 , mean±SEM ) is nominally less than the final learning level of the 13-trial group ( Figure 6C; two-tailed unpaired student t-test , p = 0 . 18 ) . Correspondingly , when comparing the mid-movement force levels , we see that the reduced 230-trial group produces significantly less force than the 13-trial group ( 1 . 5±0 . 1 N vs . 2 . 2±0 . 3 N , respectively; p<0 . 04 , two-sided unpaired student t-test; Figure S4D ) . The lateral force patterns observed at the end of Task A for these two groups reflect this difference ( Figure 6B ) . Therefore , if the final learning level hypothesis were true , it would predict that this reduced 230-trial group would experience less interference than the 13-trial group , corresponding to faster opposite-learning . However , as shown in Figure 6A , the shaded portion of the opposite-learning curve for the reduced 230-trial group is slower than its 13-trial group counterpart . Accordingly , the reduced 230-trial group experiences significantly more interference than the full 13-trial group ( Figure 6C: p<0 . 007 , one-tailed unpaired student t-test on regression data; Figure S4D: p<0 . 007 , one-tailed unpaired student t-test on mid-movement force data ) despite smaller learned changes in motor output at the end of Task A ( p<0 . 04 , as mentioned above ) . This finding is not consistent with the final learning level hypothesis . Figure 6D , which displays the interference metric plotted against the final learning for all of the groups , highlights this inconsistency . Therefore , while the amount of anterograde interference displayed by the full strength force-field groups could be interpreted as being dependent on final learning levels because they all lie along the same line , the reduced 230-trial group cannot because it is separated from this line . This is not to say , however , that the final learning level is completely independent of the amount of AI expressed . According to the multi-rate model , both Task A duration and Task A strength influence the level of the slow process , which according to the multi-rate model is the ultimate determinant of AI ( see Figure S11 ) .
We showed that the observed pattern of anterograde interference cannot be explained by a model relating anterograde interference to the level of motor output at the end of Task A ( see Figure 6 ) . However , anterograde interference has also been proposed to arise from a delay in switching between different internal models [4] , [12] . This explanation could be interpreted in two different ( and opposite ) ways . On one hand , a delay in switching could be a qualitative description of the residual positive bias of the slow learning process once it slowly begins to adapt to Task B . In this case , our findings would not only support this delayed switching explanation , but provide a quantitative mechanism for it . On the other hand , if we interpreted the delayed switching explanation as meaning a delay in switching between any internal models , then such a mechanism would appear to be at odds with the rapid improvement in performance that can occur under conditions that produce savings . For example , a saccadic gain experiment using an A1BA2 learning paradigm found faster relearning of Task A during the second exposure , even though this second instance of Task A immediately followed exposure to Task B [3] , and thus would require a switch between different internal models . If delays did exist when switching between different internal models , then one would expect slower relearning of Task A . While this second interpretation cannot account for the previous work on savings in the A1BA2 paradigm , our multi-rate model can [14] . Furthermore , it can explain the measured pattern of anterograde interference reported in the current work , rapid downscaling and unlearning of a learned motor task [27] , 24-hr retention of a motor task [20] , the shapes of initial learning curves , adaptation deficits in patients with cerebellar damage [26] , patterns of memory consolidation [24] , and spontaneous recovery in force-field [14] and saccadic gain adaptation [25] . See Text S1 and Figure S9 and Figure S10 for a discussion of other possible alternative explanations for anterograde interference . Interestingly , the amount of anterograde interference we observed in the reduced-230 group ( 0 . 62±0 . 11 , mean±SEM ) was significantly higher than what was predicted by the multi-rate model ( 0 . 33 , p = 0 . 03 , one-sample , two-sided student t-test; Figure S11 ) . Perhaps this discrepancy can be explained by the observation that the motor system may process larger errors differently from smaller errors [28] . For example , in a force-field adaptation task , retention is better when the force-field is gradually introduced ( i . e . small errors ) than if it is abruptly introduced with larger errors [29] . This finding suggests that the level of the slow process is elevated when adapting to small errors , yielding better retention ( which is equivalent to reduced decay ) because the slow process would decay more slowly than the fast process . In keeping with this idea , it would be likely that adaptation to the reduced-strength Task A is composed of a greater-than-expected contribution from the slow process because of the smaller errors during training , thus leading to greater-than-expected interference . How might this be achieved mechanistically ? Greater-than-expected levels of the slow process when adapting to smaller errors could be achieved if learning in the two internal processes ( i . e . , ) saturates as errors increase in size , and if the slow process saturates earlier than the fast process . Stated in another way , elevated levels of the slow process would manifest if the ratio were higher for smaller errors in the linear region than for large errors in the saturated region . This occurs only if is still rising as error increases when has already saturated , resulting in an increased gap between the learning rates in favor of the fast process . This is in contrast to our current model , which assumes a fully linear relationship between learning and error ( i . e . and are constant over the space of all possible errors ) . However , it is important to note that no biological system remains linear over all space , and evidence suggests that , in fact , motor adaptation saturates as errors get larger . For example , when subjects are exposed to increasingly strong force pulses during reaching arm movements , single-trial adaptation levels saturate even though the kinematic errors induced by these transient force perturbations steadily increase [30] . This indicates that as errors increase beyond a certain point , single-trial learning levels saturate , and learning rate decreases . Similarly , when subjects are exposed to visual feedback shifts during arm reaches , small errors induce essentially linear adaptation , but larger errors lead to saturation of single-trial learning , or even a decrease in learning for extremely large errors [31] . An even more striking example of the nonlinearity of learning with respect to error can be found in saccadic gain adaptation [32] . When monkeys are exposed to small visual errors while making eye saccades , adaptation is linear . Over an intermediate range of errors , adaptation saturates , as previously discussed . However , when errors are increased past this intermediate range , motor output actually falls back to near baseline levels , indicating that the decrease in learning rate associated with these very large errors is more than enough to counteract the benefit of a larger learning signal . While saturation clearly occurs in the learning process as errors get larger , further work is required to confirm whether the slow process does indeed saturate earlier than the fast process . If this occurs , it may simultaneously explain why gradual adaptation to a force-field with small errors leads to better retention than abrupt adaptation with large errors [29] , and why we see greater-than-expected interference in our reduced-strength 230-trial group . A recent visuomotor rotation study showed that savings occurs in an A1-washout-A2 relearning paradigm , although the amount of savings observed is less than in an A1BA2 relearning paradigm [1] . In this paper , the authors use a superposition argument to show that a linear multi-rate model cannot yield savings in the A1-washout-A2 paradigm , suggesting that the savings observed in the A1BA2 paradigm cannot be fully explained by interactions between internal adaptive processes . They suggest that some ( nonlinear ) memory of Task A or a meta-learning process may also contribute to savings . In contrast , in the current work , we show that interactions between adaptive processes appear to fully account for the observed pattern of anterograde interference ( R2 = 0 . 91 ) . The multi-rate model predicts a near-asymptotic interference level of 0 . 54 for the four longest duration full-strength groups on average , which closely matched the average interference level we observed for these groups ( 0 . 53 ) . It has been shown that patients with cerebellar deficits are significantly impaired in their ability to learn new motor skills [26] , [33]–[35] . Since a reduction in adaptation is evident after just a few trials , these findings suggest that the cerebellum might be a neural substrate for the fast process . However , even if the fast process were more affected than the slow , the dramatic reductions in motor adaptation observed in these studies suggest that both processes are likely to be affected by cerebellar damage [26] , [35] . Recent work has shown that while application of transcranial magnetic stimulation ( TMS ) to the posterior parietal cortex ( PPC ) does not impair the rapid initial learning of a viscous force-field , it does eliminate the gradual increase in learning that the multi-rate model attributes to the slow process [23] , suggesting that the slow process might depend on the PPC . Findings from several other studies indicate that primary motor cortex may also serve as a neural substrate for the slow process . These studies have shown that stimulation of primary motor cortex may cause a partial reduction in the retention factor of the slow process . For example , in a visuomotor rotation task , when TMS is applied to primary motor cortex immediately after movement offset , adaptation to the rotation is unaffected , but this adaptation washes out more rapidly when the visuomotor rotation is withdrawn [36] . Interestingly , the more rapid washout only emerges after the third or fourth trial , suggesting that the retention of the slow , but not the fast , process might be preferentially impaired by this TMS . Another study found that 24-hour retention of adaptation to a viscous curl force-field is reduced by about 15% if a 15-minute block of repetitive ( 1Hz ) TMS is applied to primary motor cortex prior to the onset of training [37] . Since 24-hour retention is specifically determined by the level of the slow ( and not fast ) process at the end of training [20] , this finding also suggests that stimulation of primary motor cortex can result in a partial reduction of the retention factor of the slow process . Consistent with these results , neurophysiologic data recorded from primary motor cortex during force-field adaptation reveal the existence of “memory cells” that retain adaptive shifts in preferred direction , even after the behavioral signs of adaptation have been washed out [38] . The activity of these memory cells is consistent with what would be expected from the output of the slow process , which is responsible for anterograde interference . This interpretation should be taken with some degree of caution , however , because reanalysis of the same data suggested that the tuning curves of neurons in primary motor cortex may drift spontaneously [39] . In an A1A2 learning paradigm , a reduced initial error and a faster learning rate can be observed in adaptation to Task A2 compared to A1 , even when prolonged time periods ( such as a day or week ) separate A2 from A1 [2] , [5] , [11] , [20] . When a second task ( Task B , which is often taken to be the opposite of Task A ) is inserted between A1 and A2 in the A1BA2 paradigm , improvement on Task A2 can be reduced . This reduction has been attributed to the ability of Task B to erase , in whole or in part , the memory of A1 [2] , . This effect is known as retrograde interference because Task B interferes with a previously-stored memory . Complete retrograde interference from Task B onto the retention of Task A1 has been reported when only 5 minutes separate the two tasks [2] , [5] , [11]–[12] , [22] , [40]–[42] . However , if 4 to 24 hrs separate A1 and B , then retrograde interference can be reduced , reflecting the consolidation of the initially fragile memory of Task A1 into a more stable form [2] , [11]–[12] . Intriguingly , one recent study found complete interference of Task B onto A2 for both 5 min and 24 hr intervals between A1 and B in visuomotor rotation and force-field adaptation paradigms [5] , in contrast with the finding that a 24 hr interval after Task A1 is sufficient for either partial or full consolidation [2] , [12] . A series of studies have attempted to reconcile the differences between these findings by suggesting that the inclusion of “catch trials” ( occasional movements during which the learned environment was unexpectedly removed ) [13] , or washout trials before Task A2 ( null-field trials to wash out contributions from anterograde interference which could mask retrograde interference effects in Task A2 ) [2] are necessary for consolidation to be observed . However , even these proposals do not provide a fully harmonious explanation for all of the available data: consolidation has been observed even when catch trials are not included [2] , and two experimental conditions with the null-field movements to washout anterograde interference failed to show evidence for consolidation , even with a 24hr interval between Tasks A1 and B [5] . Although the weight of the evidence suggests that consolidation can occur during motor adaptation , the somewhat fragmented nature of these results indicates that the mechanisms governing this resistance to retrograde interference are not yet fully understood . This may be substantially due to the fact that retrograde interference has a relatively small ( 10–20% ) effect on performance in all of these studies [11]–[13] , making resistance to retrograde interference somewhat challenging to study . In contrast , the effects of anterograde interference can be substantially larger [4]–[5] . In the current study , we found that anterograde interference reached levels of 50–60% , suggesting that anterograde interference can play a substantially greater role in modulating the rate of motor learning than retrograde interference . It is interesting to note that when washout trials are not included before Task A2 to prevent anterograde interference , performance on Task A2 has been reported to be similar to naïve performance on Task A1 in studies of visuomotor rotation [2] , [6] . It has been suggested that this occurs because anterograde interference from B onto A2 effectively cancels the performance improvement conferred by the memory of A1 [6] . An alternative hypothesis is that Task B interferes with the ability to retrieve the memory of A1 [2] , [19] . This idea is consistent with the observation that the performance on Task A2 and A1 are similar , even when one week separates B and A2 – a time period long enough for aftereffects of B to have minimal influence on performance of Task A2 [2] , [5] , and consistent with a mechanism for interference with retrieval posited for declarative memories [43]–[44] . This mechanism can be viewed as a type of hybrid between anterograde and retrograde interference because it describes a forward ( anterograde ) effect of Task B , but the effect impairs retrieval of the memory for the previously-learned ( retrograde ) Task A1 . Note that our multi-rate model would not , by itself , be able to explain such a mechanism . Intriguingly , as exposure to Task B progresses , we find that the opposite-learning curves for the different groups ( Figure 4C ) do not converge to the degree predicted by our two-rate model ( Figure 5B ) . This discrepancy could potentially be explained by the existence of slower learning processes with even more protracted timescales than the “slow” learning process in the model we applied . The residual contributions from these slower processes could hold back adaptation to Task B for even longer , leading to even slower convergence . While it is likely that more than two timescales are present in motor adaptation [45] , it is remarkable that interactions between just two adaptive processes are able to predict the shapes of initial learning curves , anterograde interference , savings , rapid downscaling and unlearning of a learned motor task , 24-hr retention of a motor task , and spontaneous recovery of learning [3] , [14] , [20] , [25] , [27] .
Fifty-eight healthy individuals ( 34 women , median age: 24 yrs old , age range: 18–64 , 52 right-handed ) participated in this study . Each of the subjects had no prior knowledge of the experiment's purpose and provided informed consent . All experiment protocols were approved by the Harvard University Committee on the Use of Human Subjects in Research . Participants were given a dynamic force-field adaptation task to learn [46] . They were asked to sit in front of a vertically-mounted computer screen while grasping the handle of a two-joint robot arm manipulandum ( Interactive Motion Technologies , Inc . ) that allowed motion in the xy-plane ( Figure 1A ) . The xy-position of the handle was indicated by the xz-position of a cursor ( 3 mm in diameter ) on the computer screen . Subjects were instructed to make 500 ms , 10 cm reaching arm movements in the y-direction ( in the midline , toward or away from the chest ) from one circular target ( 10 mm in diameter ) to another in as straight a line as possible . Although subjects made movements in both the 90° and 270° directions , only movements in the 270° direction were analyzed; all 90° movements were “error-clamped” by using the robot arm as a virtual spring ( 6 kN/m ) and damper ( 250 Ns/m ) [14]–[15] , [20]–[21] , [47] such that the maximum lateral deviation from a straight line connecting the start and end targets during the longitudinal reach motion was 1 . 2 mm ( Figure 1D ) . Initially , subjects were asked to make 160 reaching movements in the 270° direction during a baseline training period . Approximately 90% of these baseline trials were made while the manipulandum's motors were turned off ( Figure 1B ) . The other 10% of trials were error-clamp trials , during which lateral errors were restricted to no more than 1 . 2 mm . With maximal lateral kinematic errors about 1% of the reach length of 10 cm , online kinematic error feedback contributions to motor output are mostly eliminated , such that the measured force production is composed of predominantly feedforward contributions [14]–[15] , [20]–[21] , [24] , [29] , [48] . Following this baseline period , subjects were then exposed to a velocity-dependent force-field environment ( Task A ) for a variable number of trials in the 270° direction ( 13-trial group: 14 subjects; 41-trial group: 9 subjects; 112-trial group: 9 subjects; 230-trial group: 9 subjects; 369-trial group: 9 subjects ) . In this viscous force-field , the manipulandum imposed forces onto the hand that were perpendicular to the reach direction and proportional to the reach velocity ( Figure 1C ) : ( 1 ) In order to move in a perfectly straight line while being perturbed by the force-field , subjects would need to produce a compensatory force pattern that exactly counteracts the robot-produced force . Half of each experiment group experienced a clockwise force-field during Task A . The other half experienced an equal magnitude counter-clockwise force-field . Following the completion of Task A , subjects were then exposed to the opposite force-field ( i . e . if Task A was a clockwise force-field , Task B was a counter-clockwise force-field ) for about 115 trials ( 116 , 114 , 113 , 112 , 120 trials for the 13-trial , 41-trial , 112-trial , 230-trial , and 369-trial groups , respectively ) . Interspersed throughout Tasks A and B were occasional error-clamp trials ( approximately 1 out of every 7 trials ) in order to assess the learning curve associated with learned feedforward force output produced by subjects . By measuring lateral forces during error-clamp trials during this force-field adaptation task , we can assess how well the magnitude and shape of subjects' force outputs compare to the ideal velocity-dependent force pattern , which is the opposite of the robot-produced force ( Figure 1C ) . We regress the subject-produced force pattern onto the ideal force pattern in order to quantify the learning – an absence of any learning would yield a regression coefficient ( or adaptation index ) of 0 , while perfect learning would yield an adaptation index of 1 . Note that an index of 1 does not necessarily mean that subjects produced perfect compensatory forces . The actual force pattern can be decomposed into a component that is aligned with the ideal force pattern , and a component that is orthogonal to it . The regression coefficient indicates the size of the aligned component and is independent of the orthogonal component . If the regression coefficient is 1 , this indicates that the magnitude of the aligned component is exactly the ideal force profile , regardless of the size of the orthogonal component . We use these adaptation indices to generate the learning curves displayed in Figures 4 , 6 , and Figure S8 . These adaptation indices are then averaged across subjects to obtain group-averaged data . Note that at the onset of Task B , the ideal force pattern becomes opposite of that required in Task A , whereas the force patterns being produced are still appropriate for the Task A ideal force . Therefore , at the transition from Task A to Task B , the regression coefficients jump from one value to the negative of that value ( e . g . for the 369-trial group , the coefficients jump from the adaptation level at the end of Task A ( 0 . 85 ) to −0 . 85 , Figure 4A ) . To maintain continuity in the adaptation curves plotted in Figures 4A , 4B , and 6A , we multiply the regression coefficients calculated during Task B by −1 , which is equivalent to maintaining a consistent ideal force pattern throughout the duration of the plot . Therefore , the negative values observed in these adaptation curves late in exposure to Task B reflect the fact that subjects are producing motor output that is nearly equal to the ideal output for Task B , and nearly opposite the ideal output for Task A . Also note that the “rectified” opposite-learning curves shown in Figure 4C actually represent the “original” regression coefficients , equivalent to multiplying by −1 twice ( once in the manipulation just described , and once in the rectification ) . It is possible that the two-rate behavior we observe in the current paper is a result of averaging together two sub-populations of subjects with different single-rate behaviors . However , we observed interference even when comparing individual initial and opposite-learning curves to each other ( i . e . opposite-learning curves are significantly slower than the initial-learning curves within each group; one-sample , one-sided student t-tests , p-values between 2 . 8×10−6 and 0 . 02; because the 13-trial group did not complete 25 trials in Task A , we compared initial and opposite-learning over the first 13 trials in that case ) . Note that the subject-produced force patterns we present are baseline-subtracted , where the baseline is the average force pattern measured during the last 5 error-clamp trials before the onset of Task A . Subjects who first learned a CCW force-field and then a CW force-field had their force patterns multiplied by negative one so that their data could be aligned with the subjects who first learned the CW and then CCW force-field . All force and velocity profiles used in the analysis were 2 . 25 seconds long and centered at the peak longitudinal velocity point ( where longitudinal velocity refers to the component of the velocity vector in the target direction ) . We also used a mid-movement force metric defined as the average force produced during a 250ms time window centered at the peak velocity point . The data for this force metric can be found in Figure S4 . Note that the force levels associated with this metric would be expected to be somewhat smaller on average than the force levels observed right at the peak velocity point . An additional group ( 8 subjects ) learned the same Tasks A and B as the 230-trial group , except that the force-field in Task A was halved in magnitude: ( 2 ) See the Results section for the reason why this additional group was studied . The adaptation indices for this reduced-strength group were calculated by comparing the subject-produced force patterns to the full-strength ideal patterns for Tasks A and B in order to allow for comparison with the full-strength groups . We recently proposed a two-process , multi-rate learning model that provides a potential explanation for anterograde interference , along with several other motor learning phenomena [14] . The model states that a force disturbance of the motor system introduces a motor error that drives the evolution of two constituent learning processes that have different rates of learning and retention . This motor error is the difference between the overall motor output ( i . e . the combined contributions of the two processes ) and the desired output necessary to compensate for the force disturbance . One of these processes , , learns quickly from error , but rapidly forgets the previous learning . The other process , , learns slowly from the error , but retains what it previously learned very well . This occurs because , is greater than , and is less than , leading to multiple timescales in the learning process . The values for these parameters , , , , and , were taken from a previous study [14] in which anterograde interference did not occur rather than being fit to the current data set . See Text S1 for model parameters fit to the current data set . We calculate the percent reduction in the opposite-learning curves for Task B with respect to the initial-learning curve for Task A in order to quantify the level of anterograde interference . Specifically , we use the portions of the opposite-learning curves beginning from the zero-crossing point ( i . e . when the performance level has returned to baseline levels ) in this analysis and rectify them such that comparisons with the initial learning curve can be made directly . This percent reduction , or anterograde interference metric , is measured over the first 25 trials because the difference between the curves is greatest early on ( Figure 3 ) . A metric value of 0 corresponds to no interference , and a metric value of 1 corresponds to a complete lack of opposite learning , or 100% interference . ( 3 ) represents the average initial learning between trials 1 and 25 , and represents the average opposite learning within the same boundaries , and k indicates the subject group . Note that we interpolate between trials 1 and 25 for the initial-learning and opposite-learning curves to find the average learning . In addition , note that because this average learning is proportional to the area under the curves over that same trial span , a normalized AI metric based on average learning ( Equation 3 ) is identical to a normalized AI metric based on area under the curve which is illustrated in Figure 3B . was found by combining the initial-learning curves for all subjects exposed to a full-strength force-field in Task A . If after task transition the learning curve crossed zero , went back above zero , and then crossed zero again ( as in the 230-trial and 369-trial groups ) , we used the last zero-crossing as the beginning of the opposite-learning curve . See Text S1 and Figure S7 for a discussion of our rationale for choosing to use this particular AI metric , as opposed to the time constant of the opposite-learning curves . To compare the experimentally-obtained interference metrics with simulation predictions , we found 1000 different sets of model parameters by bootstrapping previously-obtained data [14] and calculating the associated interference metrics predicted by the multi-rate model . We then found the 95% confidence interval for the simulation predictions by selecting the metrics representing the 2 . 5% and 97 . 5% percentiles as the interval boundaries . We use one-tailed , paired t-tests to compare the initial-learning and opposite-learning between groups , using the average interpolated learning between trials 1–25 , with the exception of the 13-trial group . Because the initial-learning period for this group only spans 16 trials ( 13 force-field trials , 3 error-clamp trials ) , we compare the learning between trials 1–16 . The Average Final Learning metric ( Figure 6 ) is obtained by averaging together the force patterns measured during the error-clamp trials in the last 30% of trials during initial learning , corresponding to trials 9–13 , 28–41 , 78–112 , 161–230 , and 259–369 for the 13-trial , 41-trial , 112-trial , 230-trial , and 369-trial groups , respectively . | The act of learning one task can not only have direct effects on the performance of other tasks , but it can also affect the ability to learn other tasks . One example of the latter is the phenomenon of anterograde interference in motor adaptation , in which the learning of one adaptation can substantially reduce the rate at which the opposite adaptation can be learned . Here we show that the amount of anterograde interference depends systematically on the strength of a particular component of the initial adaptation rather than on the total amount of adaptation that is achieved . This component of the motor memory evolves more slowly than the overall learning and acts in combination with a quickly evolving component of memory to produce the observed improvement in task performance . We proceed to show that a simple computational model of the interactions between these adaptive processes predicts greater than 90% of the variance in the observed interference patterns , suggesting that this quantitative model may enable the development of improved training and rehabilitation paradigms that mitigate unwanted interference . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neuroscience/behavioral",
"neuroscience",
"neuroscience",
"neuroscience/motor",
"systems"
] | 2010 | Reduction in Learning Rates Associated with Anterograde Interference Results from Interactions between Different Timescales in Motor Adaptation |
Computational analysis of neural systems is at its most useful when it uncovers principles that provide a unified account of phenomena across multiple scales and levels of description . Here we analyse a widely used model of the cerebellar contribution to sensori-motor learning to demonstrate both that its response to intrinsic and sensor noise is optimal , and that the unexpected synaptic and behavioural consequences of this optimality can explain a wide range of experimental data . The response of the Marr-Albus adaptive-filter model of the cerebellar microcircuit to noise was examined in the context of vestibulo-ocular reflex calibration . We found that , when appropriately connected , an adaptive-filter model using the covariance learning rule to adjust the weights of synapses between parallel fibres and Purkinje cells learns weight values that are optimal given the relative amount of signal and noise carried by each parallel fibre . This optimality principle is consistent with data on the cerebellar role in smooth pursuit eye movements , and predicts that many synaptic weights must be very small , providing an explanation for the experimentally observed preponderance of silent synapses . Such a preponderance has in its turn two further consequences . First , an additional inhibitory pathway from parallel fibre to Purkinje cell is required if Purkinje cell activity is to be altered in either direction from a starting point of silent synapses . Second , cerebellar learning tasks must often proceed via LTP , rather than LTD as is widely assumed . Taken together , these considerations have profound behavioural consequences , including the optimal combination of sensori-motor information , and asymmetry and hysteresis of sensori-motor learning rates .
The uniformity of the cerebellar microcircuit [1] has long been attractive to modellers . The original Marr-Albus framework [2] , [3] continues to be influential , particularly in the adaptive-filter form developed by Fujita [4] to deal with time-varying signals [5] , [6] . However , although variants of the cerebellar adaptive-filter model are widely used and show great promise for generic motor control problems [7]–[13] , they are typically constructed in a distributed form that makes mathematical analysis of their properties difficult . It is therefore still unclear whether the adaptive-filter model has the power and robustness needed to underlie the computational capacities of the cerebellum . One method of addressing this question is to use a lumped version of the model , in simulated tasks that are simplified as much as possible while still retaining the computational demands of the real-world equivalent . This approach has indicated that , when wired in a recurrent architecture , the adaptive filter can use the sensory consequences of inaccurate movements for adaptive feedfoward control [14]–[17] , thereby solving the classic problem of the unavailable motor-error signal [18] , [19] . The recurrent architecture allows the filter to decorrelate an efference copy of motor commands from the sensory signal , ensuring that any remaining movement inaccuracies are not the result of the inadequate commands . The translation of ‘simple’ motor commands into the detailed instructions required for accurate movements has long been considered a central function of the cerebellum [2] , and this translation entails the adaptive compensation of time-varying biological motor plant ( muscles , tendons , linkages , etc . ) . The demonstration that the adaptive filter in a recurrent architecture can achieve adaptive compensation using only physically available signals is thus an important step towards establishing its computational suitability as a model of the cerebellar microcircuit . A second requirement of a cerebellar model is robustness in the face of typically biological features of motor control problems . One ubiquitous example of such a feature is the presence of noise in biological signals [20] . In the modelling examples given above , both input and internal signals were assumed to be noise free . Here we investigate the performance of the model when noise is added to these signals . The investigation is in two parts . First , we show that an adaptive filter using the standard covariance learning rule behaves optimally with respect to input and internal noise . Secondly , we show there are important consequences of this computational optimality for both the neuronal implementation of the adaptive-filter , and for behavioural learning rates . These findings are significant for understanding not only cerebellar function , but also the relationship between computational and implementational aspects of neural modelling in general [21] .
The linear adaptive-filter model of the cerebellar microcircuit [4] , [22] is outlined in Figure 1 . Filter inputs correspond to mossy fibre signals , conveying information about the current sensory and motor state of the organism . These inputs are recoded by a bank of linear filters representing the granular layer , whose outputs ( PF signals ) are weighted ( PF synapses on Purkinje cells ) then summed to constitute the filter output ( Purkinje cell firing ) . Weights are adjusted in response to an error signal ( climbing fibre input to Purkinje cell ) , using the covariance learning rule [23] . This rule , which assumes that signals are carried by modulation of a tonic firing rate so that positive and negative values can be coded , is identical in form to the powerful Least Mean Square rule of adaptive control theory [24] . It requires bidirectional plasticity ( that is both LTD and LTP ) at synapses between parallel fibres ( PFs ) and Purkinje cells [25] , so that synaptic weights decrease when climbing fibre input is positively correlated with parallel fibre input , and increase when the correlation is negative . If the filter is properly connected , this learning rule learns weights which combine parallel fibre inputs so that the PC output has minimal mean square error . It should be noted that in Figure 1 we follow the convention of referring only to parallel fibre synapses , without mentioning the synapses between the ascending axons of granule cells and Purkinje cells . However , the arguments in the paper would not be affected by inclusion of ascending axon synapses , provided their behaviour conformed to the covariance learning rule . The uniformity of the cerebellar microcircuit implies that a model can be tested using any convenient cerebellar task . Adaptation of the vestibulo-ocular reflex ( VOR ) is relatively simple , has been extensively modelled and investigated [26] , and previously used to investigate the computational properties of adaptive-filter models [14] , [15] , [17] . The simplified architecture used for the simulations is shown in Figure 2 . Horizontal VOR accuracy requires that motor commands to eye muscles compensate for changes in the dynamic properties of both the oculomotor plant P and of vestibular processing V . We have previously shown that plant compensation can be learnt by an adaptive filter version of the Marr-Albus algorithm using the recurrent pathway illustrated in Figure 2 , in which the filter receives an efference copy of the motor commands to the plant . In contrast , the forward pathway shown in Figure 2 is suitable for compensating for changes in vestibular processing . In the simulations below both architectures are used although , since these simulations deal only with changes in scalar gain , this is not a crucial distinction . In general , appropriately connected adaptive-filters using the covariance learning-rule will achieve optimal filter weights that minimise the error measure e ( Figures 1 and 2 ) . Since e is a measure of task performance , these weights enable the filter to perform the task accurately . This optimal behaviour clearly generalises to the situation where noise is present in PF signals: because this noise affects the filter output , minimising e will also tend to minimise the effect of PF noise , by choosing weights that are optimal for eliminating disturbances due to PF noise . The optimality principle can be illustrated by considering the case where a number of PFs carry signals pi with different levels αi of a signal of interest s but contaminated by independent noise components ni of power σi2 . It is shown in the Methods section ( Equation 6 ) that mean square output error is minimised when the weights on these input signals have the ratios wi∶wj = αi/σi2∶αj/σj2 . Figure 3 shows the time course of this learning , for the case where plant gain is suddenly decreased from 1 . 0 to 0 . 5 . and the filter has four PF channels carrying differing amounts of efferent-copy signal and noise . Figure 3A illustrates a general phenomenon for low levels of PF noise , namely the existence of fast and slow phases of learning ( see Methods ) . Initial learning is fast , producing a 5-fold drop in retinal slip error in ∼60 batches , nominally about 1 hour of input . During this phase the weight vector converges close to the subspace of weight combinations which performs the task in the absence of noise . Thus , the values of the weights attained after this early fast phase of learning are sufficient to achieve a near optimal VOR gain of just below 1 . 0 . Subsequent learning is much slower ( note log scale for x-axis ) , as the weight vector moves essentially within this subspace to bring all weights to the optimal values determined by Equation 6 . During this learning phase performance improves , but less dramatically , as the smaller noise contribution to task errors is reduced . The slowest time constant for this phase of learning is lengthened by a factor approximately equal to the signal to noise ratio ( Equation 10 ) . If the signals carried by parallel fibres correspond to a set of noisy sensory estimates of an environmental property , and appropriate cost information is carried on the climbing fibre , the adaptive-filter behaviour above leads to the optimal linear estimator in the Bayesian sense . Our analysis shows this explicitly for the simplest case of a minimum least square error estimator when the sensory estimate noises are independent . Such statistically optimal performance has been observed for humans integrating visual and haptic information [27] , and the above result suggests that the adaptive-filter model of the cerebellum can match the performance of the whole subject . This result has particular relevance to smooth pursuit , a class of eye-movement known to be dependent upon the cerebellum , whose accuracy ( in the initial open-loop phase ) appears to be limited primarily by sensory noise [28] . This example is considered further in the Discussion . It can be seen from Figures 3 and 4 that even weights for parallel fibres carrying relevant signals are driven to low values if they also carry high amounts of additive noise . We now consider a second type of noise , namely potentially useful signals carried by PFs but which are irrelevant to the current task ( termed ‘nuisance’ signals in the control-theory literature ) , these signals could be correlated between different PFs . A simple example would be a parallel fibre that carries information about the conditioned stimulus in classical conditioning . Conditioned stimuli are deliberately chosen on the basis of their not having prior influence on the response to be conditioned , so before acquisition commences the corresponding parallel-fibre signal is essentially all noise . Its weight will therefore have been set to zero at the fast time scale before the start of formal training . A more interesting example is provided by the case of two parallel fibres , one carrying irrelevant information n and a second fibre carrying the same information with the opposite sign −n . Here the total contribution to the task will be zero if the weights are equal . From any arbitrary non-zero starting weights this state will be reached on the fast time scale . However if these PFs also carry an independent second component of noise ( as they surely will ) these redundant weights will go on changing to become zero on the slow time scale ( illustrated in Figure 4 ) . In general all non-zero weight combinations for which nuisance sources cancel will be unstable due to intrinsic noise . In a similar way large numbers of nuisance sources might cancel to good accuracy due to the central limit theorem , but their weights will nevertheless eventually converge to zero due to intrinsic noise . Parallel fibres are thought to carry a widespread array of information about the sensorimotor context in which motor activity takes place , including sensory signals , copies of motor commands , and signals about the state of the organism such as arousal [29] . The fact that there are so many ( ∼170 , 000 ) parallel-fibre inputs to a given Purkinje cell [6] implies that most parallel fibres will inevitably carry information which is only weakly related ( low signal to noise ) or is simply unrelated ( all noise ) to a given task . From the analysis above the long-term optimal synaptic weights for such synapses will be small or zero . Hence it is a consequence of the optimal performance of the model that most synapses between parallel fibres and Purkinje cells are expected to be silent , consistent with experimental evidence [30]–[32] . The second consequence of optimal performance is related to the first . In simplified computational models it is often assumed that a given synaptic weight can be either positive or negative . The fact that actual synapses do not change between excitatory or inhibitory forms can be finessed if the weights vary around some intermediate positive ( or negative ) value . However , if many of them are typically zero at the start of learning , the model can only be properly implemented if there is a second pathway from granule cells to Purkinje cells of opposite sign to the first , else learning would only be possible when it required Purkinje cell excitability to increase . Fortunately , this requirement appears to be consistent with recent experimental evidence indicating that there is climbing-fibre controlled plasticity in the synapses between parallel fibres and stellate and basket cells , which are inhibitory interneurons that project to Purkinje cells [31] , [32] . Thus there is a second , indirect , pathway from granule cells to Purkinje cells via inhibitory interneurons that can support the learning required by the adaptive-filter model . The final consequence is almost a triviality . Clearly if most synapses are silent they are not available for long-term depression ( LTD ) . Hence for a large class of tasks learning must initially proceed via long-term potentiation ( LTP ) , in either the direct or indirect pathway from granule cells to Purkinje cells . LTP in the direct excitatory pathway would increase Purkinje cell excitability , whereas LTP in the indirect inhibitory pathway would reduce Purkinje cell excitability . The covariance learning rule thus implies that LTP and LTD are in general of equal significance , rather than cerebellar LTP merely playing a book-keeping role by normalising an LTD-lead learning process . The predominance of silent granule synapses goes further by implying that LTP may be particularly important for new learning . The basic simplicity of the Marr-Albus mechanism as exemplified by the adaptive-filter model is substantially modified by the implementation issues just considered , in particular by the presence of both direct excitatory and indirect inhibitory pathways from granule cells to Purkinje cells . We have shown that synaptic positivity requires an indirect pathway whenever a task requires synaptic weights to be negative . Hence the locus of synaptic plasticity , in the direct or indirect pathway , will depend on the direction of the change to be learnt . This means that any differences between direct and indirect pathways will lead to asymmetries in learning behaviour . An example is given in Figure 5 , which illustrates the behaviour of a system with vestibular inputs arriving on both the direct excitatory pathway and an indirect inhibitory pathway . Signs were chosen so that gain down would initially be learnt by LTP on the direct pathway , consistent with [33] . It is further assumed that the learning rate in the direct pathway is smaller than that in the indirect pathway . The effect of these assumptions is to produce asymmetrical learning rates , with gain-up learning being about twice as fast as gain down ( a similar result can be obtained using equal learning rates but with the indirect pathway carrying a more powerful signal than the direct pathway ) . This difference is similar to that found for VOR adaptation in the mouse [33] . Figure 5 therefore shows how , in principle , the presence of a direct excitatory and indirect inhibitory pathway could contribute to an observed asymmetry in learning rate . Additional differences between these pathways with respect to , for example , generalisation could also contribute to other kinds of experimentally-observed learning asymmetries ( see Discussion ) . Finally , we have argued above that , if most synapses are inactive , learning novel tasks must proceed mainly via LTP . However once learning has taken place , these newly active synapses become available for learning via LTD . Hence the number of active synapses and the magnitude of the synaptic weight available for LTD will depend on previous experience , ensuring that learning rates will depend on previous learning history . An example of this hysteresis mechanism is given in Figure 6 , which illustrates learning rates for an increase in VOR gain in the dark from 1 . 0 to 1 . 5 , followed firstly by a decrease back to 1 . 0 , then by another gain increase to 1 . 5 . It is assumed that all weights are zero at the start of learning , and that the direct excitatory and indirect inhibitory pathways have identical signal strengths and learning rates . It can be seen that the initial learning of the gain increase ( ‘acquisition’ ) is slower than learning the subsequent decrease ( ‘extinction’ ) , and also slower than re-learning the gain increase ( ‘re-acquisition’ ) . As with the previous figure , Figure 6 shows how in principle the presence of direct and indirect pathways could contribute to hysteresis in learning rates .
Analysis of inaccuracies in open-loop smooth pursuit movements indicates that more than 90% of the variance arises from errors in sensory estimation of the speed , timing and direction of target motion [28] , and that pursuit thresholds are similar to perceptual thresholds [34] . Since smooth pursuit is dependent upon the cerebellum ( e . g . , [35] ) , these findings suggest that the cerebellum can process noisy sensory information as well as the perceptual system as a whole . Moreover , at least in some instances perceptual processing of this kind has been shown to be statistically optimal ( e . g . , [27] ) . Recordings from smooth-pursuit related Purkinje cells in the cerebellar floccular complex suggest that variability in their open-loop responses is also driven primarily by sensory noise , with noise downstream from the Purkinje cells being of minor importance [36] . These findings together suggest that smooth pursuit performance is close to optimal given the noise present in sensory measurements , and that the cerebellum can make optimal use of those measurements . An important criterion , therefore , for assessing cerebellar models is their computational ability to reproduce such optimality . A second feature of the present findings is the implication of the model's computational power for its implementation and performance . After long periods of training most of the model's weights are likely to be small or zero , consistent with recent experimental evidence [30]–[32] . We comment on four features of this finding . The possibility that cerebellar learning can proceed via at least 4 separate processes ( LTP and LTD in either pathway ) complicates the interpretation of behavioural studies in which one or more of those processes are compromised . As can be seen from Figures 5 and 6 , the contribution of each process depends both on the direction of learning , and the organism's past history . For example , the neural bases of a new learning task ( possibly the initial acquisition of eyeblink conditioning to a tone ) may differ from those of an ongoing familiar task ( VOR or saccadic calibration ) . This complication may contribute to the difficulty of identifying these neural bases using behavioural studies of mutants [44] , though again it must be emphasised that there are a number of other possible sources contributing to difficulty in this area . A related issue concerns the processes underlying the fast and slow phases of learning illustrated in Figures 3 and 4 . It can be seen that in principle there could be some tasks where early learning uses a single process , whereas later learning uses a mixture ( e . g . , Figure 3A ) . Although a distinction between fast early learning ( ‘acute’ ) and slow subsequent learning ( ‘chronic’ ) is familiar in the cerebellar literature [26] , [44] , the mechanisms illustrated in Figure 3 have not so far been considered as a possible basis . One additional implication of this figure is that the slow acquisition of many motor skills ( to expert level ) might be caused in part by cerebellar input noise . The cerebellar algorithm we have described necessarily inherits the well-known optimality properties of the adaptive filter [24] . We have demonstrated statistical optimality explicitly and examined its consequences for a class of noisy inputs likely to be of importance in cerebellar learning . As far as we are aware , the cerebellar model described here is at present the only one demonstrated to guarantee statistical optimality in dealing with noisy inputs , and thus the only one known to be capable of , for example , the optimal smooth pursuit performance described experimentally [28] , [34] , [36] . There is an alternative account , however , of the experimentally observed preponderance of silent synapses between parallel fibres and either Purkinje cells or interneurons . The relation between weight distribution and storage capacity has been examined for perceptron models [45] , and the optimal distribution has been shown to contain a high proportion of very weak or silent synapses . This analysis is based on the assumptions i ) that the cerebellar microcircuit acts like a perceptron in which both inputs and outputs are binary and ii ) that weights are distributed so as to achieve maximum storage capacity . Under these assumptions it is shown that coding capacity is maximised when 50% of weights are silent , and that this proportion increases if a noise threshold is introduced to increase reliability of classification . Although the derivation is rigorous , there is a question of how far the Perceptron is in fact a suitable representation of the cerebellar microcircuit in a motor control context . Although Perceptrons have been used as models for cerebellar cortex based on the Marr-Albus framework [46] , [47] they are not usually applied to motor control problems where continuous time-varying signals are required . In general the adaptive filter interpretation is more suited to these sensori-motor applications , and it is more closely linked to theoretical developments in adaptive control . Moreover , the task of learning the coefficients of an adaptive filter is very unlike that of coding many random bit patterns with a single template . For example in a motor control problem the inputs would generally be confined to a low dimensional subset of input space , an assumption that is basic to current machine learning algorithms such as locally weighted linear regression [48] . In these circumstances the requirement of maximising coding capacity is not relevant . Although the simplicity of the Marr-Albus algorithm may seem to imply correspondingly simple learning behaviour , we have shown how constraints at the hardware level can mask this algorithmic simplicity so that Marr-Albus systems exhibit complex phenomena such as multiple time scales , asymmetry and hysteresis . Marr [21] distinguished between the computational , algorithmic and hardware levels of description in models of neural information processing . In fact models often have the greatest explanatory power when they integrate information across all three levels . Our previous work has concentrated primarily on the interaction between the two higher levels [14]–[17] . Here we have extended this work to include two important hardware level constraints , namely system noise and weight positivity , and show that they have computational consequences which are critical to understanding neuronal and behavioural aspects of cerebellar learning . It is of interest that recent experimental work on VOR adaptation has emphasised the complexity of the learning processes involved [26] . The results here suggest that such complexity is not in principle incompatible with the original Marr-Albus framework .
In the simulations the model architecture shown in Figure 2 was programmed in MATLAB with V , P , and B taken as scalar gains . In recurrent architecture V was a unit gain and the forward pathway through C was not used giving an overall loop gain of BP/ ( 1−BC ) . Initially P = 1 , B = 1 so the plant is initially perfectly compensated when C = 0 . For example when P is reduced to 0 . 5 exact compensation requires C = B−1−P = 0 . 5 . In adaptive filter models the cerebellar filter C analyses its input m ( t ) into many parallel fiber signals pi which are re-synthesized to form the output z = Σ wipi . Since the simulations here deal only with scalar gains we do not require pi containing information about the past history of m as in our previous work . Assumptions about the nature of the pi are described separately for each simulation . Since the time dependence of the inputs is irrelevant to learning a scalar gain the input was taken to be constant . All noise signals were represented as white noise , results would be the same for other types of noise with the same variance and correlations . The learning rule ( Equation 2 below ) at the parallel fibre/Purkinje cell synapse was implemented as a batch update rule , accumulating the total change in weight over the batch for fixed weights and then updating at the end of the batch . A batch consisted of 6 , 000 time steps so that with dt = 0 . 01 s a batch had a nominal duration of 1 min . The teaching signal e was retinal slip vhead-veye . The learning rate β was chosen to fix the fast time scale for each simulation . Although batch update was used for efficiency the results are essentially identical for continuous time update . The code for the all the simulations is available in Dataset S1 . The mossy fibre inputs to the granule cell layer are expansion-recoded as parallel fibre signals pi ( note that these signals are assumed to be carried by modulation of a tonic firing rate so that both positive and negative signal values can be coded ) . These parallel fibre inputs are re-combined by the Purkinje cell to produce its output ( 1 ) If the desired output is γs ( i . e . , the required gain is γ ) , the error in PC output is z−γs . Learning stability requires that the climbing fibre input e is an approximation to this output error; that is , e≈z−γs . The level of approximation required is that these quantities be related by a strictly positive real transfer function [49] . It has been shown that in recurrent architecture e can be an error in task space , that is , a sensory error , while forward architectures such as feedback-error learning require that e be a motor error signal . This distinction ( discussed further in [15] , [16] ) is not relevant to the phenomena discussed here . The learning rule is the covariance learning rule ( 2 ) If the strict positive realness condition is satisfied this learning rule can be shown to minimise mean square error E = 〈e2〉 . We consider an illustrative situation in which each parallel fibre carries a combination of the signal of interest s and uncorrelated noise ni ( 3 ) ( the noise sources are assumed to be pairwise uncorrelated ) . The mean square error has the form ( 4 ) whose minimum is at ( 5 ) so that the optimal weights are in the ratios ( 6 ) Note that in general the optimal weights give an optimal gain , which is smaller than γ , this is due to the usual trade-off between bias and variance for an optimal estimator . The rate of approach to the optimal weights is determined by the covariance learning rule which takes the form ( 7 ) Rigorous bounds on the time constants of this system can be obtained using the eigenvalue interlacing theorem [50] , here we use a simpler heuristic approach . Suppose there was zero noise . Then weight update would take place entirely in the direction ( αi ) with time constant ( 8 ) Superimposed on this is a motion in each coordinate direction generated by the noise term with time constants ( 9 ) ( given subscripts fast and slow because noise power will usually be much smaller than signal power ) . The ratio of the slow to fast time constants is thus determined by the signal to noise ratio: ( 10 ) | The cerebellum or “little brain” is a fist-sized structure located towards the rear of the brain , containing as many neurons as the rest of the brain combined , whose functions include learning to perform skilled motor tasks accurately and automatically . It is wired up into repeating microcircuits , sometimes referred to as cerebellar chips , in which learning alters the strength of the synapses between the parallel fibres , which carry input information , and the large Purkinje cell neurons , which produce outputs contributing to skilled movements . The cerebellar chip has a striking resemblance to a mathematical structure called an adaptive filter used by control engineers , and we have used this analogy to analyse its information-processing properties . We show that it learns synaptic strengths that minimise the errors in performance caused by unavoidable noise in sensors and cerebellar circuitry . Optimality principles of this kind have proved to be powerful tools for understanding complex systems . Here we show that optimality explains neuronal-level features of cerebellar learning such as the mysterious preponderance of “silent” synapses between parallel fibres and Purkinje cells and behavioural-level features such as the dependence of rate of learning of a motor skill on learning history . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neuroscience/motor",
"systems",
"neuroscience/theoretical",
"neuroscience",
"neuroscience/sensory",
"systems",
"computational",
"biology/computational",
"neuroscience"
] | 2008 | Silent Synapses, LTP, and the Indirect Parallel-Fibre Pathway: Computational Consequences of Optimal Cerebellar Noise-Processing |
The conserved DAF-16/FOXO transcription factors and SIR-2 . 1/SIRT1 deacetylases are critical for diverse biological processes , particularly longevity and stress response; and complex regulation of DAF-16/FOXO by SIR-2 . 1/SIRT1 is central to appropriate biological outcomes . Caenorhabditis elegans Host Cell Factor 1 ( HCF-1 ) is a longevity determinant previously shown to act as a co-repressor of DAF-16 . We report here that HCF-1 represents an integral player in the regulatory loop linking SIR-2 . 1/SIRT1 and DAF-16/FOXO in both worms and mammals . Genetic analyses showed that hcf-1 acts downstream of sir-2 . 1 to influence lifespan and oxidative stress response in C . elegans . Gene expression profiling revealed a striking 80% overlap between the DAF-16 target genes responsive to hcf-1 mutation and sir-2 . 1 overexpression . Subsequent GO-term analyses of HCF-1 and SIR-2 . 1-coregulated DAF-16 targets suggested that HCF-1 and SIR-2 . 1 together regulate specific aspects of DAF-16-mediated transcription particularly important for aging and stress responses . Analogous to its role in regulating DAF-16/SIR-2 . 1 target genes in C . elegans , the mammalian HCF-1 also repressed the expression of several FOXO/SIRT1 target genes . Protein–protein association studies demonstrated that SIR-2 . 1/SIRT1 and HCF-1 form protein complexes in worms and mammalian cells , highlighting the conservation of their regulatory relationship . Our findings uncover a conserved interaction between the key longevity determinants SIR-2 . 1/SIRT1 and HCF-1 , and they provide new insights into the complex regulation of FOXO proteins .
The Insulin/Insulin-like Growth Factor-1 ( IGF-1 ) signaling ( IIS ) cascade is one of the most highly conserved and best characterized longevity pathways in eukaryotes . When stimulated , the insulin/IGF-1 like receptors initiate a kinase cascade that leads to the phosphorylation , and cytoplasmic retention of the main downstream effectors , Forkhead box , Class O ( FOXO ) transcription factors . Reduction in IIS signaling leads to the dephosphorylation of FOXO , allowing nuclear translocation and transcriptional activation of FOXO [1] , [2] . The C . elegans FOXO ortholog DAF-16 , as well as the Drosophila , mouse , and human FOXO transcription factors are all critical for longevity , metabolism , and stress response [3]–[12] , suggesting that the mechanisms underlying FOXOs' ability to affect physiology are highly conserved across species . Indeed , much of our understanding of FOXO regulation comes from studies done on C . elegans DAF-16 . When activated , DAF-16 selectively regulates the transcription of a large number of genes which cumulatively act to elevate stress resistance , alter metabolic and developmental responses , improve immunity , and extend lifespan [13]–[16] . To integrate many different environmental stimuli and coordinate proper transcriptional responses , DAF-16 activity must be tightly controlled . DAF-16 activity is known to be regulated by post-translational modifications , nuclear/cytoplasmic translocation and association with transcriptional co-regulators . Although necessary for its activation , translocation of DAF-16 into the nucleus is not sufficient to stimulate its transcriptional activity [17] . Association with additional co-factors is also necessary for nuclear DAF-16 activation [18]–[23] . Little is known about the interplay between DAF-16 and its nuclear regulators and how these multiple factors coordinately act on DAF-16 to ensure proper transcriptional outcomes . SIR-2 . 1 , the C . elegans homolog of the yeast NAD+-dependent protein deacetylase Sir2p , is an important DAF-16 co-factor . SIR-2 . 1 is thought to activate DAF-16 in conferring longevity as well as stress resistance [18] , [24] , [25] . Heat stress stimulates the physical association of SIR-2 . 1 with DAF-16 via the scaffolding protein 14-3-3 , which promotes the transactivation of DAF-16 through an unknown mechanism [18] , [25] . Overexpression of Sir2 homologs in worms , yeast and flies extends lifespan [18] , [24] , [26] , [27] , emphasizing the evolutionarily conserved role of Sir2 in longevity determination . In mammals , SIRT1 associates with and directly deacetylates FOXO1 , 3 , and 4 in a stress-dependent manner [28]–[31] . However , the exact mechanism whereby SIR-2 . 1/SIRT1 affects DAF-16/FOXO activity and whether additional factors are involved in the regulation of DAF-16/FOXO by SIR-2 . 1/SIRT1 is not well understood . Host Cell Factor-1 ( HCF-1 ) belongs to a family of highly conserved HCF proteins and acts as a nuclear co-repressor of DAF-16 [21] , [32] . Inactivating hcf-1 robustly extends lifespan and confers oxidative stress resistance in a daf-16-dependent manner in C . elegans . In the nucleus , HCF-1 associates with DAF-16 and limits its access to a subset of target gene promoters [21] . C . elegans HCF-1 shares high structural homology with two mammalian counterparts , HCF-1 and HCF-2 [32] . Although mammalian HCF-1 has been studied extensively , HCF-2 functions remain largely unknown . Mammalian HCF-1 was originally identified as a binding partner of the Herpes Simplex Virus VP16 transcription factor [33] . Apart from VP16 , HCF-1 has been shown to associate with a number of transcription factors to stimulate or repress their transactivation properties [34]–[39] . HCF-1 is an important regulator of cellular proliferation as it promotes progression through multiple phases of the cell cycle via assembling transcriptional complexes to modulate E2F transcription factor activities [38] , [40] . Whether mammalian HCF proteins function as conserved FOXO regulators has yet to be determined . In this study , we sought to examine whether the two conserved DAF-16/FOXO nuclear regulators , HCF-1 and SIR-2 . 1/SIRT1 , functionally interact in worms and whether this interaction is conserved in mammals . We found that hcf-1 acts downstream of sir-2 . 1 to regulate daf-16 and thereby modulates lifespan and oxidative stress response in C . elegans . We showed that HCF-1 and SIR-2 . 1 regulate a common subset of DAF-16 target genes important for ensuring longevity and stress response . Furthermore , we demonstrated that mammalian HCF-1 affects the expression of several SIRT1/FOXO transcriptional targets and physically associates with both FOXO3 and SIRT1 . Our findings uncover a new regulatory mechanism between the critical longevity determinants DAF-16/FOXO and SIR-2 . 1/SIRT1 , and implicate an important role of HCF-1 in aging and age-related diseases in diverse organisms .
In C . elegans , inactivation of hcf-1 results in a robust lifespan extension , as well as improved survival upon exposure to oxidative stress , in a manner dependent on daf-16 . In its role in longevity and stress response , HCF-1 inhibits DAF-16 activity by physically associating with DAF-16 and diminishing DAF-16 localization to a subset of downstream target promoters [21] . In the context of cell cycle progression , mammalian HCF-1 is known to regulate the activities of various transcription factors by promoting the formation of transcriptional regulatory complexes [39] , [41] . We reasoned that HCF-1 in C . elegans may function similarly and , in conjunction with other transcriptional regulators , act to fine tune DAF-16 activity . As SIR-2 . 1 is a well-known , evolutionarily conserved longevity determinant that activates DAF-16 [18] , we explored whether HCF-1 and SIR-2 . 1 functionally interact to regulate DAF-16 . As a first step , we examined the putative functional connection between hcf-1 and sir-2 . 1 in lifespan modulation by performing genetic analyses . We compared the lifespan of hcf-1 ( pk924 ) and sir-2 . 1 ( ok434 ) single mutants to that of sir-2 . 1 ( ok434 ) hcf-1 ( pk924 ) double mutants . Both hcf-1 and sir-2 . 1 alleles used in this analysis are putative null mutants [21] , [42] . As previously described , hcf-1 ( pk924 ) mutant worms displayed a mean lifespan >20% longer than that of wild type and the hcf-1 ( pk924 ) long-lived phenotype was fully suppressed by daf-16 ( mgDf47 ) mutation ( Figure 1A and [21] ) . sir-2 . 1 ( ok434 ) mutants exhibited lifespan similar to that of wild-type worms and always substantially shorter than that of hcf-1 ( pk924 ) ( Figure 1A; Table S1A ) . We found that all four independent lines of the double mutants exhibited lifespans similar to that of hcf-1 ( pk924 ) single mutant worms ( Figure 1A , Table S1A ) , suggesting that sir-2 . 1 is not required for hcf-1 ( pk924 ) mutation to extend lifespan . Our genetic data suggest two possibilities: one is that hcf-1 and sir-2 . 1 may work independently and that sir-2 . 1 inactivation does not affect hcf-1 ( pk924 ) mutant longevity . On the other hand , since the lifespan of the double mutant is similar to that of hcf-1 ( pk924 ) single mutant , hcf-1 may act downstream of sir-2 . 1 . To distinguish between these two possibilities , we examined the effect of overexpressing sir-2 . 1 in worms harboring the hcf-1 mutation . In C . elegans , overexpressing sir-2 . 1 confers a lifespan extension phenotype that is dependent on daf-16 [18] , [24] . We reasoned that if hcf-1 and sir-2 . 1 work independently , then combining hcf-1 inactivation with sir-2 . 1 overexpression should further increase lifespan . By contrast , if hcf-1 and sir-2 . 1 work in the same pathway , and hcf-1 is genetically downstream of sir-2 . 1 , then overexpression of sir-2 . 1 should not cause further lifespan extension in hcf-1 ( pk924 ) mutants . To examine this , we utilized the long-lived , low-copy sir-2 . 1 overexpressor strain NL3909 pkIs1642 [unc-119 sir-2 . 1] ( pkIs1642[sir-2 . 1 ( O/E ) ] ) [18] , [43] to generate hcf-1 ( pk924 ) ;pkIs1642[sir-2 . 1 ( O/E ) ] strains . As a control , we outcrossed the pkIs1642 strain and showed that it continues to extend lifespan compared to its transgenic control NL3908 pkIs1641 [unc-119] ( pkIs1641[sir-2 . 1 ( wt ) ] ) under our assaying conditions ( Figure S2A; Table S1G ) . Furthermore , we knocked-down sir-2 . 1 in the pkIs1642 strain to show that the lifespan increase is indeed dependent on sir-2 . 1 ( Figure S2B–S2D; Table S1H ) . hcf-1 ( pk924 ) and pkIs1642[sir-2 . 1 ( O/E ) ] worms lived longer than N2 wild type or pkIs1641[sir-2 . 1 ( wt ) ] transgenic controls by 28% and 17% , respectively ( Figure 1B; Table S1B , S1G ) . Interestingly , the hcf-1 ( pk924 ) ;pkIs1642[sir-2 . 1 ( O/E ) ] ) worms exhibited a lifespan very similar to , or in some cases shorter than , that of hcf-1 ( pk924 ) mutants ( Figure 1B; Table S1B ) . However , in none of the hcf-1 ( pk924 ) ;pkIs1642[sir-2 . 1 ( O/E ) ] ) isolates generated did we observe a lifespan longer than that of hcf-1 ( pk924 ) mutants ( Table S1B ) . These data support the hypothesis that hcf-1 acts in the same genetic pathway as sir-2 . 1 . Considering our previous findings that hcf-1 can robustly extend the lifespans of long-lived insulin signaling and germline proliferation mutants [21] , our current observation that overexpression of sir-2 . 1 cannot further enhance longevity in worms lacking hcf-1 indicates that the genetic interaction between hcf-1 ( - ) and sir-2 . 1 ( O/E ) is specific . In addition to their lifespan effects , both HCF-1 and SIR-2 . 1 regulate the ability of DAF-16 to respond to a variety of environmental stress cues . Adult hcf-1 ( pk924 ) mutant worms are resistant to oxidative- and heavy metal-stress [21] . Likewise , sir-2 . 1 overexpression is protective against exposure to oxidative as well as heat stress , while sir-2 . 1 mutation increases sensitivity to oxidative , heat , and UV-induced environmental insults [18] , [42] . To further investigate the genetic relationship between hcf-1 and sir-2 . 1 , we analyzed the response of sir-2 . 1 ( ok434 ) hcf-1 ( pk924 ) double mutants and hcf-1 ( pk924 ) ;pkIs1642[sir-2 . 1 ( O/E ) ] ) worms to treatment with two oxidative-stress inducing agents , paraquat and tert-Butyl hydroperoxide ( t-BOOH ) . Paraquat induces cellular damage by elevating intracellular superoxide levels [44] , and t-BOOH damages cellular lipids and proteins through peroxidation [45] . Under the paraquat or t-BOOH conditions where sir-2 . 1 ( ok434 ) mutants were sensitive and hcf-1 ( pk924 ) worms resistant to the treatments , sir-2 . 1 ( ok434 ) hcf-1 ( pk924 ) worms survived the paraquat or t-BOOH exposure as well as hcf-1 ( pk924 ) single mutants did , and were significantly more resistant than N2 or sir-2 . 1 ( ok434 ) worms ( Figure 1C , 1E; Figure S1A , S1C; Table S1C , S1E ) . Furthermore , overexpressing sir-2 . 1 in hcf-1 ( pk924 ) mutants did not further enhance the paraquat or t-BOOH-resistance of hcf-1 ( pk924 ) worms ( Figure 1D , 1F; Figure S1B , S1D; Table S1D , S1F ) . Overall , our observations are consistent with a model in which hcf-1 acts downstream of sir-2 . 1 to modulate longevity and oxidative stress responses in C . elegans . In C . elegans , 14-3-3 proteins are required for lifespan extension and stress resistance conferred by extra copies of sir-2 . 1 , as well as for facilitating the association of SIR-2 . 1 and DAF-16 [18] , [25] . Our findings that hcf-1 and sir-2 . 1 act together to regulate daf-16 raise the possibility that hcf-1 may also functionally interact with 14-3-3 . To address this question , we examined the genetic relationship between hcf-1 and 14-3-3 in lifespan . The 14-3-3 homologs in C . elegans are encoded by two highly similar genes ftt-2 and par-5 , which share ∼80% sequence identity [46] . RNAi constructs targeting the coding sequences of ftt-2 and par-5 are not specific and will knockdown both genes , whereas RNAi constructs targeting the 3′ UTR of each are gene-specific ( Figure S4A and [47] ) . We found that knocking down either ftt-2 or par-5 alone did not substantially reduce hcf-1 ( pk924 ) lifespan , yet simultaneously diminishing the function of both genes through the non-specific RNAi completely abrogated the longevity effect of hcf-1 inactivation ( Figure 2A , 2B; Table S2A , S2B ) . The RNAi data are corroborated by our findings that a null mutation of ftt-2 , n4426 , was only able to slightly decrease the lifespan of hcf-1 mutants ( Figure S3D; Table S2D ) . Therefore , we conclude that both 14-3-3 genes are necessary for the longevity increase conferred by hcf-1 mutation and likely act downstream of hcf-1 . DAF-16 responds to different upstream stimuli by selectively activating and repressing groups of target genes , and hence ensuring appropriate responses to specific signals [14]–[16] . We previously proposed that C . elegans HCF-1 acts as a specificity factor for DAF-16 and negatively regulates DAF-16 on a select set of its target genes [21] . Similarly , C . elegans SIR-2 . 1 is thought to promote DAF-16 regulation of a subset of transcriptional targets [18] . As our genetic data suggest that hcf-1 and sir-2 . 1 act in the same genetic pathway to modulate longevity in a daf-16-dependent manner , we hypothesized that hcf-1 inactivation and sir-2 . 1 overexpression would have similar effects on DAF-16-mediated transcription . To test this hypothesis , we compared the daf-16-dependent global transcriptional changes occurring in the long-lived hcf-1 ( pk924 ) mutant to those occurring in the long-lived sir-2 . 1 overexpressor strain . We identified the genes whose expression was changed in hcf-1 ( pk924 ) mutants in a daf-16-dependent manner by comparing the expression profiles of synchronized hcf-1 ( pk924 ) mutants to those of daf-16 ( mgDf47 ) ;hcf-1 ( pk924 ) double mutants using Agilent C . elegans gene expression microarrays . In addition , to pinpoint the genes that are responsive to the hcf-1 ( pk924 ) mutation , instead of those that show expression changes simply due to daf-16 deletion , we focused on genes that showed a similar trend of expression change both in the hcf-1 ( pk924 ) vs . N2 and hcf-1 ( pk924 ) vs . daf-16 ( mgDf47 ) ;hcf-1 ( pk924 ) comparisons ( henceforth referred to as hcf-1 ( - ) profile ) ( Data are available at NCBI Gene Expression Omnibus , accession number GSE30725 ) . Likewise , the genes which were differentially regulated by DAF-16 in response to sir-2 . 1 overexpression were identified by comparing the strains pkIs1642[sir-2 . 1 ( O/E ) ] to daf-16 ( mgDf50 ) ;pkIs1642[sir-2 . 1 ( O/E ) ] and pkIs1642 [sir-2 . 1 ( O/E ) ] to its transgenic control pkIs1641[sir-2 . 1 ( wt ) ] ( henceforth referred to as sir-2 . 1 ( O/E ) profile ) . To identify the genes that show consistent and significant expression changes across the independent biological replicates of hcf-1 ( - ) or sir-2 . 1 ( O/E ) , we used Significance Analysis of Microarrays ( SAM ) [48] with a stringent criteria of expected false discovery rate ( FDR ) set at 0% . SAM analysis identified 1 , 032 significantly affected genes in hcf-1 ( - ) and 1 , 042 genes in sir-2 . 1 ( O/E ) ( Figure 3A; Table S3 ) . Next , we compared the two datasets to determine the extent of overlap . Strikingly , we found 866 genes ( 473 upregulated and 390 downregulated ) whose expression changed similarly in hcf-1 ( - ) and sir-2 . 1 ( O/E ) profiles , suggesting that the vast majority ( >80% ) of the genes regulated by DAF-16 in response to hcf-1 inactivation or sir-2 . 1 activation are shared ( Figure 3B ) . Of the genes that were expressed in a dissimilar manner between hcf-1 ( - ) and sir-2 . 1 ( O/E ) profiles , ∼10% displayed an opposite expression change and ∼10% were unique to either hcf-1 ( - ) or sir-2 . 1 ( O/E ) ( Figure 3A , 3B ) . The finding that the transcriptional outcomes conferred by DAF-16 in response to hcf-1 mutation or sir-2 . 1 overexpression are largely similar corroborates our genetic data suggesting that SIR-2 . 1 and HCF-1 act in the same pathway to regulate DAF-16 . In addition to being regulated by SIR-2 . 1 and HCF-1 , DAF-16 activity is also controlled by the insulin/IGF-1 signaling ( IIS ) pathway . In response to reduced IIS , DAF-16 translocates into the nucleus and regulates the expression of a large number of genes that together contribute to the diverse functions of IIS , including the regulation of development , metabolism , stress response , and longevity [14]–[16] . To determine how the hcf-1- and sir-2 . 1-responsive DAF-16- target genes compare with the IIS-responsive DAF-16 targets , we further compared the hcf-1 ( - ) and sir-2 . 1 ( O/E ) profiles to that of the daf-2 ( - ) profile ( microarray data from daf-2 ( e1370 ) vs . daf-16 ( mgDf50 ) ;daf-2 ( e1370 ) [49] ) . Interestingly , expression of the majority of the shared hcf-1 ( - ) /sir-2 . 1 ( O/E ) -regulated genes ( 693/866 = 80% ) were also changed in daf-2 ( - ) in the same direction , yet this represented only a fraction of all daf-2 ( - ) -induced changes ( 693/2515 = 28% ) ( Figure 3C , 3D ) . This indicates that , among a large number of potential DAF-16 targets , hcf-1 and sir-2 . 1 converge to co-regulate a distinct subset of these genes . Our findings from the microarray comparisons support the model that HCF-1 and SIR-2 . 1 antagonize each other to control a particular aspect of the DAF-16-regulated transcriptional program . To examine the biological processes that can be carried out by genes affected by hcf-1 ( - ) and sir-2 . 1 ( O/E ) , we queried their Gene Ontology ( GO ) terms using Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) [50] . We focused on the GO term categories most significantly enriched in our dataset compared to the C . elegans genome . Our analyses revealed that for the DAF-16 target genes co-regulated by HCF-1/SIR-2 . 1 , GO terms for aging , cellular detoxification ( in particular phase 1 & 2 detoxification ) and stress response were highly overrepresented among both the upregulated and downregulated genes ( Figure 3E; Table S4 ) [51] , [52] . To test whether the DAF-16 targets that are co-regulated by HCF-1/SIR-2 . 1/DAF-2 might participate in biological functions distinct from the targets uniquely regulated by DAF-2 ( and not affected by HCF-1/SIR-2 . 1 ) , we compared the GO terms represented in the hcf-1 ( - ) /sir-2 . 1 ( O/E ) -shared genes to those in daf-2 ( - ) . Among the genes induced by DAF-16 , the most prominent functional categories represented in the hcf-1 ( - ) /sir-2 . 1 ( O/E ) /daf-2 ( - ) -overlapping set were very similar to those in the hcf-1 ( - ) /sir-2 . 1 ( O/E ) -co-regulated set ( i . e . aging , detoxification , stress response ) ( Figure 3E; Table S4 ) . By contrast , the DAF-16 target genes that are uniquely upregulated in daf-2 ( - ) are enriched for GO categories for developmental , metabolic ( amino acid anabolism/catabolism ) and cellular ion transport processes ( Figure 3E; Table S4A ) . Among the genes repressed by DAF-16 , the hcf-1 ( - ) , sir-2 . 1 ( O/E ) and daf-2 ( - ) overlapping set is also enriched with GO terms in aging and stress responses , as well as a new category in fatty acid/lipid/amino acid metabolic processes . Interestingly , the daf-2 ( - ) -specific downregulated genes are highly enriched for GO terms in protein biosynthesis , protein degradation , unfolded protein response , protein homeostasis , development and cell division ( Figure 3E; Table S4B ) . Thus , our results suggest that in response to hcf-1 inactivation and sir-2 . 1 overexpression , DAF-16 specifically induces longevity assurance genes to combat toxic cellular insults/stressors and extend lifespan without strongly affecting developmental , and protein homeostasis pathways . DAF-16 directly binds a consensus DAF-16 binding element ( DBE ) to regulate the expression of many downstream target genes [53] , [54] . To further investigate how the HCF-1/SIR-2 . 1-coregulated vs . the IIS-specific DAF-16 target genes might be regulated , we analyzed the 1 . 5 kb upstream promoter sequences of genes in each group to identify any transcription factor binding sites and regulatory elements that are overrepresented . We submitted the upstream sequences of all genes in hcf-1/sir-2 . 1-coregulated or daf-2-specific categories to two de novo motif finding algorithms , BioProspector [55] and Regulatory Sequence Analysis Tools ( RSAT ) [56] and focused on the top highest-scoring motifs from each algorithm . These analyses revealed four common motifs enriched in the promoters of DAF-16 targets , regardless of their responsiveness to HCF-1 & SIR-2 . 1 ( Table S4C ) , suggesting that DAF-16 likely collaborates with additional yet-to-be identified co-factors in gene regulation . We were particularly interested in the motifs that are uniquely enriched in the different groups of genes analyzed . The most notable motif highly enriched in the hcf-1/sir-2 . 1/daf-2-overlapping group , but not in the daf-2-unique group , was the DAF-16-associated element ( DAE ) ( CTTATCA or TGATAAG ) , previously discovered as a sequence overrepresented in the promoters of DAF-16-regulated genes [16] , [54] and shown to be directly bound by DAF-16 in in vitro gel shift assays [54] ( Table S4C ) . Interestingly , the DAE represents a GATA-factor binding motif that is highly enriched in promoters of genes whose expression show age-dependent changes and whose transcription is controlled by C . elegans GATA-factor homologs elt-3 , elt-5 , and elt-6 [57] . We further compared the expression profiles of hcf-1 ( - ) and sir-2 . 1 ( O/E ) to that of aging worms [57] , and found that 23% of genes that show age-dependent changes were also represented in our hcf-1/sir-2 . 1 co-regulated set ( p-value<2 . 2e-16 as determined by Chi2 analysis ) . The large representation of genes that show age-dependent expression changes in the hcf-1/sir-2 . 1 group correlates well with our observation that HCF-1 and SIR-2 . 1 together regulate aging- and stress response-specific DAF-16 downstream targets ( Figure 3E ) . Results from the motif analysis also suggest that HCF-1 and SIR-2 . 1 likely engage additional transcriptional partners , such as GATA factors , in their regulation of DAF-16 . Our genetic and microarray analyses suggest that SIR-2 . 1 likely antagonizes HCF-1 to regulate DAF-16 activity . To elucidate the molecular mechanism by which SIR-2 . 1 may inhibit HCF-1 , we first tested whether HCF-1 expression or stability is affected by SIR-2 . 1 . We found that the mRNA and protein levels of HCF-1 did not significantly differ in strains lacking or overexpressing sir-2 . 1 ( data not shown ) . Since both SIR-2 . 1 and HCF-1 are known to form a protein complex with DAF-16 in C . elegans [18] , [21] , we next examined whether SIR-2 . 1 may also physically associate with HCF-1 . We performed co-immunoprecipitation ( co-IP ) experiments using an affinity-purified anti-HCF-1 antibody and immunoprecipitated HCF-1 from lysates of geIn3[sir-2 . 1 ( O/E ) ] , worms overexpressing SIR-2 . 1 to a greater extent than the pkIs1642[sir-2 . 1 ( O/E ) ] strain we used for lifespan analysis , hcf-1 ( pk924 ) ;geIn3[sir-2 . 1 ( O/E ) ] , worms overexpressing SIR-2 . 1 but lacking hcf-1 , and sir-2-1 ( ok434 ) , worms lacking sir-2 . 1 . SIR-2 . 1 was co-immunoprecipitated with HCF-1 only in the geIn3[sir-2 . 1 ( O/E ) ] lysate ( Figure 4A , left panel ) . A similar complex formation was also detected in reciprocal co-immunoprecipitation experiments ( Figure 4A , right panel ) . Since 14-3-3 proteins are proposed to bridge the physical interactions between SIR-2 . 1 and DAF-16 , especially under stress conditions [18] , [25] , and our genetic data revealed that 14-3-3 likely function downstream of HCF-1 in longevity modulation , we tested a possible physical association of HCF-1 with 14-3-3 proteins . We immunoprecipitated GFP-fused HCF-1 using anti-GFP antibodies from hcf-1::gfp;ftt-2::mCherry or hcf-1::gfp strains and blotted with anti-mCherry or anti-PAR-5 antibodies to monitor mCherry-tagged FTT-2 and endogenous PAR-5 respectively . HCF-1 was able to form a protein complex with either FTT-2 or PAR-5 ( Figure 4B , 4C ) . Consistent with the co-IP results , a search for HCF-1 binding partners using immunoprecipitation of HCF-1::GFP followed by mass spectrometrical analysis of co-purifying proteins identified the two 14-3-3 proteins FTT-2 and PAR-5 ( Figure S4B ) . Interestingly , sequence analysis ( by scansite . mit . edu ) predicts that HCF-1 contains a highly significant consensus 14-3-3 binding site , suggesting HCF-1 may directly bind 14-3-3 . Taken together , our data reveal that HCF-1 is a new component in the regulatory network involving SIR-2 . 1 , 14-3-3 , and DAF-16 . C . elegans HCF-1 belongs to a highly conserved family of proteins [38] , [58] , [59] . In mammals , two homologs of HCF-1 are present: HCF-1 and HCF-2 [32] , [60] . Mammalian HCF-1 plays a role in transcriptional regulation and cell cycle progression , whereas the functions of HCF-2 remain unknown . SIRT1 , the mammalian homolog of SIR-2 . 1 , is known to interact with and deacetylate the DAF-16 homologs FOXO1 , FOXO3 , and FOXO4 and in doing so affects FOXO transcriptional activity [28] , [30] . Given that HCF-1 , DAF-16 and SIR-2 . 1 are highly conserved between C . elegans and mammals , we tested whether mammalian homologs of HCF-1 could affect the transcription of FOXO- and SIRT1- co-regulated target genes . Since mammalian HCF-1 is required for proper cell cycle progression , we employed a transient knockdown approach by transfecting siRNA duplexes targeting the HCF-1 gene into INS-1 rat insulinoma cells . We used two different HCF-1 siRNA duplexes to control for specificity , and found that HCF-1 knockdown did not substantially affect the expression of HCF-2 mRNA as assessed by reverse transcription-quantitative PCR ( RT-qPCR ) ( Figure S5 ) . We examined the expression of Bim , a proapoptotic factor , Gadd45a , which is involved in DNA damage repair , IGFBP1 , an insulin-like growth factor-binding protein , and p27 , a cyclin-dependent kinase inhibitor . These represent FOXO target genes which are affected by SIRT1 deacetylation of FOXO [28] , [30] . Depletion of HCF-1 resulted in a significant increase in the levels of Bim , Gadd45a , and IGFBP1 transcripts , but did not affect p27 expression ( Figure 5A ) . Consistent results were obtained with the two different HCF-1-targeting siRNA duplexes . We next tested whether HCF-2 could also affect FOXO target gene expression . Similar to HCF-1 knockdown , cells treated with HCF-2 siRNA exhibited increased expression of Gadd45a and no change in p27 . However , unlike the case with HCF-1 , cells depleted of HCF-2 did not show any significant changes in Bim , or IGFBP1 transcripts ( Figure 5B ) . Our data reveal that HCF proteins negatively regulate the expression of a subset of FOXO and SIRT1 transcriptional target genes . Furthermore , HCF-1 appears to play a more substantial role in regulating FOXO target genes relative to HCF-2 . The observation that HCF-1 and HCF-2 have specific effects on a subset of FOXO targets tested is also consistent with our findings in C . elegans suggesting HCF-1 to be a specificity factor for DAF-16/FOXO . In C . elegans , HCF-1 is able to physically associate with both DAF-16 and SIR-2 . 1 ( Figure 4A and [21] ) . We therefore hypothesized that mammalian HCF proteins will also participate in protein complexes with FOXO3 and SIRT1 . To examine the physical interactions between these proteins , we transfected HEK293T cells with plasmids encoding either Flag-tagged FOXO3 or Flag-tagged SIRT1 . We then performed co-immunoprecipitation experiments with these cell lysates by using Flag-antibody conjugated agarose beads . Both FOXO3 and SIRT1 were found to interact with the endogenous mammalian HCF-1 protein ( Figure 6A , 6B; Figure S6A ) . We also tested whether the closely related HCF-2 protein could also physically interact with FOXO3 and SIRT1 . Since antibodies capable of detecting endogenous HCF-2 are not available , we performed co-immunoprecipitation experiments using overexpressed Flag-FOXO3 , Flag-SIRT1 , and HA-tagged HCF-2 . We found that HCF-2 was also present in a protein complex with FOXO3 and SIRT1 when overexpressed ( Figure S6B ) , similar to HCF-1 . These results indicate that the physical associations between HCF-1 , DAF-16 and SIR-2 . 1 are highly conserved between C . elegans and mammals .
The highly conserved FOXO transcription factors are master regulators of diverse biological processes [61] and as such , their transcriptional activities are tightly controlled [18]–[23] . Although a number of different transcriptional co-factors of DAF-16/FOXO have been identified , little is known about how they functionally interact to fine-tune DAF-16/FOXO activity , and in particular , how they may collaborate to affect DAF-16-mediated lifespan extension . In this study , we identified the DAF-16 nuclear co-repressor HCF-1 as an integral component of the regulatory network involving SIR-2 . 1/SIRT1 , 14-3-3 , and DAF-16/FOXO with major consequences to both organismal aging and stress response . Our data indicate that in C . elegans , HCF-1 likely functions downstream of SIR-2 . 1 , and upstream of 14-3-3 , to regulate a distinct subset of DAF-16 target genes to affect longevity and oxidative stress response . This regulatory pathway is highly conserved , as mammalian HCF proteins also impact the expression of SIRT1/FOXO co-regulated transcriptional targets , and HCF proteins participate in protein complex formation with SIR-2 . 1/SIRT1 , 14-3-3 , and DAF-16/FOXO in worms and in mammals ( Figure 7 ) . Our expression profiling studies indicate that the set of DAF-16 target genes co-regulated by sir-2 . 1 , hcf-1 , and daf-2 ( area “a” of Figure 3D ) is enriched for previously identified longevity-associated genes ( annotated as “aging” in GO ) , whereas the IIS-specific targets ( area “g” of Figure 3D ) are not . This is somewhat unexpected as the hcf-1 mutant and sir-2 . 1 overexpressor strains exhibit lifespan extension phenotypes that are much milder than that of the daf-2 mutant . Interestingly , this correlates well with the degree of expression change observed for many of the shared DAF-16 target genes , as they often exhibit more robust expression changes in the daf-2 ( - ) profile compared to the sir-2 . 1 ( O/E ) or hcf-1 ( - ) profiles . An implication from this observation is that the co-regulated gene set is particularly important for longevity determination , and may thus contain additional targets important for prolonged lifespan that are not currently known to affect aging . Our previous genetic findings indicated that reduced insulin signaling synergizes with inactivation of hcf-1 to affect longevity and DAF-16-mediated gene regulation [21] . We interpreted those results to suggest that IIS and hcf-1 likely act independently to regulate DAF-16/FOXO . However , a caveat of that interpretation is that the daf-2 mutant we examined was not a null mutant , and formally , loss of hcf-1 can further decrease IIS signaling to further increase lifespan . Similarly , the genetic relationship between the insulin signaling pathway and sir-2 . 1 has been unclear due to several conflicting reports [18] , [24] . In the current study , a comparison of the DAF-16-regulated gene expression changes in response to either daf-2 mutation , hcf-1 inactivation , or sir-2 . 1 overexpression indicates that a large majority of the HCF-1/SIR-2 . 1 co-regulated DAF-16 target genes are similarly regulated by reduced IIS . It is possible that upon downregulation of IIS , the majority of DAF-16 migrates into the nucleus but is still subject to regulation by nuclear co-factors . Under this scenario , SIR-2 . 1 and HCF-1 may be acting as additional “gate keepers” to control DAF-16 activation in the face of reduced IIS . In addition , we saw that the insulin/IGF-1-like peptide , ins-7 , which was shown to act as a daf-2 agonist [16] , was significantly repressed by hcf-1 inactivation and sir-2 . 1 overexpression ( Table S3 ) . Thus , a possible feedback mechanism in which hcf-1 inactivation or sir-2 . 1 activation leads to further inhibition of IIS may also explain the genetic results observed with reduced IIS and hcf-1 inactivation or sir-2 . 1 overexpression . Our motif analyses revealed additional factors that are likely involved in the regulation of DAF-16 by HCF-1 and SIR-2 . 1 in C . elegans , in particular the aging-related GATA-factor homologs ( ELT-3 , -5 , -6 ) known to bind the DAE element , a consensus motif enriched in many of the HCF-1/SIR-2 . 1 co-regulated genes [57] . Of note , the DAE sequence also shares close resemblance to the mammalian transcription factor Evi1 binding site . Although the C . elegans Evi1 homolog , egl-43 , has been shown to be involved in early development [62] , a function in longevity and stress response has not been reported . Future functional analysis of HCF-1/SIR-2 . 1 and ELT-3 , -5 , -6 , and EGL-43 will likely yield new insights into additional layers of DAF-16 regulation . Considering the high conservation of DAF-16/FOXO-related pathways , it is not surprising that the regulatory relationship among HCF-1 , SIR-2 . 1 and DAF-16 we uncovered in worms turns out to be conserved in mammals . Our findings in mammalian cells are nevertheless very exciting as they implicate the HCF proteins to be key components linking FOXO and SIRT1 , two critical master regulators of physiology in mammals . Our results indicate that while both mammalian HCF-1 and HCF-2 are able to interact with SIRT1 and FOXO3 , HCF-1 has a greater effect on FOXO target gene expression . Interestingly , while both mammalian HCF-1 and HCF-2 as well as C . elegans HCF-1 are able to support the formation of the Herpes Simplex Virus VP16-transcriptional complex , only mammalian and C . elegans HCF-1 are able to promote VP16 transcriptional activity [32] . Thus , it appears that the evolutionarily conserved functions of HCF proteins are retained in mammalian HCF-1 . Alternatively , HCF-1 and HCF-2 likely have tissue-specific functions and are regulated differently under different cellular contexts . While our data indicate that parallel regulatory mechanisms are shared between C . elegans and mammalian HCF-1 , they also suggest the modes of regulation between HCF-1 , SIRT1 , and FOXO in mammals are likely more complex than what is observed in C . elegans . We note that in the case of the mammalian FOXO target genes Bim and IGFBP1 , HCF-1 and SIRT1 appear to affect FOXO target gene expression in a similar manner ( Figure 5A and [28] , [30] ) , and thus would appear to act in concert rather than antagonistically . On the other hand , HCF-1 and SIRT1 appear to have antagonistic effects on the FOXO target gene Gadd45a . It is important to keep in mind that in mammals , SIRT1 regulation of FOXO transcription factors is complex; in some instances SIRT1 acts as a repressor and in other cases as an activator of FOXO [28] , [30] , while in C . elegans the predominant role of SIR-2 . 1 is as an activator of DAF-16 . It is likely that in mammals , the interplay between SIRT1 and HCF-1 results in collaborative as well as antagonistic effects on FOXO transcriptional activity in a gene- and context-dependent manner . Future genome-wide studies examining the effects of HCF-1 on FOXO/SIRT1-regulated gene expression will provide further insights into the relationship between HCF-1 and SIRT1 . We found that HCF-1 physically associates with DAF-16/FOXO and SIR-2 . 1/SIRT1 in both worms and mammals . Previous studies in C . elegans indicate that 14-3-3 proteins act as bridging molecules that bring SIR-2 . 1 and DAF-16 into a protein complex in the nucleus [18] , [25] . Interestingly , our data suggest 14-3-3 proteins also physically associate with HCF-1 . This raises the question of how these different molecules coordinately interact to affect each other's activities . An intriguing model may be that HCF-1 normally binds 14-3-3/DAF-16 and dampens the ability of DAF-16 to activate its target genes; upon appropriate upstream signals , SIR-2 . 1 ejects HCF-1 off the complex and induces full activation of DAF-16 . Whether 14-3-3 proteins are also involved in the regulation of FOXO by SIRT1 and HCF in mammals remain to be investigated . In addition , SIRT1 is known to regulate FOXO transcriptional activity by directly deacetylating FOXO proteins and the FOXO co-activator PGC1α in mammals [63]–[65] . SIRT1 may affect multiple FOXO responses by deacetylating FOXO and specific FOXO co-regulators to achieve activation and/or repression of the appropriate target genes . Future investigation into whether SIRT1 also regulates HCF-1 via deacetylation and whether deacetylation will disrupt protein complexes involving SIRT1/HCF-1/FOXO will provide new insights into the functional interactions among these key longevity determinants . In conclusion , our findings establish a novel link between two evolutionarily conserved DAF-16/FOXO regulators . This study expands our understanding of the complex role that nuclear factors play in determining the specificity of DAF-16/FOXO activity . These results further implicate HCF-1 as a novel factor that may affect mammalian aging and age-related pathologies through interactions with SIRT1 and FOXO .
All strain stocks were kept at 16°C and grown under standard growth conditions [66] . The strains used are: Wild type N2 , hcf-1 ( pk924 ) , daf-16 ( mgDf47 ) ;hcf-1 ( pk924 ) [21] , IU372 . 1 sir-2 . 1 ( ok434 ) ( 7 times outcrossed in our lab ) , NL3908 pkIs1641 [unc-119] , NL3909 pkIs1642 [unc-119 sir-2 . 1] [18] , IU91 . 1 pkIs1641 [unc-119] ( 1X outcrossed in our lab ) , IU94 pkIs1642 [unc-119 sir-2 . 1] ( 1X outcrossed in our lab ) , geIn3[sir-2 . 1 rol-6 ( su1006 ) ] [24] ( 1X outcrossed in our lab ) , ftt-2 ( n4426 ) [18] ( 3X outcrossed in our lab ) , rwIs23 [hcf-1 ( pk924 ) ;Phcf-1::GFP unc-119] , GR1680 rwIs23[Phcf-1::GFP; unc-119]; IsB[pCR270 ( Pftt-2::ftt-2:: Spep-TEV-mCherry::ftt-2-3′UTR; Cb_unc-119 ) ] , rwIs9[Phcf-1::hcf-1::GFP Pmec-7::RFP] . Standard genetic methods were utilized to construct the following strains: sir-2 . 1 ( ok434 ) hcf-1 ( pk924 ) , hcf-1 ( pk924 ) ;pkIs1642[sir-2 . 1 ( O/E ) ] , hcf-1 ( ok559 ) ;geIn3[sir-2 . 1 rol-6 ( su1006 ) ] , ftt-2 ( n4426 ) ;hcf-1 ( pk924 ) . daf-16 ( mgDf50 ) ; pkIs1642[sir-2 . 1 ( O/E ) ] was a gift from M . Viswanathan and L . Guarente at MIT [43] . All lifespan assays were performed at 25°C , unless otherwise noted , on Nematode Growth Media ( NGM ) plates seeded with E . coli OP50 or RNAi bacteria . For experiments using OP50 , bacteria was grown overnight at 37°C , OD measured after growth and concentrated to OD 7 . 5 ( 5X OP50 ) or used directly , at OD 1 . 5 ( 1X ) . 35 mm NGM plates were seeded with 150 uL of OP50 for egglay plates and dried at room temperature . Plates that would be used for transferring worms throughout the lifespan assay were prepared by adding FUDR to OP50 culture to a final concentration of 50 ug/mL per plate , seeding 150 uL/plate , drying at room temperature , and storing at 4°C until use . For RNAi experiments , HT115 bacteria containing vectors expressing dsRNA were grown at 37°C in LB with 100 ug/mL carbenicillin and 15 ug/mL tetracycline to OD 0 . 8 , induced with 4 mM IPTG for 4 hrs at 37°C , and either concentrated to OD 7 . 5 and seeded , or seeded at OD 1 . 5 ( 1X ) . RNAi plates were also induced with 4 mM IPTG before use . Well-fed gravid adult worms were allowed to lay eggs at room temperature and the progeny were grown at 25°C until young adult/early gravid adult stage . The synchronized adults were transferred to fresh FUDR-containing plates at Day 0 , 2 , and 4 of adulthood . For lifespan assays carried out at 20°C , worms were incubated at 25°C for the first three days of adulthood to reduce vulva protrusion defects . The adult worms were scored every other day and worms that did not move when gently prodded by a platinum wire pick were recorded as dead . Worms that bagged , crawled onto the wall of the plate , or had a large protruding vulva were censored on the day of the event . All survival data were analyzed using Kaplan-Meier statistics ( SPSS software ) to generate statistical values and survival curves . p-values were calculated using the log-rank test . Kaplan-Meier log rank test was employed to determine whether independent experiments displayed statistically similar trends using a cutoff of p-value>0 . 05 . Based on these criteria , data from independent experiments were pooled whenever possible to increase statistical power . For hcf-1 ( - ) microarrays , total RNA was purified from synchronized L4 or young adult ( YA ) worms . Worms were synchronized by allowing hypochlorite-treated eggs to hatch in M9 buffer for 20 hrs at 16°C , and plating 500 L1 stage worms onto each of 5–6 10 mm NGM plates seeded with 3X OP50 bacteria . 6 biological replicates of hcf-1 ( - ) /daf-16 ( - ) ;hcf-1 ( - ) , two replicates of hcf-1 ( - ) /N2 were prepared . The synchronized populations were grown to L4 or YA stage at 25°C and harvested by washing off the plates with M9 buffer and freezing the worm pellet in liquid nitrogen . Total RNA was isolated using Tri-reagent ( Molecular Research Center , Inc . ) [68] and purified with the RNeasy kit ( Qiagen ) . cRNA synthesis/amplification , Cy3/Cy5 dye labeling , and hybridization onto Agilent 4X44K C . elegans oligonucleotide microarrays were performed as previously described [49] . Half the arrays were dye-flip replicates in each comparison . Details on sir-2 . 1 ( O/E ) microarrays will be published elsewhere ( Rogers* , Jan* , Ashraf , and Murphy , in preparation ) . daf-2 ( - ) microarray data were published in [49] . Immunoprecipitation was performed as described [21] . For HCF-1/SIR-2 . 1 co-IPs , mixed stage worms were grown on plates , harvested , and sonicated in IP lysis buffer ( 50 mM HEPES pH 7 . 5 , 1 mM EDTA , 150 mM NaCl , 10% Glycerol , 0 . 1% Triton X-100 , 1 mM sodium fluoride , 2 . 5 mM sodium orthovanadate , 1 mM PMSF , and Complete ( EDTA-free ) protease inhibitor cocktail ) and lysates cleared by centrifugation . Lysates were incubated with either affinity purified guinea-pig anti-HCF-1 antibody [21] or rabbit anti-SIR-2 . 1 antibody ( Novus Biologicals ) at 4°C overnight . Immunocomplexes were incubated with trysacryl protein A-agarose beads ( Pierce ) at 4°C for four hours , washed four times with IP lysis buffer , and eluted by boiling in SDS sample buffer . Eluted protein complexes were analyzed by western blotting using the anti-HCF-1 , anti-SIR-2 . 1 , or anti-actin ( Chemicon , clone C4 ) antibodies . For mass spectrometry and 14-3-3 co-IPs , GFP-tagged HCF-1 was purified from mixed stage C . elegans , using a previously reported method [74] with slight modifications . In short , worms were grown in liquid culture as mixed stages to a density of 4000 worms/mL . Worms were washed into lysis buffer ( 50 mM HEPES at pH 7 . 4 , 1 mM EGTA , 1 mM MgCl2 , 150 mM KCl , 10% ( v/v ) glycerol , protease and phosphatase inhibitors ) , drop-frozen in liquid nitrogen , and ground using a mortar and pestle . Resulting powder was thawed and NP-40 was added to 0 . 05% ( v/v ) . Immunoprecipitations were conducted on a 20 , 000 g supernatant of this extract , using monoclonal mouse-anti-GFP antibody ( Invitrogen ) coupled to Protein A resin ( Biorad ) . Immunoprecipitated proteins were eluted using 100 mM glycine at pH 2 . 6 . For co-IPs , eluted protein complexes were analyzed by western blotting using anti-mCherry ( Ruvkun Lab , MGH Boston ) or rabbit anti-PAR-5 ( a kind gift from K . J . Kemphues , Cornell University ) antibodies . For mass-spectrometrical analysis , immunoprecipitated proteins were eluted using 100 mM glycine at pH 2 . 6 . Eluted proteins were visualized by silver-stained SDS-PAGE and identified by mass spectrometry . For the latter , samples were digested using trypsin and the resulting peptides were separated via nano-capillary liquid chromatography and identified by online tandem mass spectrometry ( LTQ-XL , Thermo ) . Mass spectra were searched against the current wormpep database using Sequest ( Thermo ) and DTASelect [75] . As a negative-control for the mass-spectrometrical analysis , an identical purification was conducted using C . elegans expressing only untagged endogenous HCF-1 . IP and negative-control were compared using Contrast [75] . Flag-FOXO3 and Flag-SIRT1 were obtained from Addgene and have been described previously [28] . The plasmids encoding HA-HCF-1 and HA-HCF-2 were generated by cloning the human HCF-1 and HCF-2 cDNA into the vector pCMV-HA ( Clontech ) . The plasmid encoding the short-hairpin RNA targeting the human SIRT1 gene was generously provided by W . L . Kraus [8] . The plasmid encoding shRNA targeting the firefly luciferase gene was generously provided by L . Qi ( Cornell University ) . siRNA duplexes directed against rat HCF-1 and HCF-2 were purchased from Dharmacon and targeted the following sequences: siHCF-1 #1: 5′-GGAAGAGACTGAAGGCAAA-3′; siHCF-1 #2: 5′-AGAACAACATTCCGAGGTA-3′; siHCF-2: 5′- GGGAATGGTTGAATATGGA-3′ . Non-targeting control siRNA was also from Dharmacon . Cells were collected 48 hours post-transfection , or treated for an additional 6 hours with nicotinamide ( 10 mM , Sigma ) . HEK293T were maintained in DMEM containing 4 . 5 g/L glucose and 10% calf serum and were transfected with the indicated plasmids using calcium phosphate . INS-1 cells were maintained in RPMI-1640 medium containing 11 . 1 mM glucose , 10% fetal bovine serum , 1 mM pyruvate , 10 mM HEPES , and 50 µM 2-mercaptoethanol . INS-1 cells were transfected with siRNA at a concentration of 10 nM using Lipofectamine RNAiMax ( Invitrogen ) . siRNA transfections were performed twice , 24 hours apart , and cells were collected 24 hours after the second transfection . RNA was isolated from mammalian cells using Trizol reagent and was reverse-transcribed using Superscript III First-Strand kit ( Invitrogen ) . cDNAs were analyzed by quantitative-PCR using the SYBR Green system on a Roche LightCycler 480 real time PCR machine and quantified relative to a standard curve . β-actin was used as an internal control . The following primers were used: β-actin forward: 5′- CTAAGGCCAACCGTGAAAAG-3′; : β-actin reverse: 5′-AACACAGCCTGGATGGCTAC-3′; HCF-1 forward: 5′-GCTGGAAAAGCTCCTGTCAC-3′; HCF-1 reverse: 5′- CACTCATCTGTGGGTTGCTG-3′; HCF-2 forward: 5′- TTGAAAGCAGAGCAATGGTG-3′; HCF-2 reverse: 5′- AGTCGGGTACGTCTGCATTT-3′; Bim forward: 5′- GCCCCTACCTCCCTACAGAC-3′; Bim reverse: 5′- CAGGTTCCTCCTGAGACTGC-3′; p27 forward: 5′- GTGGACCAAATGCCTGACTC-3′; p27 reverse: 5′- TTCTGTTCTGTTGGCCCTTT-3′; Gadd45a forward: 5′- GCAGAGCTGTTGCTACTGGA-3′; Gadd45a reverse: 5′- TGTGATGAATGTGGGTTCGT-3′; IGFBP1 forward: 5′- CTGCCAAACTGCAACAAGAA-3′; IGFBP1 reverse: 5′- TTCCCACTCCATGGGTAGAC-3′ . For co-immunoprecipitation experiments , HEK293T cells were transfected with the indicated plasmids . 48 hours after transfection , cells were lysed in lysis buffer ( 50 mM Tris-HCl pH 8 . 0 , 100 mM NaCl , 2 mM EDTA , 1% TritonX-100 , 10 mM NaF , 1 mM sodium orthovanadate , 1 mM PMSF , 10 mM nicotinamide , 1 mM trichostatin A , and Roche complete protease inhibitor cocktail ) . Cell extracts were incubated with either Flag- or HA-conjugated agarose beads ( Sigma ) overnight at 4°C . Beads were washed 5 times in lysis buffer and eluted by boiling in SDS sample buffer . Immunoprecipitates were analyzed by western blotting using the following antibodies: anti-HA ( Covance ) , anti-FOXO3 ( Upstate ) , anti-SIRT1 ( gift from W . L . Kraus ) , anti-HCF-1 ( Bethyl Labs ) . | The nematode C . elegans has been instrumental in identifying and characterizing genetic components that influence aging . Studies in worms have been successfully extended to complex mammalian organisms allowing for the identification of genetic factors that impact longevity in mammals . DAF-16/FOXO transcription factors are among the best characterized longevity factors , and their increased activity leads to a longer lifespan and improved stress resistance in many organisms . Elucidating how the activities of DAF-16/FOXO are regulated will provide new insights into the basic biology of aging and will aid future therapeutic developments aiming to improve healthy aging and alleviate age-related diseases in humans . We utilized both C . elegans and mammalian cell culture systems to dissect the functional and molecular interactions between two important DAF-16 regulators , HCF-1 and SIR-2 . 1/SIRT1 . We demonstrated that HCF-1 and SIR-2 . 1/SIRT1 physically associate and antagonize each other to properly regulate DAF-16/FOXO-mediated expression of genes important for longevity and stress response . We further showed that the functional relationships among these three proteins are conserved in mammals . Our work implicates HCF-1 as an important player in the regulation of FOXO by SIRT1 , and thereby a potential longevity determinant in humans , and prompts further characterization of HCF-1's functions in aging and age-related pathologies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology"
] | 2011 | The Evolutionarily Conserved Longevity Determinants HCF-1 and SIR-2.1/SIRT1 Collaborate to Regulate DAF-16/FOXO |
Reunion Island is a French overseas territory located in the south-western of Indian Ocean , 700 km east of Madagascar . Leprosy first arrived on Reunion Island in the early 1700s with the African slaves and immigration from Madagascar . The disease was endemic until 1980 but improvement of health care and life conditions of inhabitants in the island have allowed a strong decrease in new cases of leprosy . However , the reintroduction of the disease by migrants from endemic neighbouring countries like Comoros and Madagascar is a real and continuing risk . This observational study was then conducted to measure the number of new cases detected annually on Reunion Island between 2005 and 2013 , and to describe the clinical features of these patients . Data were collected over two distinct periods . Incident cases between 2005 and 2010 come from a retrospective study conducted in 2010 by the regional Office of French Institute for Public Health Surveillance ( CIRE of Indian Ocean ) , when no surveillance system exist . Cases between 2011 and 2013 come from a prospective collection of all new cases , following the implementation of systematic notification of all new cases . All patient data were anonymized . Among the 25 new cases , 12 are Reunion Island residents who never lived outside Reunion Island , and hence are considered to be confirmed autochthonous patients . Registered prevalence in 2014 was 0 . 05 /10 000 habitants , less than the WHO’s eradication goal ( 1/10 000 ) . Leprosy is no longer a major public health problem on Reunion Island , as its low prevalence rate indicates . However , the risk of recrudescence of the disease and of renewed autochthonous transmission remains real . In this context , active case detection must be pursued through the active declaration and rapid treatment of all new cases .
Leprosy , also known as Hansen’s disease , is a chronic infectious disease caused by Mycobacterium leprae . Its most likely route of transmission is the upper respiratory tract , and it has a long incubation period [1] . The disease primarily affects the skin and peripheral nerves , causing sensory loss [2] . If not treated , it can cause progressive and permanent damage to the skin , nerves , limbs or eyes , and may lead to amputations and disabilities . Because of these visible symptoms , leprosy has always been strongly stigmatized , preventing patients to seek treatment . Leprosy can be cured using multidrug therapy ( MDT ) , an association of different antibiotics including rifampin , dapsone and clofazimine [3] . Human leprosy has been documented for millennia and is probably the oldest human-specific infection [4] . The disease was distributed worldwide during the Middle Ages , but its prevalence has considerably decreased since MDT became available in the early 1980s [5] and national campaigns and disease surveillance systems were developed in most endemic countries . At the beginning of 2012 , the registered prevalence of leprosy at global level was around 180 , 000 cases . The majority of new cases ( 95% ) were reported from 16 countries . Some areas remain highly endemic , such as the Comoros and Mayotte in the Indian Ocean[6] . Reunion Island is a French overseas territory located in the South West Indian Ocean , 700 km to the East of Madagascar . Leprosy first arrived on Reunion Island in the early eighteenth century with African slaves and immigrants from Madagascar [7] . Leprosy continued to be a serious concern on Reunion Island until the 1960s , when about 148 patients were still followed in 1966 [8]; The disease was still endemic on Reunion Island until 1980 [9] . Improvements in the health care and living conditions of residents of the Island led to a significant decrease in the number of new cases of leprosy . However , since the prevalence of the illness on Reunion Island has been poorly documented due to the lack of an adequate surveillance system long preventing proper reporting of the illness , which means that it was impossible to know if the World Health Organisation’s goal to eradicate the disease ( i . e . , prevalence rate <1/10 000 ) has been truly achieved on Reunion Island . In this context , the Regional Office of the French Institute for Public Health Surveillance ( CIRE Indian Ocean ) conducted in 2010 a retrospective study to collect information on all cases diagnosed between January 2005 and December 2010 . This retrospective study showed that leprosy was still present on Reunion Island . A prospective surveillance system was then implemented in January 2011 [10] . The aim of the present study was to estimate the number of new cases of leprosy detected annually on Reunion Island between 2005 and 2013 , describe the clinical features of patients and finally to evaluate eradication of leprosy on Reunion Island
This article is based on a descriptive study of new leprosy cases diagnosed between 2005 and 2013 on Reunion Island . The study was conducted retrospectively between 2005 and 2010 and prospectively between January 2011 and December 2013 . In 2010 , as the lack of an adequate surveillance system made impossible to know if WHO objective for eradication was achieved , the CIRE Indian Ocean decided to conduct a retrospective study on all cases diagnosed in the last 5 years ( 2005–2010 period ) . This study involved health professionals who were in a position to collect diagnosed cases of leprosy . Private and hospital dermatologists , infectiologists and anti-tuberculosis centres were first informed about the study by letter and then contacted by telephone to report all cases of leprosy that occurred during this period by completing a standardized questionnaire . This retrospective study showed that leprosy was still present on Reunion Island and that the implementation of a prospective surveillance system was needed [10] . From January 2011 , health professionals reported systematically all newly diagnosed cases using the same standardized questionnaire as in the retrospective study . In addition to clinicians , pathology laboratories are now requested to report the histologic diagnoses of new leprosy cases in order to ensure the completeness of data collection . All new leprosy patients must be sent for treatment to a referent physician in the anti-tuberculosis centre of the University Hospital of Reunion Island . The diagnosis of leprosy is based on the World Health Organization’s criteria: “patient presenting skin lesion consistent with leprosy and with definite sensory loss , with or without thickened nerves and/or positive skin smears” [11] . In our study , all patients had a skin biopsy and/or a nose and ear smear to confirm the diagnosis , except for 2 patients who had been diagnosed several years earlier and presented clinical features of relapse . When the bacteriological index was positive on the biopsy or nose and ear smear , the patient was classified as multibacillary . Patients showing clinical manifestations of leprosy but negative smears were classified as paucibacillary . The retrospective study was based on data collected at CIRE Indian Ocean . The prospective study was based on systematic reporting . All the information was collected using a standardized questionnaire . The following data were collected for each patient: socio-demographic data ( age , country of birth , country of residence , sex , profession ) , type of leprosy according to WHO classification ( multibacillary for patients with positive smears and paucibacillary for patients with negative smears ) , and clinical data ( method of diagnosis , degree of disability evaluated at the time of reporting , i . e . , before treatment ) . Disability was classified according to the WHO grading system ( grade 1: decrease or loss of sensibility in the eyes , hands and/or feet; grade 2: Disability or deformity in the eyes , hands and/or feet ) [11] . All patient data were anonymized . Quantitative variables were expressed as mean and standard deviation or median and interquartile range . Qualitative variables were expressed as proportions and 95% confidence interval . We performed separate analyses for each period of collection . Registered prevalence was calculated by dividing the number of yearly cases reported by Reunion Island’s population that year , and multiplying that number by 10 , 000 .
Results are summarized in Table 1 . From January 2005 to December 2013 ( 9 years ) , 25 new cases of leprosy were diagnosed on Reunion Island . During the first period of our study ( 2005–2010 ) 18 cases were diagnosed and 7 during the second period ( 2011–2013 ) . The median age of patients at the time of diagnosis was 48 . 2 years in the first period versus 44 . 3 years in the second; moreover , male patients were predominant in the entire period ( 68% , 17/25 ) . Only 1 child under 15 years of age was diagnosed with leprosy in the first period , and none in the second . This child was born in the Comoros and had recently migrated to Reunion Island; hence he was probably contaminated in the Comoros . Among the 25 new cases , 12 are Reunion Island residents who never lived outside the Island , and are therefore considered to be autochthonous patients ( “Confirmed autochthonous cases” ) . There were 10 new autochthonous cases in the first period ( 6 years: 2005–2010 ) , and only 2 in the second ( 3 years: 2011–2013 ) . 6 of these autochthonous patients lived in the same area of Saint-Louis , a popular city in the southwest of the island that constituted a focus of transmission . Among the 13 patients born or having resided outside Reunion Island ( Comoros , Mayotte or Madagascar ) , 10 cases arrived on Reunion Island less than 5 years before the diagnosis . Considering the mean duration of leprosy incubation ( about 5 years according to the WHO ) , these cases were then considered as imported cases . 2 other cases arrived on Reunion Island 9 and 12 years before the diagnosis; and for the last one the date of arrival on Reunion Island was not specified . Those 3 cases were doubtful and were then , taking the most pessimistic option , considered as possible autochthonous cases . Skin biopsy was largely available on the island; the majority of diagnoses were therefore made with skin biopsy ( 84% , 21/25 ) . However , some patients had a complementary ear and nose biopsy to classify the case based on WHO criteria . The multibacillary form was predominant ( 72% , 18/25 ) . The rate of new cases with grade 2 disability was 24% ( 6/25 ) , and 56% ( 14/25 ) of patients had a grade 1 or 2 disability at the time of detection .
Although leprosy is now diagnosed in less than 1/10 000 inhabitants on Reunion Island , it is important to follow indicators of active transmission in the community . Our study shows that few autochthonous cases of patients are still present . These autochthonous cases are proportionally fewer in the second period than in the first with no more focus of transmission which suggests that autochthonous transmission on the Island is disappearing . Interestingly , no cases of patient under 15 years of age were detected in the second period of the study , indicating that there has been no active transmission for the last 3 years . These 2 keys indicators supports that autochthonous transmission of leprosy has stopped on Reunion Island . However , regarding clinical features of patients , a high rate of disability at the time of diagnosis has been reported for 24% of the patients ( grade 2 disability ) , which is indicative of late detection . This might be explained by general practitioners’ poor knowledge of the disease . Following the study , communication has been performed to renew GPs awareness of the risk of leprosy on Reunion Island . Our study presents some limitations . Indeed , the possibility that some misdiagnosed or undeclared cases cannot be excluded . Furthermore , the prospective period ( 3 years ) is too short to conclude that the illness has been lastingly eradicated from the island . This result has to be re-evaluated regularly . In our opinion , this favourable evolution can be explained by one major historical reason . Reunion Island was the first overseas territory to become an administrative French department in 1946; as a result , the island has benefitted from the French public health system for more than 50 years . For example , dermatologic consultation has been available on the entire territory for over 3 decades . The skin biopsy became the gold standard for diagnosis in routine . This important access to dermatologists has played a large role in the early detection of new cases , which is the key for preventing aggravation and transmission [12 , 13] . In addition , improvements in quality of life , better housing conditions and lower promiscuity have played an important role in the reduction of autochthonous transmission . Indeed household and dwelling contact are among the most important risk factors for transmission [14] . By contrast , Mayotte Island has just gained the same administrative status as Reunion Island , and the living conditions of the majority of its inhabitants are still rudimentary , with small homes for large families and poor access to medical care . This situation may well explain partly why leprosy is still endemic in Mayotte [15] . Moreover , given their long-standing historical ties , Mayotte has seen an important number of imported cases from the Comoros , where the disease is also endemic ( Fig 1 ) . In fact , Mayotte and the Comoros have the highest prevalence rate of leprosy of the South Indian Ocean area . Lastly , the implementation of a tuberculosis and leprosy control program—which includes active surveillance , early declaration of new cases prompting the screening of household contacts and rapid access to MDT , —can also explain the progressive eradication of the disease . However , Reunion Island remains highly exposed to resurgence . Indeed , the reintroduction of the disease through immigration from endemic neighbouring countries such as the Comoros , Mayotte or Madagascar is a real and continuing risk . This risk is illustrated by the prevalence rates of Leprosy in Indian Ocean in 2014 ( Fig 1 ) . Main immigration in Reunion Island comes from Madagascar , Comoros , and Mayotte . In 2014 , Comoros and Mayotte were still highly endemic with prevalence rates of 3 . 54 and 7 . 25/10 000 inhabitants . With a rate of 0 . 83/10000 inhabitants , Madagascar is also among the endemic countries . Constant vigilance should then be maintained to assure that the disease does not reappear in the community . Leprosy is no longer a major public health problem on Reunion Island , as indicated by the low prevalence rate and the absence of active transmission . Improvements in living conditions and access to health care meeting French metropolitan standards have put an end to autochthonous transmission . However , given the significant influx of migrants from leprosy-endemic neighbouring countries , the risk of resurgence of the disease and of renewed autochthonous transmission is real . In conclusion , our experience shows that “active detection , systematic declaration and rapid treatment” are the 3 key measures to obtain eradication of leprosy in a community . In our opinion , those measures must be maintained to consolidate eradication . | Leprosy was still endemic on Reunion Island 30 years ago but improvements in health care and treatments led to a significant decrease in the number of new cases of leprosy . Nevertheless , the long-standing lack of a surveillance system prevents a real evaluation of endemicity . This is the first study to evaluate eradication of Leprosy on Reunion Island . The prevalence rate of less than one case per 10000 inhabitants is necessary , but not sufficient to claim eradication . Remaining active transmission of the disease is to be explored . The most widely used indicator of active transmission , the absence of new cases detected in children younger than 15 years of age , and the lack of focus of transmission , confirmed the eradication assumption . Improvements in quality of life , better housing conditions and lower promiscuity have played a key role in the reduction of autochthonous transmission . Active detection among relatives , systematic declaration and rapid treatment are the most effective way of preventing disabilities and further transmission of the disease . However , if elimination of leprosy is no longer a major public health problem on Reunion Island , the risk of reintroduction of the disease through immigration from endemic neighbouring countries is a real and continuing risk . Preventing resurgence is now the challenge . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"disabilities",
"biopsy",
"tropical",
"diseases",
"oceans",
"geographical",
"locations",
"surgical",
"and",
"invasive",
"medical",
"procedures",
"bacterial",
"diseases",
"research",
"design",
"comoros",
"bodies",
"of",
"water",
"neglected",
"tropical",
"diseases",
"africa",
"research",
"and",
"analysis",
"methods",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"marine",
"and",
"aquatic",
"sciences",
"madagascar",
"people",
"and",
"places",
"earth",
"sciences",
"leprosy",
"retrospective",
"studies",
"indian",
"ocean"
] | 2016 | Leprosy on Reunion Island, 2005-2013: Situation and Perspectives |
There is no effective vaccine against Buruli ulcer . In experimental footpad infection of C57BL/6 mice with M . ulcerans , a prime-boost vaccination protocol using plasmid DNA encoding mycolyltransferase Ag85A of M . ulcerans and a homologous protein boost has shown significant , albeit transient protection , comparable to the one induced by M . bovis BCG . The mycolactone toxin is an obvious candidate for a vaccine , but by virtue of its chemical structure , this toxin is not immunogenic in itself . However , antibodies against some of the polyketide synthase domains involved in mycolactone synthesis , were found in Buruli ulcer patients and healthy controls from the same endemic region , suggesting that these domains are indeed immunogenic . Here we have analyzed the vaccine potential of nine polyketide synthase domains using a DNA prime/protein boost strategy . C57BL/6 mice were vaccinated against the following domains: acyl carrier protein 1 , 2 , and 3 , acyltransferase ( acetate ) 1 and 2 , acyltransferase ( propionate ) , enoylreductase , ketoreductase A , and ketosynthase load module . As positive controls , mice were vaccinated with DNA encoding Ag85A or with M . bovis BCG . Strongest antigen specific antibodies could be detected in response to acyltransferase ( propionate ) and enoylreductase . Antigen-specific Th1 type cytokine responses ( IL-2 or IFN-γ ) were induced by vaccination against all antigens , and were strongest against acyltransferase ( propionate ) . Finally , vaccination against acyltransferase ( propionate ) and enoylreductase conferred some protection against challenge with virulent M . ulcerans 1615 . However , protection was weaker than the one conferred by vaccination with Ag85A or M . bovis BCG . Combinations of these polyketide synthase domains with the vaccine targeting Ag85A , of which the latter is involved in the integrity of the cell wall of the pathogen , and/or with live attenuated M . bovis BCG or mycolactone negative M . ulcerans may eventually lead to the development of an efficacious BU vaccine .
Buruli ulcer ( BU ) is a necrotizing bacterial skin disease caused by Mycobacterium ulcerans . M . ulcerans produces a diffusible macrolide toxin , called mycolactone ( ML ) which is essential for bacterial virulence [1] . BU has been documented in 33 countries worldwide , although most of the cases occur in West Africa , primarily Benin , Côte d'Ivoire , Ghana and more recently Gabon . According to the World Health Organization , about 5000 cases annually are reported from 15/33 countries . The incidence in endemic regions of Ghana has been estimated at 150 cases/100 000 inhabitants . However , as the disease is not notifiable in many countries and most patients live in remote , rural areas with little medical infrastructure , the actual number of cases is likely to be much higher . Regardless , as the disease burden is mostly localized to certain geographical areas , the impact of vaccination and treatment efforts can be very high [2] . Prevention of BU is complicated by the fact that while M . ulcerans is present in the environment in disease endemic areas [3] , [4] , the route of transmission is largely unknown . In Australia , infection following contamination of a golf course irrigation system was reported [5] while many cases elsewhere are related to disruption of the environment , e . g . due to deforestation and building of dams [4] . Possible sources of infection include aquatic insects , mosquitoes and mammals [6] , [7] . In temperate south-eastern Australia ( State of Victoria ) ringtail and brushtail opposums presenting typical ulcerative lesions have been identified and M . ulcerans DNA was detected at high level by real-time qPCR in faeces of these animals [8] . Person-to-person transmission appears to be extremely rare [9] . M . ulcerans is distinct from other mycobacteria in that it produces a lipid toxin ( ML ) , which is synthesized by three large polyketide synthases encoded by mlsA1 , mlsA2 and mlsB localized on the 174 kb pMUM001 virulence plasmid [10] . These synthases are composed of different modules , which each have a particular sequence of enzymatic domains . ML locally suppresses T cell responses at non-toxic levels [11] . This T cell suppression induced by ML is not completely understood , but it is clear that ML can alter both early signaling at the T cell receptor level by activation of the Src-family kinase Lck as well as blocking cytokine responses at a post-transcriptional level [11] . At higher concentrations , the toxin is cytotoxic Using a semiquantitative reverse transcription-PCR analysis of mRNA isolated from BU lesions , we have shown that production of IL-10 rather than production of IL-4 or IL-13 by Th2-type T cells may be involved in the low M . ulcerans-specific IFN-gamma response in Buruli disease patients [12] . A more in depth study by R . Phillips et al on serum from 37 BU patients from Ahafo Ano North District of Ghana demonstrated by use of Luminex technology that patients with active ulcers display a distinctive profile of immune suppression , marked by the down-modulation of four inflammatory chemokines: macrophage inflammatory protein ( MIP ) 1β , IL-8 , monocyte chemoattractant protein ( MCP ) 1 , and ( to a lesser extent ) fractalkine [13] . These immunological defects were induced early in the disease and resolved after anti-BU therapy [13] . An impaired capacity to produce Th1 , Th2 , and Th17 cytokines on stimulation with the mitogen Phytohaemagglutinin PHA was also observed in the Phillips' study ( be it on a limited number of 4 patients with BUD and 4 healthy control participants ) [13] . Interestingly , some of the defects in cytokine and chemokine response could be mimicked in vitro by incubation of CD4+ peripheral blood lymphocytes with ML [13] . ML is an obvious candidate for a BU vaccine , but by virtue of its chemical composition and possibly because of its immunosuppressive properties , the toxin is not immunogenic and in neither infected mice nor humans ML-specific antibodies have been found . However , antibodies against some of the polyketide synthase domains involved in ML synthesis , were found in BU patients and healthy controls from the same endemic region , suggesting that these domains are indeed immunogenic [14] . Aiming to interfere with ML synthesis , we have used a DNA prime/protein boost strategy targeting nine of these polyketide synthase domains . C57BL/6 mice were vaccinated against three variations of the acyl carrier protein domain ( ACP1 , ACP2 , ACP3 ) , against three acyltransferase domains ( ATac1 , ATac2 , and ATp ) , against the enoylreductase domain ( ER ) , against one of the ketoreductase domains ( KR A ) and against the ketosynthase load module domain ( KS ) .
C57BL/6 mice were bred in the Animal Facilities of the WIV-ISP ( Site Ukkel ) , from breeding couples originally obtained from JANVIER SAS in Le Genest Saint Isle , France . Mice were 8–10 weeks old at the start of the experiments . Female mice were used for immune analysis and male mice for the protection studies . Virulent M . ulcerans 1615 strain ( Malaysia ) [10] was kindly given to us by Dr . P . Small ( University of Tennessee ) . Bacteria were maintained and amplified in vivo in mouse footpad [15] . M . bovis BCG strain GL2 was grown for 2 weeks as a surface pellicle at 37°C on synthetic Sauton medium and homogenized by ball mill as described before and kept at −80°C in 20% of glycerol until used [16] . Bacterial expression vector pET-DEST42 encoding the genes of 8 enzymatic modules , ACP1 , ACP2 , ACP3 , ATac1 , ATac2 , ATp , ER and KS or pDEST17 vector encoding KR A ( all as C-terminally Histidine-tagged proteins ) , were constructed at the University of Melbourne , Australia and used for transformation and selection in E . coli BL-21 . Following induction with IPTG for 2–4 hours , cells were lysed and recombinant proteins were purified according to standard protocol on immobilized metal affinity chromatography ( IMAC ) using gravity flow . Recombinant Ag85A protein from M . ulcerans ( MUL 4987 ) was kindly given to us by Dr . G . Pluschke ( Swiss Tropical and Public Health Institute , Basel , Switzerland ) . Figure 1 shows the IMAC purified Pks domains and MUL4987 separated in 15% ( left figure ) or 12 . 5% SDS-PAGE ( right figure ) and stained with Protein Staining Solution ( Thermo Scientific , Rockford , Illinois , USA ) . The genes encoding the nine enzymatic modules of the polyketide synthases were cloned in the eucaryotic expression vector pV1 . Jns-tPA [17] . In this plasmid , the genes are expressed under the control of the promoter of IE1 antigen from cytomegalovirus , including intron A , preceded by the signal sequence of human tissue plasminogen activator Briefly , sequences were amplified by PCR ( Expand High Fidelity PCR System , Roche ) , from the corresponding pET-DEST42 and pDEST17 constructs . Primers used for cloning are shown in Table 1 . The amplified sequences were digested with Bgl II , Bcl I , or BamH I , purified on agarose ( QIAkit PCR Purification kit , Qiagen ) and T4 ligated into pV1 . Jns-tPA vector digested with Bgl II . After ligation and transformation into DH5-α chemically competent E . coli cells ( Invitrogen ) , clones were screened on LB-kanamycin medium ( 50 µg/mL ) and plasmid was checked by restriction digestion and sequencing . Plasmid DNA encoding the mature 32 kD Ag85A from M . ulcerans in V1J . ns-tPA vector was prepared as described before [18] . C57BL/6 were anesthesized by intraperitoneal injection of ketamine-xylazine and injected intramuscularly ( i . m ) in both quadriceps muscles with 2×50 µg plasmid V1-Jns-tPA encoding one of the nine polyketide synthase domains , empty vector as negative control and V1-Jns-tPA-Ag85A ( MUL4987 ) as positive control on day 0 and day 21 . On day 42 , mice were injected subcutaneously ( s . c . ) in the back with 10 µg of corresponding , recombinant protein emulsified in Gerbu adjuvant , i . e . water miscible , lipid cationic biodegradable nanoparticles , completed with immunomodulators and GMDP glycopeptide ( GERBU Biochemicals ) . C57BL/6 mice were vaccinated intradermally with 1×105 colony forming units ( CFU ) of M . bovis BCG strain GL2 on day 0 . Vaccinated mice were sacrificed 3 or 6 weeks after the third immunization . Spleens were removed aseptically and homogenized in a loosely fitting Dounce homogenizer and cells were adjusted to 4×106 white blood cells/ml in RPMI-1640 medium ( Gibco , Grand Island , NY ) supplemented with 10% fetal calf serum ( FCS ) , 5×10−5 M 2-mercapto-ethanol , penicillin , streptomycin and Polymyxin B sulphate ( 30 µg/ml , Sigma ) . Cells were cultivated at 37°C in a humidified CO2 incubator in round-bottom microwell plates individually and analyzed for Th1 type cytokine response to corresponding recombinant protein ( 5 µg/ml ) . Supernatants from at least three wells were pooled and stored frozen at −20°C . Cytokines were harvested after 24 h ( IL-2 ) and 72 h ( IFN-γ ) , when peak values of the respective cytokines can be measured . IL-2 activity was quantified by sandwich ELISA using coating antibody anti-mouse interleukine-2 ( 14-7022 , eBioscience ) and biotinylated detection antibody anti-mouse IL-2 ( JES6-5H4 , 13-7021 , eBioscience ) . The detection limit of the IL-2 ELISA is 5 pg/ml . IFN-γ activity was quantified by sandwich ELISA using coating antibody R4-6A2 and biotinylated detection antibody XMG1 . 2 ( both BD Pharmingen ) . The detection limit of the IFN-γ ELISA is 5 pg/ml . Antigen-specific spleen cell IFN-γ secretion was also assayed by ELISPOT as described earlier . Briefly , 96-well flat-bottomed nitrocellulose plates ( MAHA S4510 , Millipore , Billerica , MA ) were incubated overnight at 4°C with 50 µl of capture purified anti-mouse IFN-γ ( 15 µg/ml; BD Pharmingen , Erembodegem , Belgium ) in phosphate-buffered saline ( PBS ) and then saturated with 200 µl/well of RPMI-complete medium 2 h at 37°C . 180 µl of spleen lymphocytes ( pool of four mice per group ) were added at a cell concentration of 4 . 106 cells/ml in the presence or absence of 20 µl proteins ( 5 µg/ml ) and plates were incubated for 48 h at 37°C , 5% CO2 . After extensive washing , plates were incubated 2 h at 37°C , 5% CO2 with 50 µl of biotinylated rat anti-mouse IFN-γ ( 2 µg/ml ) ( BD Pharmingen ) , washed and incubated for 45 min at 37°C , 5% CO2 with alkaline phosphatase labelled ExtrAvidine ( Sigma-Aldrich , Bornem , Belgium ) . After washing , spots were revealed with Bio-Rad ( Hercules , CA ) alkaline phosphatase conjugate substrate kit , following the manufacturer's instructions and plates were analysed on a Bioreader-3000 LC ( BioSys , Germany ) . Results are shown as mean spot-forming cells ( SFC ) per million lymphocytes . Sera from C57BL/6 mice were collected by tail bleeding three and six weeks after the protein boost or six weeks after M . ulcerans challenge . Antigen-specific total immunoglobulin G ( IgG ) was determined by an enzyme-linked immunosorbent assay ( ELISA ) on serial dilutions of individual sera . The corresponding recombinant protein was used for coating ( 500 ng/well ) . Total antibody was detected using peroxidase-labeled rat anti-mouse immunoglobulin IgG ( Experimental Immunology Unit , Université Catholique de Louvain , Brussels , Belgium ) and orthophenylenediamine ( Sigma ) for revelation . Data are presented as the mean optical density at 490 nm ( O . D490 nm ) for 3–5 vaccinated mice tested individually for serum diluted 1∶50 and for 9 serial twofold dilutions thereof . Six weeks after the protein boost ( 12 weeks after BCG ) , 15 mice/group were challenged with M . ulcerans 1615 . 105 acid fast bacilli ( AFB ) obtained by in vivo passage in footpad , were injected in the right footpad of the vaccinated mice . The number of bacilli injected , suspended in Dubos Broth Base medium ( Difco ) , was determined by counting under a microscope after Ziehl-Neelsen staining . Viability of the M . ulcerans inoculum was checked by plating on 7H11 Middlebrook agar , supplemented with oleic-acid-albumin-dextrose-catalase enrichment medium . Yellow colonies were counted after 8 weeks of incubation at 32°C . The number CFU equaled the number of AFB . Five mice per group were sacrificed for enumeration of AFB six weeks after M . ulcerans challenge in the footpad . Briefly , the skin and bones were removed from infected footpad . Tissues were homogenized in a Dounce homogenizer and suspended in 2 ml of Dubos broth based medium containing glass beads . The number of AFB was counted on microscope slides after Ziehl-Neelsen staining . Protection was also evaluated in ten mice/group by monitoring footpad swelling after M . ulcerans 1615 infection . The swelling was measured with a calibrated Oditest apparatus with a resolution of 0 . 01 mm as described previously [19] . Animals were euthanized when footpad swelling exceeded 4 mm according to the rules of the local ethical commission and survival curves were established . For cytokine production analysis , antibody production and AFB counting , statistical analysis was made according to one-way ANOVA test . Subsequent multiple comparisons between the different groups of animals and the antigens used was made by a Tukey's correction test . Statistical results are represented in the figure by *** ( p<0 . 001 ) , ** ( p<0 . 01 ) and * ( p<0 . 05 ) . Median survival time was calculated using GraphPad , Log-rank ( Mantel-Cox ) test .
As shown in Figure 2 , vaccination against some of the Pks domains induced significant IgG antibodies . In particular , strong responses were found at three weeks after the protein boost in mice vaccinated against ATac2 and ATp . Vaccination against ACP1 and ER induced a weak IgG response ( only 1/4 mice reactive ) , whereas IgG levels induced by vaccination against ACP2 , ACP3 , ATac1 KR A and KS were not different from IgG levels in naïve mice . Confirming previous findings [18] , vaccination against Ag85A also induced strong antibody levels . At six weeks post vaccination ( Supplementary Figure S1 ) , IgG antibodies directed against ATac2 and ATp were still present , but lower than at week 3 . ACP1 and Ag85A specific antibody levels remained at the same level , and ER specific antibodies were clearly higher at week 6 than at week 3 post protein boost ( albeit with more variation between the 6 mice , antibody levels in 2/6 mice being lower than in the other 4 mice ) . IgG levels induced by vaccination against ACP2 , ACP3 , ATac1 , KR A and KS remained negative . Production of two Th1 type cytokines was analyzed in spleen cell culture supernatant of vaccinated mice stimulated with their respective antigens: Interleukin-2 and IFN-γ . IL-2 is a pleiotropic cytokine produced after antigen activation that plays pivotal roles in the immune response . Discovered as a T cell growth factor , IL-2 additionally promotes CD8+ T cell and natural killer cell cytolytic activity and modulates T cell differentiation programs in response to antigen , promoting naïve CD4+ T cell differentiation into T helper 1 ( Th1 ) and T helper 2 ( Th2 ) cells while inhibiting T helper 17 ( Th17 ) and T follicular helper ( Tfh ) cell differentiation . Moreover , IL-2 is essential for the development and maintenance of T regulatory cells and for activation-induced cell death , thereby mediating tolerance and limiting inappropriate immune reactions [20] . The macrophage-activating cytokine IFN-γ on the other hand together with TNF-α is a well known pivotal cytokine in the control of mycobacterial infections , as illustrated by the increased susceptibility to tuberculosis in IFN-γ gene disrupted mice [21] , [22] . Whereas IL-2 is produced exclusively by CD4+ T cells , IFN-γ can be produced by both CD4+ and CD8+ T cells , and therefore analysis of both cytokines may give complementary information . Vaccination against ATac2 , ATp , KR A , KS and Ag85A resulted in significant spleen cell IL-2 production in 24 hr culture supernatant , ranging between 400 and 1000 pg/ml when cells were stimulated in vitro with the corresponding antigen ( Figure 3 ) . The same two Pks domains ATac2 and ATp , that induced strong antibodies , were also good inducers of IL-2 . In contrast , vaccination against KR A ( which did not induce an antibody response ) also induced a good IL-2 response . Vaccination against ACP1 , ACP2 , ACP3 , ATac1 and ER induced only very modest IL-2 levels between 100 and 200 pg/ml . Stimulation of cells from unvaccinated mice with the recombinant proteins induced IL-2 levels were close to the detection limit ( 5 pg/ml ) . Cytokine levels of the other Th1 cytokine IFN-γ were analyzed in 72 h spleen cell culture supernatants ( Figure 4 ) . Vaccination against all nine Pks domains induced some antigen-specific IFN-γ responses . Whereas vaccination against ACP1 , ACP2 , ATac1 , ATac2 , ER and KS resulted in mean IFN-γ levels of 2 . 500 pg/ml at most , responses against KR A and ACP3 mounted to 5 . 000 pg/ml and 7 . 500 pg/ml respectively . Finally vaccination against ATp and Ag85A resulted in mean IFN-γ levels of more than 10 . 000 pg/ml . The number of IFN-γ producing cells was also examined by ELISPOT ( Figure 5 ) . Some IFN-γ producing cells could be detected after vaccination against all Pks domains , except ACP1 . High numbers ( between 150 and 200 SFC/106 cells ) were measured in response to KS and Ag85A and highest numbers were observed in response to ATp ( 350 SFC/106 cells ) . Mice were challenged in the footpad with virulent M . ulcerans 1615 six weeks after the protein boost and the number of AFB was enumerated 6 weeks later . Vaccination against the ER domain , encoding an enoyl reductase , conferred significant protection at this early time point after challenge . Confirming previous findings , vaccination against Ag85A and vaccination with BCG also resulted in significantly reduced AFB numbers in footpad as compared to AFB numbers in naive mice ( Figure 6 ) . Protection was also evaluated in ten mice/group by monitoring footpad swelling after M . ulcerans 1615 infection . The swelling was measured with a calibrated Oditest apparatus and animals were euthanized when footpad swelling exceeded 4 mm according to the rules of the local animal ethics committee . Of all the PKS domains , only vaccination against ATp conferred a modest , but significant protection as measured by a delay in footpad swelling and median survival time increased from 47 days in the control group to 58 days in mice that received the ATp vaccine . Vaccination against Ag85A ( MST 66 days ) and vaccination with BCG ( MST 99 days ) significantly prolonged the survival time ( Figure 7 and Table S1 ) .
Buruli ulcer is a neglected tropical disease [23] for which there is no effective vaccine [2] . The M . bovis BCG vaccine , used for the prevention of tuberculosis , has been reported to offer a short-lived protection against the development of skin ulcers [24] , [25] and to confer significant protection against disseminated cases of BU , e . g . osteomyelitis , both in children and in adults [26] , [27] . Also in mice , BCG vaccine protects to some extent against infection with M . ulcerans [15] although a booster vaccination with the same BCG vaccine cannot increase the protective effect and mice finally succumb to the infection [19] . We have previously shown that vaccination with plasmid DNA encoding Ag85A from M . bovis BCG can protect , albeit transiently , C57BL/6 mice against footpad challenge with M . ulcerans [15] . Antigen 85 is a major secreted component in the culture filtrate of many mycobacteria such as M . bovis BCG , M . tuberculosis and M . avium subsp . paratuberculosis [28] . The antigen 85 complex ( Ag85 ) of M . tuberculosis is actually a family of three proteins , Ag85A , Ag85B and Ag85C , which are encoded by three distinct but highly paralogous genes and that display an enzymatic mycolyl-transferase activity , involved in cell wall synthesis [29] , [30] . Using a DNA prime/protein boost regimen , we have reported that a species specific vaccine composed of Ag85A from M . ulcerans was more effective than a vaccine composed of Ag85A of M . bovis BCG , conferring a protection , comparable to the protection conferred by the BCG vaccine [18] . Mycolactone is poorly immunogenic , but some of the polyketide synthase domains involved in its synthesis do induce antibodies in BU patients and healthy controls living in endemic regions of Buruli ulcer [31] [14] . Using a plasmid DNA prime/recombinant protein boost protocol , we have confirmed their immunogenicity in an experimental mouse model and have shown that vaccination can induce strong antigen-specific antibodies and Th1 type cytokine responses . Furthermore , a modest protection against a challenge infection with virulent M . ulcerans 1615 could be observed in mice vaccinated against ER ( reduced AFB numbers early after challenge ) and ATp ( delayed footpad swelling and increased median survival time ) . Interestingly , ER was the only M . ulcerans-specific antigen leading to an IgG response discriminating ulcerative patients from endemic controls and antibodies against ATp could distinguish healthy controls living in endemic regions of Buruli ulcer from healthy controls living in a non-endemic region [14] . To what extent antibodies directed against these PKS domains can actually inhibit ML synthesis is not clear , but this question certainly warrants further analysis . Two studies have reported that the mycolactone PKS multienzymes are associated with the mycobacterial cell wall [31] , [32] . Each mycolactone PKS domain is a component of a large , contiguous polypeptide that makes up the complete multienzyme . The structure of this enzyme is not known , but it is possible that some of the component domains might be orientated such that they are more readily accessed by host immune cells than others . Based on biopsy specimens , M . ulcerans was originally described as an extracellular bacillus . However , the pathogen has an initial intracellular growth phase in macrophages and therefore , recognition of this early intracellular stage by an effective Th1 type immune response , could be an effective means to control the initial infection [33] . Following this proliferation phase within macrophages , M . ulcerans induces the lysis of the infected host cells and mycolactone-associated cytotoxicity is responsible for its subsequent extracellular localization [34]–[36] . [37] , [38] . This extracellular phase suggests that humoral responses might also be important for protection against M . ulcerans . However , as for M . tuberculosis infection , the correlates of protection against M . ulcerans infection are also unknown . In our study , strongest protection was conferred by vaccines [perhaps use ‘antigens’ instead of ‘vaccines’ ? ? ] that induced consistently strong cellular and humoral responses ( see also Table S2 ) . However , to formally proove the importance of these two cell compartments , transfer studies with specific antibodies or T cell populations would be needed . Finally , even the most immunogenic vaccine can exert its protective effect only when its cognate epitopes are generated and presented to the sensitized immune system upon infection . We speculate that the temporal differences in protection conferred by the ER and ATp vaccine might be related to variations in this antigenic presentation . In favour of this hypothesis is the finding that at the early time point after M . ulcerans infection , infected control mice produce stronger IFN-γ responses to the ER than to ATp domain ( data not shown ) . Hence , a combination vaccine targeting both the early intracellular and the subsequent extracellular stage , through the induction of strong Th1 T cells and antibodies respectively , may be needed to control the infection effectively . A vaccine composed of the mycolyl transferase Ag85A combined with the most immunogenic polyketide synthase domains is an interesting possibility that requires further study . Also priming with pDNA followed by boosting with M . bovis BCG or live , attenuated mycolactone-negative M . ulcerans mutants [33] could be envisaged , as has been reported in tuberculosis vaccine development [39] , [40] . | Buruli ulcer ( BU ) is an infectious disease , characterized by deep , ulcerating skin lesions , particularly on arms and legs , which are provoked by a toxin . BU is caused by a microbe of the genus that also cause tuberculosis and leprosy . The 33 countries where Buruli ulcer has been detected , especially in West Africa , have mainly tropical and subtropical climates , although the disease is also present in temperate areas of Australia and Japan . There is no effective vaccine against BU and it is still not fully understood which immune defence mechanisms ( antibodies and/or T cells ) are needed to control the infection . The identification of microbial components that are involved in immune control is an essential step in the development of an effective vaccine . In this paper , we used an experimental mouse model to demonstrate the immunogenicity and the vaccine potential of enzymes involved in the toxin synthesis . Combinations with other vaccine candidates , such as a subunit vaccine against Ag85A targeting cell wall synthesis or with live , attenuated M . bovis BCG or mycolactone negative Mycobacterium ulcerans remain to be tested . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2014 | Analysis of the Vaccine Potential of Plasmid DNA Encoding Nine Mycolactone Polyketide Synthase Domains in Mycobacterium ulcerans Infected Mice |
Increased ambient temperature is inhibitory to plant immunity including auto-immunity . SNC1-dependent auto-immunity is , for example , fully suppressed at 28°C . We found that the Arabidopsis sumoylation mutant siz1 displays SNC1-dependent auto-immunity at 22°C but also at 28°C , which was EDS1 dependent at both temperatures . This siz1 auto-immune phenotype provided enhanced resistance to Pseudomonas at both temperatures . Moreover , the rosette size of siz1 recovered only weakly at 28°C , while this temperature fully rescues the growth defects of other SNC1-dependent auto-immune mutants . This thermo-insensitivity of siz1 correlated with a compromised thermosensory growth response , which was independent of the immune regulators PAD4 or SNC1 . Our data reveal that this high temperature induced growth response strongly depends on COP1 , while SIZ1 controls the amplitude of this growth response . This latter notion is supported by transcriptomics data , i . e . SIZ1 controls the amplitude and timing of high temperature transcriptional changes including a subset of the PIF4/BZR1 gene targets . Combined our data signify that SIZ1 suppresses an SNC1-dependent resistance response at both normal and high temperatures . At the same time , SIZ1 amplifies the dark and high temperature growth response , likely via COP1 and upstream of gene regulation by PIF4 and BRZ1 .
Ambient temperature is a major factor that affects plant growth and development , but also plant immunity [1 , 2] . In particular , the temperature range of 16-32ºC modulates the output of many plant immune receptors . For example , the tobacco N ( Necrosis ) gene fails to trigger resistance against Tobacco mosaic virus ( TMV ) at 30°C , while conferring resistance at 23°C [3] . This is accompanied by the loss of the hypersensitive response ( HR ) above 27°C . This HR includes a localized cell death that appears to be associated with recognition of pathogen effectors resulting in effector-triggered immunity ( ETI ) [4–7] . Multiple examples of high temperature suppression of ETI have been described for the TNL-type of immune receptors ( Toll Interleukin-1 receptor [TIR] , NB-LRR-type ) [2] , including the tobacco immune receptor N against Tobacco mosaic virus ( TMV ) [7 , 8] , but also resistance mediated by the Arabidopsis immune receptor RPS4 , which recognizes the avirulence protein AvrRPS4 from Pseudomonas , is suppressed at high temperature [9] . Finally , SNC1 ( Suppressor of npr1-1 , constitutive 1 ) dependent auto-immunity in the gain-of-function mutant snc1-1 is suppressed at high temperature [10] . Auto-immunity in the snc1-1 mutant was caused by hyperaccumulation of a mutant variant of SNC1 resulting in a dwarf stature of the mutant plant with curly leaves at 22°C [11]; At 28°C this auto-immune phenotype of snc1-1 is fully suppressed yielding plants with wild type rosettes without any macroscopic lesions or microscopic cell death . Importantly , HR activation by SNC1 required nuclear localization of SNC1 , which appeared to be compromised when plants were kept at 28°C [6 , 7 , 12] . In non-infected plants , SNC1 levels are tightly controlled at both the transcript and protein level to prevent spurious immune signalling [13] . The expression of SNC1 is , for example , indirectly negatively regulated by the plasma membrane-localized protein BON1 ( Bonzai 1 ) [14] , but also the protein levels of SNC1 are regulated e . g . by the immune adaptor SRFR1 ( Suppressor of RPS4-RLD 1 ) [15 , 16] , several protein folding chaperones [17] , and the F-box protein CPR1 ( Constitutive expressor of Pathogenesis-related ( PR ) proteins 1 ) [11 , 18] . Mutations in the corresponding genes ( e . g . snc1-1 , bon1 , srfr1-4 and cpr1-2 ) cause SNC1-dependent auto-immunity ( hereafter SNC1auto-I ) . SNC1auto-I relies on EDS1 and PAD4 ( Enhanced disease susceptibility 1 , Phytoalexin-deficient 4 ) [19] . Upon recognition of biotrophic pathogens , EDS1 translocates from the cytoplasm , where it is sequestered by the related protein PAD4 , to the nucleus [20–23] . Nuclear localization of EDS1 is necessary for transcriptional reprogramming to trigger SA biosynthesis and other plant defence responses . Strikingly , high temperature suppression of auto-immunity depends for the snc1-1 mutant on the central growth regulator PIF4 ( Phytochrome Interacting Factor 4 ) , a transcription factor ( TF ) that is essential for thermomorphogenesis at 28°C [24] . This implies that plant growth is prioritized over SNC1-dependent auto-immunity at 28°C via transcriptional regulation . High ambient temperature increases PIF4 activity by controlling both its transcript levels and protein levels in a diurnal dark/light cycle [25] . This process is directly affected by relocalization of the ubiquitin E3 ligase COP1 ( Constitutive Photomorphogenesis 1 ) to the nucleus in dark conditions . In the nucleus COP1 targets key regulators of both PIF4 protein activity and PIF4 gene expression for degradation [26] . Recent data highlight that COP1 is not only essential for the dark-induced growth response , but also at high ambient temperature in a normal diurnal dark/light cycle [27] . Here we studied auto-immunity in a mutant of the Arabidopsis SUMO E3 ligase SIZ1 . Auto-immunity of siz1 highly resembles SNC1auto-I [28 , 29] , i . e . the mutant shows enhanced resistance to Pseudomonas infection due to high levels of SA , its rosette adopts a very similar morphology ( including lesions and spontaneous cell death ) as the SNC1auto-I mutants , and this auto-immune phenotype depends on PAD4 . Auto-immunity in the siz1 mutant is likely caused by the absence of sumoylation on one or more of its substrates , as the sumo1/2KD knock-down mutant also displays auto-immunity [29] . SIZ1 is the major SUMO E3 ligase in Arabidopsis [30] , affecting SUMO conjugation of many substrates including pivotal regulators of growth [31–33] . For example , COP1 is a direct substrate of SIZ1 and its sumoylation enhances the intrinsic ubiquitin E3 ligase activity of COP1 [34 , 35] . As PIF4 controls the high temperature-mediated recovery of snc1-1 auto-immunity and SIZ1 controls the activity of a key regulator of PIF4 , namely COP1 , we assessed here ( i ) whether the siz1 auto-immune phenotype requires a functional SNC1 gene copy at normal and high temperature . Moreover , we tested ( ii ) if loss of SIZ1 function suppresses the COP1/ PIF4 mediated growth response at high temperature and in dark conditions . We found that siz1 auto-immunity is sustained at 28°C resulting in enhanced resistance to bacteria , which depended on both SNC1 and EDS1 . The dwarf stature of siz1 also hardly recovered at 28°C . Moreover , we found that siz1 shows a compromised thermosensory growth response , which was independent of SNC1 and PAD4 . This positive regulatory role of SIZ1 in growth regulation was suppressed by the TF HY5 ( Elongated hypocotyl 5 ) at 22°C , while it depended on COP1 function at 28°C ( and in dark conditions ) . HY5 is a direct substrate for COP1 targeted protein degradation . Finally , we found that high temperature induced transcriptome changes are both attenuated and delayed in the siz1 and sumo1/2KD mutants and that a substantial subset of the affected genes are known genomic targets for PIF4 binding and regulation .
A hallmark of SNC1auto-I is a dwarf stature and curled leaves . These morphological defects disappear when SNC1auto-I mutants like cpr1-2 , bon1 , snc1-1 , and srfr1-4 are grown at 28°C , adopting a wild type stature ( Fig 1A and 1B ) . Here we tested if also for siz1 these morphological defects are rescued when it grows at high temperature . In contrast to the four aforementioned SNC1auto-I mutants , we observed that siz1 remains significantly smaller than the wild type control at 28°C ( Fig 1A and Fig 1B , compare group ‘cd’ with group b ) . At 22°C , the rosette weight of siz1 was indistinguishable from these four SNC1auto-I mutants ( Fig 1B , group ‘d’ ) . Previous work by others had shown that the auto-immune phenotype of these SNC1auto-I mutants depends on ( i ) a functional gene copy of PAD4 and EDS1 , and ( ii ) accumulation of the defence hormone SA [10 , 16 , 36 , 37] . Likewise , Lee and co-workers demonstrated that the siz1 phenotype ( partially ) depends on PAD4 and SA accumulation [28] , but the role of EDS1 remained unknown . Since EDS1 is the major nuclear actor of the PAD4/EDS1 hub [22 , 38] and SIZ1 is considered to primarily act in the nucleus [39] , we examined if siz1 auto-immunity depends on EDS1 . The siz1 growth defect partially recovered when it was crossed with the eds1-2 mutation in the Col-0 background , but this recovery did not significantly differ from the recovery seen for the double mutants siz1 pad4 and siz1 NahG ( a transgene encoding salicylate hydroxylase that effectively prevents SA accumulation by converting it to catechol ) at 22°C ( Fig 1C and 1E; all post hoc group ‘c’ ) . We also crossed siz1 with a mutant for SID2 ( Salicylic acid induction deficient 2 ) , which encodes the key enzyme for SA synthesis in plant immunity [40] . As seen by others for other auto-immune mutants [41] , introduction of the sid2 mutation did not rescue the siz1 growth defect seen at 22°C ( Fig 1C and 1E , group d ) . Importantly , at 28°C none of the siz1 double mutants showed any additional growth recovery compared to siz1 alone ( Fig 1D and 1E , group c ) . This suggests that the small growth recovery of siz1 seen at 28°C ( Fig 1E , from only ‘d’ at 22°C to ‘cd’ at 28°C ) is potentially linked to suppression of its auto-immune phenotype , which in turn would depend on EDS1/PAD4 and SA accumulation . Hence , we assessed if other hallmarks of the SNC1auto-I phenotype are also partially rescued when siz1 is grown at 28°C . We looked at spontaneous cell death , expression of defence-related genes ( PR1 , PR2 , and SNC1 ) , and accumulation of the encoded PR proteins . Both spontaneous cell death and PR1 expression are known ( i ) to strongly depend on EDS1/PAD4 and SA accumulation , and ( ii ) to be suppressed at 28°C in the aforementioned SNC1auto-I mutants . Spontaneous cell death was fully suppressed when siz1 was grown at 28°C ( Fig 1F ) . At 22°C spontaneous cell death was lost in the double mutants siz1 pad4 , siz1 eds1 and siz1 NahG ( Fig 1F ) , indicating that EDS1/PAD4 and SA accumulation are required for the spontaneous cell death in siz1 . At 22°C expression of PR1 and PR2 was also strongly up-regulated in siz1 compared to the control ( Col-0 ) and expression of both genes required EDS1 , PAD4 and SA accumulation ( Figs 2E , S1A and S1B ) . At 28°C , PR1 expression was completely suppressed in siz1 , but PR2 expression partially remained ( S1B Fig ) . This situation was reflected in their protein levels , i . e . PR1 levels were high in siz1 at 22°C while undetectable at 28°C ( Fig 1G ) . In contrast , PR2 levels were elevated in siz1 both at 22°C and 28°C albeit to a lower level at 28°C . In the case of the four SNC1auto-I mutants , PR1 and PR2 did not accumulate when these mutants were grown at 28°C ( Figs 1G and 2D ) . Thus , the siz1 auto-immune response is ( partially ) temperature sensitive , but it does not simply mimic the ‘classic’ behaviour of SNC1auto-I mutants . As elevated expression of SNC1 triggers auto-immunity at 22°C [42] , we measured SNC1 expression in siz1 . SNC1 expression proved to be induced by nearly 5-fold in siz1 at 22°C ( S1C Fig ) , suggesting that an increase in SNC1 transcript levels could be causal for the siz1 dwarf stature and auto-immunity . To determine if the SNC1 gene is indeed required for the siz1 phenotype at 22°C/28°C , we crossed siz1 with a loss-of-function mutant of SNC1 , snc1-11 ( SALK_04705 ) . This mutant has a T-DNA insertion in the first exon , which results in a severely truncated transcript [42] . When grown at 22°C , the siz1 snc1-11 double mutant displayed a small but significant growth recovery compared to siz1 ( Fig 2A and 2B; group ‘c’ and ‘d’ , respectively ) , which is more apparent when the plants are flowering ( S2 Fig ) . However , in our conditions the snc1-11 mutant itself also displayed a small but significant increase in biomass compared to the wild type control ( Col-0 ) at 22°C ( Fig 2B ) . More importantly , both siz1 and the siz1 snc1-11 double mutant largely kept their dwarf stature when grown at 28°C . This is striking , as the growth defects of the SNC1auto-I mutants cpr1-2 , bon1 and srfr1-4 recovered strongly ( to wild type levels ) when the snc1-11 mutation was introduced in these mutants by crossing [10 , 15 , 18] . The increase in SNC1 transcript levels can , therefore , not be the main or sole cause of the dwarf stature of siz1 . Nonetheless , spontaneous cell death was fully suppressed in siz1 snc1-11 at 22°C ( Fig 2C ) , while PR2 and to a lesser extent PR1 still accumulated in siz1 snc1-11 at 22°C ( Fig 2D ) . Also at 28°C PR2 still accumulated to some extent in siz1 snc1-11 , similar to siz1 ( Fig 2D ) . The PR1 and PR2 protein levels were again mirrored by their gene expression levels ( Fig 2E ) , i . e . at 22°C the expression of PR1 was roughly 50% in siz1 snc1-11 in comparison to siz1 , which in both cases was fully suppressed when these two mutants were grown at 28°C . On the other hand , PR2 expression remained detectable when both mutants were grown at 28°C . Also the ( truncated ) transcript of SNC1 still accumulated to higher levels in siz1 snc1-11 than in siz1 . For snc1-11 , 2–3 samples showed up-regulation of PR1 and PR2 , while the remaining samples 5 samples showed hardly any up-regulation suggesting that the latter samples reflect the general trend . Increased SNC1 protein levels are known to trigger auto-immunity [11] . SNC1 levels are negatively controlled by the HSP90/SGT1/SRFR1 chaperone-complex of which some components were reported to be SUMO substrates [43 , 44] . We therefore examined whether siz1 auto-immunity was attenuated when mutants for SGT1a , SGT1b , and RAR1 were introduced by crossing . Introduction of these mutants in siz1 ( i . e . siz1 rar1 , siz1 sgt1aKO and siz1 sgt1beta3 ) partially compromised cell death induction ( S3A Fig ) , while it hardly enhanced rosette growth in these siz1 chaperone double mutants ( S3B Fig ) . Hence , the chaperones contribute to the siz1 phenotype , but they are not essential for spontaneous cell death . Clearly , the siz1 auto-immune phenotype partially depends on SNC1 , but not all of the elements of the auto-immune phenotype disappear when SNC1 is non-functional . As the PR1 levels were down in siz1 at 28°C , we tested if enhanced resistance of siz1 to the pathogen Pseudomonas syringae pv . syringae strain DC3000 ( PstDC3000 ) is compromised at high temperature . In order to inoculate similar looking plants , all plants were grown at 28°C and half of the plants was shifted to 22°C twenty-four hours prior to the inoculation . In this way extreme differences in rosette size , morphology , or tissue structure had no impact on the disease assay ( compare the plants grown at 28°C in Fig 1A ) . The 24 hours pre-incubation at 22°C was sufficient to re-activate auto-immunity in the SNC1auto-I mutants tested ( cpr1-2 , bon1 , snc1-1 ) resulting in reduced susceptibility to PstDC3000 ( Fig 3A , post hoc groups ‘cd’ and ‘d’ ) . As expected , the three tested SNC1auto-I mutants ( cpr1-2 , bon1 , and snc1-1 ) were as susceptible as the wild type control ( Col-0 ) at 28°C ( Fig 3B ) . However , siz1 displayed enhanced resistance to PstDC3000 both at 22°C and 28°C ( Fig 3A and 3B ) . This resistance was compromised in siz1 at 22°C when PAD4 , EDS1 or SNC1 were mutated ( Fig 3A ) . At high temperature , only siz1 pad4 retained enhanced resistance to PstDC3000 , while siz1 eds1-2 and siz1 snc1-11 were both as susceptible as the wild type control ( Fig 3B ) . This means that enhanced resistance of siz1 to the pathogen PstDC3000 at 28°C was still dependent on EDS1 and SNC1 . In the case of snc1-1 , high temperature suppression of immunity and restoration of growth were both reported to depend on PIF4 [24] . Therefore , we also tested if the pif4-2 mutant showed altered resistance to PstDC3000 at 22°C/28°C . The pif4-2 plants showed a clearly compromised thermomorphogenesis response at 28°C , i . e . ( i ) the hypocotyl length was reduced ( Fig 4A and 4B ) , ( ii ) the rosette showed no hyponasty and ( iii ) the leaf blades and petioles failed to elongate in comparison to Col-0 . However , the pif4-2 mutant was as susceptible to PstDC3000 as the wild type control ( Col-0 ) at either temperature in our conditions ( Fig 3 ) . As snc1-1 auto-immunity is inhibited by PIF4 at high temperature [24] , the enhanced immunity of siz1 to PstDC3000 at 28°C might also be due to reduced PIF4 function . In line with this notion , we found that siz1 and the sumo1/2KD mutant both showed reduced hypocotyl elongation at 28°C in normal diurnal dark/light cycles ( Fig 4A , light; 4B , compare 22C L with 28C L ) , implying that SIZ1 and the two archetype SUMO proteins , SUMO1 and SUMO2 ( hereafter SUMO1/2 ) , act as positive regulators of thermomorphogenesis similar to PIF4 ( pif4-2 was included as control for the loss of thermosensitive hypocotyl elongation; Fig 4A and 4B ) . SIZ1 and SUMO1/2 were both also needed for skotomorphogenesis ( dark-induced hypocotyl elongation ) at 22°C and 28°C ( Fig 4A , dark; 4B , compare 22C L with 22C D ) . The compromised dark and high temperature growth responses were both independent of PAD4 and SNC1 , as they still occurred to same extent in siz1 pad4 and siz1 snc1-11 ( Fig 4B ) . This means that not the auto-immune phenotype of siz1 is responsible for the compromised thermo/skotomorphogenesis , but rather that SIZ1 itself acts as positive regulator of these growth responses . In support of this notion , we confirmed that the SNC1auto-I mutants cpr1 , bon1 , and srfr1-4 display a normal thermomorphogenesis response ( S4 Fig ) , indicating that PIF4 function is unaffected in them . Moreover , the sumo1/2KD consistently showed a stronger reduction in hypocotyl elongation than siz1 nearing pif4-2 at the 28°C in a normal dark/light cycle ( Fig 4B , 28°C L ) . The mutants siz1 and sumo1/2KD also displayed a strong reduction in hypocotyl elongation when they were kept in the dark at 22°C and 28°C ( Fig 4B; panels 22C D , 28C D ) . As SIZ1 stimulates COP1 activity and the nuclear function of COP1 is activated in the dark [34 , 35] , we examined whether loss of SIZ1 function could enhance the thermo/skotomorphogenesis phenotype of a strong but not lethal COP1 mutant , cop1-4 [45] . Hypocotyl elongation was indeed more reduced in siz1 cop1-4 than in cop1-4 alone in dark conditions at 22°C and 28°C ( Fig 4C; panels 22C D , 28C D ) . Thus , COP1 is critical for the thermosensory growth response–as recently reported [27] , while SIZ1 appears to primarily enhance this response ( as further detailed below ) . In light conditions , the TF HY5 is known to inhibit hypocotyl elongation by inhibiting PIF4 expression [25] . COP1 targets HY5 for proteasomal degradation when COP1 is active in the nucleus . We found that SIZ1 function is needed for the full hypocotyl elongation of the HY5 loss-of-function mutant hy5-215 in a diurnal light/dark cycle at 22°C ( Fig 4C , panel 22C L ) . This means that in a diurnal light/dark cycle at 22°C the stimulatory role of SIZ1 on hypocotyl growth is masked by the inhibitory role of HY5 . We also compared the rosette size and morphology of siz1 cop1-4 and siz1 hy5-215 with the single mutants at both temperatures ( S5 Fig ) . At 22°C siz1 cop1-4 and siz1 hy5-215 both adopted a siz1 rosette size/morphology . At 28°C growth was recovered for siz1 hy5-215 , but to a lesser extent than for siz1 . In contrast , siz1 cop1-4 failed to respond to the high temperature and this mutant still closely resembled cop1-4 mutant ( having a compact rosette with hardly any petioles and no hyponasty; S5A Fig ) . This is consistent with a model in which COP1 primarily conveys the thermosensory growth response and that SIZ1 amplifies the output of this response . As biosynthesis of the hormones gibberellic acid ( GA3 ) and the brassinosteroids is needed for the temperature induced hypocotyl elongation [46] , we checked if the positive regulatory role of SIZ1 and SUMO1/2 in thermomorphogenesis requires these two hormones . First , we inhibited GA3 or BR biosynthesis by adding paclobutrazol ( PAC ) or propiconazole ( PPZ ) , respectively . Irrespective of the genetic background , we found that biosynthesis of both hormones was essential for the temperature-induced hypocotyl elongation in the lines tested including the residual elongation in pif4-2 ( Fig 4D and 4E ) . GA3 is known to reduce the abundance of the DELLAs by triggering their degradation [47] . In turn the DELLAs restrain cell growth by reducing protein abundance of the PIFs ( including PIF4 ) and the TF BZR1 ( Brassinazole resistance 1 ) [48 , 49] . A combined treatment of 28°C+GA3 resulted in increased hypocotyl elongation for each of the four tested lines compared to the 28°C control ( - ) ( Fig 4D ) . However , hypocotyl elongation was still impaired for siz1 , sumo1/2KD and pif4-2 in the combined treatment 28°C+GA3 ( Fig 4D ) . This implies that the positive role of SIZ1 on temperature-induced hypocotyl growth is independent of DELLA accumulation . The combined treatment of 28°C plus the brassinosteroid Brassinolide ( 28°C+BL ) triggered a small but significant increase in hypocotyl elongation in the control ( Col-0 ) plants compared to the mock treatment ( Fig 4E , —vs . BL ) . However , the SUMO mutants ( siz1 and sumo1/2KD ) showed no additional response to the combined treatment 28°C+BL ( Fig 4E ) . Strikingly , the pif4-2 mutant did respond to the BL treatment ( from post hoc group D to C ) , suggesting that in siz1 and sumo1/2KD brassinosteroid signalling is apparently already at its maximum physiological level . To elucidate how SIZ1 and SUMO1/2 conjugation affect high temperature-induced gene expression , we grew siz1 pad4 and the sumo1/2KD pad4 mutants for two weeks at 22°C and then shifted them to 28°C ( 4 hrs after light onset ) to trigger a temperature induced transcriptional response . To avoid that constitutive ( auto- ) immune signalling impedes the thermosensory transcriptional response at t = 0 , we performed the experiment in the pad4 background , which largely blocked siz1 auto-immunity at 22°C ( i . e . the enhanced accumulation of PR1 and PR2 , spontaneous cell death and the increased resistance to PstDC3000 are suppressed in siz1 pad4; Figs 1F , 1G and 3A ) , but it only partially restored the dwarf stature . Importantly , increased resistance to PstDC3000 was not lost in siz1 pad4 at 28°C , similar to siz1 ( Fig 3B ) . The plants were sampled at the shift to 28°C ( day 0 ) and 24hrs ( day 1 ) and 96 hrs ( day 4 ) after the shift . Catala et al . had previously shown that siz1 shows a strong up-regulation of defence-related genes ( like PR genes and immune receptors ) , while genes involved in BR biosynthesis/signalling are down-regulated [50] . We first determined which genes are differentially expressed at 22°C in siz1 pad4 in comparison to the control ( pad4 ) . We found that a small set of genes encoding for TNL immune receptors , Receptor-like kinases ( RLKs ) , and Receptor-like proteins ( RLPs ) remained up-regulated in siz1 pad4 in comparison to the control ( pad4 ) at 22°C ( S1A Table ) . SNC1 or immune receptors of the CNL type ( Coiled-coil NB-LRR-type ) were not amongst the up-regulated genes in the microarray data . Real time PCR revealed that SNC1 was roughly two-fold induced in siz1 pad4 ( close to the cut-off value for differential gene expression ) , while SNC1 showed no up regulation in siz1 eds1 or siz1 NahG ( S1C Fig ) . As SNC1 was 5-fold induced in siz1 at 22°C ( S1C Fig ) , we conclude that this requires feedback regulation via EDS1 and SNC1 . There was no broad up-regulation of TF families linked to plant immunity ( WKRY , TGA or MYC family ) in siz1 pad4 at 22°C . Likewise , PR genes like PR2 , PR3 , or PR4 were no longer strongly up-regulated in siz1 pad4 at 22°C . The genes involved in BR biosynthesis and signalling were also no longer collectively down-regulated except for two genes , which encode for two rate-limiting enzymes of the Brassinosteroid ( BR ) biosynthesis pathway ( DWF4 or DWARF 4; and BR6OX2 or BRASSINOSTEROID-6-OXIDASE 2 ) [51–53] . This suggests that the BR levels might be reduced in siz1 pad4 . In agreement with this , we found that the TFs BEE1 , BEE3 , and TCP1 are down-regulated in siz1 pad4 ( S1B Table ) . BEE1 and -3 are two closely related bHLH TFs that act as early response TFs required for the full BR response [54] . TCP1 encodes a TF that directly positively regulates the expression of DWF4 [55] . Combined , these data argue that the siz1 pad4 phenotype may be ( partially ) due to BR-deficiency . We then selected the set of thermosensitive genes by identifying the genes that are differentially expressed ( DEGs , q ≤ 0 . 01 ) in pad4 in response to the shift to 28°C ( comparing day 1 to day 0 , day 4 to day 1 , and day 4 to day 0 ) . The DEGs were clustered based on their expression profile and their expression dynamics was revealed by plotting their standardized expression values in a clustered heat map ( Fig 5A , red-to-blue ) . To detect differences in the gene expression profiles of siz1 pad4 and sumo1/2KD pad4 we plotted the same gene expression heat maps for the two mutants while retaining the gene clustering ( Fig 5A ) . We also plotted the difference in gene expression ( Δ ) between the mutants ( siz1 pad4 and sumo1/2KD pad4 ) and pad4 ( brown-to-cyan heat maps ) . Fig 5A reveals that overall the gene expression profiles of the thermosensitive genes do not differ strongly between the two SUMO conjugation mutants and the control pad4 ( blue-to-red heat maps ) . In other words , most of the thermosensitive genes also respond to the shift to 28°C in siz1 pad4 or sumo1/2KD as they do in pad4 . However , most of the thermosensitive genes appear to show an attenuated response in siz1 pad4 and sumo1/2KD pad4 at day 1 and/or 4 . For example the up-regulated genes ( changing from red at day 0 to blue at day 4 in the heat maps ) show less expression in siz1 pad4 and sumo1/2KD pad4 than pad4 at day 4 ( brown colour in the ‘ΔExpr ( mut-WT ) ’ heat maps ) . Likewise , the down-regulated genes ( shift from blue at day 0 to red at day 4 ) show increased expression in siz1 pad4 and sumo1/2KD pad4 at day 4 ( cyan colour in the ‘ΔExpr ( mut-WT ) ’ heat maps ) . To confirm this notion , we selected for each time point the DEGs in siz1 pad4 and sumo1/2KD pad4 in comparison to pad4 . A large set of these DEGs was shared between the two mutants ( siz1 pad4 and sumo1/2KD pad4 ) , as can be seen in the VENN diagrams ( Fig 5B ) . Strikingly , the largest number of DEGs was obtained for both mutants at day 1 rather than at day 4 . To visualize the dynamic response of these DEGs in response to high temperature , we plotted in a scatter plot the fold change in expression of these DEGs for siz1 pad4 and sumo1/2KD pad4 ( both y-axis ) versus pad 4 ( x-axis ) ( by separately combining the DEGs for the different time points for the two mutants ) . The left panel in Fig 5C and 5D depicts the change in expression from day 0 to day 1 , while the right panel depicts the change from day 0 to day 4 . This revealed that primarily in the control ( pad4 ) at day 1 the expression of the DEGs changed due to the increase in temperature , while in siz1 pad4 and sumo1/2KD pad4 these genes largely failed to respond at this time point ( Fig 5C and 5D , panel day 1–0 ) . This is best seen in the global expression profiles ( top and right side of the scatter plot ) revealing a double hump in pad4 , while the expression profile displays a single Gaussian curve around zero for both SUMO mutants . In contrast , at day 4 we find a positive correlation for the change in expression of all DEGs ( Pearson R = 0 . 73; linear regression ) with a slope = 0 . 61 for siz1 pad4 versus pad4 . This means that at day 4 the DEGs responded in siz1 pad4 to the high temperature , but their response was overall attenuated . A similar situation is seen for sumo1/2KD pad4 at day 4 ( Pearson R = 0 . 79; slope = 0 . 87 ) . Thus , SIZ1 and SUMO1/2 both appear to control in a similar manner both the timing and the amplitude of the temperature-induced transcriptional response . We then examined if the direct genomic targets of the TFs PIF4/BZR1/ARF6 are differentially expressed in siz1 pad4 and sumo1/2KD pad4 . The direct genomic targets of these tree TFs , which form a trimeric transcriptional hub , were obtained from published chromatin-immunoprecipitation ( ChIP ) datasets of these TFs [56 , 57] . As shown in Fig 5E , nearly 25% of the genomic targets of these three TFs was differentially expressed in siz1 pad4 during the course of the temperature shift experiment . This overlap was very significant with p-values of 3 . 07e-21 ( PIF4 ) , 2 . 11e-8 ( BZR1 ) , 9 . 24e-11 ( ARF6 ) using a hypergeometric test ( based on 26859 annotated probes; TAIR9 ) . The overlap was still significant but less strong for sumo1/2KD pad4 ( with an overlap of ±12% , Fig 5E ) and p-values of 1 . 17e-6 ( PIF4 ) , 6 . 92e-4 ( BZR1 ) , 1 . 46e-4 ( ARF6 ) . Thus , there is a significant enrichment for the genomic targets of these three TFs amongst the DEGs in both our mutants in response to shift to temperature 28°C ( Fig 5E ) . The change in expression of these genomic targets of these three TFs in the mutants versus the control ( pad4 ) mirrored largely the global pattern seen for all the DEGs combined ( S6 and S7 Figs ) . Thus , the response of the misexpressed genomic targets of PIF4 , BZR1 , and ARF6 in the siz1 pad4 and sumo1/2KD pad4 mutants follows the same trend as the global response ( i . e . their expression is largely delayed till day 4 and the response remains attenuated at day 4 ) . This corroborates our hypothesis that the PIF4-dependent high-temperature growth response is compromised in siz1 and sumo1/2KD . We also looked at the genomic targets of the ‘cold’ regulator HY5 that binds to and competes ( at low temperature ) for the same genomic targets as PIF4 [58 , 59] . The HY5 genomic targets largely failed to respond in siz1 pad4 at day 1 , while at day 4 their response was largely attenuated in siz1 pad4 compared to pad4 ( S8C and S8D Fig ) . This effect on the expression of the HY5 genomic targets was less clear for sumo1/2KD pad4 ( S9C and S9D Fig ) . While examining the list of DEGs we noted that many SAUR ( Small auxin up RNA ) genes were present among the top of the gene lists . PIF4 is known to regulate auxin biosynthesis via the SAUR family [60] . The differentially expressed SAUR genes showed a strong deregulation in siz1 pad4 and sumo1/2KD pad4 at both time points , with very distinct global expression profiles in the mutants versus the control ( pad4 ) ( S8E , S8F , S9E and S9F Figs ) . Combined , our data revealed that the siz1 pad4 and the sumo1/2KD pad4 mutants display a delayed and attenuated transcriptional response to high temperature ( in comparison to pad4 ) , which runs in part over the PIF4/BZR1 transcriptional hub .
Here , we describe an interconnected dual role for SIZ1 and SUMO1/2 conjugation in the switch between plant immunity and high temperature induced growth ( as summarized in the model of Fig 6 ) . Our data unveil that both SIZ1 and SUMO1/2 conjugation are positive regulators of thermo- and skotomorphogenesis upstream of the PIF4/BZR1 growth regulation hub . In this hub , BZR1 is activated by the hormone BL , while PIF4 is activated by dark conditions and high ambient temperature . In line , these two TFs share a large number of genomic targets that are synergistically regulated by them [56] . We find that loss of SIZ1 and SUMO1/2 both delays and attenuates this transcriptional response to high temperature affecting many targets of PIF4 and BZR1 . This suggests that SIZ1 activity acts as a positive regulator of PIF4 function in thermomorphogenesis and that PIF4 function is apparently compromised/inhibited in siz1 at high temperature ( Fig 6 , siz1-2 ) . Importantly , the PIF4 protein abundance is positively regulated by COP1 E3 ligase activity [58] , while COP1 activity is stimulated by SIZ1-dependent sumoylation ( Fig 6 , wild type route c . ) [34 , 35] . Our data unveil that COP1 is essential to convey this high temperature signal , as recently reported by others [27] , while SIZ1 enhances the high temperature and dark signal . This role of SIZ1 in thermo/skotomorphogenesis is distinct from its reported role on cell elongation due to constitutive defence signalling [61] , as hypocotyl elongation was still compromised at high temperature when PAD4 or SNC1 were mutated . Likewise , we noted that the rosette of siz1 pad4 , siz1 eds1 , and siz1 NahG remained compact at 28°C ( without strong petiole elongation or hyponasty as seen for Col-0 ) . Interestingly , part of the siz1 auto-immune phenotype is sustained at high temperature resulting in enhanced resistance to bacteria ( Fig 3 ) . This enhanced resistance still required SNC1 and EDS1 function at 28°C ( Fig 3 , Fig 6 wild type route a . ) . The latter is relevant , as both SNC1 and EDS1 immune signalling depend on their nuclear localization , while SNC1 nuclear localization is impaired at high temperature [7 , 12 , 22 , 62] . High temperature suppression of snc1-1 auto-immunity and concomitantly rescue of its growth phenotype requires PIF4 function [24] ( Fig 6 wild type route b . ) . SNC1-dependent auto-immunity , including enhanced resistance to the bacterial pathogen Pseudomonas , is normally fully suppressed in the mutants bon1 , crp1-2 and snc1-1 at 28°C , resulting in normal rosette growth ( e . g . Figs 1–3 ) [7 , 63] . However , siz1 fails to resume normal growth at 28°C and this is independent of PAD4/EDS1 , SNC1 or SA accumulation . This implies that the ‘high temperature’ signal is not properly conveyed in siz1 . At the same time , SIZ1 suppresses expression of a small subset of immune receptors at 22°C , even when PAD4 is mutated . It remains an open question if elevated expression of one of these immune receptors ( S1A Table ) is causal for the auto-immune phenotype of siz1 , rather than the misexpression of SNC1 . Biochemically , SUMO conjugation was already implied as a regulator of photomorphogenesis [34 , 35] . Our data suggest that the role of SIZ1 in thermomorphogenesis is mechanistically independent of light sensing , as hypocotyl elongation in siz1 was also reduced in the dark . Previous works had indicated that sumoylation of phyB allows PIF5 to bind its target promoters resulting in root growth stimulation . These authors demonstrated that sumoylation of the Pfr state ( red light activated state ) of phyB suppresses the interaction between phyB and PIF5 , the closest homologue of PIF4 [32 , 64 , 65] . Our GA3 treatment experiment also suggests that SIZ1 controls thermomorphogenesis response independent of DELLA accumulation ( Fig 4D ) . The DELLAs control the stability of the PIFs , while they themselves are also controlled by sumoylation [31 , 49] . Other ( putative ) sumoylation substrates implicated in PIF4 function are ELF3 ( Early flowering 3 ) [43 , 66] , HFR1 [67] and LAF1 [68] , HY5 and HY5-like ( HYL ) [69] . The role of sumoylation has not yet been determined for ELF3 . Both HFR1 and LAF1 are also targets for COP1-mediated degradation . The link between their degradation and sumoylation remains to be studied . Nevertheless , it is evident that ( i ) SUMO conjugation acts at multiple levels as a regulator of growth and that ( ii ) certain COP1 substrates are also targets for sumoylation . Finally , we found that several actors in BR biosynthesis and signalling are still down-regulated ( DWF4 , BEE1 , BEE3 , and TCP1 ) in siz1 pad4 . CESTA , a close homologue of BEE1 and BEE3 , is another SUMO substrate that directly binds to BEE1 to control BR biosynthesis [70] . Catala and co-workers had previously reported that from the nearly 1600 differentially expressed genes in siz1 ( >two-fold change ) , eleven down-regulated genes were known to be critical for BR biosynthesis and signalling [50] . In addition , they found in their genome-wide expression analysis that both PIF4 and PIF5 were underexpressed in siz1 [50] . These data warrant further research on the role of sumoylation on BL signalling and biosynthesis . To conclude , SIZ1 and SUMO1/2 both act as important positive regulators of growth , while SIZ1 also acts as negative regulator of an SNC1-dependent immune response at high temperature . SIZ1 thus plays an interdependent dual role in growth and immunity at elevated ambient temperature .
The genetic resources for this research were wild type Arabidopsis ( Arabidopsis thaliana ) ecotype Col-0 , siz1-2 [71] , cop1-4 [45] , cpr1-2 [18] , bon1-1 [10] , hy5-215 [72] , snc1-1 [19] , srfr1-4 [15] , pad4-1 [73] , eds1-2 ( backcrossed in Col-0 ) [74] , sid2-1 [40] , 35Spro::NahG [75] , snc1-11 ( SALK_047058 ) [10] , sgt1a-3 [16] , sgt1b ( eta3 ) [76] , rar1-21 [77] , pif4-2 [78] , and sumo1/2KD [aka sum1-1 amiR-SUMO2 line B][29 , 79] . The double mutants pad4 siz1 , NahG siz1 [28] , cop1-4 siz1-2 and hy5-215 siz1-2 [35] are described elsewhere . Arabidopsis plants were grown under white light with 120 μmol m-2 sec-1 under short-day ( SD ) light conditions ( 11 hr light , 13 hr dark ) at 22°C or 28°C on a compost/perlite soil mixture . After crossing , the plants were genotyped according to the primer combinations and primer sequences presented in the S2 and S3 Tables , respectively . The fresh rosette weight of plants ( minimum 8 ) grown individually in single pots was measured . The rosette was sampled from 5-week-old plants grown in parallel at 22°C or 28°C . Statistical analyses were made using two-way ANOVA ( genotype , temperature , interaction GxT ) followed by Tukey post hoc test in Prism7 . Significantly different groups are indicated by letters . For immunoblot analysis , seedlings or leaf material was homogenized in liquid nitrogen , thawed on ice in extraction buffer ( 10% glycerol , 50 mM K2HPO4/KH2PO4 pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 2% w/v polyvinylpolypyrrolidone K25 , 1× protease inhibitors ( Roche ) , 1% v/v Nonidet P-40 , 0 . 1% SDS and 5 mM DTT ) , and centrifuged for 10 min at 13 , 000g . The supernatant was mixed 1:1 with 2× SB ( 125 mM Tris-HCl pH 6 . 8 , 4% SDS , 20% v/v glycerol , and 100 mM DTT ) , and the samples were boiled for 10 min . Proteins were separated on 15% SDS-PAGE and blotted onto Polyvinylidene fluoride ( Immobilon-P , MIllipore ) membranes . Secondary immunoglobulins conjugated to horseradish peroxidase were visualized using ECL Plus ( GE Healthcare ) . Primary antibodies against PR1 ( αPR1 ) were described previously [80] and αPR2 was obtained from Agrisera ( #AS12 2366 , ~35kDA ) . Incubation of both primary and secondary antibodies were done in Tris-buffered saline with 0 . 05% Tween-20 ( TBST ) followed by three rinses of 10 minutes in TBS . Equal protein loading was confirmed for the samples by Ponceau S staining of the membranes and when needed the loaded total protein amounts were standardized using BCA protein analysis on the total protein extracts prior to protein loading of the gels . The primary antibodies αPR1 , αPR2 and the secondary antibody Goat-anti-Rabbit HRP ( Fisher ) were used at 1:5000 , 1:2000 and 1:5000 dilutions , respectively . For the gene expression analysis , total RNA was extracted from 100–200 mg of leaf material of 5-week-old plants grown at 22/28°C using TRIzol LS reagent ( Fisher ) . The RNA was treated with DNase ( ThermoFisher ) according to the supplier’s protocol and RNA concentrations were determined by measuring the Abs ( 260 ) on a Nanodrop . cDNA was synthesised from 1 μg total RNA using RevertAid H reverse transcriptase in the presence of the RNAse inhibitor Ribolock ( both ThermoFisher ) following the supplier’s protocol . All biological samples were measured in technical replicate with 3–4 biological replicates per experiment . The PCR amplification was followed using Hot FIREPol EvaGreen qPCR ( Solis Biodyne ) in a QuantoStudio3 ( ThermoFisher ) . Gene expression was normalized using two genes: Actin2 ( At3g18780 ) and beta-Tub4 ( At5g44340 ) . The primers used are given in the S2 Table . The Ct values were corrected for primer efficiencies . All expression data were analysed using the pipeline in qBASE+ ( Biogazelle ) . Cold-stratified ( 3 days at 4°C ) sterilized seeds ( ~50 per line ) were placed on vertical plates with 1/2 MS medium supplemented with 1% w/v sucrose and 1% w/v Daishin agar ( Duchefa ) . Seeds were irradiated with white light for 6 hrs to promote germination and then incubated in the specified light/temperature conditions for 5 days . The used seeds were fresh and from the same seed harvest . Seedlings were scanned and the hypocotyl lengths were measured using ImageJ ( http://rsb . info . nih . gove/ij ) . Sensitivity to the Gibberellin biosynthesis inhibitor Paclobutrazol ( Pac , Duchefa ) and the hormone Gibberellic acid ( GA3 , Duchefa ) was analysed by growing the seedlings on 0 . 5 μM PAC or 10 μM GA3 , respectively . Likewise , sensitivity to the Brassinosteroid biosynthesis inhibitor Propiconazole ( PPZ , Sigma-Aldrich ) or the hormone 24-epiBrassinolide ( BL , #b1439 , Sigma-aldrich ) was analysed by adding 2 μM PPZ or 0 . 1 μM BL to the plates , respectively . Pseudomonas syringae pv . tomato DC3000 ( PstDC3000 ) [81] ( carrying the empty vector pVSP61 ) was freshly grown overnight at 28°C with 200 rpm in 10 mL Kings B broth [82] supplemented with rifampicin ( 50 μg/mL ) and kanamycin ( 40 μg/mL ) to reach an OD600 of ~0 . 9–1 . 2 . Directly prior to infiltration , the bacterial suspensions were spun down , washed with 10 mM MgSO4 , and resuspended at OD600 = 0 . 0002 ( 1×105 CFU/mL ) in 10 mM MgSO4 for syringe leaf infiltrations . For the Pst disease assays the plants were germinated and grown at 28°C constant temperature ( with 11L/13D ) for 5 weeks in soil . Twenty-four hours prior to inoculation ( 9:00 am ) , one batch of plants was moved to 22°C ( SD ) and both plant sets were placed in propagators to increase humidity ( >90% ) . The two plant batches were simultaneously infiltrated at 22°C and 28°C using the same bacterial suspension . Upon infiltration the plants were left to dry for 1 . 5–2 hrs after which they were again covered with lids for 72 hours to increase humidity ( >90% relative humidity ) . Humidity and temperature was followed using a data logger inside the propagators for the duration of the experiment . Leaf discs were taken 1 hour after dipping ( t = 0 ) at both temperatures and 72 hrs post infiltration ( t = 3 ) . At least 6 plants were infiltrated per condition . In total 8 samples were taken for each condition combining 2–3 leaf discs with a diameter of 5 mm . Leaf discs were taken from different leaves and only ‘mature’ fully elongated rosette leaves were sampled . The first-formed round shaped leaves were excluded from tissue sampling . The sampled intact leaves were surface-sterilized prior to taking leaf discs ( 10 sec dip in 70% ethanol followed by two washes with sterile water ) . The disease assays were performed with at least two independent replicates with similar results . The rosette leaves were stained with a 1:1 mixture ( v/v ) of ethanol and lactic acid–phenol–trypan blue solution ( 2 . 5 mg mL−1 trypan blue , 25% v/v lactic acid , 25% phenol , 25% glycerol , and water ) and boiled for 5 min . For destaining , the trypan blue solution was replaced with a chloral hydrate solution ( 2 . 5 g mL−1 in water ) , as described [83] . The siz1 pad4 , sumo1/2KD pad4 , and pad4 plants were grown on soil in SD conditions at 22°C for 2 weeks and then transferred to 28°C at noon ( t = 0 ) . Leaf samples were taken in triplicate for total RNA extraction at t = 0 , 24 hrs ( 1d ) , and 96 hrs ( 4d ) . Total RNA was purified using the RNAeasy mini kit ( QIAGEN ) . The RNA quality was examined by monitoring Abs ( 260/280 ) and the Abs ( 260/230 ) ratios . Total RNA ( 100 ng ) was amplified using the GeneChip WT PLUS kit ( Affymetrix ) generating biotinylated sense-strand DNA targets . The labelled samples were hybridized to Arabidopsis Gene 1 . 1 ST arrays ( Affymetrix ) . Washing , staining and scanning was performed using the GeneTitan Hybridization , wash , and stain kit for WT Array Plates , and the GeneTitan Instrument ( both Affymetrix ) . All arrays were subjected to a set of quality control checks , such as visual inspection of the scans , checking for spatial effects through pseudo-color plots , and inspection of pre- and post-normalized data with box plots , ratio-intensity plots and principal component analysis . Normalized expression values were calculated using the robust multi-array average ( RMA ) algorithm [84] . The experimental groups were contrasted to test for differential gene expression . Empirical Bayes test statistics were used for hypothesis testing [85] using the Limma package in R 3 . 2 . 1 ( http://cran . r-project . org/ ) , and all p-values were corrected for false discoveries according to Storey and Tibshirani [86] . Downstream statistical analyses ( e . g . hypergeometric tests on enrichment ) were performed in Python using the Scipy . stats module ( https://scipy . org/scipylib/ ) . The microarray data were deposited in Gene Expression Omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo/ ) under the accession number GSE97641 and Github ( DEGs and scripts used to prepare Fig 5; https://github . com/LikeFokkens/Siz1_immunity-vs-growth_temperature ) . | Ambient temperature is a major actor in plant immunity and in growth regulation . Foremost , high temperature ( >27°C ) is known to block plant defence responses . High temperature also alters the plant morphology by inducing elongation growth , which facilitates plant ‘cooling’ . This process is called thermomorphogenesis . Importantly , the SUMO E3 ligase SIZ1 suppresses plant immunity at normal conditions ( 22°C ) , but its role in immunity at high temperature was unknown . SIZ1 was recently shown to sumoylate and activate the ubiquitin E3 ligase COP1 , a key player in thermomorphogenesis affecting the accumulation and/or stability of key transcription factors for this process ( PIF4 and HY5 ) . At high temperature PIF4 suppresses SNC1-dependent growth defects and auto-immunity for the snc1-1 mutant . We report that part of the SNC1-dependent auto-immune response is retained and activated in the siz1 mutant at high temperature resulting in enhanced resistance to Pseudomonas . In addition , we find that SIZ1 controls the thermomorphogenesis response and it affects expression of a substantial subset of PIF4 and BZR1 gene targets in response to high temperature . Our data imply that SIZ1 acts upstream of the PIF4/BZR1 hub . Combined the data highlight that SIZ1 has a dual role in the trade-off between SNC1-dependent immunity and growth at elevated temperature , where the latter aspect potentially runs via COP1 . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"cell",
"death",
"plant",
"anatomy",
"plant",
"growth",
"and",
"development",
"plant",
"embryo",
"anatomy",
"cell",
"processes",
"brassica",
"membrane",
"proteins",
"developmental",
"biology",
"plant",
"science",
"model",
"organisms",
"sumoylation",
"experimental",
"organism",
"systems",
"plants",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"arabidopsis",
"thaliana",
"plant",
"embryogenesis",
"plant",
"development",
"proteins",
"gene",
"expression",
"leaves",
"cell",
"membranes",
"biochemistry",
"eukaryota",
"plant",
"and",
"algal",
"models",
"cell",
"biology",
"post-translational",
"modification",
"embryogenesis",
"genetics",
"biology",
"and",
"life",
"sciences",
"biosynthesis",
"hypocotyl",
"organisms",
"fruit",
"and",
"seed",
"anatomy"
] | 2018 | The Arabidopsis SUMO E3 ligase SIZ1 mediates the temperature dependent trade-off between plant immunity and growth |
True incidence of leprosy and its impact on transmission will not be understood until a tool is available to measure pre-symptomatic infection . Diagnosis of leprosy disease is currently based on clinical symptoms , which on average take 3–10 years to manifest . The fact that incidence , as defined by new case detection , equates with prevalence , i . e . , registered cases , suggests that the cycle of transmission has not been fully intercepted by implementation of multiple drug therapy . This is supported by a high incidence of childhood leprosy . Epidemiological screening for pre-symptomatic leprosy in large endemic populations is required to facilitate targeted chemoprophylactic interventions . Such a test must be sensitive , specific , simple to administer , cost-effective , and easy to interpret . The intradermal skin test method that measures cell-mediated immunity was explored as the best option . Prior knowledge on skin testing of healthy subjects and leprosy patients with whole or partially fractionated Mycobacterium leprae bacilli , such as Lepromin or the Rees' or Convit' antigens , has established an acceptable safety and potency profile of these antigens . These data , along with immunoreactivity data , laid the foundation for two new leprosy skin test antigens , MLSA-LAM ( M . leprae soluble antigen devoid of mycobacterial lipoglycans , primarily lipoarabinomannan ) and MLCwA ( M . leprae cell wall antigens ) . In the absence of commercial interest , the challenge was to develop these antigens under current good manufacturing practices in an acceptable local pilot facility and submit an Investigational New Drug to the Food and Drug Administration to allow a first-in-human phase I clinical trial .
Detection of pre-symptomatic leprosy continues to be identified by the World Health Organization ( WHO ) as a priority [1] . With the introduction of multiple drug therapy ( MDT ) by the WHO in 1982 to subvert extensive resistance of Mycobacterium leprae resulting from years of dapsone monotherapy , the prevalence of leprosy began a dramatic decline [2] . Over the past 30 years , prevalence has dropped by about 98% from an estimated historical high of 11 . 5 million cases in 1983 [3] to the current figure of 192 , 246 registered cases [1] . Contrary to this remarkable achievement , leprosy incidence as defined by new case detection ( NCD ) remained relatively constant or increased slightly from 1985 at 555 , 188 new cases in the top 33 endemic countries [4] to 571 , 792 in 1990 [5] and 620 , 672 in 2002 [6] . A significant decline of 51 . 4% in new cases was then observed between 2002 and 2005 to 299 , 036 , followed by another decline of 23 . 6% to the current figure of 228 , 474 [1] . A total decline of 58 . 8% in detection of new cases from 1985 to 2010 has been observed . Although many investigators have questioned the value of these numbers based on confounding operational factors [7] , one fact remains; NCD has generally exceeded prevalence . Of added concern is the increased NCD of childhood leprosy signifying active and recent transmission of disease [8] , [9] . These findings provide evidence that transmission of M . leprae from infected to susceptible individuals remains a problem . Little is known of the extent of pre-symptomatic [10] leprosy in the endemic regions of the world , or reservoirs of infection , or bacterial or immunological basis of the distinctive pathogenesis of leprosy , notably nerve damage [11] , [12]; however , we do know that early detection and treatment does reduce transmission [13] and disease sequelae [14]–[16] . Clinical leprosy is a bacteriological and pathological polar disease ranging from the tuberculoid ( TT ) to the lepromatous ( LL ) forms , with intermediate stages [17] , [18] . This spectrum of disease is determined by the immunological status of the host [19] . The lepromatous forms are marked by high antibody titers , but T cell hyporesponsiveness ( anergy ) [20]; whereas the tuberculoid form show little evidence of M . leprae specific antibodies , but a vigorous Th1 response . Likewise , some household contacts ( HC ) of leprosy patients also demonstrate a specific T-cell response . These changes in the immune response along the continuum of disease suggest that a cell mediated immunity ( CMI ) test may be adequate to detect early leprosy infection . Our approach toward development of an early diagnostic tool for leprosy has been focused on the delayed hypersensitivity ( DTH ) immune response , because it is considered to be sensitive , simple , cost-effective , and inexpensive when applied as Tuberculin Purified Protein Derivative ( PPD ) , skin test for tuberculosis [21] . A DTH type IV reaction is initiated when antigen is injected into subcutaneous tissue and processed by antigen presenting cells . A Th1 effector cell recognizes the antigen and releases cytokines IL-2 , IFN-γ , and TNF , which act on vascular endothelium causing erythema and recruitment of T-cells , phagocytes , fluid , and protein . This cascade of events causes a measurable induration response within 48–72 hours in humans . A lack of DTH response to recall antigen is evidence of anergy [22] . Early leprosy skin test studies with whole bacilli preparations , such as Lepromin-H ( Mitsuda ) [23] and Lepromin A [24] , [25] , had proven utility in classification of disease with the 21 day Mitsuda granulomatous reaction . However , the Lepromin antigen tends to prime the immune response and moreover is not specific for leprosy . Lepromin Dharmendra ( Dharmendra ) [26] , Convit's Soluble Protein Antigen ( SPA ) or Leprosin , and Rees's M . leprae soluble antigen ( MLSA ) [27] evoked a 48 hour DTH reaction ( the Fernandez reaction ) [28] . TT leprosy patients had a characteristic DTH response to SPA and MLSA; whereas LL leprosy patients were anergic to these antigens , but not to other mycobacterial antigens such as PPD [29] . The DTH responses of borderline patients typically fell within the spectrum of their disease classification [30] , [31] . Promising features of the MLSA and SPA included: neither had sensitizing potential [32]; both were potent immunologically; and , both were found to be safe in human vaccine trials in Venezuela , Malawi , and India [33] , [34] . Shortcomings included inconsistent readings due to soft rather than hard DTH reaction in some individuals; variations in potency between batches due to quality control issues; and , lack of adequate sensitivity and specificity . Two refined leprosy skin test antigens were identified [35] . The first antigen was a modified Rees antigen: MLSA-LAM ( MLSA devoid of lipoglycans , primarily the immunosuppressive and cross-reactive lipoarabinomannan ( LAM ) , and also lipomannan ( LM ) , and phosphatidylinositol mannoside ( PIM ) and other lipids [36]–[38] ) . The second antigen was MLCwA ( M . leprae cell wall antigen ) , consisting of the powerful immunogens of the cell wall devoid of lipoglycans [39] , [40] . Active ingredients of these two intradermal skin test antigens are proteins of M . leprae . MLSA-LAM contains soluble protein antigens; over 100 individual proteins were initially recognized on two-dimensional gels , and about 30 of these had been sequenced and the immunological responses studied in part [41] , [42] . Foremost among these antigens are the 70 kDa ( DnaK ) , 65 kDa ( GroEL ) , 45 kDa , 38 kDa , 35 kDa major membrane protein-I ( MMP-I ) , 23 kDa superoxide dismutase ( SOD ) , 18 kDa small heat shock protein ( SmHSP ) , 18 kDa bacterioferritin ( Bfr ) , 10 kDa ( GroES ) , and the ribosomal proteins S7/S12 [43]–[48] . More recently , the full spectrum of proteins in soluble and insoluble subcellular fractions of M . leprae have been demonstrated and many more identified through the modern-day “proteomics” approach [49]–[51] . MLCwA contains many of the same proteins as MLSA-LAM , particularly the 70 kDa and 65 kDa and degradation products of these , the export/secretory proteins ( notably the 30/31 kDa , multigene antigen 85 complex ) , and also some larger , uncharacterized proteins [50] . Details of the full spectrum of MLCwA constituent proteins have since been published [51] . Both leprosy antigens were chosen as skin test candidates based on adequate yield and biological justification with a robust DTH response in M . leprae sensitized compared to M . tuberculosis infected guinea pigs [52] and strong induction of lymphocyte proliferation and secretion of IFN-γ from TT leprosy patient immune cells [53] , [54] . These early studies led to the development and manufacturing of these antigens [35] . The neglected tropical disease of leprosy is a disease of the poor , living in marginalized countries [55]; hence , commercial interest in the development of new products was lacking . Despite limited experience and resources , product development was implemented in this academic setting [56] , The researchers overcame the challenges of developing and manufacturing skin test antigens suitable for human application [57] .
Protocols for animal use were reviewed and approved by the Animal Care and Use Committee ( ACUC ) at Florida Institute of Technology ( FIT ) and Colorado State University ( CSU ) . The CSU approved ACUC protocol number was 02-167A-02 . The FIT Armadillo Facility was in-compliance with United States Department of Agriculture-American Public Health Association ( USDA-APHA ) , United States Public Health Service-Office for Protection from Research Risks ( USPHS-OPRR ) , and International ACUC ( IACUC ) standards . The CSU Laboratory Animal Facility followed IACUC regulations and guidelines . The Phase I trial ( registration number: NCT01920750 ) and phase II trial ( registration number: NCT00128193 ) were registered with ClinicalTrials . gov . The phase I trial was not registered prior to implementation , because the trial was completed ( February , 1999 ) , before ClinicalTrials . gov registry was made available to the public ( February , 2000 ) . Retrospective registration of the phase I trial was requested for publication . The clinical Phase I Protocol , Protocol S1 , and Phase II Protocol , Protocol S2 , are attached as Supportive Information; although details of the clinical study will follow in subsequent articles . M . leprae cannot grow axenically , but can be propagated in the nine-banded armadillo , Dasypus novemcinctus [58] . At the Florida Institute of Technology ( FIT ) , Melbourne , Florida , Eleanor . E . Storrs and subsequently Arvind Dhople et al . under National Institutes of Health ( NIH ) , National Institutes of Allergy and Infectious Diseases ( NIAID ) with regard to support and authorization , captured armadillos from state or nationally managed land areas in Central Florida for propagation of M . leprae . Animals were treated for parasites , quarantined , and tested prior to release by: 1 ) acid fast staining of ear snips , nasal swabs , and blood for evidence of acid-fast bacilli; 2 ) culturing of blood samples for sterility in Trypticase Soy Broth and thioglycollate broth; 3 ) hematology; 4 ) serodiagnosis for IgM antibodies to phenolic glycolipid-I; and , 5 ) Lepromin test to determine susceptibility to M . leprae [58] , [59] . The source of M . leprae was a untreated lepromatous leprosy individual from Guyana with large numbers of highly bacilliferous subcutaneous nodules and lepromas . Genetic evidence has since indicated that M . leprae isolates are antigenically homogeneous [60] , [61] . Infected armadillos were sacrificed and the livers and spleens were homogenized and fractionated to separate M . leprae bacilli to serve as the Master Seed Stock in 2 ml volumes ( 3×108 bacilli/ml ) frozen at −70°C . Subsequently infected armadillos with disseminated leprosy were sacrificed and the tissues ( liver and spleen ) , aseptically removed . The infected armadillo tissues were shipped to the Pilot Plant Skin Test Antigen Facility at CSU . A total of 242 g of M . leprae infected tissue ( spleen , 19 g; liver , 223 g ) from three infected armadillos [animal nos . A563 ( 19 g spleen , one preparation ) , A572 ( 109 g liver , divided into three preparations ) , and A581 ( 114 g liver , divided into three preparations ) ] were fractionated using a modified 3/77 Draper protocol [62] ( Figure 1 ) , except for omission of the step involving protease digestion with chymotrypsin and trypsin and alterations in buffer composition . Protease digestion of homogenate was removed since no difference was seen between treated and untreated tissue preparations in terms of purity , protein content , and immunological potency of the recovered M . leprae . Tissue sections ranging from 19 g to 36 . 5 g were homogenized with 10 mM disodium ethylenediaminetetraacetic acid ( EDTA , Sigma , St . Louis , Mo . ) , pH 8 . 0 at 3 ml/g of tissue . Homogenates were tested for sterility on brain heart infusion agar , blood agar , and Lowenstein-Jensen agar ( BD , Franklin Lakes , NJ ) . Tissue fragments were pelleted and washed twice with 10 mM EDTA by centrifugation ( Sorvall RC5 , Thermo Fisher Scientific , Inc . , Rockford , IL ) at 15 , 000× g for 10 min at 4°C in 50 ml Teflon Oakridge tubes , followed by extraction with 0 . 1 M sodium hydroxide ( Mallinckrodt Baker Inc . , Phillipsburg , NJ ) in 10 mM EDTA while stirring at room temperature for 2 h to remove pigment and to separate M . leprae from tissue . The suspension was pelleted and washed twice with 0 . 1 mM sodium phosphate/0 . 1% Tween 80 ( Mallinckrodt/Fisher ) followed by digestion with 20 mg collagenase ( Sigma , St . Louis , Mo . ) and 0 . 23 mM calcium chloride ( Sigma ) in 200 ml of the sodium phosphate Tween 80 buffer while stirring overnight at 37°C . The digest was again pelleted and washed prior to two-phase extraction with 6% polyethylene glycol 6 , 000 and 8% Dextran T-500 ( Sigma ) in 0 . 1 M sodium phosphate/150 mM sodium chloride at 10 ml/g of tissue in a separatory funnel . The upper phase containing bacteria was removed and an equal volume of 0 . 2% Tween 80 added prior to centrifugation at 27 , 000× g for 30 min at 4°C . Purified M . leprae was washed twice at 15 , 000× g with buffered water and the concentration of bacilli estimated with a 1∶100 and 1∶200 dilution by optical density at A540 using an empirically determined conversion factor of 0 . 362 based on dry weight , i . e . , A540 of 1 . 0 = 0 . 362 mg M . leprae/ml multiplied by the dilution factor . Samples of the bacilli were tested for sterility by culturing on brain heart infusion agar , blood agar , and Lowenstein-Jensen agar . Purity was subjectively determined by acid fast staining using methlyene blue as a counterstain for residual tissue , with acceptance criteria of ≥90% [63] , [64] . M . leprae ( 128 . 43 mg ) from seven such preparations were pooled and washed twice with 25 ml phosphate buffered saline ( PBS ) by centrifugation at 27 , 000× g for 15 min at 4°C ( Figure 2 ) . Bacteria were suspended in 5 ml PBS and disrupted by sonication on cold packs with an ultrasonic processor ( Sanyo Soniprep 150 , MSE Ltd . , Lower Sydenham , London ) at 1 . 5 MHz , 50% duty , and 1 second pulse intervals over six 5 min cycles with 5 min cooling between each cycle . Pre and post-sonicated bacteria were stained using the TB Acid Fast Stain Kit ( Thermo Fisher Scientific Inc . ) for counting to verify greater than 80% breakage . Disrupted bacteria were centrifuged at 27 , 000× g for 30 min . Supernatant consisting of cytosol and membrane was transferred to a fresh tube and centrifugation repeated . The pellet consisting of M . leprae cell wall was washed three times with 10 ml PBS . The cytosol/membrane containing supernatant was transferred to an Ultra Clear 5 ml ( 13×51 mm ) tube and ultracentrifuged ( Optima TLX 120 , Beckman Coulter Inc . , Brea , CA ) at 100 , 000× g for 2 h at 4°C to pellet the membrane . To remove lipoglycans [65] cold 20% condensed Triton X-114 ( Baxter , Deerfield , IL ) was added to the supernatant ( cytosol ) to a final concentration of 4% , followed by rocking at 4°C overnight . The tube was placed in a beaker of water at 37°C for 10 min to condense the Triton X-114 followed by centrifugation for 15 min at 3 , 900× g at 22°C to separate detergent and aqueous layers . The top layer was transferred onto tandem 1 ml Extracti-gel D ( Fisher ) columns to remove residual detergent . Extraction and removal of residual detergent was then repeated . Cell wall pellet was resuspended with 2 ml of 2% sodium dodecyl sulfate ( SDS , Fisher ) /PBS and stirred while heating at 56°C for 1 h followed by centrifugation for 15 min at 27 , 000× g at 22°C to remove the SDS solubilized M . leprae cell wall antigens; the residual M . leprae cell walls consisting of the mycolylarabinogalactan-peptidoglycan complex has been the subject of much research [66] , [67] . The supernatant was transferred to a fresh tube and the extraction was repeated . The MLCwA preparation was passed over two 1 ml Extracti-gel D columns to remove residual SDS and finally subjected to two rounds of TX-114 extraction followed by removal of residual detergent as described above . The protein concentration of each of the antigen preparations was assessed by the Bicinchoninic Acid assay ( Fisher ) . Antigens were diluted with PBS containing 0 . 0005% Tween 80 to a final dosage of 10 . 0 , 5 . 0 , 2 . 5 , 1 . 0 , and 0 . 1 µg protein per 0 . 1 ml followed by 0 . 2 µm filtration to remove residual particulates . A total of 1 ml of each of the antigen doses was aliquoted into prewashed and sterilized 2 ml borosilicate vials with 13 mm silicon rubber stoppers and aluminum caps ( Wheaton , Millville , NJ ) . Vials were labeled in accordance with Food and Drug Administration ( FDA ) labeling requirements , including the statement , “Caution: New Drug-Limited by Federal Law to Investigational Use” [68] , autoclaved for 20 min at 121°C; cooled at room temperature , and placed at −70°C for storage as MLSA-LAM and MLCwA batch no . 23 and lot no . 051297 . Vials used in the phase I clinical trial remained at CSU , while those used in the phase II clinical trial were sent to Fisher Bioservices Repository ( Rockville , MD ) for relabeling with randomly assigned codes and shipment to the phase II clinical site ( Figure 3 ) .
Options for manufacturing the two new leprosy skin test antigens under cGMP , suitable for human application , were limited . Costs for using a contract manufacturing organization ( CMO ) were prohibitive; it was difficult to find any with an open schedule , and few had biosafety level 2 ( BSL-2 ) /cGMP clean rooms required for safe manufacturing of these antigens . In addition , service providers acknowledged that they were fearful of working with M . leprae . Consequently , a retired BSL-3 research laboratory was converted to a cGMP Pilot Facility ( Figure 4 ) at CSU for the sole purpose of manufacturing these leprosy skin test antigens . To this end , the manufacturing and testing process for MLSA-LAM and MLCwA was developed to meet 21 CFR parts 210 , 211 for current Good Manufacturing Practices ( cGMP ) [85] , [86] . Details of Pilot Plant Facility renovation are available from the authors . The Pilot Facility consisted of a suite of five rooms , 1 ) Gowning and Material Transfer Room , 2 ) Manufacturing Suite A , 3 ) Manufacturing Suite B , 4 ) Quarantine/Released Goods Room , and 5 ) Quality Control Laboratory . Both the manufacturing and quality control rooms were under positive pressure cascading from the innermost room to the entry foyer . Air was supplied by a dedicated heating ventilation air conditioning system with single pass air flow monitored with gauges in the entry room and an anemometer prior to entry of the manufacturing suite . High efficiency particulate air filters were positioned on both the supply and exhaust air streams to purify air entering and exiting the clean rooms . The manufacturing rooms were classified [87] as international standard organization ( ISO ) 7 clean rooms . The innermost manufacturing room was used for downstream processing ( antigen purification , formulation , and vialing ) , while the outermost manufacturing room was used for upstream processing ( tissue fractionation and bacteria sonication ) . The gowning and material handling room was classified as an ISO8 clean room for personnel aseptic Tyvek gowning , wipe down and transfer of materials and equipment into the manufacturing area , and entering and exiting of personnel . The innermost quality control room , an ISO8 clean room was used for testing raw materials , intermediate product , and final product , while the quarantine/released goods room was a clean , non-classified clean room used for quarantine and release of raw materials . Commissioning of the cGMP Pilot Plant for manufacturing skin test antigens was performed . Rooms were decontaminated with para-formaldehyde . The Pilot Plant was cleaned and the environment was monitored on three consecutive days and three consecutive weeks following directive documents to assess the cleanliness of the facility . Monitoring viable airborne organisms was performed with the Rotary Centrifugal Air Sampler ( Biotest Diagnostics , Brooklyn Park , MN ) and settling plates , both using Trypticase Soy Agar strips/plates . Monitoring viable surface organisms was performed with Rodac plates containing Trypticase Soy Agar and neutralizer for cleaning agents . Isolates were identified to the genius and species level using API Test Kits ( Biomerieux , Etolile , France; distributed by VWR ) . Total particle counts in each clean room were measured using a Particle Counter ( Metone Instruments , Grants Pass , Oregon ) . Acceptance criteria were met with each test enabling release of the Pilot Plant for cGMP manufacturing . A quality system [88] was created for processing and testing leprosy skin test antigens in the renovated pilot plant [89] . The documentation system addressed: facility and equipment , materials , production , product labeling , laboratory control , and quality [90] . Two batch records were written , one for fractionation of tissues and the other for bacteria . A total of 255 supporting standard operating procedures ( SOPs ) were written to cover the quality system and manufacture of antigens . Facility and equipment SOPs were written for operation , maintenance , and calibration of dedicated equipment . SOPs for directing and tracking the chain of custody for raw materials transferred through purchasing , receiving , quarantine , release , and storage were created . Process directives supporting environmental monitoring , gowning , transferring material , manufacturing , in-process testing , and release testing were written into SOP format with data forms to collect relevant information . Explicit details for product labeling were captured in the batch record . All levels of training , including equipment use , biosafety , good laboratory practice ( GLP ) , cGMP , and good clinical practice ( GCP ) were directed through SOPs . Logs were created to track part numbers , documents , raw materials , sample submission , equipment and room usage . Documents were subjected to the mandated review and approval process prior to implementation [91] . The manufacture of antigens was a two step process beginning with receipt , tracking , and release of raw materials . The primary raw material was spleen and liver tissues laden with M . leprae propagated in armadillos at FIT . Upon aseptic harvest , tissues were tested for the presence of contaminating bacteria using microbiological medium and then sent to the Pilot Plant , where they were frozen at −70°C in a qualified freezer until use . Manufacturing reagents were United States Pharmacopeia grade or equivalent , if available; otherwise , the highest purity was specified . Each reagent was released for use based on a certificate of analysis provided by the vendor , per an approved in-house specification sheet . Materials were tracked using a receiving code and part number system . Tissue fractionation under the respective batch record was performed to release and purify M . leprae from armadillo tissue . A total of seven tissue runs were performed to accumulate 100–150 mg bacteria . Tissue weights ranged from 19–36 . 5 g for manageability and to maximize yields . A total of 128 . 4 mg of M . leprae was purified from 242 g tissue , resulting in a yield of 0 . 05% ( Table 1 ) . Sterility testing was performed on each bacterial lot , and material was stored at −70°C until use . Bacterial fractionation under the respective batch record was performed using the pooled intermediate product . Totals of 4 . 6 mg of MLSA-LAM and 5 . 0 mg of MLCwA were obtained , representing a yield of 3 . 57% and 3 . 88% from intact bacteria , respectively . Assays to assess MLSA-LAM and MLCwA critical quality attributes of identity , purity , sterility , potency , and safety were performed [92] . Ten vials of each antigen dose ( 2 . 5 , 1 . 0 , and 0 . 1 µg/0 . 1 ml ) planned for clinical studies were tested on all assays with two exceptions . Identity testing by gel electrophoresis and immunoblotting was performed on samples taken prior to autoclaving , which degrades proteins resulting in smearing of bands on gels and immunoblots . A representative silver stained gel of both antigen preparations is shown in Figure 5 . Immunoblotting results showed that neither antigen preparation had detectable armadillo tissue or LAM present , both contained MMP-I , and only MLSA-LAM contained GroES and SOD , while only MLCwA contained GroEL proteins . Purity testing for adventitious agents was performed on tissue homogenates and concentrated final product ( 10 . 0 µg and 5 . 0 µg/0 . 1 ml ) ; both were free of detectable human viral pathogens . The presence of collagenase , Triton X-114 , and SDS were tested and found to be less than the lower limit of detection . Extracti gel D ligand was not tested , because if released , it would be removed by filtration prior to vialing . Calcium chloride , polyethylene glycol , and Dextran T-500 were not tested , because following multiple washes , the calculated residual concentration in the purified bacteria suspension had decreased by 46-fold and was found to be harmless as demonstrated in animal safety studies . Antigen preparations were found to be sterile under aerobic and anaerobic conditions and potent when assessed for a DTH response in guinea pigs sensitized with M . leprae or infected with M . tuberculosis . Stability , although not a critical quality attribute was assessed during product development using a research batch and prior to and during clinical testing , resulting in 4 years of satisfactory results . All test results were used to complete the regulatory package . The Lot Release Summary and stability results for both MLSA-LAM and MLCwA can be found in Table 2 . In 1994 , a draft Investigational New Drug ( IND ) [93] was formulated and specific questions related to IND enabling studies , manufacturing , and phase I clinical trial design was sent to our NIH , NIAID , DMID program officer at the time ( the late Dr . Darryl Gwinn ) and Regulatory Affairs Specialist ( Ms . Carol Manning ) for submission to the FDA Center for Biologics and Evaluation Research ( CBER ) for preliminary review and comment . A FDA Response Letter with a comprehensive list of queries was received . The first topic of focus was the armadillo infected tissue and included questions on the following subject matters: 1 ) the origin , isolation , and characterization of the M . leprae strain; 2 ) creation , storage , maintenance , and viability testing of the master seed stock; 3 ) armadillo quarantine , test for human pathogens , and general health status; 4 ) potential human infectivity of indigenous armadillo microorganisms; 5 ) armadillo inoculation procedures and biosafety procedures for staff; and 6 ) test for viral adventitious agents . The second topic of concern centered on the manufacturing and characterization process , including questions on: 1 ) procedural flow charts; 2 ) potential or known human toxicities and quantitative tests for reagents used in the manufacturing process; 3 ) qualitative compositional analyses for each skin test antigen; 4 ) presence of cross-reactive antigens; 5 ) level of host contamination , endotoxin , and sterility; 6 ) in-vitro and in-vivo potency assays conforming to intended clinical use in humans; 7 ) stability testing prior to clinical studies; and 8 ) preclinical testing of clinical lots for safety , activity , and skin test conversion in a dose ranging study . Further questions were raised regarding the clinical phase I study design: 1 ) clinical study details; 2 ) potential impact of anergy regarding leprosy and HIV patients; 3 ) consent form and Institutional Review Board for each study site; 4 ) Case Report Forms for data collection; 5 ) references supporting related antigens and clinical studies; and 6 ) distinguishing subjects that are infected or harboring live bacilli from those who are infected and cured . A reply to the FDA Response Letter was satisfactory and a Pre-IND Meeting followed to review details of the manufacturing and testing process . Skin test antigens were manufactured in May , 1997 . The IND chemistry , manufacturing , and control ( CMC ) section was then completed and our DMID Study Sponsor submitted the IND Application to CBER for review . In September , 1998 , FDA allowed the clinical investigation of two new drugs , MLSA-LAM and MLCwA , to proceed each at 3 doses ( 2 . 5 , 1 . 0 , and 0 . 1 µg ) initially in a phase I clinical trial with ten healthy subject residing in a non-endemic region for leprosy , and subsequently in a phase II clinical trial with healthy subjects , leprosy patients , leprosy patient contacts , and tuberculosis patients residing in an endemic region for leprosy .
A tool for the detection of pre-symptomatic leprosy is an urgent need [94] , [95] . How to address the treatment of individuals with evidence of specific leprosy exposure is a matter of debate [10]; chemoprophylaxis is proving highly efficacious in the short term , as applied to household contacts [96] . Individuals positively identified as pre-symptomatic could be a tool in the identification and further management of the disease , particularly reduction of incidence , i . e . NCD . Serological and gene approaches had not proven satisfactory for the purpose of diagnosing leprosy [56]; although these and other test methods are continually being refined and evaluated , in particular: details of new M . leprae antibodies [97] , [98] , new approach in the application of M . leprae specific DNA polymerase chain reaction [99]–[101] , and cell-mediated immune response assays primarily based on IFN-γ release [102] , [103] . While tests for PGL-I IgM antibodies have found favor for certain applications , most are not suitable for epidemiological application [104] . However , the two new leprosy antigens described here , MLSA-LAM and MLCwA , showed promise in guinea pig DTH studies and IFN-γ release assays [53] , [54] , [105] . Skin testing is the only means for mass epidemiological screening . Antigens for this purpose were targeted for product development as new leprosy skin test antigens . Notwithstanding significant challenges , the development and manufacturing of these two leprosy skin test antigens suitable for human application was successfully accomplished . Securing adequate funding , identifying a large team of experts , and establishing a product development plan were key achievements that benefited the entire development phase . Changing the focus and practices of the research staff from basic to applied research enabled production of the skin test antigens . The magnitude of effort necessary in meeting regulatory requirements , in particular , substantial documentation , compounded by limited staff , funding , and experience was demanding A special attribute of this undertaking was the oversight of NIH , NIAID , DMID sponsor who provided financial , technical , and regulatory assistance , and served as a conduit to the FDA for cGMP and IND related questions . The positive impact of developing and manufacturing these two new leprosy skin test antigens in an academic setting was realized only after successful implementation . The effort produced knowledge , skill , and understanding of the product translational process at the academic institutional level . Students were a valuable asset and in return , gained a unique learning opportunity . Looking forward , this work provides a product development template for products of neglected tropical diseases . Academic institutions cannot carry the heavy load of full product development alone , but this prototype presents alternative opportunities to move viable product ideas from the bench to the clinic . The outcome was two new leprosy skin test antigens , suitable for human application , produced in a setting inexperienced in the manufacture of products for human use . This was necessitated by our focus on one of the major neglected tropical diseases of our time , and one of little commercial value . A consequence of this effort was the establishment of a contract manufacturing organization , Biopharmaceutical Manufacturing in an Academic Research Center ( BioMARC ) , at Colorado State University for developing and manufacturing biological products to test in early clinical studies . | Despite reaching the global elimination target for leprosy , the need for a diagnostic tool to detect pre-symptomatic disease remains . Transmission has not been completely intercepted despite over 30 years of extensive curative treatment . With limited resources , two new leprosy skin test antigens , MLSA-LAM and MLCwA , suitable for human application were developed and manufactured in a local pilot plant . Requirements for manufacturing and clinical testing were met and an Investigational New Drug was established with the Food and Drug Administration to test both antigens in a phase I clinical trial for safety in a non-endemic region for leprosy and a phase II clinical trial for safety and efficacy in an endemic region for leprosy . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"biotechnology",
"research",
"design",
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"clinical",
"medicine",
"clinical",
"research",
"design",
"clinical",
"trials",
"biology",
"and",
"life",
"sciences",
"microbiology",
"research",
"and",
"analysis",
"methods"
] | 2014 | The Challenge of Producing Skin Test Antigens with Minimal Resources Suitable for Human Application against a Neglected Tropical Disease; Leprosy |
A single co-administered dose of ivermectin ( IVM ) plus diethylcarbamazine ( DEC ) plus albendazole ( ALB ) , or triple-drug therapy , was recently found to be more effective for clearing microfilariae ( Mf ) than standard DEC plus ALB currently used for mass drug administration programs for lymphatic filariasis ( LF ) outside of sub-Saharan Africa . Triple-drug therapy has not been previously tested in LF-uninfected individuals from Africa . This study evaluated the pharmacokinetics ( PK ) , safety , and efficacy of triple-drug therapy in people with and without Wuchereria bancrofti infection in West Africa . In this open-label cohort study , treatment-naïve microfilaremic ( >50 mf/mL , n = 32 ) and uninfected ( circulating filarial antigen negative , n = 24 ) adults residing in Agboville district , Côte d’Ivoire , were treated with a single dose of IVM plus DEC plus ALB , and evaluated for adverse events ( AEs ) until 7 days post treatment . Drug levels were assessed by liquid chromatography and mass spectrometry . Persons responsible for assessing AEs were blinded to participants’ infection status . There was no difference in AUC0-inf or Cmax between LF-infected and uninfected participants ( P>0 . 05 for all comparisons ) . All subjects experienced mild AEs; 28% and 25% of infected and uninfected participants experienced grade 2 AEs , respectively . There were no severe or serious adverse events . Only fever ( 16 of 32 versus 4 of 24 , P<0 . 001 ) and scrotal pain/swelling in males ( 6 of 20 versus 0 of 12 , P = 0 . 025 ) were more frequent in infected than uninfected participants . All LF positive participants were amicrofilaremic at 7 days post-treatment and 27 of 31 ( 87% ) remained amicrofilaremic 12 months after treatment . Moderate to heavy W . bancrofti infection did not affect PK parameters for IVM , DEC or ALB following a single co-administered dose of these drugs compared to uninfected individuals . The drugs were well tolerated . This study confirmed the efficacy of the triple-drug therapy for clearing W . bancrofti Mf and has added important information to support the use of this regimen in LF elimination programs in areas of Africa without co-endemic onchocerciasis or loiasis . ClinicalTrials . gov NCT02845713 .
Lymphatic filariasis ( LF ) is a mosquito-borne parasitic disease caused by nematode parasites . Host responses to the adult worms in lymphatic vessels cause stigmatizing morbidity ( lymphedema , hydrocele , and elephantiasis ) that can lead to chronic disability . Parasites that cause LF ( Wuchereria bancrofti , and less commonly Brugia malayi and Brugia timori ) are currently estimated to infect 80 million people in 52 tropical countries , with about 850 million at risk [1] . The World Health Organization ( WHO ) has targeted LF for global elimination by 2020 [2 , 3] . The elimination effort is based on a mass drug administration ( MDA ) approach that uses one of three anti-filarial drug regimens; i ) ivermectin ( IVM ) plus albendazole ( ALB ) in regions of Africa where Onchocerca volvulus is co-endemic , ii ) ALB alone in areas where LF is co-endemic with Loa loa , and iii ) diethylcarbamazine ( DEC ) plus ALB in LF endemic areas outside of Africa and in regions of Africa where L . loa and O . volvulus are not present [4] . Often , five or more annual rounds of MDA are required to reduce the community microfilarial reservoir to a level that cannot support sustained transmission of new infections by mosquitoes . Recently , a one time co-administered dose of IVM plus DEC plus ALB , or triple-drug therapy , was shown to achieve sustained microfilariae ( Mf ) clearance for three years in 96% of individuals with moderate to heavy LF infections in Papua New Guinea ( PNG ) , compared to a lower cumulative clearance of Mf with standard therapy of DEC plus ALB administered once a year over the same period of time [5 , 6] . Although the frequency of mild adverse events ( AEs ) was higher in the triple-drug regimen compared to the standard treatment of DEC plus ALB ( 27% versus 5% ) [5] , there were no serious AEs . Finally , it is unknown how LF infection itself might affect the pharmacokinetics ( PK ) and pharmacodynamics of these drugs . For example , inflammatory responses to chronic infections and the killing of Mf could affect the P450-mediated metabolism and PK of ALB in its first-pass conversion to ALB sulfoxide , the active metabolite [7] . This study evaluated the PK , safety , and efficacy of triple-drug therapy in men and women with and without Wuchereria bancrofti infection .
This was an open-label cohort study of treatment naïve Wuchereria bancrofti-infected ( n = 32 ) and uninfected ( n = 24 ) adults residing in the Agboville district of Côte d’Ivoire , which is endemic for LF and non-endemic for onchocerciasis . The primary outcomes were drug levels and safety . The secondary outcomes were reduction in circulating Mf and parasite antigen levels , and inactivation of adult worm nests . Individuals who assessed adverse events and measured drug levels were blinded to LF infection status . The study protocol and related documents were approved by institutional review boards in Cleveland , USA ( University Hospitals Cleveland Medical Center IRB #03-16-09 ) and in Côte d’Ivoire ( Comité National d’Ethique et de la Recherche , CNER , N/Ref:022/MSLS/CNER-kp ) . This trial is registered at Clinicaltrials . gov ( NCT02845713 ) . DEC ( Banocide GlaxoSmithKline ) was purchased for the study . ALB ( GlaxoSmithKline ) and IVM ( Merck & Co . Inc . ) were obtained from Ministry of Health stocks in Côte d’Ivoire used for the LF MDA program . A fixed dose of 400 mg ALB was used for all participants . IVM and DEC doses were 200 μg/kg and 6 mg/kg , respectively . Individuals were prescreened in their home villages for W . bancrofti infection with a rapid diagnostic test that detects circulating filarial antigenemia ( the Alere Filariasis Test Strip , FTS , Alere , Inc , Waltham , MA , USA ) [8] . Persons with positive FTS results had blood collected between 21:30 and 23:00 for Mf testing by membrane filtration with 1 mL of anticoagulated venous blood ( 5μM , Nuclepore Corp . , Pleasanton , CA , USA ) . Two microscopists independently read Giemsa-stained filters to assess Mf load . The study took place at the Centre de Recherche de Filariose Lymphatique d’Agboville , located at General hospital of Agboville , Côte d’Ivoire . Eligible participants included adults 18–70 years , with no acute illness , and no treatment with ALB or IVM within the past two years . Infected participants required >50 Mf/mL . Participants were considered uninfected if FTS strip was negative in whole blood and confirmed with plasma . Exclusion criteria included a positive pregnancy test; a history of chronic kidney or liver disease; a serum alanine transaminase , aspartate transaminase , or creatinine level >1 . 5 times the upper limits of normal; or blood hemoglobin <7 gm/dL . Biochemical tests were performed with a Piccolo biochemistry machine ( Abbott Labs , Lake Bluff , IL , USA ) and hemoglobin with a Hemocue Hb 201+ machine ( HemoCue America , Brea , CA , USA ) . Individuals were also excluded if they had taken medications that could interfere with test drug metabolism within one week of study onset or if they had evidence of urinary tract infection on a spun urine sample ( >10 neutrophils per high powered field by microscopy , 400x ) or 3+ nitrate on dipstick ( Diastix , Bayer Inc . ) . Because DEC can cause serious AEs in people infected with onchocerciasis [9] , all individuals were tested by microscopic examination of skin snips taken from both iliac crests and by the presence of antibodies to a recombinant O . volvulus antigen ( Ov16 Rapid Test , Standard Diagnostics Inc . Youngin , South Korea ) [10] . Persons with microfiladermia or a positive Ov16 antibody test ( n = 3 ) were excluded . All participants signed a written consent prior to screening and enrollment into the study . Enrollment began on April 17 , 2015 and continued through June 4 , 2015 . Groups of five participants of the same sex were brought to the research center the night prior to treatment for screening with baseline laboratory tests . For men , ultrasound examinations were performed . Investigators evaluating AEs and performing ultrasound examinations and laboratory tests were blinded to participants’ infection status . Participants remained at the research center until 72 hours post-treatment , then returned to their village for passive follow up until repeat examinations on day 7 . The first treatment dose of triple-drug therapy was given on April 18 , 2015 . Starting at 7 a . m . , and within about 30 minutes after eating a typical Ivorian breakfast of wheat bread and eggs , all participants were treated with a single co-administered dose of triple-drug therapy . Venous blood samples were collected at 1 , 2 , 3 , 4 , 6 , 8 , 12 , 24 , 36 , 48 , 72 hours and 7 days after treatment , with aliquots of plasma stored at -20°C for later testing of drug levels . A peripheral intravenous catheter was placed for the first 12 hours due to frequent blood draws . Additional venous blood was collected between 21:30 and 23:00 at 39 hours , 7 days and 1 year for Mf testing with two 1 mL samples by membrane filtration . Biochemistry and urine tests were repeated at 24 and 48 hours and 7 days post-treatment . After treatment , participants were monitored for AEs every 6 hours for the first 48 hours , then every 12 hours until 72 hours , and again at day 7 post-treatment . Passive surveillance for potential AEs was conducted by trained community health workers located in the participants’ home villages on days 4–6 . New or worsening symptoms , changes in vital signs , or new abnormal findings on physical examination were considered to be treatment emergent adverse events ( TEAEs ) and were scored using a modified version of the National Cancer Institute Common Terminology Criteria for Adverse Events , v4 . 0 . Blood pressure and heart rate were taken with an Omron-7 automatic blood pressure cuff with the participant in a seated position . Auricular temperatures were obtained using a digital thermometer . Scrotal ultrasound examinations were performed on men in the supine position using a SonoScape S8 portable ultrasound system equipped with a 5–7 . 5Hz liner array transducer ( International Diagnostic Devices , Inc , Las Vegas , NE , USA ) . Color and pulsed wave Doppler modes were used to differentiate lymphatic vessels from blood vessels . Adult worm nests were identified based on the characteristic bidirectional movement of the worms ( the “filarial dance sign” ) [11] . Abnormal ultrasound findings were digitally recorded . The presence or absence of worm nests was recorded , along with the number and size of worm nests , lymphatic dilatation , and hydroceles . Ultrasounds were repeated at 24 , 48 , 72 hours and 7 days and 1 year after treatment . Microfilarial counts were expressed as Mf/mL and log transformed after adding 1 , and geometric mean values ( GM ) were used as measures of central tendency to normalize the results . Baseline sample characteristics between individuals infected or uninfected with LF and the impact of treatment of Mf and worm nests were compared using the chi-squared test or the Mann-Whitney test using GraphPad Prism , version 6 . For the analysis of the impact of treatment on the inactivation of worm nests , only men who had detectable worm nests at baseline were included . For the sample size calculation we used methods as previously described [14] . We wanted to observe less than 50% difference in clearance of drugs between LF-infected and uninfected individuals . Considering a variance of ALB-OX ( the most variable of the drugs [6] ) of approximately 50% from the mean , with an alpha = 0 . 05 and power = 0 . 8 , the number of participants required would be 20–24 per study population . The PK parameters were determined by noncompartmental analysis using Phoenix WinNonlin version 6 . 3 ( Pharsight Corporation , CA , USA ) . For analytes that did not have at least eight calculated values , the mean and standard deviation were not calculated . Where R2adj was <0 . 85 , this parameter was reported as NE ( not estimated ) . Total drug exposure up to the last measured concentration ( AUC0-last ) was calculated using the linear trapezoidal method for ascending concentrations and the logarithmic trapezoidal method for descending concentrations . The AUC0-last was defined as the area under the concentration-time curve from the time of dose until the last concentration above lower limit of quantitation . Area under the concentration-time curve from 0 to infinity ( AUC0-inf ) was calculated using the formula AUC0-t + C ( last ) / Kel , where C ( last ) is the last quantifiable concentration . Half-life ( t1/2 ) was calculated using the formula ln ( 2 ) / Kel . Maximum concentration ( Cmax ) and time to reach maximum concentration ( Tmax ) were taken directly from the observed data . Apparent volume of distribution ( Vz/F ) was calculated as dose/ ( Kel *AUC0-inf ) and apparent clearance ( CL/F ) calculated as dose/AUC0-inf , according to standard procedures . PK estimate comparisons between uninfected and infected participants were performed using the Kruskal-Wallis test using the JMP software version 13 ( Cary , NC , USA ) .
Screening of 1 , 534 adults yielded 70 eligible individuals , with 36 LF-infected ( FTS+ , Mf+ ) and 34 uninfected ( FTS- , Mf- ) . These individuals then underwent full screening ( Fig 1 ) . Three participants were excluded due to evidence of onchocerciasis infection and one individual was excluded because of elevated creatinine . Excluded participants were treated with IVM + ALB and returned to their village . Ten participants initially treated in the LF-uninfected ( FTS- ) group were subsequently found to be LF-infected based on repeated examination of plasma with FTS assay and excluded from subsequent analysis per protocol . Baseline demographics were similar in the two groups ( Table 1 ) . Thirty-two ( 57% ) of all participants were men . One year following treatment , 31 of 32 ( 97% ) infected and 22 of 24 ( 92% ) uninfected participants were re-examined for the presence of LF infection ( FTS , Mf , and ultrasound in males ) . The three individuals not examined had all moved from the area . Two uninfected men seen at follow-up did not have a repeat ultrasound . The mean plasma concentration-time profiles of ALB-OX ( the active metabolite of ALB ) , DEC , and IVM are shown in Fig 2 , and of ALB and ALB-ON are shown in S1 Fig . There was no difference in plasma concentration for all the drugs between infected and uninfected individuals for all time points examined . Maximum concentrations of ALB-OX were substantially higher than corresponding concentrations for ALB , but showed less variability compared to ALB and ALB-ON , a secondary metabolite . The main PK parameters ( median and range ) of ALB , ALB-OX , ALB-ON , DEC , and IVM for all 56 subjects are shown in S1 Table . The main PK parameters ( median and range ) stratified by infection status or by sex are shown in S2 and S3 Tables . Overall , the DEC Tmax occurred at a median time of 4 . 0 hours post-treatment , with a reported Cmax of 1 , 522 ng/mL . The median t1/2 for DEC was 9 . 5 hours . The CL/F and Vz/F of DEC were 8 . 1 L/hour and 111 L , respectively . The median Cmax for DEC was not different based on infection status ( Fig 3A ) ; however , Cmax was higher in female compared to male participants ( P< 0 . 05 , Fig 4A ) . The AUC0-t for DEC with LF and without LF ( Fig 3B ) , or based on sex ( Fig 4B ) was not different ( P>0 . 05 ) . The median Tmax for IVM was 6 . 0 hours , with a median t1/2 of 48 . 1 hours ( S1 Table ) . The CL/F and Vz/F of IVM were 6 . 8 L/hour and 466 . 7 L , respectively . IVM Cmax and AUC0-t was not significantly different ( P>0 . 05 ) when comparing participants with and without LF ( Figs 3C and 3D ) , as well as treatment group comparisons based on sex ( Fig 4C and 4D ) . The median Tmax for ALB-OX was 5 . 0 hours , with a median t1/2 of 8 . 9 hours ( S1 Table ) . The CL/F and Vz/F of IVM wer2 L/hour and 848 L , respectively . For ALB-OX , the Cmax and AUC0-t parameters were not significantly different ( P>0 . 05 ) in the presence or absence of LF ( Fig 3E and 3F ) and for sex basis ( Fig 4E and 4F ) . Since mild subjective complaints were common at baseline , new subjective findings ( e . g . symptoms ) and objective findings ( e . g . fever , presence of hematuria , hemodynamic changes ) , or worsening after treatment of objective and/or subjective observations compared to baseline , were considered to be study-related AEs . Every participant had at least one AE ( Table 2 ) . Headache , abdominal pain , and muscle/joint pain were the most common symptoms , followed by diarrhea and fatigue . There was no difference in the frequency of subjective AEs between LF-infected and uninfected individuals . Women were more likely to have multiple subjective AEs ( 21 ( 88% ) versus 22 ( 69% ) for men , P = 0 . 02 ) , but there was no difference in the severity of AEs between sexes . For all participants , AEs were most common between 18 and 48 hours post-treatment . With respect to objective AEs , 16 of 32 ( 50% ) LF-infected subjects had fevers versus 4 of 24 ( 17% ) uninfected participants post-treatment ( P = 0 . 01 ) . Individuals with fevers were not treated with antipyretics to allow for evaluation of the kinetics of post-treatment fever . Fevers occurred most commonly between 18 and 42 hours . Frequency and severity peaked at 36 hours in LF-infected subjects . All fevers resolved by 72 hours after treatment . Scrotal swelling and pain occurred in 6 of the 20 ( 30% ) LF- infected men , but this was not observed in uninfected men ( P = 0 . 04 ) . The onset of scrotal swelling and pain occurred from 48 to 96 hours post-treatment . Hematuria ( based on urine dipstick , but not by microscopy ) was detected in 10 of 32 ( 31% ) LF- infected individuals , compared to 3 of 24 ( 13% ) uninfected participants ( P = 0 . 1 , Table 2 ) . Hematuria was predominantly seen at 24 and 48 hours and was resolved by day 7 . Pre-treatment infection parameters are shown in Table 1 . Infected men and women had very similar Mf counts and circulating filarial antigen test scores ( geomean = 141 Mf/mL and average FTS of 2 . 3 , and geomean = 141 . 82 Mf/mL and mean FTS of 2 . 4 , respectively ) . At 39 hours and 7 days post-treatment , all LF-infected participants were Mf negative , and 27 of 31 ( 87% ) were Mf negative 1 year after treatment ( Fig 5A ) . FTS scores decreased significantly after treatment ( Fig 5B , P<0 . 001 ) , and 6 of 31 ( 19% ) participants were FTS negative . All 32 men enrolled in the study had scrotal ultrasound at baseline . Adult worm nests were detected in 14 of 20 ( 70% ) infected men and in 0 of 12 uninfected men . The mean number of worm nests at baseline in positive men was 2 . 6 ( range 1–6 ) . The mean maximum diameter of worm nests was 3 . 8 mm ( range 1 . 4–7 . 6 mm ) . Ultrasounds were repeated on all men at 24 , 48 , 72 hours and 7 days following treatment . No significant changes were seen in the number or size of worm nests , the degree of lymphatic dilatation or the development of hydroceles at those time points . Ultrasound examinations were performed for 30 men at 1 year after treatment ( 20 of 20 infected men and 10 of 12 uninfected men ) . No new worm nests were seen in men of either group . Of the 14 men who had worm nests at baseline , 11 had no detectable worm nests at one year ( 79% reduction , p<0 . 001 , Fig 5C ) . Of the three men who had worm nests visible at 1 year , two had a reduced number relative to baseline , and one had no change . One of the men who still had detectable worm nests , though decreased , also had detectable Mf . The other two men were amicrofilaremic at 1 year . Four individuals failed to have sustained clearance of Mf at 1 year following triple-drug treatment ( Fig 5 ) . To determine whether incomplete Mf clearance might result from reduced drug levels compared to levels in individuals with sustained clearance , we calculated the variance of all three drugs from the mean AUC0-inf for each participant ( Fig 6 ) . Although there was considerable variability in drug levels among individuals , persons who were microfilaremic at 1 year ( circled ) had similar overall drug levels to those observed in participants with sustained Mf clearance .
Results from this study show that the presence of moderate to heavy LF infection does not affect IVM , DEC or ALB drug levels or their PK parameters following a single co-administered dose of the triple-drug regimen . Triple-drug therapy was well tolerated in both LF-infected and uninfected individuals and was effective for clearing Mf of W . bancrofti in Ivorian participants for up to 1 year after treatment . These studies provide important additional information in support of the use of triple-drug therapy for MDA in LF endemic areas where onchocerciasis and loaisis are not present . LF infection did not affect the PK and pharmacodynamics ( PD ) of IVM , DEC or ALB , based on the observation that the kinetics of drug levels and derived PK parameters for all three drugs were the same irrespective of whether an individual was LF-infected or not . There was considerable variability in plasma ALB and IVM drug levels among individuals . Both drugs , and especially ALB , have been shown to have highly variable gastrointestinal absorption [15] , although this has not been shown to affect drug efficacy . By contrast , plasma levels of DEC showed relatively little variation among individuals . This is likely a consequence of good drug bioavailability of DEC . Drug levels did not differ between sexes with the exception of Cmax for DEC , which was higher in women . Compared to ALB and IVM , which are lipophilic and thus have a large Vz/F [16] , DEC has a low Vz/F that is more closely associated with the ideal body weight of an individual [17] . This suggests that high DEC concentrations might occur in persons who are overweight if they are dosed based on actual body weight with no maximum dose . However , future studies are needed to evaluate the relation with weight , DEC dosing , and systemic concentrations . Of note , previous studies have shown that the addition of IVM to DEC plus ALB or IVM to ALB does not significantly alter the PK parameters of individual drugs compared to those observed in various combinations , including the triple-drug combination [6 , 18] . All participants complained of one or more AEs , which is comparable to that observed in PNG study participants who were treated with the triple-drug regimen ( 83% ) in a similarly designed PK study [6] . The high rate of reported AEs in infected and uninfected participants is probably related to the high frequency of symptom assessment and to effects of confining normally active adults to a hospital ward for three days . One of the most common AEs was back pain , which was relieved by getting up and walking around . Another common AE was dyspepsia , which could possibly be attributed to changes from the normal village diet to that provided while in the study clinic . The two AEs that differed between infected and uninfected adults were fever and scrotal pain and swelling . Fever is a well-known side effect of LF treatment that is associated with the host inflammatory reaction to dying Mf [19 , 20] . The inflammatory response is exacerbated in both prevalence and severity with higher blood Mf counts [6 , 21 , 22] . Scrotal pain is likely a reaction to dying adult worms . The ingestion of the drugs themselves have well-known side effects independent of their impact on helminth infections; for IVM this can include dizziness , joint pain , and skin irritation , and for DEC and ALB , common symptoms are nausea , abdominal discomfort , and headache . Treatment with triple-drug therapy rapidly cleared Mf in all participants by 39 hours , and all but four individuals remained amicrofilaremic ( 87% clearance ) at 1 year following treatment . This is comparable to , although somewhat less than , the sustained blood Mf clearance levels ( 96% ) of LF-infected subjects in PNG 1 year post-treatment with triple-drug therapy , even though participants from PNG had 4 . 5 to 10 times higher Mf levels [6 , 22] . Failure to sustain Mf clearance was not attributable to reduced drug levels ( Fig 6 ) . It is possible that some participants were re-infected during the follow-up period , although this is less likely in one year because the prepatent period for W . bancrofti is about 4 months [23] and we failed to observe any new worm nests in participants . Results from a larger clinical trial of triple-drug therapy in Côte d’Ivoire may shed further light on variability in responses to this regimen . Our results suggest that triple-drug therapy killed many adult worms , as evidenced by reduced circulating filarial antigen levels as assessed by lower FTS scores and the inactivation of worm nests observed by ultrasound after treatment . Inactivation of worm nests following treatment has been interpreted as evidence of a macrofilaricidal effect [24] , and one study confirmed this by histological examination of surgically removed worm nests after DEC treatment [25] . Measurement of circulating filarial antigen levels provides a second means of assessing macrofilaricidal activity of antifilarial medications , and antigen levels are believed to correlate with adult filarial burdens [26] . Limitations of this study include the exclusion of children and people with chronic disease ( who would normally be included in MDA programs ) and the relatively small sample size . Community-wide safety trials including almost 26 , 000 people in five countries have now been completed . These showed that triple-drug therapy was well tolerated and suggested that it should be as safe as the two-drug MDA regimen DEC + ALB that has been widely used outside of sub-Saharan Africa . Based on these and other studies , WHO changed its policy to recommend triple-drug therapy for MDA in areas that are non-endemic for onchocerciasis or loiasis and unlikely to eliminate LF by the year 2020 [27] . | Lymphatic filariasis is a mosquito-borne infection that causes disability in the form of lymphedema , hydroceles , and elephantiasis . It has been targeted for global elimination based on mass drug administration in the total population at risk including many people uninfected with LF . Recently , a single co-administered dose of IVM + DEC + ALB has been shown to be much more effective than the standard treatment with DEC + ALB for sustained clearance of Mf for 3 years based on studies in Papua New Guinea . This study confirms the efficacy and safety of triple-drug therapy for clearing of Wuchereria bancrofti Mf in an African population . The presence of LF did not affect drug levels and the medicines were well tolerated , with 28% and 25% rate of moderate AEs in infected and uninfected individuals respectively , and no severe or serious AEs , supporting the use of triple-drug therapy for mass drug administration . This study shows for the first time that triple-drug therapy also has a potent macrofilaricidal effect , as determined by the reduction in circulating filarial antigen and inactivation of worm nests one year following treatment . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"pathology",
"and",
"laboratory",
"medicine",
"clinical",
"research",
"design",
"tropical",
"diseases",
"parasitic",
"diseases",
"animals",
"research",
"design",
"signs",
"and",
"symptoms",
"pharmaceutics",
"drug",
"administration",
"neglected",
"tropical",
"diseases",
"onchocerciasis",
"research",
"and",
"analysis",
"methods",
"wuchereria",
"bancrofti",
"wuchereria",
"adverse",
"events",
"blood",
"plasma",
"helminth",
"infections",
"eukaryota",
"diagnostic",
"medicine",
"blood",
"anatomy",
"fevers",
"physiology",
"nematoda",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy",
"organisms"
] | 2019 | Pharmacokinetics, safety, and efficacy of a single co-administered dose of diethylcarbamazine, albendazole and ivermectin in adults with and without Wuchereria bancrofti infection in Côte d’Ivoire |
By necessity , the ancient activity of type II topoisomerases co-evolved with the double-helical structure of DNA , at least in organisms with circular genomes . In humans , the strand passage reaction of DNA topoisomerase II ( Topo II ) is the target of several major classes of cancer drugs which both poison Topo II and activate cell cycle checkpoint controls . It is important to know the cellular effects of molecules that target Topo II , but the mechanisms of checkpoint activation that respond to Topo II dysfunction are not well understood . Here , we provide evidence that a checkpoint mechanism monitors the strand passage reaction of Topo II . In contrast , cells do not become checkpoint arrested in the presence of the aberrant DNA topologies , such as hyper-catenation , that arise in the absence of Topo II activity . An overall reduction in Topo II activity ( i . e . slow strand passage cycles ) does not activate the checkpoint , but specific defects in the T-segment transit step of the strand passage reaction do induce a cell cycle delay . Furthermore , the cell cycle delay depends on the divergent and catalytically inert C-terminal region of Topo II , indicating that transmission of a checkpoint signal may occur via the C-terminus . Other , well characterized , mitotic checkpoints detect DNA lesions or monitor unattached kinetochores; these defects arise via failures in a variety of cell processes . In contrast , we have described the first example of a distinct category of checkpoint mechanism that monitors the catalytic cycle of a single specific enzyme in order to determine when chromosome segregation can proceed faithfully .
Type II topoisomerases make a transient double-strand break in one DNA helix ( the Gate-segment ) , pass a second helix through the break ( the Transported-segment ) , then re-ligate the G-segment ( Figure 1 ) [1]–[3] . This Strand Passage Reaction ( SPR ) has been widely studied because it is the target of important classes of anti-microbial and anti-cancer drugs , as well as a large array of natural products [4] , [5] . Upon chemical inhibition of Topo II , cellular checkpoint response pathways are activated that attempt to delay the cell cycle and thus prevent chromosome mis-segregation and/or cell death . Firstly , the DNA damage checkpoint response [4] is activated by a class of Topo II inhibitor , so-called Topo II “poisons” , that trap Topo II-DNA cleavage complexes . When locked in this conformation , the ternary DNA-protein-drug complex can deteriorate to produce DNA breaks that are recognized by the DNA damage checkpoint machinery . This cellular response has been extensively studied and its induction is not specific to DNA damage that results from Topo II poisons . A second class , referred to as Topo II catalytic inhibitors , including the bisdioxopiperazines , do not induce enzyme-mediated DNA cleavage , but block the overall catalytic activity of Topo II by trapping the enzyme in a state in which the N-terminal gate is closed ( see Figure 1 ) . These inhibitors activate alternative checkpoint controls that arrest the cell cycle [6]–[10] , but the cell cycle control mechanisms that are employed are not well understood [6] and it is of particular interest to determine how checkpoint signaling occurs in the absence of DNA cleavage . A current subject of much controversy is whether the checkpoint detects dysfunctional Topo II directly or if cells utilize other well-characterized mechanisms , for example the spindle assembly checkpoint , to indirectly monitor Topo II activity via topological changes in chromosomal DNA . Using the bisdioxopiperazine ICRF-193 to activate checkpoint signaling in human cells , one study has demonstrated binding of the DNA damage checkpoint signaling protein MDC1 to the C-terminus of Topo II , which thus induces cell cycle arrest in G2-phase [11] . Because the divergent C-terminal region is dispensable for the strand passage reaction of the enzyme , one explanation of these data is that inefficient or arrested catalysis is structurally transferred to the C-terminus where signaling complexes then assemble . However , the bisdioxopiperazines can bind to Topo II enzymes that are not associated with DNA [12] and are therefore not engaged in the strand passage reaction . Thus , it has not been determined whether checkpoint controls have the ability to directly assess the strand passage reaction . A further complication is that a mitotic ( as opposed to a G2-phase ) checkpoint response is also activated by ICRF-193 in human cells and in yeast cells , Topo II ( top2 ) mutants arrest mitotic progression via Mad2-dependent inhibition of anaphase onset [9] , [13] . Mad2 is a spindle checkpoint protein that prevents chromatid disjunction when chromosomes are not properly bioriented on the mitotic spindle . The well-understood mechanisms that activate Mad2 can be initiated by aberrant tension at kinetochores [14] , [15] . This indicates that Topo II ( named Top2 , in yeast ) dysfunction in yeast mutants , or in mitotic human cells treated with ICRF-193 , may be sensed indirectly , via structural changes at the centromere regions of chromosomes . This is an attractive hypothesis given that Topo II is concentrated at the centromere regions [16]–[18] , that the strand passage reaction alters DNA topology [19] , [20] , and that some yeast top2 mutant strains have altered centric chromatin structure [21] . It therefore remains unknown whether checkpoints that respond to Topo II inhibition involve novel signaling mechanisms or simply activate the already well-understood mitotic checkpoint pathways . Here we demonstrate that , in yeast , the strand passage reaction of Topo II is directly monitored and that checkpoint signaling originates from the defective homodimeric enzyme at specific conformational states within the strand passage reaction . Checkpoint activation under these defined conditions requires the C-terminal region of the enzyme , supporting the hypothesis that a structural transference of defective catalysis to the enzymatically inert C-terminus allows checkpoint signaling . This is the first example of a checkpoint mechanism that directly monitors the catalytic cycle of a single enzyme .
We previously studied yeast strains harboring the top2-B44 hypomorphic allele of Topo II which induces a Mad2-dependent cell cycle delay in G2 ( equivalent to the metaphase stage of mitosis in mammalian cells ) . This checkpoint was not activated due to the accumulation of DNA stand breaks [13] . Correspondingly , the DNA damage checkpoint was not activated and the major kinases involved in the DNA damage response ( Rad53 and Mec1 ) were dispensable for G2 checkpoint activation in top2-B44 cells [13] . In contrast , the spindle checkpoint protein Mad2 was essential for G2 checkpoint activation , suggesting that aberrant chromosome structure , especially kinetochore structure , may be defective under conditions of limited Topo II activity . To test this hypothesis we examined chromosome attachment to the mitotic spindle apparatus in top2-B44 cells and surprisingly found no evidence in favor of aberrant attachment of kinetochores to the mitotic spindle [13] . Intriguingly , the checkpoint delay was at least partially independent of other effectors of the spindle checkpoint response; Pds1 and Bub3 [13] . To understand the mechanism of checkpoint activation in top2-B44 cells , we first examined if reduced Topo II activity affected chromosome condensation . We employed an assay characterized previously in which chromosome compaction is monitored using marked chromosomal loci on the long arm of chromosome IV in yeast [22] . This analysis revealed that temporally concomitant with checkpoint activation in top2-B44 cells , there was no defect in chromosome linear compaction ( Figure S1 ) . In fact , chromosome condensation occurred just as efficiently as in wild type cells . Because a wide variety of assays failed to provide any evidence that aberrant DNA topology or DNA breaks in top2-B44 cells were the cause of G2 checkpoint activation ( i . e . indirect consequences of perturbed Topo II activity ) , we next considered the possibility that cells may directly monitor the enzyme activity of Topo II . A system was established to control expression and degradation of Top2 in yeast using a degron allele ( top2deg ) expressed from the MET3 promoter ( Figure 2a ) . Top2deg contains a Ubiquitin-Arg-DHFR fusion which destabilizes folding at high temperature and becomes poly-ubiquitylated by the E3 ligase , Ubr1 [23] . Tight control of top2deg expression and Top2deg degradation was achieved ( Figure 2b and Figure S2 ) . After G1 synchronization , Top2deg was abolished to undetectable levels , allowing effects on progression through a subsequent cell cycle in the absence of Top2 to be observed , as previously described [24] . Under such conditions , if cells reached G2 unable to resolve DNA catenations arising during DNA replication , then upon anaphase entry these cells ought to fail to segregate their chromosomes . This was indeed the outcome in ∼90% of cells examined ( Figure 2c ) , indicating that they reached G2 with extensive DNA topological defects arising under conditions of Top2-deficiency . To confirm this conclusion via biochemical means , we asked if top2deg cells grown under the above conditions ( i . e . released from G1 after the depletion of Top2 ) reached G2 with catenated DNA . We isolated genomic DNA from such cells and following separation on CHEF gels , probed resulting Southern blots to detect the endogenous yeast circular 2-micron plasmid ( as previously used to assess the catenation state of yeast genomic DNA; see Material and Methods ) . As a positive control we examined genomic DNA from top2-4 cells grown in parallel under restrictive conditions . In both cases , extensively catenated 2-micron circular DNA was detected , whereas none was observed in samples from either mutant strain grown under permissive conditions or from wild type cells grown at high temperature ( Figure 2d ) . Based on this molecular analysis of DNA catenation and the analysis of chromosome segregation in vivo , we conclude that following depletion of Top2deg in G1 , cells progress through the cell cycle with severely limited , if not absent , Top2 activity , resulting in persistent DNA hyper-catenation and failed chromosome segregation . The presence of DNA hyper-catenation must result in extensive chromosome topological defects , particularly at the centromere regions of chromosomes where Topo II is concentrated in mitosis . It can therefore be predicted that DNA hyper-catenation activates the spindle assembly checkpoint via aberrant centric ( centromeric ) chromatin . To test this hypothesis , we used two different assays to measure the approximate duration of G2 following depletion of Top2deg in G1 and release of the cells from G1 synchrony . First we used a commonly used “population assay” where samples were taken from the population every 10–20 min and cells categorized into G1 , G2 and anaphase morphologies based on mitotic spindle characteristics [24] . This yielded plots that revealed the approximate timing of anaphase spindle elongation relative to spindle assembly , which defines the duration of G2 ( Figure 3a–c , Figure S3 ) . Surprisingly , the length of G2 after Top2 depletion was very similar to that in the same strain but carrying an additional copy of wild type TOP2 expressed from its endogenous promoter . Thus , the absence of Top2 did not induce a G2 cell cycle delay . In a second assay , we studied cell cycle progression in single cells by digital time-lapse imaging ( Figure 3d–f ) . Here , we recorded cells expressing GFP-tagged tubulin , acquiring z-stacks of images at a temporal resolution of 2 min . Except for growth in a micro-fluidic chamber , the experimental conditions of synchrony and Top2 depletion were identical to those used in the population assay . The resulting time-lapse movies were analyzed as follows to determine the average length of G2 . First , we identified cells that completed the cell cycle within the duration of each movie . Second , we located the first time point ( movie frame ) in which two Spindle Pole Bodies ( SPBs ) could be observed ( Figure 3d , second frame ) and we then measured SPB diameter in the previous five movie frames to obtain a baseline SPB size . Next , we measured spindle length in all subsequent movie frames until full spindle elongation in anaphase had occurred . Approximate G2 length could then be determined by averaging the time interval from SPB separation to the onset of spindle elongation . We found that variation between cells was minimal as long as at least twenty cells were analyzed per strain . In addition to displaying simple histogram plots of G2 duration , we found that it was informative to plot average spindle length versus time , after the cells analyzed were aligned at the time of SPB separation ( Figure 3e ) . In this manner , spindle lengths were largely very similar at each time point until a fraction of the cells initiated anaphase . Because spindle length differences are large in anaphase and are much larger than the length of the spindle in cells that were still in G2 at such time-points , standard deviation of spindle length became large , indicative of the trend towards anaphase onset among the cells recorded ( Figure 3e , right side of the plots ) . In these single-cell assays , not unexpectedly , the kinetics of cell cycle progression was elongated due to growth within a static chamber , versus vigorous shaking used in the population assays . Nevertheless , these studies also revealed that the duration of G2 is very similar in cells depleted of Top2deg versus cells producing Top2 at endogenous levels ( Figure 3e , f ) . We conclude that anaphase onset was not delayed when cells were allowed to progress from G1 after depletion of Top2 ( Figure 3a–f , Figure S3 ) . Presumably any centromeric chromatin defects that are a consequence of hyper-DNA catenation are not sufficient to activate a pre-anaphase checkpoint , including the spindle checkpoint . This conclusion is consistent with a previous study indicating that lack of Top2 in yeast does not delay mitotic progression [25] and studies in human cells in which checkpoint activation was not observed upon depletion of Topo II [10] . Because Top2 depletion did not activate checkpoint signaling but certain top2 alleles are known to induce a Mad2-dependent cell cycle delay [13] , we asked if a checkpoint response can be initiated in the top2deg strain after introduction of the top2-B44 allele . When Top2deg was depleted in G1 and Top2-B44 was expressed at endogenous levels ( Figure 4a ) , population analysis revealed that anaphase initiation in the subsequent cell cycle was indeed delayed , indicating checkpoint activation ( Figure 4a , Figure S3 ) . Identical strains , but lacking the MAD2 gene , did not delay before anaphase , confirming that the transient cell cycle arrest was Mad2 checkpoint-dependent . Single-cell assays confirmed these results ( Figure 4b , c ) . The above experiments demonstrated that hyper-catenated DNA was not sufficient for the induction of cell cycle arrest , but the presence of Top2-B44 protein did trigger checkpoint activation . A possible explanation is that cells can directly detect the aberrant activity of Top2-B44 and respond by initiating checkpoint signaling via Mad2 . Similar to the effects of the drug ICRF-193 , which can bind to Topo II in the absence of DNA [12] , checkpoint activation in top2-B44 cells could be due to the detection of abnormal Top2-B44 protein structure , independent of Top2 activity , or could be a result of direct monitoring of the SPR enzyme cycle . To distinguish between these alternatives , we took advantage of SPR mutants that have been characterized previously in structural/biochemical studies [26]–[32] ( see Figure 1 ) and we performed both population and single-cell assays to confirm our findings . First , we asked whether Top2-B44 not only needs to be physically present in the cell , but also must be bound to DNA in order for checkpoint activation to occur . We introduced a K651A substitution into top2-B44 . The conserved residue , K651 , is present in a small , flexible linker region between the highly structured TOPRIM and WHD domains of Top2 that form the deep positively charged groove in which the G-segment DNA is housed [27] . Top2K651A has a vastly reduced affinity for DNA [30] and cannot support cell viability ( ref . 30 and data not shown ) . However , the K651A substitution is predicted not to destabilize the overall structure of Top2 [30] . Consistent with this prediction , Top2-B44K651A was stable in vivo suggesting that it is correctly folded ( Figure 4a ) . In the top2deg strain , Top2-B44K651A was present at similar levels to Top2-B44 , but did not induce a pre-anaphase delay ( Figure 4a–c , Figure S3 ) , indicating that the checkpoint is only activated when Top2-B44 is associated with DNA . A possible explanation is that the checkpoint monitors defective enzymatic cycles of Top2 . We next asked if initiation of the SPR is required for checkpoint signaling to occur . The catalytic tyrosine of Top2 [29] was substituted with phenylalanine ( Top2Y782F ) , resulting in an enzyme unable to cleave the G-segment DNA and capture a T-segment , though binding to the G-segment , binding of ATP and closure of the N-gate are not affected ( Figure 1 , Figure 5a ) . Top2Y782F arrests bound to the G-segment but before the SPR can be initiated [29] . Population and single-cell analysis of top2deg cells expressing Top2Y782F close to endogenous levels revealed a lack of checkpoint delay ( Figure 5b–d , Figure S3 ) . Because a previous study reported that over-expression of TOP2Y782F does activate a checkpoint response [25] , we asked if this is the case in our strain background using a GAL-TOP2Y782F construct . Indeed , we did observe a G2 delay similar to the previous study , in agreement with the result that over-produced Top2Y782F induces checkpoint activation ( Figure 5c , d ) . We conclude that at least when present at endogenous levels , a Top2 enzyme that binds the G-segment but is trapped within a futile cycle of N-Gate opening and closure does not activate checkpoint signaling . We next substituted Y782 with phenylalanine in the Top2-B44 enzyme ( Top2-B44Y782F ) . Similar to top2deg cells expressing Top2Y782F , this enzyme , expressed near endogenous levels , did not trigger checkpoint activation ( Figure 5b–d , Figure S3 ) . We infer that a defective step of the SPR of Top2-B44 , subsequent to G-segment cleavage , is required for checkpoint activation , presumably because a downstream step of the SPR enzyme cycle is directly monitored . Top2Y782F cannot cleave G-segment DNA and as a consequence it cannot adopt the closed-clamp form of the enzyme with a captured T-segment within the closed N-gate; there appears to be insufficient space within the N-terminal orifice of the dimeric enzyme when bounded by an intact G-segment [27] . To ask if T-segment capture and closure of the N-gate must occur for checkpoint activation , we analyzed top2G144I , a mutant that cannot bind ATP/ADP and thus cannot close the N-gate , but can , albeit inefficiently , cleave the G-segment ( Figure 6a ) [28] . Interestingly the checkpoint was activated by Top2G144I similar to Top2-B44 ( Figure 6b , c , i , j and Figure S4 ) . Thus , N-gate closure with a captured T-segment is not required for checkpoint activation . From biochemical and structural predictions , the Top2G144I mutant ought not to produce frank DNA breaks and activate the DNA damage checkpoint . Indeed , Top2G144I has reduced DNA cleavage activity because T-segment capture is inefficient [28] . Nevertheless , we asked if the activated checkpoint is Mad2 dependent , as is the case for Top2-B44 [13] , or dependent on the DNA damage checkpoint kinase Rad53 . Double mutants of top2G144I combined with a deletion of MAD2 did not activate the checkpoint ( Figure 6d , i , j and Figure S5 ) , whereas double mutants combined with the checkpoint defective rad53-1 allele or a rad53-null allele ( rad53Δ ) retained checkpoint signaling ( Figure 6g , Figure S5 and Figure S9 ) . Consistent with these findings , we failed to detect Rad53 phosphorylation in cells expressing Top2G144I ( Figure S9 ) . Therefore , like top2-B44 , the top2G144I cells arrest the cell cycle due to the activation of Mad2 , and likely do not activate the DNA damage checkpoint . Consistent with these findings for Top2G144I which cannot bind ATP , we observed that Top2E66Q , defective in ATP hydrolysis ( Figure 6a ) [26] , activates the checkpoint in a Mad2-dependent , but Rad53-independent manner ( Figure 6b , f , e , h–j , Figure S6 and Figure S9 ) . Top2E66Q and Top2G144I have in common the inability to utilize the energy of ATP hydrolysis and consequently both enzymes transit very slowly through the coupled mechanism that drives the T-segment transport step of the SPR ( Figure 1 and Figure 6a ) [26] , [28] . To ask if perturbed ATP hydrolysis per se , or conformational changes in the enzyme that promote T-segment transport , are monitored by the checkpoint machinery , we examined strains harboring top2L475A/L480P , which encodes an enzyme proficient in ATP hydrolysis , but defective in T-segment transport ( Figure 7a ) [32] . Strikingly this mutant activated the checkpoint to the same degree as the ATP binding and hydrolysis mutants , again in a Mad2-dependent , Rad53-independent manner , suggesting that the checkpoint is activated by inefficient T-segment transport ( Figure 7b–f , Figure S7 and Figure S9 ) . We then compared these data with other top2 mutants in which the overall rate of the SPR is slow , but that are predicted not to be defective in T-segment transport through the DNA-gate: Top2P824S and Top2G738D ( Figure 1 ) [31] . Neither mutant led to checkpoint activation ( Figure 8a–c , Figure S8 ) . Based on the analysis of these mutants , checkpoint activation does not require: ( 1 ) DNA strand breaks , ( 2 ) a defect in N-gate opening or closure , or ( 3 ) a defect in ATP binding or hydrolysis . Furthermore , an overall reduced rate of strand passage is not sufficient for checkpoint activation . The mutants that activate the checkpoint have in common a defect in the T-segment transport step of the SPR , which is associated with specific conformational states of the enzyme ( Figure S10 ) . Top2-B44 had not been characterized biochemically , but based on our analysis of the other mutants , we would predict that Top2-B44 is defective in the T-transport step of the SPR . To address this hypothesis , we purified wild type and Top2-B44 enzymes to homogeneity from yeast ( see Material and Methods ) . First , we asked if Top2-B44 has an altered propensity to cleave supercoiled DNA in the presence of the Topo II poison etoposide . This assay can determine whether the enzyme has a propensity to stall with a cut G-segment . This cleavage activity assay ( Figure 9a ) revealed that Top2-B44 does not display increased DNA breakage in the presence of etoposide , which is consistent with our failure to detect DNA damage in top2-B44 cells [13] ( Figure S9 ) and is consistent with Mad2-dependent checkpoint activation rather than Rad53-dependent checkpoint activation . We then performed relaxation activity time course experiments to determine the rate of relaxation of supercoiled plasmid DNA ( Figure 9b ) . This analysis demonstrated that Top2-B44 relaxes supercoiled DNA more slowly ( ∼2 . 5 to 3-fold ) than wild type Top2 at 37°C . Most interestingly , however , Top2-B44 had a reduced rate of ATP hydrolysis ( Figure 9c ) , consistent with checkpoint activation resulting from a defect in the T-transport step of the SPR , and consistent with the biochemical defects of Top2G144I , Top2E66Q and Top2L475A/L480P that all activated a Mad2-dependent G2 checkpoint . Topo II undergoes major conformation changes as part of the SPR , particularly during the step of the enzyme cycle where the T-segment is transported through the holoenzyme [27] , [28] . Our data indicate that a specific conformation might be recognized by the checkpoint machinery . In this case , a determinant may exist within the Topo II quaternary structure that participates in checkpoint signaling . This hypothesis stems from the discovery that , in human cells , the checkpoint signaling protein MDC1 binds to the C-terminus of Topo II [11] . We therefore tested the hypothesis that specific top2 mutants activate a Mad2-dependent checkpoint response via the catalytically inert C-terminal region ( CTR ) . Because the CTR ( residues 1321 to the C-terminus ) is dispensable for the SPR and for cell viability in yeast [33] , we could construct strains expressing either wild type TOP2 with a truncated CTR ( top2ΔCTR ) or top2-B44 with a truncated CTR ( top2-B44ΔCTR ) . Strikingly , we observed that Top2-B44ΔCTR does not activate checkpoint signaling , demonstrating that the CTR is required for Mad2 activation ( Figure 10a–c ) . The C-terminal regions of eukaryotic Topo II enzymes are divergent , and thus it is not clear whether the CTRs of human and yeast Topo II possess a conserved binding site for checkpoint proteins . Nevertheless , if checkpoint signaling complexes are recruited to the CTR of yeast Top2 , then over-production of the CTR in isolation may sequester the relevant factor ( s ) and perhaps abrogate checkpoint signaling . This was indeed the outcome when the CTR was expressed from the strongly inducible GAL1 promoter in the top2-B44 strain ( Figure 10a–c ) . These experiments therefore provide evidence that specific defects in the SPR transduce to the C-terminal region , resulting in the activation of checkpoint signaling . This could occur via the physical interaction of checkpoint proteins with the CTR , although other explanations of the data remain , such as over-produced CTR having dominant effects on chromatin that interfere with checkpoint signaling . Dissecting the role of the CTR and the molecular basis of Mad2 activation , remain important future goals . We previously found that checkpoint activation in top2-B44 cells does not coincide with a defect in chromosome biorientation , and that some spindle checkpoint proteins ( including Bub3 and the spindle checkpoint target , Pds1 ) are dispensable for the observed G2 delay [13] . Because these data suggested that a non-conventional spindle checkpoint is activated in top2-B44 cells , we asked if the G2 delay requires the kinetochore protein Ndc10 . In the absence of Ndc10 , although kinetochores lose their structural integrity , cells are able to progress through the cell cycle and complete mitosis [34] . However , since Mad2 activation must occur at kinetochores when the spindle checkpoint is triggered , ndc10 mutant cells cannot arrest prior to anaphase in the presence of microtubule poisons such as nocodazole [34] . We confirmed these data in our strain background in order to determine conditions that efficiently inactivate the temperature sensitive Ndc10-1 protein in conjunction with cell cycle synchrony in G1-phase . Upon release into the cell cycle , such cells failed to arrest in the presence of nocodazole , progressing into a second cell cycle almost as quickly as untreated cells ( Figure 11a ) . Strikingly , however , under identical conditions but in the absence of nocodazole , top2-B44 ndc10-1 cells delayed in G2-phase ( Figure 11b ) . Therefore , checkpoint activation in top2-B44 cells is independent of Ndc10 . The above data led to the prediction that Mad2 becomes activated in top2-B44 cells independently of kinetochores . We therefore asked if Mad2 is recruited to kinetochores upon checkpoint activation in top2-B44 cells . First we expressed Mad2 tagged with three tandem GFP epitopes in wild type cells in order to verify that we could observe re-localization of Mad2 upon activation of the spindle checkpoint . Indeed , as previously reported [35] , within 90 minutes of nocodazole treatment , most cells possessed a single discrete focus of Mad2-GFP ( Figure 12a , right panel , and data not shown ) . In the absence of nocodazole , we did not observe discrete foci of Mad2-GFP in either wild type or top2-B44 cells . Rather , Mad2-GFP was either diffusely localized or localized to structures at the periphery of the nucleus ( Figure 12a , left panel , Figure 12b and data not shown ) , consistent with previous studies showing that Mad2 localizes to nuclear pores in the absence of spindle checkpoint activation [35] . To ensure that Mad2-GFP does not localize to discrete foci in top2-B44 cells , we filmed such cells by time-lapse microscopy at 2 . 5 minute intervals to observe complete cell cycles ( Figure 12b , Movie S2 ) . Analysis of more than 50 cells failed to reveal any Mad2-GFP foci in top2-B44 cells . We conclude that Mad2 does not re-localize to kinetochores and that kinetochores are dispensable for checkpoint activation in top2-B44 cells . These findings are consistent with the genetic analyses described above , and suggest a novel mechanism of Mad2-dependent checkpoint activation via Top2-B44 . It will be important to extend the studies presented here to improve our understanding of mammalian mitotic checkpoint controls . It has been known for some time that inhibition of Topo II in human cells induces a G2 checkpoint delay that is dependent on the DNA damage checkpoint kinase ATM [36] . The checkpoint signaling pathway activated in G2 human cells is therefore clearly distinct from the Mad2-dependent checkpoint that becomes activated in yeast top2 mutants . This likely is the case because the yeast cell cycle is organized quite differently from human cells . Since budding yeast cells assembly their mitotic spindles during S-phase and achieve chromosome biorientation ( equivalent to mammalian metaphase chromosome alignment ) before the completion of DNA synthesis , they do not possess a G2 cell cycle phase that temporally precedes mitosis . As a result there is no true cell cycle transition between G2 and mitosis and thus no opportunity for biochemical regulation at an equivalent stage to the mammalian G2-prophase transition . Perhaps for this reason , budding yeasts seem to rely heavily on regulation of anaphase onset via Mad2 and other mechanisms that control stability and activity of anaphase inhibitors . Nevertheless , an equivalent cell cycle control to the one we have described in yeast does appear to function in human cells . When metaphase human cells are treated with the Topo II inhibitor ICRF-193 , a transient cell cycle delay prior to anaphase onset is observed that endures for approximately 45–60 minutes before anaphase is attempted and cells exit mitosis [37] . Little is known about this cellular response , however , and it will be intriguing to determine how similar it may be to the yeast checkpoint . In addition , it should be noted that much higher doses of ICRF-193 have been reported to silence the spindle checkpoint under conditions where cohesin proteins have been depleted , which results in defective tension at kinetochores and thus activates the spindle checkpoint [38] . The mechanistic basis of this phenomenon remains poorly understood and it is not clear how different doses of ICRF-193 can have very different consequences in vivo . However , it is interesting to note that parallels can be drawn between the yeast Top2E66Q mutant and a wild-type enzyme in the presence of ICRF-193 , since in both cases ATP can bind to the topoisomerase but cannot be hydrolyzed . The Top2E66Q mutant , which activates the Mad2-dependent checkpoint in yeast , therefore phenocopies a situation where each Topo II holoenzyme is bound to ICRF-193 , without there being an excess of the drug . It is clear that a comprehensive understanding of the interplay between mitotic checkpoints and Topo II function will provide important information in terms of the consequences of inhibiting the enzyme in the context of anti-microbial and anti-cancer treatments . In this study , we have presented evidence that a Topo II responsive checkpoint is activated through a novel mechanism that detects the enzyme cycle rather than a DNA lesion . DNA topology defects , including hyper-catenation , are not sufficient to trigger checkpoint signaling . Rather , the evidence supports a mechanism whereby the checkpoint machinery directly monitors the SPR of Topo II to ensure that sister chromatid separation can occur in anaphase . The data also indicate that the checkpoint is distinct from other checkpoints . Such a mechanism of checkpoint control could have evolved due to the abundance of naturally occurring Topo II inhibitors such as plant secondary metabolites that interrupt the enzyme cycle [4] , [5] . The SPR mutants described here may mimic some of the effects of these inhibitors in that they accumulate specific SPR intermediates that may form a structural platform for assembly of a signaling complex . We propose that checkpoint sensor proteins bind to Topo II when blocked or delayed at specific steps in the SPR . If the assembly of signaling complexes at the CTR of Topo II is a conserved mechanism of checkpoint activation across eukaryotes , then the nature of interaction with the Topo II CTR must have diverged since no homolog of the human MDC1 checkpoint protein exists in yeast . Further understanding of such Topo II-checkpoint protein interactions could be clinically valuable because current efforts aim to identify drugs that inhibit Topo II without activating the checkpoint controls that are protective to tumors cells .
The yeast strains used in this study are haploid derivatives of BF264-15 15DU ( see Table S2 ) . Yeast strains were modified according to standard yeast genetic approaches [39] . Plasmids were mutagenized by site-directed mutagenesis according to Liu and Naismith [40] . Step 1: cells were grown to an OD of 2 . 0–6 . 0 overnight at 26°C in synthetic raffinose medium lacking methionine and tryptophan ( Figure S2 ) . Step 2: for synchrony , cells were diluted to OD 0 . 2 in rich raffinose medium at 26°C with α factor ( concentration varying from 1∶2200 to 1∶3500 of a 1 mg/ml stock ) . After 1 h , galactose was added to a final concentration of 4% ( Step 3 ) . Following another 30 min growth the temperature was raised to 35°C ( Step 4 ) . After a further 30 min growth ( 2 hours total with α factor ) α factor was washed off with water that was pre-warmed to 35°C . The cells were released into YPG at 35°C ( Step 5 ) . Spindle morphologies were visualized using TUB1-GFP [41] as described previously [24] . Nuclear morphologies were visualized using a method from Juan Martinez ( Purdue University ) . Cells were grown under the conditions specified previously and were collected ( 500 µl ) by centrifugation . The resulting pellet was washed once with 1 ml 1× PBS , resuspended in 1 . 4 ml of filter-sterilized 4% p-formaldehyde solution ( 4% p-formaldehyde , 3 . 5% sucrose in water ) , and incubated for 20 min at room temperature . Cells were then centrifuged at 3000 g and the pellet was washed with 1 ml wash buffer ( 1 . 2 M sorbitol , 100 mM potassium phosphate , pH 7 . 5 ) . Cells were resuspended in 1 ml 1× PBS containing 5 µl of 1 mg/ml DAPI solution and incubated at room temperature for 3 min in the dark . Cells were washed once with 1× PBS and resuspended in the remaining 1× PBS after decanting . Anaphase cells were identified based on spindle morphology and were scored for separated or un-separated nuclei . During a normal mitosis , anaphase cells go through an intermediate stage where the nuclei are stretched , but have not separated completely ( this accounts for the low percentage of anaphase cells in wild-type with un-separated nuclei ) . Proteins were extracted by collecting 15 mls of cells and resuspending in 1 mL 0 . 25 M NaOH and 1% BME solution . Resuspensions were placed on ice for 10 min . 160 µl of 50% TCA was added , the solution inverted , and placed on ice for 10 min . The extracts were then pelleted at 14 , 000 g for 10 min at 4°C . Supernatant was decanted and the pellet resuspended in 1 mL of ice-cold acetone . Extracts were pelleted again and supernatant decanted . The extract was dried for 3 min at 55°C . The dried extract was resuspended in 100 µl of 2× protein-loading buffer and neutralized with 5 µl of 1 M Tris base . Western blots were performed using the following antibodies: 1∶1000 dilution of anti-Top2 ( TopoGen ) , 1∶6000 dilution of anti-Flag ( Pierce ) and a 1∶2500 dilution of anti-GFP ( Clontech ) . Secondary antibody , HRP-conjugated goat anti-rabbit ( Pierce ) was used at 1∶5000 and HRP-conjugated goat anti-mouse IgG ( Invitrogen ) was used at 1∶5000 . Preparation of yeast cell agarose plugs for CHEF gel electrophoresis was performed as preciously described [42] and were run on a CHEF DRIII electrophoresis system ( Bio-Rad ) at 11°C , 60–120 sec switch for 18 hours at 6 V/cm with 120° angle . Southern blots were probed to detect catenated 2-micron circle DNA as previously described [43] . Wild type Top2 and Top2-B44 were purified after over-expression in yeast as previously described [44] . Top2 reactions were carried out as described [44] using supercoiled pBR322 to monitor ATP-dependent relaxation and cleavage activity . ATPase assays were performed as described by Osheroff et al . [45] . Reaction mixtures contained 45 nM yeast topoisomerase II , 5 nM negatively supercoiled pBR322 DNA , and 1 mM [γ-32P] ATP in a total of 40 µl of relaxation buffer . Mixtures were incubated at 28°C and 37°C , and 2 µl samples were removed at time intervals up to 15 min and spotted on polyethyleneimine-impregnated thin layer cellulose chromatography plates ( EMD Chemicals ) . Plates were developed by chromatography in freshly made 400 mM NH4HCO3 and analyzed using a Bio-Rad molecular imager FX . ATP hydrolysis was monitored by the release of free phosphate . | Several major classes of anti-cancer drugs kill tumor cells by binding to the enzyme DNA topoisomerase II , but at the same time , cellular responses are activated that protect the tumor cells . How checkpoint activation occurs under circumstances of topoisomerase II perturbation is not well understood . We show that a novel checkpoint mechanism directly monitors the enzyme reaction of topoisomerase II . This is the first example of a checkpoint mechanism that directly monitors specific steps of the catalytic cycle of a single enzyme . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Material",
"and",
"Methods"
] | [] | 2013 | Direct Monitoring of the Strand Passage Reaction of DNA Topoisomerase II Triggers Checkpoint Activation |
To follow the fate of CD8+ T cells responsive to Plasmodium berghei ANKA ( PbA ) infection , we generated an MHC I-restricted TCR transgenic mouse line against this pathogen . T cells from this line , termed PbT-I T cells , were able to respond to blood-stage infection by PbA and two other rodent malaria species , P . yoelii XNL and P . chabaudi AS . These PbT-I T cells were also able to respond to sporozoites and to protect mice from liver-stage infection . Examination of the requirements for priming after intravenous administration of irradiated sporozoites , an effective vaccination approach , showed that the spleen rather than the liver was the main site of priming and that responses depended on CD8α+ dendritic cells . Importantly , sequential exposure to irradiated sporozoites followed two days later by blood-stage infection led to augmented PbT-I T cell expansion . These findings indicate that PbT-I T cells are a highly versatile tool for studying multiple stages and species of rodent malaria and suggest that cross-stage reactive CD8+ T cells may be utilized in liver-stage vaccine design to enable boosting by blood-stage infections .
Malaria is a mosquito-transmitted disease found in a range of animals including man , non-human primates and rodents . It is caused by multiple Plasmodium species , several of which may infect the same animal species . For humans , the two most prevalent Plasmodium species are P . falciparum and P . vivax , with the former responsible for the bulk of lethal disease . Mice have been used as a convenient animal model for studying malaria , with three rodent Plasmodium species in use: ( i ) P . chabaudi , which can cause a disease that shows recrudescence and has many features in common with human malaria including anemia , sequestration of parasites , and metabolic acidosis [1]; ( ii ) P . yoelii , which has two very closely related strains that differ in their capacity to infect red blood cells and cause lethal disease [2]; and ( iii ) P . berghei , particularly the ANKA strain ( PbA ) , which has been used as a model for human cerebral malaria [3] , [4] , [5] , a lethal complication of P . falciparum infection . While there is much debate as to the relevance of the PbA rodent infection model to human disease , the pathological processes underlying human cerebral malaria are relatively poorly characterized , making it difficult to accurately compare human and murine diseases . However , like human severe malaria , high parasite burden is required for multi-organ pathology in the PbA model [6] , [7] , [8] . In itself , the pathological process underlying experimental cerebral malaria ( ECM ) seen in PbA infections also offers insight into immune-mediated pathology in general , providing a rigorous experimental approach that can be easily manipulated to decipher various cellular and molecular contributions . In this rodent model , various cell types and cytokines have been reported to contribute to lethal ECM , with CD8+ T cells a major and essential contributor [9] , [10] , [11] . Infection with PbA leads to the activation of parasite-specific T cells that first expand in the spleen and then migrate to the brain , where they cause pathology [11] . Depletion of CD8+ T cells shortly before the onset of ECM prevents disease [11] , supporting a role for these cells in the effector phase of disease pathology . Plasmodium species have a complex life cycle with several distinct stages: a mosquito stage , from which sporozoites emerge to enter the mammalian hosts during a blood meal; a liver-stage where sporozoites enter hepatocytes and eventually develop into a large cohort of merozoites; and a blood stage , where merozoites are released into the blood and cause cyclic infection of erythrocytes . Disease symptoms and immune mediated pathology associated with malaria are limited to the blood-stage of infection , with the preceding liver stage being asymptomatic [12] . Despite this , sporozoite infection is not immunologically silent , with evidence that following pathogen entry via a mosquito bite , the immune response is initiated in the skin draining lymph nodes of mice [13] , generating protective immunity that depends on CD8+ T cells and the cytokines TNFα and IFNγ [14] . Sporozoite-specific immunity can control infection in mice [15] , non-human primates [16] and humans [17] , [18] , preventing development of blood-stage infection and its associated disease . As a consequence , researchers have explored the use of live sporozoites attenuated by irradiation or genetic engineering [19] , [20] , [21] or non-attenuated sporozoites controlled by drug curing , as potential approaches to vaccination [22] . Administration of irradiated cryopreserved sporozoites via the intravenous route was shown to provide superior immunity compared to cutaneous injection in non-human primates and mice [19] . More recently , vaccination of humans by the intravenous route demonstrated protection [21] . The success of the intravenous route was speculated to result from the direct access of parasites to the liver for development of immunity at this site . However , direct examination of where immunity was generated to this effective route of vaccination was not attempted . During the different life-cycle stages , Plasmodium parasites adopt distinct morphologies and as a consequence express many stage-specific proteins , which are often the focus of immunity and vaccine design . However , many proteins are expressed throughout multiple stages of the life cycle [23] and in the mammalian host may be expected to contribute to immunity across multiple stages . While it has been suggested that blood-stage immunity may impair responses to liver-stage antigens [24] , others have shown protection against liver-stage infection by prior blood-stage infection and cure [25] , supporting the idea that antigens expressed at both stages may be capable of inducing protective immunity . However , direct demonstration of this capacity was not provided . Here we describe the development of an MHC I-restricted , T cell receptor ( TCR ) transgenic murine line specific for PbA . We show that transgenic T cells from this line recognize an antigen expressed in both the blood-stage and the liver-stage of infection , demonstrating the potential for T cells with blood-stage-specificity to protect against sporozoite infection . T cells from this line detect a conserved antigen expressed by several rodent Plasmodium species including P . chabaudi and P . yoelii , rendering it a highly versatile immunological tool for dissecting CD8+ T cell immunity in malaria .
An MHC I-restricted TCR transgenic mouse line specific for blood-stage PbA ( termed PbT-I ) was generated using TCR genes isolated from a Kb-restricted hybridoma termed B4 ( Figure S1 ) originally derived from a T cell line isolated from a B6 mouse infected with blood-stage PbA . Analysis of spleen and lymph node ( LN ) cells from PbT-I mice showed a strong skewing towards CD8+ T cells ( Figure 1A ) , with essentially all splenic ( Figure 1B ) and lymph node ( Figure S2 ) CD8+ T cells expressing the Vα8 . 3 and Vβ10 transgenes . The few CD4+ T cells detected in the spleen and lymph node also expressed these transgenic receptors , though at a lower level indicative of co-expression of endogenous receptors . There was no reduction in spleen or lymph node cellularity relative to wild-type mice , with CD8+ T cells substituting for the lack of CD4+ T cells ( Figure S3 ) . Peripheral skewing towards CD8+ T cells was reflected in the thymus , where a large population of mature CD8+CD4− T cells with high TCR expression was evident ( Figure S4 ) . In this case , total thymocyte numbers were reduced to about one third of wild-type ( Figure S3 ) , consistent with the cellularity of other TCR transgenic mice we have generated , and likely due to efficient positive selection [26] . To determine if PbT-I cells responded to blood-stage PbA , purified CD8+ T cells from PbT-I mice were labeled with CFSE and then stimulated in vitro with dendritic cells and lysate from either infected red blood cells ( iRBC ) of mixed stages or enriched as schizonts ( Figure S5 ) . This showed a dose-dependent proliferative response to both forms of antigen , though schizont lysate was more efficient . To test whether PbT-I cells also responded to PbA in vivo , PbT-I cells were labeled with CFSE and adoptively transferred into B6 mice one day before infection with blood-stage PbA . Three or 5 days later , mice were killed and the spleen and blood examined for proliferating PbT-I cells ( Figure 2A , B ) . This revealed a vigorous response by PbT-I cells , which entered the blood from the spleen after day 3 . The specificity of PbT-I cells for malarial antigen was demonstrated by their lack of response to intravenous ( i . v . ) infection with herpes simplex virus type I ( HSV-1 ) , an infection that efficiently stimulated viral glycoprotein B-specific transgenic T cells ( gBT-I cells ) in the same mice ( Figure S6 ) . To more precisely determine where PbT-I cells were activated during the primary response to blood-stage PbA infection , B6 mice were injected with CFSE-labeled PbT-I cells one day before i . v . infection with blood-stage PbA , then various tissues were harvested 2 days later to examine expression of the early activation marker CD69 on PbT-I cells ( Figure 2C , D ) . This showed that blood-stage infection caused T cell activation in the spleen , although some CD69 up-regulation was observed in liver-draining lymph nodes ( portal and celiac LNs ) . Other lymph nodes showed no evidence of T cell activation . To test whether PbT-I cells induced by blood-stage infection made cytokines and were able to degranulate , as required for lytic activity , mice were adoptively transferred with small numbers of GFP-expressing PbT-I cells and infected i . v . with blood-stage PbA . On day 8 post-infection , PbT-I cells were recovered from the spleen and briefly restimulated with anti-CD3 mAb to test for production of IFNγ , TNFα and CD107a , the latter of which is a surrogate marker for degranulation ( Figure S7 ) . This revealed that most PbT-I cells were able to produce both cytokines and degranulate . As CD8+ T cells have been implicated in the pathology of ECM , we asked whether transfer of PbT-I cells into B6 mice could accelerate this disease . B6 mice were injected with a high ( 2×106 ) or low ( 2×104 ) number of PbT-I cells or a high number of a herpes simplex virus-specific gBT-I cells , then infected with blood-stage PbA and monitored for disease ( Figure 3A ) . This showed that PbT-I cells significantly accelerated disease onset , though only by about one day . ECM was accompanied by infiltration of PbT-I cells and endogenous CD8+ T cells , but not gBT-I cells into the brain of infected mice on days 5–6 post-infection ( Figure 3B and Figure S8 ) . To determine whether PbT-I cells could themselves cause ECM , endogenous CD8+ T cells were depleted from mice with anti-CD8 mAb and 7 days later replaced with PbT-I cells , control gBT-I cells or no T cells . One day later , these mice were infected with blood-stage PbA and examined for ECM onset . All mice given PbT-I cells developed ECM , while very few other CD8-depleted mice developed disease ( Figure 4 ) . Onset of ECM in a small fraction of the latter was likely due to incomplete depletion of endogenous CD8+ T cells in some mice . This could not be avoided because the dose of depleting anti-CD8 antibody had to be sufficient to deplete virtually all endogenous CD8+ T cells while leaving little antibody to persist until adoptively transfer of PbT-I cells a week later ( otherwise remaining anti-CD8 mAb would have depleted these PbT-I cells ) . H&E staining of the brains of mice that received PbT-I cells showed typical features of CM , such as haemorrhages and intravascular accumulation of RBC and leukocytes ( Figure S9 ) . These data clearly showed that PbT-I cells were able to cause ECM . As the precise specificity of PbT-I cells was unknown , we determined whether they recognized other species of Plasmodium . CFSE-labeled PbT-I cells were adoptively transferred into B6 mice that were then infected with blood-stage P . chabaudi AS; 6 or 7 days later proliferation of PbT-I cells was assessed in the spleen ( Figure 5 ) . This showed that PbT-I cells could proliferate in response to blood-stage P . chabaudi AS . In a similar set of experiments , PbT-I cells were also shown to respond to blood-stage infection with P . yoelii XNL ( Figure S10 ) . These findings indicated that PbT-I cells have specificity for multiple Plasmodium species that cause rodent malaria . While our PbT-I line was generated to blood-stage parasite infection , a proportion of antigens expressed in the blood stage are also expressed by sporozoites and during the liver-stage of infection [23] . To address whether sporozoites could stimulate PbT-I cells , we adoptively transferred CFSE-labeled PbT-I cells into B6 mice and then injected them i . v . with radiation-attenuated PbA sporozoites ( RAS ) . On day 4 post-infection , proliferating PbT-I cells were detected in the spleen indicating their capacity to respond to sporozoites ( Figure 6 ) . Additional mice examined on day 7 did not progress to patency , indicating that day 4 responses were induced by sporozoites and not by break-through blood-stage parasites . Eight days after infection , PbT-I cells harvested from the spleen produced IFNγ , TNFα and CD107a ( Figure S11 ) , indicating their development of effector function . A recent report suggested that the efficiency of intravenous vaccination with irradiated sporozoites relative to subcutaneous vaccination may be because the former route allows more parasites to reach the liver for priming of protective immunity [19] . To test whether priming by irradiate parasites occurred in the liver , we injected irradiated sporozoites intravenously and then 1–4 days later examined the activation ( CD69 expression ) ( Figure 7A , C ) and proliferation ( Figure 7A , B ) of PbT-I cells in the liver and various lymphoid tissues including the spleen and lymph nodes . Upregulation of CD69 was seen as early as one day after infection and was primarily detected in the spleen , with some expression also seen in the liver draining lymph nodes ( celiac LN , portal LN and the 1st mesenteric LN ) [27] . Proliferation closely followed on day 2 , almost entirely in the spleen . These data suggested that PbT-I cells responded to sporozoites by CD69 upregulation and extensive initial proliferation in the spleen and to a lesser extent in the liver-draining lymph nodes , but not in the liver nor other lymph nodes . Divided cells were only evident in the liver once they were present in the blood and had divided extensively , suggesting initiation of proliferation elsewhere , most likely in the spleen . CD8α+ DC are critical for generating immunity to blood-stage infection [28] , [29] and recently the human DC subset equivalent , BDCA3+ DC , have been implicated in severe malaria in humans [30] , [31] . To address whether CD8α+ DC also participated in responses to sporozoites , we examined proliferation of PbT-I cells in Batf3-/- mice , which lack this DC subset ( Figure 8 ) . The poor proliferation in Batf3-/- mice compared to wild-type mice revealed that this response was dependent on CD8α+ dendritic cells . It has been reported that blood-stage infection can impair immunity to liver-stage antigens [24] , though this is disputed by evidence that there is an equivalent response by liver-stage-specific transgenic T cells to sporozoites in the presence or absence of a subsequent blood-stage infection [32] . To resolve this issue with respect to CD8+ T cell-mediated immunity , we examined the expansion of PbT-I cells after exposing mice to live sporozoites ( which will infect the liver then generate blood-stage infection ) , or irradiated sporozoites alone or followed by blood-stage ( iRBC ) infection 2 days later , mimicking the time for blood-stage egress after live sporozoite infection ( Figure 9 ) . Our results clearly showed that naïve PbT-I cells proliferated to reach greater numbers if additionally exposed to blood-stage infection , indicating that T cells with cross-stage specificity can show cumulative expansion to the liver and bloods stages . Since sporozoite antigen has been shown to persist in other models [33] , and we could demonstrate some proliferation of PbT-I cells transferred 2 days but not 7 days after injection of irradiated sporozoites ( Figure S12A , B ) , indicating at least short-term persistence of the PbT-I antigen , it remained possible that augmented proliferation of PbT-I cells due to blood-stage infection might simply relate to additional inflammatory effects , rather than provision of antigen . To test whether inflammation alone could boost PbT-I expansion to irradiated sporozoites , 20 nmol of 1668 CpG oligonucleotide ( CpG ) was used as an inflammatory signal on day 2 and its effect on expansion of PbT-I cells examined ( Figure S12C ) . CpG-mediated inflammation failed to induce a significant increase in PbT-I cell numbers in mice given irradiated sporozoites two days earlier , suggesting that antigen provided by blood-stage infection may be important for enhanced proliferation . This did not , however , formally excluding a role for inflammatory signals distinct from CpG that are associated with blood-stage infection . Because only one parasitized hepatocyte needs to survive to deploy thousands of merozoites into the blood and seed blood-stage infection , it is very difficult to prevent malaria with vaccines directed at pre-erythrocytic stages . It follows that any vaccine targeting pre-erythrocytic stages of infection must generate sterile immunity to be effective . As the antigen recognized by PbT-I cells was expressed by sporozoites , we asked whether this antigen might represent a vaccine candidate capable of eliciting sterile hepatic immunity . To assess this , we asked whether PbT-I cells could provide protective immunity to liver-stage infection . First , we determined an infectious dose of sporozoites that would lead to just under 100% blood-stage infection in the absence of PbT-I cells ( Figure 10A ) . From this we chose 520 sporozoites as our infectious dose . To test the protective capacity of PbT-I cells , these cells or control virus-specific gBT-I cells were activated in vitro and then 7×106 cells adoptively transferred into naïve B6 mice that were subsequently challenged with 520 live sporozoites ( Figure 10B ) . By monitoring these mice for blood parasitemia , we showed that PbT-I cells , but not gBT-I cells , could prevent progression to blood-stage infection , protecting mice from infection . This indicated that the antigen recognized by PbT-I cells has the potential to generate sterilizing immunity to liver-stage infection . To identify the antigenic determinant recognized by PbT-I cells , we used an octamer combinatorial peptide library scan [34] to identify amino acid residues important for PbT-I activation as measured by MIP1β production ( data not shown ) . These residues were then used to generate a octamer motif ( x-x-x- ( CD ) - ( WF ) -N-x- ( LMIV ) ; where x is any amino acid and residues in brackets are valid for that position ) to search the genomes of the three rodent malaria species for which PbT-I cells showed reactivity . 151 peptides fitting this motif were then examined for their capacity to stimulate PbT-I cells either by CD69 expression or MIP1β production ( data not shown ) . Six peptide sequences caused some T cell activation but only one of these ( NCYDFNNI ( NCY ) ) was found to act as a target antigen for endogenous killer T cells generated in normal B6 mice infected with PbA ( Figure 11A and data not shown ) . This sequence also induced IFNγ production from endogenous T cells ( Figure 11B ) and PbT-I T cells ( Figure 11C ) responding to blood-stage infection . Note that tetramers made with Kb containing NCY were able to stain PbT-I cells , confirming the Kb-restriction of this specificity ( data not shown ) . The NCY peptide was derived from a protein of 745 amino acids ( PBANKA_071450 ) , which is now our leading candidate for the antigen responsible for priming PbT-I cells .
Here we characterize a new TCR transgenic mouse that produces CD8+ T cells specific for both the blood and liver stages of rodent malaria . PbT-I cells responded in vivo to the blood-stage of three different rodent Plasmodium species , PbA , P . yoelii XNL and P . chabaudi AS . In addition , PbT-I cells responded to mosquito-derived sporozoites of PbA and were able to provide protection against sporozoite infection . It remains to be tested as to whether PbT-I cells also recognize sporozoites from the other rodent Plasmodium species , but it seems likely that this will be the case given their blood-stage cross-reactivity . Recognition of blood-stage parasites as well as mosquito-derived sporozoites , and the ability to protect against liver-stage infection , suggests that the protein recognized by PbT-I cells is widely expressed throughout the parasite life cycle and is potentially well conserved . Identification of NCYDFNNI as a peptide recognized by PbT-I cells and by endogenous PbA-induced T cells suggests the protein encoded by PBANKA_071450 , which is of unknown function and undefined expression pattern , may be the source of the PbT-I epitope . Construction of parasites deficient in this epitope will be required for formal proof . It is notable that while the source protein is encoded in the genomes of PbA and P . chabaudi , the ortholog appears severely truncated in P . yoelii and consequently lacks the region containing NCYDFNNI found in other species . As PbT-1 cells were able to respond to P . yoelii , the authentic antigen must be present in this species . Whether this invalidates the gene product of PBANKA_071450 as the authentic PbT-I antigen , or is explained by sequencing error within the P . yoelii genome , or has some other basis remains to be established . Whatever the case , the NCYDFNNI epitope is clearly recognized by PbT-I cells and can be used to stimulate these transgenic T cells as well as endogenous T cells specific for PbA . Evidence that immunization with live blood-stage parasites can protect against the liver-stage infection [25] , suggests that multi-stage antigens like that recognized by PbT-I cells can be protective . Our study extends this concept by indicating that CD8+ T cells of a single specificity for a blood-stage antigen can protect against liver-stage infection when the antigen is also expressed during the liver stage . It has been reported that blood-stage infection can impair immunity to liver-stage antigens [24] , though this is disputed by the above study , which uses blood-stage infection to induce anti-sporozoite immunity [25] and by another study that shows an equivalent response by liver-stage-specific transgenic T cells to sporozoites in the presence or absence of a subsequent blood-stage infection [32] . The availability of PbT-I cells will give us the opportunity to examine this relationship when the relevant antigen is expressed during both blood- and liver-stages and to determine how antigens presented during the blood-stage might influence the effector function of T cells capable of recognizing liver-stage antigens . Clearly , in our experiments , exposure of cells primed to liver-stage parasites did not impair their capacity to respond to blood-stage parasites , but increased the expansion of PbT-I cells . This raises the possibility that CD8+ T cells specific for antigens expressed in both stages of infection may have a selective advantage for expansion over single stage specific T cells . The broad cross-reactivity of this TCR transgenic line means that it is suited to exploring the role of CD8+ T cells in several rodent malaria models . For blood-stage infection , this is most relevant to PbA , where ECM is dependent on CD8+ T cells . However , CD8+ T cells have been implicated in protective immunity to blood-stage infection by P . yoelii 17XL [35] , raising the possibility that this protective process could be explored using PbT-I cells . These transgenic T cells should also be highly relevant for analysis of liver-stage immunity , as CD8+ T cells are critical for protection at this stage of infection [15] , [36] . Here we used PbT-I cells to investigate the site of priming and T cell expansion after intravenous administration of irradiated sporozoites . This study was prompted by the implication that the effectiveness of this route of immunization was related to its capacity to prime in the liver [19] . Our analysis revealed that T cells showed signs of activation in the spleen and in the liver draining lymph nodes , but not in the liver itself , and subsequent examination of T cell proliferation showed that most PbT-I T cell proliferation occurred in the spleen . While our study does not exclude a role for the liver in tailoring the response , it suggests that at least the initial priming steps are unlikely to occur in this site . Thus , efficient priming via this route most likely derives from the large load of irradiated sporozoites deposited in the spleen after intravenous administration and the high frequency of T cells found in this organ . This contrasts infection by mosquito bite , which favors priming within skin draining lymph nodes [13] , probably as a consequence of local deposition of sporozoites within the dermis of the skin . Our findings suggest that the spleen is the main site for priming sporozoite specific T cells after intravenous administration of parasites , but they do not formally exclude the liver draining lymph nodes or the liver as important sites of activation for protective immunity . Initiation of PbT-I proliferation in the spleen in response to intravenous injection of irradiated PbA sporozoites also demonstrated that the sporozoites themselves expressed the antigen recognized by PbT-I cells and that conversion to later liver stages of development was not necessary to provide antigen capable of stimulating these T cells . Furthermore , it showed that the same DC subset as required for priming CD8+ T cell immunity to blood-stage infection , i . e the CD8α+ DC [28] , [29] , was responsible for inducing CD8+ T cell responses to the liver-stage parasites . Extraction of putative CD8α+ DC from the liver 6 days after sporozoite infection also suggested that these DC might contribute to antigen presentation in the liver at late time points after infection [37] , though this idea should be taken with caution as CD8 T cells can express CD11c when activated and can be easily mistaken for DC . This common use of CD8α+ DC probably reflects their dominant capacity to cross-present antigens [38] . The ability of PbT-I cells to protect against infection by PbA sporozoites is encouraging because sterilizing immunity requires destruction of all infected hepatocytes . Our experiments used 7×106 activated PbT-I cells to demonstrate protection , which is a relatively high number of cells but certainly achievable by vaccination . Identification of the antigen recognized by this TCR transgenic line should allow development of vaccination strategies to test the protective power of this potentially conserved antigen expressed in multiple stages of the life cycle . This approach has the potential to be highly effective since both stages of infection are shown to boost responses by CD8+ T cells with such multi-stage specificity . One concern with this type of multi-stage antigen , however , is that priming of T cells by sporozoites may enhance the potential for development of ECM mediated by the same cells during the blood-stage of the infection . While directly relevant for PbA infection where ECM is commonplace , this might not be of relevance to infection models where ECM is not seen e . g . P . yoelii XNL infection . Given the strongly argued lack of adaptive immune involvement in human cerebral malaria , this concern may also be irrelevant for human vaccination approaches . However , caution should be adopted here since our understanding of pathology in human cerebral malaria is still somewhat limited . In conclusion , the PbT-I TCR transgenic line represents a versatile tool for studying CD8+ T cell immunity to a multitude of rodent Plasmodium species during both the liver- and blood-stages of infection . The current study highlights the spleen as a major organ of priming for intravenously-introduced blood- or liver-stage parasites and suggests that T cells with specificity for antigens expressed in both stages may contribute to pathology or protection , depending on the stage of life cycle .
All procedures were performed in strict accordance with the recommendations of the Australian code of practice for the care and use of animals for scientific purposes . The protocols were approved by the Melbourne Health Research Animal Ethics Committee , University of Melbourne ( ethic project IDs: 0810527 , 0811055 , 1112347 , 0911527 ) . C57BL/6 ( B6 ) mice , B6 . Ly5 . 1 mice , MHC I-/- mice , Kb-/- mice , Batf3-/- mice and the transgenic strains gBT-I [39] and PbT-I were used between 6-12 weeks and were bred and maintained at the Department of Microbiology and Immunology , The University of Melbourne . Batf3-/- mice used in this study had been backcrossed 10 generations to B6 . Animals used for the generation of the sporozoites were 4–5 week old male Swiss Webster mice were purchased from the Monash Animal Services ( Melbourne , Victoria , Australia ) and maintained at the School of Botany , The University of Melbourne , Australia . Anopheles stephensi mosquitoes ( strain STE2/MRA-128 from The Malaria Research and Reference Reagent Resource Center ) were reared and infected with PbA as described [40] . Sporozoites were dissected from mosquito salivary glands [41] , resuspended in cold PBS , irradiated with 20 , 000 rads using a gamma 60Co source , and administered to mice i . v . The rodent malaria lines PbA clone 15cy1 , P . chabaudi AS and P . yoelii XNL were used in this study . Transgenic PbT-I mice were generated using the V ( D ) J segments of the TCRα- and β-genes of a CD8+ T cell hybridoma ( termed B4 ) specific for an unidentified blood-stage PbA antigen . This hybridoma was derived from T cells extracted from the spleen of a B6 mouse at day 7 after infection with PbA . 3×106 splenocytes from a mouse previously infected with PbA were co-cultured with 5×105 conventional DC ( extracted from the spleen of FMS-like tyrosine kinase 3 receptor ligand ( Flt3-L ) treated B6 mice ) that were pre-loaded for 2 hours with 2×106 PbA schizont lysate as previously described [42] in complete RPMI at 6 . 5% CO2 , 37°C . One week later , cultured cells were re-stimulated for a week with fresh DC and PbA schizont lysate . To generate PbA-specific hybridomas , in vitro cultured cells were then fused with the BWZ36 . GFP fusion partner and exposed to drug selection [43] . This led to isolation of the Kb-restricted B4 hybridoma ( Figure S1 ) from which PbT-I T cell receptor genes were derived . TCR Vα usage was defined using 5′ RACE PCR on cDNA converted from the RNA of the B4 hybridoma . Sequencing analysis revealed that the TCR α-chain consisted of AV8S6 and Jα17 and Cα2 gene segments . The TCR α region was amplified by PCR from the cDNA of the B4 hybridoma using the forward primer GGATCCAGTGTCATTTCTTCCCT containing a BamHI recognition sequence at the 5′ end , designed to bind the 5′ UTR region of AV8S6 , and the reverse primer CAGATCTCAACTGGACCACAG containing a BglII recognition sequence at the 5′ end , specific for the Cα region . The AV8S6-Jα17-Cα2 segment was cloned into the BamHI site of the pES4 cDNA expression vector , comprising the Ig-H chain enhancer , the H2-Kb promoter and the polyadenylation signal sequence of the human β-globulin gene [44] . To prepare the α-chain transgenic construct for microinjection , the pES4-VJC construct was digested with the restriction enzymes ClaI and NotI , and the digested mix was subjected to agarose gel electrophoresis . The ∼5 . 6 kb transgenic insert containing the VJC sequence , the promoter and enhancer sequences was excised from the gel , purified and quantitated for microinjection . TCR Vβ usage was defined by PCR on cDNA converted from the RNA of the B4 hybridoma using the forward primer CCTGCCTCGAGCCAACTATGGG specific for the Vβ10 gene and the reverse primer CCAGAAGGTAGCAGAGACCC specific for the Cβ gene . Sequencing analysis revealed that the TCR β-chain consisted of Vβ10 ( BV10S1A1 ) , Dβ2 and Jβ2 . 2 . The TCR β-chain was amplified by PCR from the genomic DNA of the B4 hybridoma using the forward primer GATCGATGTCCTAGGCCAGGAGATATGA specific for the Vβ10 , incorporating a ClaI restriction enzyme site at the 5′ end , and the reverse primer GATCGATAAGCTCAGTCCAAGA specific for Jβ2 . 2 and incorporating a ClaI site at the 5′end . The Vβ10 ( BV10S1A1 ) , Dβ2 and Jβ2 . 2 segment was cloned into the ClaI site of the p3A9CβTCR gDNA expression vector , comprising the TCR β-chain enhancer , the 2B4-derived 5′ region and leader sequence and the B3-derived promoter and coding regions [45] . To prepare the construct for microinjection , the p3A9CβTCR VDJ construct was digested with the restriction enzymes ApaI and NotI , and the digested mix was subjected to agarose gel electrophoresis . The larger fragment ( ∼11 kb ) transgenic insert containing the VDJ sequence , Cβ sequence and the promoter and enhancer sequences was excised from the gel , purified and quantitated for microinjection . Cells were labeled with monoclonal antibodies specific for CD8 ( 53-6 . 7 ) , CD4 ( RM 4-5 ) , Thy1 . 2 ( 30-H12 ) , CD45 . 1 ( A20 ) , Vα8 . 3 ( B21 . 14 ) , Vβ10 ( B21 . 5 ) or CD69 ( H1 . 2F3 ) . Dead cells were excluded by propidium iodide staining . Cells were analyzed by flow cytometry on a FACsCanto or Fortessa ( BD Biosciences ) , using the Flowjo software ( Tree Star Inc . ) . Unless otherwise stated , mice were infected i . v . with 106 PbA infected RBC ( iRBC ) in 0 . 2 ml of Hank's balanced salt solution ( HBSS ) . In some experiments , mice were infected i . v . with 105 P . chabaudi iRBC or i . v . with 104 P . yoelii iRBC or with 300 , 520 , 900 , 5×104 or 105 PbA sporozoites as stated in the figure legends . Mice infected with 104 PbA infected RBC were injected i . p . with 0 . 4 mg chloroquine dissolved in water on days 6 and 7 , before being euthanized for analysis on day 8 post-infection . CD8+ T cells were negatively enriched from the spleens and lymph nodes of transgenic mice and labelled with CFSE as described [46] . Purified cells were injected i . v . in 0 . 2 ml HBSS . To deplete endogenous CD8+ T cells before adoptive transfer , B6 mice were injected i . p . with 100 µg of anti-CD8 antibody ( clone 2 . 43 ) 7 days prior to the transfer of PbT-I or gBT-I cells . 1–2×106 CFSE-labelled Ly5 . 1+ PbT-I cells were adoptively transferred into Ly5 . 2+ B6 mice a day before mice were infected with blood-stage PbA , P . chabaudi , or P . yoelii or with PbA sporozoites . In other experiments , 5×104 or 1×106 uGFP PbT-I cells labelled with CellTracker Violet stain ( Invitrogen ) were adoptively transferred into Ly5 . 2+ B6 or Batf3-/- mice a day before infection with irradiated sporozoites , or 3 days before infection with PbA iRBC . Spleens and other organs were harvested on various days post-infection for the analysis of PbT-I proliferation by flow cytometry . Dendritic cells were purified from the spleens of mice as previously described ( 28 ) . Briefly , spleens were finely minced and digested in 1 mg/ml collagenase 3 ( Worthington ) and 20 µg/ml DNAse I ( Roche ) for 20 min at room temperature . After removing undigested fragments by filtering through a 70 µm mesh , cells were resuspended in 5 ml 1 . 077 g/cm3 nycodenz medium ( Nycomed Pharma AS , Oslo , Norway ) , layered over 5 ml nycodenz medium and centrifuged at 1700×g at 4°C for 12 min . The light density fraction was collected and DC were negatively enriched by incubation with a cocktail of rat monoclonal anti-CD3 ( clone KT3-1 . 1 ) , anti-Thy-1 ( clone T24/31 . 7 ) , anti-Gr1 ( clone RB68C5 ) , anti-CD45R ( clone RA36B2 ) and anti-erythrocyte ( clone TER119 ) antibodies followed by immunomagnetic bead depletion using BioMag goat anti-rat IgG beads ( Qiagen ) . 5×104 DC extracted from the spleens of naive WT , MHC-I-deficient or Kb-deficient mice and resuspended in complete DMEM medium supplemented with 10% foetal calf serum ( FCS ) were cultured for 1 h with titrated amounts of lysed whole blood containing mixed stages of PbA parasites before adding 5×104 B4 hybridoma cells . After culture for 40 h at 37°C in 6 . 5% CO2 supernatants were collected and concentrations of IL-2 were assessed using the Mouse IL-2 ELISA Ready-Set-Go kit ( eBiosciences ) following manufacturer's instructions . PbA mixed blood-stages and schizont enriched parasite lysate were prepared as previously described [42] . Conventional DC isolated from the spleen of FMS-like tyrosine kinase 3 receptor ligand ( Flt3-L ) treated Ly5 . 2+ B6 mice [42] were incubated with titrated amounts of lysate from either the mixed blood-stages or the schizont-enriched parasites for 2 hours before the addition of CFSE-labeled Ly5 . 1+ PbT-I cells . After 60 hours of incubation at 6 . 5% CO2 , 37°C , cells were harvested for analysis by flow cytometry . To detect degranulation and the production of cytokines IFNγ and TNFα from antigen specific cells , splenocytes from mice ( either normal B6 mice or those adoptively transferred with PbT-I ) infected for 7 days with irradiated sporozoites or 7–8 days with blood-stage PbA were restimulated by 5 µg/ml plate-bound anti-CD3 or 1 µg/ml peptide for 5 hours at 37°C in the presence of 10 µg/ml brefeldin A , monensin and anti-CD107 antibody ( clone eBio1D4B ) . Cells were then surface labeled with antibodies and intracellular cytokine staining was performed to detect intracellular IFNγ and TNFα using Cytofix/Cytoperm Fixation and Permeabilization Solution ( BD ) according to the manufacturer's instructions . Results were represented in Venn diagrams using the online tool at www . venndiagram . tk . PbT-I or gBT-I isolated from the spleen and lymph nodes were stimulated with media containing 10% FCS , 10 U/ml IL-2 and 5 µg/ml anti-CD28 in 75 cm2 tissue culture flasks pre-coated with 10 µg/ml anti-CD3 ( clone 2c11 ) , anti-CD8 ( clone 53-6 . 7 ) and anti-CD11a ( clone 121/7 . 7 ) . 40 hours later , cell cultures were divided into two equal volumes and given an equivalent volume of fresh medium before culturing for 24 hours . Cells were then harvested and centrifuged over lymphocyte separation media to remove dead cells . In vitro activated cells generated using this method were routinely >90% pure . Mice were perfused intracardially with 10 ml PBS prior to harvesting of the brain . Brains were cut into fine fragments , washed once with media and digested with collagenase/DNAse ( 1 mg/ml collagenase III ( Roche ) ; 20 µg/ml DNAse I , ( Worthington ) for 1 hour at room temperature with rotation . Samples were filtered through 75 µm nylon mesh to remove undigested fragments and then centrifuged once at 596 g , 5 minutes at 4°C . The pellet was resuspended in 7 ml 33% Percoll diluted in media , and centrifuged at 400 g for 20 minutes at room temperature with low brake . The supernatant was discarded and the pellet containing RBC was incubated with 500 µl RBC lysis buffer for 2 minutes on ice . Cells were washed twice with FACS buffer followed by surface staining with various antibodies . Mice infected with blood-stage PbA were monitored daily for the development of ECM . Mice were considered to have ECM when showing signs of neurological symptoms such as ataxia and paralysis , evaluated as the inability of mice to self-right . Brains were fixed in 4% paraformaldehyde followed by 70% ethanol overnight and then stained by H&E . An octamer combinatorial peptide library in positional scanning format [34] was synthesized ( Pepscan Presto , Netherlands ) . For combinatorial peptide library screening , splenocytes from transgenic PbT-I mice were purified , washed and rested overnight in RPMI 1640 containing 100 U/ml penicillin , 100 µg/ml streptomycin , 2 mM L-glutamine and 2% heat inactivated fetal calf serum ( all Life Technologies ) . In 96-well U-bottom plates , 6×104 splenocytes target cells were incubated with 160 library mixtures ( at 100 µM ) in duplicate for two hours at 37°C . Following peptide pulsing , 3×104 PbT-I splenocytes were added and the assay was incubated overnight at 37°C . The supernatant was then harvested and assayed for MIP-1β by ELISA according to the manufacturer's instructions ( R&D Systems ) . 5×105 GFP-expressing PbT-I lymph node and spleen cells together with 105 B6 spleen cells and peptide ( titrated in 10-fold steps from 5–5000 pM ) were pelleted together in a 96-well U-bottom plate and incubated for 3 hours at 37°C ( 6 . 5% CO2 ) . Cells were then stained with antibodies specific for CD8 and CD69 and the proportion of CD69+CD8+GFP+ cells determined . To detect peptide-specific lytic activity in vivo , mice were infected for 7 days with 106 blood-stage PbA and cured by chloroquine treatment from day 4–6 before adoptive transfer of target cell populations . In vivo cytotoxicity was performed essentially as described [42] , with the modification that target cells were a mixture of CFSElo B6 spleen cells , DsRed-expressing splenocytes and GFP-expressing splenocytes , the latter two populations coated with test peptides at 1 µg/ml . Equal numbers of cells were combined and 2 . 4×107 cells were injected into host mice and 18 h later spleen cells were harvest for flow cytometric assessment of lysis 18 h later within the spleen . | Malaria is a disease caused by Plasmodium species , which have a highly complex life cycle involving both liver and blood stages of mammalian infection . To prevent disease , one strategy has been to induce CD8+ T cells against liver-stage parasites , usually by immunization with stage-specific antigens . Here we describe a T cell receptor specificity that recognizes an antigen expressed in both the liver and blood stages of several rodent Plasmodium species . We generated a T cell receptor transgenic mouse with this specificity and showed that T cells from this line could protect against liver-stage infection . We used this novel tool to identify the site and cell-type involved in priming to a recently developed intravenous attenuated sporozoite vaccine shown to have efficacy in humans . We showed that CD8+ T cells with this specificity could protect against liver-stage infection while causing pathology to the blood stage . Finally , we provided evidence that T cells with cross-stage specificity can be primed and boosted on alternative stages , raising the possibility that antigens expressed in multiple stages might be ideal vaccine candidates for generating strong immunity to liver-stage parasites . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"immunopathology",
"clinical",
"immunology",
"immunity",
"immunity",
"to",
"infections",
"biology",
"and",
"life",
"sciences",
"immunology",
"acquired",
"immune",
"system",
"immune",
"response",
"immune",
"system"
] | 2014 | CD8+ T Cells from a Novel T Cell Receptor Transgenic Mouse Induce Liver-Stage Immunity That Can Be Boosted by Blood-Stage Infection in Rodent Malaria |
As infectious disease surveillance systems expand to include digital , crowd-sourced , and social network data , public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals . Contact networks , which are the webs of interaction through which diseases spread , determine whether and when individuals become infected , and thus who might serve as early and accurate surveillance sensors . Here , we evaluate three strategies for selecting sensors—sampling the most connected , random , and friends of random individuals—in three complex social networks—a simple scale-free network , an empirical Venezuelan college student network , and an empirical Montreal wireless hotspot usage network . Across five different surveillance goals—early and accurate detection of epidemic emergence and peak , and general situational awareness—we find that the optimal choice of sensors depends on the public health goal , the underlying network and the reproduction number of the disease ( R0 ) . For diseases with a low R0 , the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak . However , identifying network hubs is often impractical , and they can be misleading if monitored for general situational awareness , if the underlying network has significant community structure , or if R0 is high or unknown . Taking a theoretical approach , we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible . By contrast , the friends-of-random strategy offers a more practical and robust alternative . It can be readily implemented without prior knowledge of the network , and by identifying sensors with higher than average , but not the highest , epidemiological risk , it provides reasonably early and accurate information .
Public health agencies rely on diverse sources of information for detecting emerging outbreaks , situational awareness ( e . g . , estimating prevalence or severity ) , prediction of future burden , and triggering initiation of control measures . For influenza alone , the CDC has deployed at least eight different surveillance systems [1] . With the public health sector facing increasing budget constraints [2 , 3] , disease surveillance is at a critical juncture where next-generation big data can potentially be harnessed to revolutionize traditional data-limited practices and improve real-time situational awareness , early detection and forecasting of disease outbreaks . HealthMap—an event-based system that aggregates worldwide news to generate global health risk maps—was among the first effective demonstrations of internet-driven surveillance [4 , 5] . In 2009 , Google Flu Trends—a detection algorithm for internet search queries of influenza-related terms—brought next-generation indicator-based syndromic surveillance to the forefront of public health [6–11] . It generally aligns well with seasonal dynamics in the US and Europe , but fell short during the 2009 H1N1 pandemic [12–14] . In the last few years , next-generation surveillance has exploded with efforts to combine both event and syndromic indicator data from search engines [15 , 16] , crowdsourcing ( e . g . , Flu Near You in the US and Influenzanet in Europe ) [17 , 18] , Twitter ( e . g . , MappyHealth ) [19 , 20] , and Facebook [21 , 22] . While these new approaches are promising , public health agencies face the significant challenge of comprehensively integrating these diverse data sources to achieve specific surveillance objectives . Many next generation data sources , whether passively scraping data gathered for an incidental purpose or actively engaging volunteer participants , can be used to infer the underlying network through which disease , opinions or information spreads . Decades of sociology and epidemiology research have demonstrated that network structure can profoundly influence the spread of disease and behavior , and determine if and when individuals are affected [23–30] . In particular , there are diverse methods for quantifying the importance or centrality of a node ( individual ) in a network , many of which have been shown to predict epidemiological risk and indicate optimal targets for interventions such as vaccination [31–37 , 41–43] . In designing disease surveillance systems for networked populations , one seeks to identify nodes ( sensors ) that are likely to provide timely and accurate indications of epidemic activity . While analogous to the selection of efficient targets for vaccination on networked populations , the best sensors are not necessarily those most likely to be infected and infect others . Nodes that are the earliest or most often infected may be unreliable indicators of the broader epidemiological situation . Conversely , a representative cross-section of a network may provide accurate situational awareness , but the rate of detection from a representative cross-section may be too slow to serve as a timely trigger of control measures . Rapidity of targeted action during the initial phase of an outbreak is fundamental to the effectively curtailing transmission and minimizing disease burden . In previous work on livestock diseases , a network path based strategy has been proposed for identifying surveillance locations that would provide timely and accurate outbreak data [40]; in a recent analysis of disease surveillance in a high school population , Smieszek and Salathé introduce a promising sensor selection criteria ( total time students spend collocated with other students ) that is expected to yield timelier and more accurate information than alternative centrality-based criteria [47 , 48] . Christakis et al . performed an experimental comparison of two social-network-based strategies in a college population [46] . In one strategy , the sensors were a random selection of students; in the other , the sensors were identified as friends of one or more random students . The friends-of-random surveillance group was expected to be biased towards more central individuals , and provided an indication of the 2009–2010 pandemic H1N1 influenza epidemic that was two weeks earlier than the random surveillance group . Here , we use a mathematical model to systematically evaluate these and other strategies for selecting surveillance sensors across several networks and for an ensemble of common public health objectives . We quantify the timing and accuracy of the information gained by monitoring the disease states of strategically chosen sensors , as well as the robustness of the information across epidemiological scenarios characterized by different reproduction numbers , R0s . We find that the best surveillance targets are not always those with the highest epidemiological risk or those most representative of the underlying network .
We simulate disease outbreaks in contact networks using a stochastic chain-binomial model that classifies the disease status of individuals as susceptible-exposed-infected-recovered ( SEIR ) [44 , 45] . Networks consist of nodes representing individuals and edges between pairs of nodes representing contacts between individuals . The degree of a node is the number of other nodes to which it is connected via an edge . During a simulated epidemic , each node is in one of four states: susceptible ( S ) , exposed to disease but not yet infectious ( E ) , infectious ( I ) , or recovered ( R ) . If a node i in state S shares an edge with a node j in state I , then j will infect i with probability β and i will transition from S to E . After a period of l days , i will enter the infectious state I . It will remain infectious for d days , and then move to the immune state R . The reproduction number of a disease , denoted R0 , indicates the growth rate of an epidemic and the expected number of secondary infections arising from a single infected host in an entirely susceptible population . Sustained epidemics are only possible when R0 > 1 . In a random network , R0 is related to β as follows [49]: R 0 = β k 2 - k k , ( 1 ) where 〈k〉 and 〈k2〉 are the mean degree and the mean squared degree , respectively , of nodes in the network . R0 depends explicitly on both the intrinsic transmission rate of the pathogen and the structure of the network . For our analyses , we specify R0 and use Eq 1 to solve for the corresponding β . For the empirical networks considered , clustering , modularity and other non-random structures may cause the resulting R0 to differ slightly from the one initially specified . For each simulation , we fix the latent period to l = 4 days and the infectious period to d = 7 , roughly in the range of estimates for common respiratory diseases , including influenza [50 , 51] . Epidemics are initialized with a single random infected node and allowed to evolve until there are no remaining infected nodes . Social interactions often generate complex network structures , with features that impose non-trivial constraints on the flow of information , behavior and disease [52–55] . We evaluated network-based surveillance strategies using three classes of social networks with distinct topological attributes . The degree distributions of the scale-free and Montreal network resemble power laws [55 , 56] , while the student network has a relatively homogeneous ( Poisson ) degree distribution . The Montreal network , but not the other two , exhibits strong community structure [56] . We propose three strategies for designing network-based surveillance systems . Each strategy is a criteria for selecting a subset of individuals to monitor for their disease state: ( 1 ) most connected: select the highest degree individuals in the network; ( 2 ) random: select individuals at random; and ( 3 ) random acquaintance: select a random acquaintance of random individuals ( which should be biased toward high degree individuals [57] ) . These strategies are illustrated in Fig 1 for a scale-free network , where each surveillance subset includes five of the 100 nodes ( in red ) . The most connected strategy assumes complete knowledge of the network structure , whereas the random and random acquaintance strategies do not . We assess the performance of each surveillance strategy with respect to four different public health goals , listed below ( Fig 2 ) . For each strategy-network combination , we build surveillance subsets by selecting 1% of all nodes ( unless otherwise specified ) via the strategy . We then estimate performance by running stochastic SEIR simulations , and make the following four comparisons between the prevalence time-series in the whole population to that of surveillance subset: All results are averaged over 2000 stochastic SEIR simulations . At the beginning of each simulation , the surveillance subset is chosen anew according to the given strategy . For each objective function , we quantify both the magnitude of the effect and its robustness with respect to a key epidemiological quantity , R0 . High sensitivity of the information provided by a surveillance system to R0 indicates that the system may be unreliable or uninterpretable in situations where R0 is unknown or changing .
Following Newman [53] , we use percolation theory to model SIR epidemics on networks , and derive the optimal surveillance group for early detection of an epidemic . We consider a disease with transmissibility β and recovery rate γ spreading through a network of size N . During the initial outbreak , the probabilities of each node being infected at time t are approximated by the vector x ( t ) = e ( β κ - γ ) t v , ( 3 ) where κ is the leading eigenvalue of the adjacency matrix and v its corresponding eigenvector [53] . We extend this equation to calculate the time lag between a subset S of the network of size M ≤ N reaching a given prevalence threshold p and the overall population prevalence reaching p . Let 1 be the vector of length N containing all ones , 1 = ( 1 , … , 1 ) , and 1S be the binary vector of dimension N indicating which M nodes are under surveillance 1 S = 1 if node i is in the surveillance subset S 0 otherwise . For example , if the 1% most connected nodes were selected for surveillance in a network of size N = 1000 , then the entries of 1S corresponding to the ten highest degree nodes would be one , and the remaining entries would be zero . Let τ and τS be the times at which the entire population and a given surveillance group reach the prevalence threshold p and pS , respectively . Substituting into the above equation , we find p = x ( τ ) · 1 N = e ( β κ - γ ) τ v · 1 N ( 4 ) and p S = x ( τ S ) · 1 S M = e ( β κ - γ ) τ S v · 1 S M . ( 5 ) To solve for the timing of early warning achieved through surveillance Δτ = τS − τ , we equate p = pS , e ( β κ - γ ) τ v · 1 N = e ( β κ - γ ) τ S v · 1 S M . ( 6 ) This implies Δ τ = 1 β κ - γ ln c c S , ( 7 ) where c = v · 1/N and cS = v · 1S/M are the average eigenvector centralities in the network as a whole and the surveillance subset , respectively . The early season lag between the surveillance subset and the whole population can thus be positive or negative , and depends on ratio of their average eigenvector centralities . We assessed the validity of this mean field approximation by comparing the expected early warning period ( Eq 7 ) to simulated early warning periods for both the most connected subset and the subset of the 1% highest eigenvector centrality nodes . To match the assumptions of our mean field model , we simulated SIR rather than SEIR transmission dynamics . The simulations mirrored the theoretical expectations for both types of surveillance subsets in all three networks , as shown for the scale-free network ( Fig 6 ) . Next , we solve for the surveillance subset that maximizes the length of the early warning period . For a given surveillance system size M , the earliest warning is achieved when 1S indicates the M nodes in the network with the highest valued entries in v . Thus , the theoretically optimal surveillance strategy for early warning of epidemic onset selects nodes with the highest eigenvector centrality . Importantly , Δτ depends on the disease parameters β and γ . Regardless of the choice of surveillance nodes 1S , the timing of the early warning period will , therefore , increase as R0 decreases . An exception occurs when the average eigenvector centrality in the surveillance subset equals that in the population as a whole ( c = cS ) . In that case , there is no early warning ( Δτ = 0 ) . These properties are reflected in the sensitivity to R0 observed in our simulations ( Fig 4B , 4D and 4F ) . For the networks under consideration , the most connected strategy produces surveillance groups with relatively high eigenvector centrality while the random strategy yields groups with average eigenvector centrality . However , eigenvector centrality in random acquaintance groups depends on the underlying network: in homogeneous networks such as the student network , it will be average , whereas in heterogeneous networks , it will be above average . Identifying individuals with the highest eigenvector centrality is challenging in real-world populations , where the underlying network structure is generally unknown . However , finding individuals with above average degree centrality is possible using local information . If eigenvector centrality is correlated to degree centrality , as it is in the three networks we consider ( see Fig 7 ) , it may be possible to use highly connected nodes as a proxy for high eigenvector centrality nodes . One strategy for finding high degree centrality nodes is to follow chains of random acquaintances . This has been explored extensively in the context of respondent-driven sampling , such as chain-referral ( i . e . , “snowball” ) sampling [64] . In particular , consider the simple random walk in which , at each step , the walker moves to a neighboring node selected uniformly at random . For connected , undirected networks , this is equivalent to the PageRank algorithm with no damping factor [53] . Assuming the network is fully connected , the distribution of the random walker after m steps approaches a stationary distribution as m → ∞ , in which the probability of landing on a node is exactly proportional its degree [65] . Thus , the more connected the node , the more likely we are to reach it . Precisely , let ki be the degree of node i and P ( k ) the degree distribution of the network . The nth moment of the degree distribution is: ⟨ k n ⟩ = 1 N ∑ i k i n = ∑ k k n P ( k ) . ( 8 ) Let Dm denote the degree of the node at which the random walk resides on the mth step , starting from a node chosen uniformly at random . Assuming the mean degree 〈k〉 < ∞ , then the distribution of D∞ is given by kP ( k ) /〈k〉 . If 〈k3〉 < ∞ , which is true for any finite graph but will be violated for power-law networks without cutoff , the mean and standard deviation of D∞ are given by μ ∞ = ⟨ k 2 ⟩ ⟨ k ⟩ , σ ∞ = ⟨ k 3 ⟩ ⟨ k ⟩ - ⟨ k 2 ⟩ ⟨ k ⟩ 2 . ( 9 ) By comparison , the distribution of randomly sampled nodes ( D0 ) has mean μ0 = 〈k〉 and standard distribution σ 0 = 〈 k 2 〉 - 〈 k 〉 2 . Thus , the random walk sample is biased towards nodes with larger degrees . For intermediate values of m , the distribution of Dm can only be derived with full knowledge of the underlying graph . Instead , this distribution converges to that of D∞ at a rate that depends on the second largest eigenvalue of the adjacency matrix of the graph . If this eigenvalue is close to one , which is usually the case for connected networks with high modularity , convergence is very slow and random walk sampling may require numerous steps to achieve its optimal performance . Methods that bias the random walk towards higher eigenvector centrality nodes should be more effective in this setting . For example , the maximal entropy random walk samples nodes proportional to eigenvector centrality just as the simple random walk considered earlier samples nodes according to their degree centralities [66] . However , the transition probabilities of the the maximal entropy random walk require global information about the network , making it impractical to implement without approximation as part of the sampling strategy . Eq 9 provide a theoretical upper bound to the mean centrality that can be achieved when using a random walk on a network to design a surveillance system . In particular , for a random-walk surveillance subset of size M = ϵN with fixed ϵ and large N , the empirical mean of the sample will become approximately normal with mean μ∞ and standard deviation σ ∞ / M , as illustrated for our three study networks ( Fig 8 ) . The success of both traditional surveillance systems such as the U . S . Outpatient Influenza-like Illness Surveillance Network ( ILINet ) and next generation participatory systems including FluNearYou [17 , 18] , depends on targeted recruitment of reliable , informative providers . With Meaningful Use and the advent of digital disease detection , we are moving from an era of sparse , volunteer-based data into an era of data inundation [16 , 61] . Nonetheless , we still face the challenge of finding reliable data sources . Effective mining of electronic medical records , social media and other internet source data , such as Google , Twitter or Facebook , requires sifting through petabytes of data for streams that can provide early and accurate information about emerging outbreaks . While random representative sampling is a good rule-of-thumb and has guided the development of numerous surveillance systems , we can improve the timeliness of surveillance by exploiting our evolving understanding of social networks and their impacts on infectious disease dynamics [24 , 28–30 , 45 , 49 , 52–55 , 62 , 63] . In an ideal scenario where both the contact network and the reproduction number ( R0 ) of the disease are known in advance , public health agencies can monitor the most informative nodes and achieve very accurate and early assessments of emerging epidemics . For example , we find that surveillance of the most connected individuals in the Montreal WiFi network can increase lead time on detecting epidemic emergence by two to three weeks and anticipating the epidemic peak by over a week . We show analytically that the optimal strategy for early detection of emerging outbreaks is targeting individuals with the highest eigenvector centrality , a measure that considers the connectivity of a node’s neighbors , and those neighbors’ neighbors , and so on [53] . It can only be calculated with full knowledge of the network , and estimates the proportion time spent on a node during an infinitely long random walk along the edges of the network . While providing the longest lead time ( between the surveillance system crossing a prevalence threshold and the rest of the population crossing that threshold ) , the timing is highly dependent on R0 . In fact , regardless of which nodes are under surveillance , epidemiological activity becomes more synchronized and the lag time shrinks as R0 increases . This ideal scenario is generally unrealistic . When the contact network is unknown , we cannot easily identify the most central individuals , for many measures of centrality . Even if we could monitor the most connected individuals , correct interpretation of the resulting signal requires some knowledge of R0 . In general , low R0 implies a longer lag time between epidemiological events in the surveillance group and corresponding events in the general population , and a larger discrepancy between prevalence in the surveillance group and overall epidemiological activity . Several recent studies have identified epidemiologically relevant measures of centrality that can be estimated from readily obtainable school , social network , and workplace data [42 , 43 , 47 , 48] . We hypothesize that these more tractable centrality-based sensors may exhibit a similar trade off between timeliness and robustness . The random acquaintance strategy , which chooses random contacts of random nodes , provides a practical method for identifying individuals with higher than average centrality . The intuition is that when choosing a random friend of a node rather than just a random node , the choice is biased towards individuals with more friends . In heterogeneous networks , such as the scale-free and Montreal WiFi network considered here , random acquaintance groups provide some degree of early warning ( significantly more than randomly selected nodes ) and exhibit epidemic curves that reflect overall disease activity ( significantly better than the most connected nodes ) . This is corroborated by the empirical finding that friends of random students served as better outbreak sentinels than random students during 2009 H1N1 pandemic [46] . Although the timing of the early warning and the discrepancy between the estimated prevalence and true prevalence will depend on R0 , the uncertainty can potentially be quantified and incorporated into confidence intervals . In a relatively homogeneous network , such as the Venezuelan student network , the random acquaintance strategy finds fairly average nodes and does not improve upon the random strategy with respect to the surveillance objectives . This finding is consistent with basic theory on Erdős-Réyni networks: in a random network with a Poisson degree distribution , the average degree of random acquaintances will be exactly average [57] . Therefore , if a population is sufficiently homogeneous , surveillance systems should simply target random individuals or employ other methods for identifying highly connected individuals . We conclude that the friends-of-random strategy , while not optimal for all public health objectives , balances risk and representativeness , provides reasonably robust , accurate and early warning , and can be applied without knowledge of the underlying contact network . Volunteer-based surveillance systems , like Flu Near You , could potentially improve coverage by recruiting friends of existing members . Network analysis , in general , allows us to anticipate individual-level epidemiological risk and can thereby help us improve and strategically extend surveillance systems to enhance the early and reliable identification of outbreaks . | As public health agencies strive to harness big data to improve outbreak surveillance , they face the challenge of extracting meaningful information that can be directly used to improve public health , without incurring additional costs . In this article , we address the question: Which nodes in a social network should be selectively monitored to detect and monitor outbreaks as early and accurately as possible ? We derive best-case performance scenarios , and show that a practical strategy for data collection–recruiting friends of randomly selected individuals–is expected to perform reasonably well , in terms of the timing and reliability of the epidemiological information collected . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"infectious",
"disease",
"epidemiology",
"sociology",
"social",
"sciences",
"mathematical",
"models",
"mathematics",
"scale-free",
"networks",
"algebra",
"network",
"analysis",
"social",
"networks",
"infectious",
"disease",
"control",
"research",
"and",
"analysis",
"methods",
"random",
"walk",
"infectious",
"diseases",
"computer",
"and",
"information",
"sciences",
"epidemiology",
"mathematical",
"and",
"statistical",
"techniques",
"centrality",
"infectious",
"disease",
"surveillance",
"eigenvectors",
"linear",
"algebra",
"disease",
"surveillance",
"physical",
"sciences"
] | 2016 | Disease Surveillance on Complex Social Networks |
The actin capping protein ( CP ) tightly binds to the barbed end of actin filaments , thus playing a key role in actin-based lamellipodial dynamics . V-1 and CARMIL proteins directly bind to CP and inhibit the filament capping activity of CP . V-1 completely inhibits CP from interacting with the barbed end , whereas CARMIL proteins act on the barbed end-bound CP and facilitate its dissociation from the filament ( called uncapping activity ) . Previous studies have revealed the striking functional differences between the two regulators . However , the molecular mechanisms describing how these proteins inhibit CP remains poorly understood . Here we present the crystal structures of CP complexed with V-1 and with peptides derived from the CP-binding motif of CARMIL proteins ( CARMIL , CD2AP , and CKIP-1 ) . V-1 directly interacts with the primary actin binding surface of CP , the C-terminal region of the α-subunit . Unexpectedly , the structures clearly revealed the conformational flexibility of CP , which can be attributed to a twisting movement between the two domains . CARMIL peptides in an extended conformation interact simultaneously with the two CP domains . In contrast to V-1 , the peptides do not directly compete with the barbed end for the binding surface on CP . Biochemical assays revealed that the peptides suppress the interaction between CP and V-1 , despite the two inhibitors not competing for the same binding site on CP . Furthermore , a computational analysis using the elastic network model indicates that the interaction of the peptides alters the intrinsic fluctuations of CP . Our results demonstrate that V-1 completely sequesters CP from the barbed end by simple steric hindrance . By contrast , CARMIL proteins allosterically inhibit CP , which appears to be a prerequisite for the uncapping activity . Our data suggest that CARMIL proteins down-regulate CP by affecting its conformational dynamics . This conceptually new mechanism of CP inhibition provides a structural basis for the regulation of the barbed end elongation in cells .
The actin capping protein ( CP ) specifically binds to the barbed end of actin filaments with a high affinity and prevents the addition and loss of the monomers at this dynamic end [1] , [2] . CP is a heterodimeric protein composed of α- and β-subunits and the molecule displays a pseudo two-fold symmetry due to the resemblance of the tertiary structures between the two subunits [3] . CP caps the filament with its two independent actin binding sites at the C-terminus of each subunit ( “tentacles” ) . The tentacles are functionally non-equivalent: the α-tentacle is more important than the β-tentacle and is responsible for the initial contact with the barbed end [4] . A recent cryo-electron microscopy ( EM ) study provided a structural model for the barbed end capping by CP [5] . The model depicted the α-tentacle , with its surrounding residues in the β-subunit , wedged between the two end actin protomers , which represents the primary contact between CP and actin . A mutational analysis revealed that three conserved basic residues in this region , CP ( α ) Lys256 , Arg260 , and Arg266 ( in the chicken α1 isoform ) , are critical for the barbed end capping [5] . The β-tentacle was predicted to interact with a hydrophobic cleft on the surface of the terminal protomer to stabilize the capping [5] . A growing body of evidence indicates that CP is a key regulator of actin-based lamellipodial dynamics . In vitro , CP is one of the essential proteins required for the formation of the Arp2/3 complex-nucleated branched-actin arrays , which drive lamellipodial protrusion [6] . CP prevents the production of longer filaments and maintains the cytosolic G-actin pool to promote the Arp2/3 complex-based filament nucleation and branching [7] . In mammalian cells , CP depletion leads to the explosive formation of filopodia , rather than lamellipodia [8] . Thus , the local concentration of CP and its affinity to the barbed end are critical determinants of dendritic actin assembly . The dissociation of CP from the barbed end is a rare event ( t1/2∼30 min ) in actin polymerization assays using purified proteins . However , recent microscopic observations of cultured cells showed that the fluorescent speckle lifetime of CP bound to actin filament network structures is on the order of seconds [9] , [10] , suggesting that CP does not stably cap the barbed end in living cells . At present , several molecules have been identified that affect the barbed end capping activity of CP . These regulators can be categorized in two groups: ( 1 ) indirect regulators that bind to actin filaments and protect the barbed end from CP and ( 2 ) direct regulators that bind CP and modulate its capping activity . Formin is an indirect regulator because it associates with the barbed end and allows filament elongation even in the presence of CP [11] . Ena/VASP is also assumed to antagonize the capping activity without interacting directly with CP [12] . Polyphosphoinositides , such as PIP2 , bind directly to CP and reduce the capping activity in vitro [13] , [14] . The V-1 and CARMIL proteins are the only direct CP regulatory proteins that have been reported . V-1 , also known as myotrophin , is a 13 kDa ankyrin repeat protein that consists of four ankyrin repeat motifs; two full-repeats are sandwiched between additional incomplete motifs at each terminus [15] . V-1 has been implicated in a variety of cellular events , including catecholamine synthesis [16] , cerebellar development [17] , cardiac hypertrophy [18] , and insulin secretion [19] . Although the precise functional roles of V-1 in these processes have not been clarified , it is possible that V-1 acts as a CP regulator in vivo , because V-1 was found to form a complex with CP in primary-cultured cells and cell lines in murine cerebella [20] , [21] . CARMIL is a multi-domain protein that reportedly interacts with myosin I , Arp2/3 complex , and CP [22] . Down-regulation of CARMIL resulted in impaired motility in Dictyostelium and mammalian cells [22] , [23] . Although CARMIL is a large protein ( ∼150 kDa ) , its CP interaction site has been narrowed down to a small region [23] , [24] , and a ∼20 amino acid sequence in this region [CP-binding motif; LXHXTXXRPK ( 6X ) P] is shared with other proteins , CD2AP , CIN85 , and CKIP-1 [25] . All of these proteins ( CARMIL proteins ) can interact with CP via this consensus motif [25] . CD2AP and its homologue CIN85 are adaptor proteins involved in various cellular processes , such as T-cell activation , apoptosis , and actin cytoskeleton dynamics [26] . CKIP-1 interacts with casein kinase 2 and recruits the enzyme to the plasma membrane [27] . Previous studies have demonstrated that the V-1 and CARMIL proteins inhibit CP in distinct manners . ( 1 ) V-1 bound to CP blocks actin filament capping , whereas the CP/CARMIL protein complex has lower barbed end capping activity ( KD∼15 nM ) than free CP ( ∼1 nM ) [23] , [28] , [29] . ( 2 ) CARMIL acts on the barbed end-bound CP and facilitates its dissociation from the filament ( called uncapping activity ) , but V-1 lacks this activity [23] , [25] , [28] , [29] . ( 3 ) The two actin binding sites in CP , the α- and β-tentacles , are not involved in the CARMIL interaction , whereas V-1 recognizes these sites [23] , [28] . ( 4 ) The CP binding fragment of CARMIL , including the CP-binding motif , has little secondary structure . In contrast , V-1 is a structured ankyrin repeat protein [15] , [23] . Although previous studies have revealed the striking functional differences between the two direct CP regulators , the molecular mechanisms by which these proteins inhibit CP remain poorly understood . In particular , the mechanism by which the CARMIL proteins uncap the filament that is tightly bound by CP has remained enigmatic . In this study , we present the crystal structures of CP complexed with V-1 and with peptides derived from the CP-binding motif of CARMIL proteins . Together with biochemical and computational studies , we have elucidated two distinct mechanisms for CP regulation by V-1 and CARMIL proteins—steric hindrance and allosteric restriction of conformational fluctuations .
To gain insight into the structural basis for the inhibition of CP by V-1 , we solved the crystal structure of CP ( chicken α1/β1 ) in complex with V-1 ( human ) . The CP/V-1 complex was crystallized and the X-ray structure was determined at 2 . 2 Å resolution ( R = 0 . 186 , Rfree = 0 . 237 ) by molecular replacement , using the CP structure ( PDB: 1IZN ) as a search model ( Figure 1B and 1C , and Table S1 ) . CP contacts V-1 at two binding sites: ( 1 ) the basic residues at the C-terminus of the α-subunit and ( 2 ) a hydrophobic pocket adjacent to the basic contact site described above ( Figures 2A and S1 ) . Three conserved basic residues in the CP α-subunit , Lys256 , Arg260 , and Arg266 , were shown to be critical for the barbed end capping [5] . Remarkably , this “basic triad” directly participates in the V-1 interaction ( Figure 2B ) . Arg260 , the center of the “basic triad , ” forms a bidentate salt bridge with V-1 Asp44 . In addition , Lys256 and Arg266 form salt bridges with V-1 Glu78 . Furthermore , Lys256 also forms a hydrogen bond with the main chain oxygen of V-1 Asp44 . These notable ion pairs involving the “basic triad” clearly indicate that V-1 specifically binds conserved residues important for the interaction with actin , thereby effectively abolishing the barbed end capping . The importance of these ion pairs for complex formation was confirmed by a mutational analysis . We determined the CP/V-1 binding affinity by surface plasmon resonance measurements . Mutations of residues which form the “basic triad , ” or their ion-pairing residues in V-1 , reduced the affinity more than 25-fold compared with the wild type proteins ( KD = 21 nM: binding constants for the mutant proteins are summarized in Table S2 ) . The effects of mutations in the “basic triad” on the V-1 interaction are similar to those on the barbed end capping: reverse-charged mutants have lower affinities for V-1 than alanine mutants , and multiple mutations exhibit more severe defects than single mutations [5] . Another striking feature in the CP/V-1 interface is the hydrophobic contact formed around V-1 Trp8 ( Figure 2C ) . In V-1 , Trp8 on the V-1 helix 1 inserts its indole ring into a hydrophobic pocket , which is formed by CP ( α ) Ala257 and Leu258 , immediately adjacent to the “basic triad , ” and CP ( β ) Gly138 and Ile144 in “loop S5–S6” ( a loop connecting β-strands 5 and 6 of the β-subunit ) . This hydrophobic contact is further stabilized by a hydrogen bond between the aromatic nitrogen of the tryptophan and the main chain oxygen of CP ( β ) Ile144 . Mutation of this tryptophan , V-1 W8A , drastically reduced the affinity for CP ( KD = 6 . 4 µM ) . As expected , the CP binding-deficient V-1 did not inhibit CP in an actin polymerization assay ( Figure S2 ) . The wild-type V-1 allowed actin elongation from spectrin-actin seeds , even in the presence of CP . In contrast , the CP-binding deficient V-1 mutants ( V-1 W8A , D44R , or E78R ) had little inhibitory effect on CP activity . We superposed the structure of the CP/V-1 complex onto the previous EM model of CP on the barbed end of an actin filament ( Figure 3 ) [5] . This unambiguously demonstrated the collision of a major part of the V-1 molecule with the filament , mainly with subdomain 3 of the penultimate protomer . Furthermore , V-1 should prevent CP from even an initial contact with the barbed end , as it masks the “α-tentacle” by interacting with the “basic triad” residues ( Figure 2B ) . Collectively , V-1 completely inhibits CP from interaction with the actin filament . The structure also indicates that V-1 lacks uncapping activity , because the V-1 binding site on CP is buried deeply between the two end protomers when CP caps the filaments . Although the association of V-1 with CP has been reported in vivo [20] , [21] , it remains unknown whether V-1 is involved in the regulation of cellular actin assembly . We addressed this question by using the rat neuronal PC12D cell line V1-69 , which is stably transfected with V-1 cDNA and expresses a 5- to 6-fold higher amount of V-1 than the mock transfectant C-9 [16] . Initially , we measured the ratio of F-actin to G-actin by a sedimentation assay and found that more actin pelleted from extracts of V1-69 cells than mock cells ( Figure 4A ) . This indicates that the overexpression of V-1 leads to enhanced actin polymerization in PC12D cells . We next examined the amount of CP in subcellular fractions . In the V1-69 cells , the proportion of CP in the “high speed supernatant” fraction was significantly larger than that of the mock transfectant . This result was inversely correlated with a decrease in the distribution of the “high speed pellet insoluble in detergent” fraction ( Figure 4B: see Materials and Methods for the subcellular fractionation procedure ) . The overexpression of V-1 did not alter the total amount of CP in the transfectants ( unpublished data ) . These results imply that V-1 enhances actin polymerization by inhibiting the interaction of CP with the cytoskeleton structures . Moreover , we observed that , compared to the mock cells , V1-69 cells exhibited membrane protrusive structures with a thick , neurite-like appearance ( Figure 4C ) . Phalloidin staining revealed that these protrusions were enriched with actin filaments ( Figure 4C ) , implying that CP suppression caused by V-1 overexpression leads to the alteration of cell morphology presumably due to the increase in the level of actin polymerization . Taken together , our results demonstrate the possible involvement of V-1 in the regulation of actin polymerization and cellular morphology in living cells . With the exception of the mobile “β-tentacle , ” CP has been considered to be a rigid heterodimeric protein that is stabilized by many intra- and inter-subunit interactions [3] . However , we found that the overall conformation of V-1-bound CP ( CPV-1; Figure 5B ) is apparently different from the free form ( CPfull; PDB; 1IZN; Figure 5A ) ; e . g . , the “antiparallel H5s” is straighter and the “N-stalk” and “β-globule” are further apart . Superposition of the two structures was poor , with a root-mean-square displacement ( RMSD ) over the Cα atoms of 2 . 55 Å [residues 9–275 ( α ) and 3–244 ( β ) ; the “β-tentacle” was not included] ( Figure 5C ) . This unexpected finding indicates that CP has conformational flexibility . For further structural comparison , we obtained a new ligand-free CP structure crystallized under different conditions from 1IZN ( CPβΔC; at a 1 . 9 Å resolution ) ( Figure S3 ) and found that the structure of CPβΔC is substantially different from both CPfull and CPV-1 ( RMSDs of 1 . 34 Å and 1 . 87 Å , respectively ) ( Figure 5C and Table S3 ) . These values are much larger than those expected for the same protein crystallized under different conditions ( ∼0 . 8 Å ) [30] . Therefore , we conclude that CP conformational changes are not induced solely by the binding of a ligand molecule but show that CP is an intrinsically flexible molecule . A domain motion analysis revealed that CP comprises two structurally stable domains , and the conformational change can be attributed to a twisting movement between the domains ( Figures 5D–G and S4 ) . The larger domain contains roughly two-thirds of the CP residues [residues 1–258 ( α ) : 1–42 , 175–192 , and 235–277 ( β ) ] and consists of the entire “N-stalk , ” “α-globule , ” and “β-tentacle” motifs together with parts of the “central β-sheet” and “antiparallel H5s , ” whereas the smaller domain [residues 259–286 ( α ) : 43–174 and 193–234 ( β ) ] consists of the remaining portion . We refer to these larger and smaller domains as the CP-L and CP-S domains , respectively . Each domain superimposed well across the three forms ( RMSDs of 0 . 80–1 . 06 Å for the CP-L domain and 0 . 80–1 . 04 Å for the CP-S domain ) ( Table S3 ) . The boundary of the two domains does not directly correspond to the subunit interface; it resides between the “N-stalk” and “β-globule . ” The two domains are linked by flexible regions , such as a short linker [Asp43–Leu47 ( β ) ] between the “N-stalk” and “β-globule” and the helix-breaking residues [Thr253 ( α ) or Gly234 ( β ) } in “antiparallel H5s . ” These regions may act as hinges to facilitate domain movement . To explore the structural basis of CP inhibition by CARMIL proteins , we attempted to determine the structures of CP in complex with CARMIL proteins . Since the CP-binding motif of the CARMIL proteins is sufficient for the interaction with CP [25] , peptides derived from this motif were used for the crystallographic studies; mouse CARMIL ( residues 985–1005; referred to as CA21 ) , human CD2AP ( 485–507; CD23 ) , and human CKIP-1 ( 148–70; CK23 ) ( we collectively refer to these synthetic peptides derived from CARMIL proteins as CARMIL peptides ) ( Figure 6A ) . In addition , we chose CPβΔC for crystallization , since the “β-tentacle” does not participate in the CARMIL interaction [23] . All of the crystals were grown under conditions similar to those for the ligand-free CPβΔC , and the structures were solved at 1 . 7–1 . 9 Å resolutions ( R = 0 . 184–0 . 213 , Rfree = 0 . 238–0 . 263 ) ( Table S1 ) . The three crystal structures are shown in Figure 6B–D . As expected from the sequence similarity , all three peptides bound to essentially the same surface on CP . A superposition of the three structures further highlights the structural similarity , especially in their N-termini ( Figure 6E ) . In contrast , the C-termini showed some diversity , probably due to the lack of consensus residues and the different peptide lengths . The peptides in our structures are largely unfolded , as previously indicated by a circular dichroism analysis [23] . Each elongated peptide binds along a continuous curved groove on the surface of the CP β-subunit . The peptides are bent by 100° at the conserved proline residue in the middle of the CP-binding motif . The consensus motif interacts with CP across the two domains: the N-terminus with the CP-L domain and the C-terminus with the CP-S domain ( Figure 6E ) . The conformations of CP within the CP/CARMIL peptide complexes are similar to each other ( RMSDs; 0 . 71–0 . 90 Å ) and are slightly different from either CPfull or CPβΔC ( RMSDs; 0 . 97–1 . 26 Å ) ( Table S3 ) , suggesting that , unlike V-1 , the CARMIL peptides do not cause a large conformational change to CP . The binding between CP and the CARMIL peptides is primarily mediated by electrostatic interactions , which are supported by hydrophobic interactions ( Figures 7A and S5 ) . The mutation of a conserved arginine in the middle of the motif ( Arg493 in CD23; indicated by an asterisk in Figure 6A ) reportedly abolished CP binding for all of the peptides [23] , [25] , [31] . This central arginine makes multiple interactions with both the CP-L and CP-S domains , by forming a salt bridge with CP ( β ) Asp44 , and hydrogen bonds with CP ( β ) Ser41 and Tyr64 ( Figure 7B ) . We confirmed the importance of the intermolecular interface residues of CP by biochemical assays using mutant CP proteins ( Figure 8 and Table 1 ) . Among the mutant CP proteins , CP ( β ) D44N exhibited the lowest affinity for the CARMIL peptides . In addition to their extensive interactions through the CP-binding motif , CD23 and CK23 further associate with the CP “N-stalk” via the C-terminal flanking residues of the motif . In the CP/CD23 complex , CD Phe505 contacts the hydrophobic pocket formed by the CP “N-stalk” residues [CP ( β ) Ile29 , Cys36 , and Leu40] and the peptide residues ( CD Leu501 and Pro502 ) ( Figure S6A ) . In the CP/CK23 complex , the C-terminal residue of the peptide , CK Arg169 , forms an electrostatic interaction with CP ( β ) Asp30 ( Figure S5B ) . In contrast to these two peptides , CA21 does not contact CP via the C-terminal flanking region ( Figures 9A and S5A ) . We tested the importance of the C-terminal flanking regions of the CP-binding motif using a binding assay ( Table 2; the constructs used for the measurement are shown in Figure 9B ) . Surprisingly , GST-CD43 , lacking CD Phe505 but containing the entire consensus motif , bound to CP only weakly with a KD of 260 nM , suggesting that the CP-binding motif of CD2AP alone is not sufficient for stable interaction with CP . In contrast , longer constructs with extended C-terminal residues showed higher CP binding affinities than the shorter fragments . GST-CD47 , containing CD Phe505 , bound to CP with a KD of 18 nM and GST-CD56 bound tightly to CP ( KD = 4 . 7 nM ) , in good agreement with the previously reported value ( KD = 5 . 6 nM for GST-CD2AP fragment containing residues 474–513 [25] ) . The C-terminus of CD23 extends into the region between the CP-L and CP-S domains ( Figure S6B ) . Thus , the residues immediately C-terminal to CD23 ( i . e . , CD Gly508∼ ) are expected to form additional contacts with the domain boundary residues to stabilize the CP/CD2AP complex . Collectively , the C-terminal flanking region of the consensus motif is required for the stable interaction between CP and CD2AP . We also examined GST-CARMIL fragments ( Table 2 and Figure 9C ) . Both GST-CA55 and GST-CA63 , containing the entire CP-binding motif and 10 or more extra residues at either end , bind only to CP with KDs in the micromolar range . This confirms that the consensus motif alone cannot tightly bind to CP . Moreover , unlike CD2AP , the CARMIL residues immediately C-terminal to the motif do not contribute to the stable CP interaction , consistent with our structure in which CA21 does not contact CP in this region . The stable CP interaction was observed in longer CARMIL fragments . GST-CA76 was found to have modest binding affinity to CP ( KD = 80 nM ) and GST-CA92 bound strongly to CP ( KD = 3 . 3 nM ) and with a comparable KD to GST-CD56 . We next evaluated the CP-binding affinity of CK23 by a competition assay and found that both CD23 and CK23 effectively compete with immobilized GST-CA92 for CP binding , whereas CA21 was a less efficient competitor ( Figure S7 ) . Thus , CK23 appears to have CP binding affinity comparable to CD23 . The CP binding affinity of the CARMIL peptides directly correlated with their ability to inhibit the barbed end capping . CD23 and CK23 moderately inhibited barbed end capping by CP , while CA21 was a poor inhibitor ( Figure 9D ) . Furthermore , CD30 , a peptide with 7 extra residues at the C-terminus of CD23 , showed higher CP inhibition activity than CD23 ( Figure 9D ) . Although weaker than CD23 or CK23 , CA21 retained the ability to inhibit CP , since CA21 attenuated the barbed end capping by CPβΔC ( Figure 9E ) , which is a less potent capper compared to CPfull [4] . Intriguingly , all peptides tested effectively inhibited CPβΔC , suggesting that CARMIL peptides do not inhibit CP simply by preventing the “β-tentacle” from filament binding . We next tested the CP inhibitory activity of GST-CARMIL constructs . As expected from their CP binding affinities , GST-CA92 showed the strongest CP inhibitory effect ( Figure 9F ) . GST-CA92 appears to have full CP inhibition activity , because it showed a similar level of inhibition as GST-C-1 ( residues 962–1084 ) , which has the same activity as the full length CARMIL ( unpublished data [23] ) . A superposition of the crystal structures of the CP/CARMIL peptide complexes onto the EM model of the CP/actin filament structure clearly revealed that none of the peptides on CP overlap with the barbed end actin protomers ( Figure 10 ) . As described above , all of the peptides used for the crystallization have varying degrees of CP inhibition activity ( Figure 9D–F ) . Furthermore , the C-terminal flanking residues of CD23 , which greatly contribute to the CP inhibition , cannot reach the nearest surface of the actin filament . Therefore , unlike V-1 , the CARMIL peptides do not inhibit the barbed end capping activity of CP by steric hindrance . This non-overlapping CP interaction , permitting the CARMIL peptides to interact with the filament-bound CP , is a prerequisite for the uncapping activity . Furthermore , the “α-tentacle” including the “basic triad” on the top surface of CP , the primary actin binding site , is still exposed even when CP is bound with CARMIL proteins . This allows the CP/CARMIL protein complex to make an initial contact with the barbed end , and thus CARMIL proteins cannot sequester CP completely from the barbed end . The CP binding site of V-1 is located on an opposite face from the CARMIL peptide binding site , implying that CP can simultaneously bind both inhibitors . Conversely , we found that the conformation of CPV-1 is significantly different from that of the CARMIL peptide-bound CP ( CPCARMILs ) ( Table S3 ) , because the binding of V-1 induces a twisting movement of the CP-L and CP-S domains . This raises the possibility that the CARMIL peptides allosterically inhibit CP from binding V-1 by restricting the domain twisting , since the peptides bind to CP across the two domains . We tested this prediction using a surface plasmon resonance assay . We immobilized GST-V-1 on a sensor chip , and then perfused with CP premixed with CARMIL peptides . Surprisingly , CD23 and CK23 , which possess substantial affinity for CP , strongly inhibited the CP/V-1 interaction , indicating that the peptides restrict the conformation of CP to the “low affinity to V-1” form ( Figure 11A ) . This inhibition depends on the CP/CARMIL peptide interaction because CA21 , which has a lower CP binding affinity than the other peptides , exhibited minimal inhibition ( Figure 11A ) . Furthermore , none of the peptides tested could prevent CP ( β ) D44N , a mutant CP deficient in CARMIL protein interaction ( Table 1 and Figure 8 ) , from the V-1 interaction ( Figure 11B ) . Most notably , in addition to its effect on free CP , the CARMIL peptides can act on CP pre-bound to V-1 and facilitate the dissociation of the complex . When the preformed CP/V-1 complex bound on the sensor chip was perfused with CD23 or CK23 , CP dissociated from V-1 quite rapidly , as compared with the buffer control ( Figure 11C ) . Again , we found that CA21 was less effective in facilitating the dissociation ( Figure 11C ) , and that the interaction between CP ( β ) D44N and V-1 was not affected by CARMIL peptides ( Figure 11D ) . This result suggests that the CARMIL peptides possess the ability to interact with CP in a conformation different from CPCARMILs and to shift the CP conformation toward the CPCARMILs form . We further confirmed the effect of the CARMIL peptides on CP/V-1 interaction by a pull-down assay . Under equilibrium conditions , the binding of CP to GST-V-1 was inhibited by the addition of the peptides in a concentration-dependent manner ( Figure S8 ) . Collectively , we concluded that the CARMIL peptides allosterically inhibit CP binding to V-1 . To further explore the intrinsic flexibility of the CP molecule , we performed a normal mode analysis with an elastic network model ( ENM ) . In this model , a protein is considered as a simple elastic object , and the spatially neighboring residues in the native structure are connected by Hookian springs . Based on this approximation , the intrinsic fluctuations originating from the protein shape are revealed . The normal mode analysis on the ENM has been applied to various sizes of proteins , e . g . , lysozyme [32] , F1-ATPase [33] , and chaperonin GroEL [34] . Referring to the lower frequency modes , the analysis succeeded in reproducing large conformational motions that had been experimentally revealed [35] . We applied this method to the CP/CD23 complex ( Figure 12A ) and the CP structure extracted from the complex ( Figure 12B ) , and focused on the first lowest modes . The first lowest mode of CP can be described as twisting motions relative to two axes , which run through the α- and β-subunits , respectively ( Figure 12A and Table S4; see Materials and Methods for more details ) . In this mode , the directions of the twisting movements about the two axes are opposite from each other ( indicated by black and gray sets of arrows in Figure 12 ) . Among these two axes , the β-subunit axis almost coincides with the axis of the twist movement between the CP-L and CP-S domains that was revealed by the structural comparison ( red rods with asterisk in Figure 12 ) . This finding strengthens the notion that CP continually undergoes substantial twisting movements about this axis . Furthermore , we found that the CARMIL peptides alter this intrinsic mode , both in the direction of the rotational axis and the amplitude of the motion ( Figure 12B ) . These effects are observed almost exclusively in the twisting motion about the β-subunit axis , yet not about the α-subunit axis , suggesting that the CARMIL peptide suppresses the twisting movement between the CP-L and CP-S domains .
The crystal structure of the CP/V-1 complex revealed that V-1 mainly interacts with the “α-tentacle , ” the primary actin binding surface of CP , thereby sterically hindering CP from barbed end capping ( Figures 1–3 ) . The structure supports biochemical data that V-1 has no uncapping activity ( Figure 13D ) . A sequence alignment of V-1 indicates that the residues involved in the V-1 interaction are highly conserved through evolution , despite their relatively minor contributions to the protein fold ( Figure S9 ) . Furthermore , the “basic triad” in the CP α-subunit , containing the highly conserved residues critical for actin binding is also recognized by V-1 . This suggests that the architecture of the V-1 molecule is well suited for the interaction with CP , i . e . , CP inhibition is the key role for V-1 in various cellular processes . This notion is further supported by the finding that , in cultured cells , V-1 is involved in the regulation of actin assembly and cell morphology ( Figure 4 ) . We note that CARMIL peptides inhibit CP from binding V-1 ( Figures 11 and S8 ) , indicating that the effect of V-1 on CP may be under the control of other proteins which interact with CP or V-1 . Future studies will verify the role of V-1 in actin-driven cell motility . An unexpected finding in this study was the conformational flexibility of the CP molecule . A structural comparison analysis revealed that CP consists of two rigid domains , CP-L and CP-S , and undergoes conformational changes even in the absence of a ligand ( Figure 5 ) . This intrinsic twisting motion between the two CP domains was further supported by a normal mode analysis of free CP ( Figure 12A ) . Intriguingly , our analysis also predicts that , in addition to the domain twist related to the rotational axis passing through the β-subunit , there might be an analogous twisting movement about the α-subunit axis . This is plausible because CP has pseudo 2-fold rotational symmetry [3] . Thus , the CP-L domain might be further divided into two rigid subdomains , which also undergo a twisting movement relative to each other . Our data showed that the CP-binding motif of CARMIL proteins cannot bind tightly to CP , despite the multitude of intermolecular interactions present in the structures ( Figures 7 , 9 , S5 , and Table 2 ) . This is attributable to the conformational fluctuation of CP , as the consensus motif interacts with residues at the domain boundary that may act as a hinge in the twisting movement . We demonstrated that the regions C-terminal to the CP-binding motif are responsible for the strong interactions between CP and CARMIL proteins ( Table 2 ) . Thus , the consensus motif and the flanking region may reciprocally increase their affinity for CP , which in turn would inhibit CP effectively . The tight interaction between CP and the barbed end is contributed by the extensive inter-molecular surface residues [5] . Consequently , the intrinsic twisting motion between the two CP domains that can cause changes in the overall structure must affect the capping activity of CP . Therefore , for a stable filament capping , CP accommodates its shape to a favorable conformation for the barbed end interaction . Consequently , we have revised the previous two-step capping model [5] as follows: ( i ) “Basic triad” residues on the CP “α-tentacle” region interact electrostatically with the barbed end . This initial contact is followed by two independent stabilization steps: ( ii ) an adaptive conformational change to a “high affinity to the barbed end” form that is a twisting movement between the CP-L and CP-S domains and ( iii ) the supportive binding of the “β-tentacle” to the filament ( Figure 13B ) . Hence , a factor which disturbs either of the capping steps has an inhibitory effect on the filament capping activity of CP . For example , V-1 sterically hinders CP from the barbed end by blocking step ( i ) . How do CARMIL proteins inhibit the capping activity of CP in an allosteric manner ? We showed that CARMIL peptides allosterically inhibit the interaction of CP with V-1 ( Figures 11 and S8 ) . This finding indicates that , regardless of the initial CP state ( i . e . , free or V-1-bound ) , the peptides binding across the two CP domains shift the conformational distribution to within a narrow range around CPCARMILs , conformations that are unfavorable for V-1 binding . We propose that CARMIL proteins inhibit CP in a similar manner ( Figure 13C ) ; CARMIL proteins limit the conformational distribution of CP to mostly the “low affinity to the barbed end” form , leading to attenuation of the barbed end capping activity [i . e . , step ( ii ) in Figure 13B is inhibited] . Fujiwara et al . indicated that CARMIL does not affect the association of CP to the barbed end but accelerates its dissociation from the filament since the on rate of the CP/CARMIL complex to the barbed end is virtually the same as that of free CP ( 3 . 7 µM−1s−1 versus 2 . 6 µM−1s−1 ) , while the affinity of the complex to the filament is significantly lower than that of free CP ( KD = 38 nM versus 0 . 18 nM ) [29] . This is consistent with our hypothesis that the CARMIL proteins inhibit CP only by affecting the twisting motion which provides the capping stability , since our data showed that neither the “α-tentacle” ( the capping on rate determinant ) nor the “β-tentacle” ( the other capping stabilizer ) is disturbed by the CARMIL protein . Furthermore , our prediction that the conformation CPCARMILs is substantially different from the “high affinity to the barbed end” form is consistent with the concept that CARMIL binding to free CP must involve some surface or conformation that is not available when CP is bound to a barbed end [23] . This is because the affinity of CARMIL for the barbed end-bound CP has been estimated to be 10- to 100-fold [23] or 200-fold [29] lower than that for free CP . To better understand the mechanism of CP inhibition by the CARMIL proteins , it would be helpful to know the conformation of CP on the barbed end . As such , we fitted all known crystal structures of CP to the 3D electron density map of the CP/actin filament [5] and found that all of the structures tested fit similarly to the model except for CPV-1 , which did not fit as well ( Figure S10 ) . The mismatch between the EM envelope and CPV-1 is largely due to the shift of the CP-S domain relative to the CP-L domain , suggesting that the CP in the “high affinity to the barbed end” form may not adopt such an “open” conformation as in CPV-1 . In this study , we cannot provide structural information about CP bound to the full activity CARMIL fragments . During the submission of this manuscript , Robinson and colleagues reported a crystal structure of CP in complex with a CARMIL fragment with an extended C-terminal portion ( CBR115; human CARMIL residues 964–1078 ) [36] . This structure revealed that , in addition to the CP-binding motif , a 15 residue motif serves as a second CP binding site ( CARMIL-specific interaction motif , residues 1021–1035; highlighted by orange in Figure S11 ) . The motif binds to the CP “N-stalk” in the CP-L domain , on the side opposite to where the CP-binding motif binds . This result also supports the concept that CARMIL proteins inhibit CP in an allosteric manner ( see Text S1 for a detailed discussion about the role of the C-terminal flanking region of the CP-binding motif of the CARMIL proteins for CP inhibition ) . Recently , intrinsically unstructured proteins or segments of proteins have been recognized to play critical roles in many cellular processes such as transcriptional regulation and signal transduction [37] . These disordered regions usually fold into ordered secondary or ternary structures upon binding to their targets ( termed coupled folding and binding processes ) . We revealed , however , that the CARMIL peptides are functional in suppressing the conformational flexibility of CP , although they have an extended backbone conformation . Consequently , our results provide new insights into the functional expression of intrinsically unstructured proteins . An important implication of this study is that conformational restraints placed on CP lead to an attenuated affinity of the protein for the barbed end . This raises the possibility that other CP regulators , such as PIP2 , also modulate the capping activity . Moreover , the state of the actin filament would also affect the affinity of CP towards the filament; i . e . , a certain actin binding protein that changes and/or restricts the structure of the barbed end to an unfavorable form for CP binding can antagonize the filament capping . We assume that such a mechanism may account for the rapid turnover rate of CP in lamellipodia [9] , [10] . In this study , we have described the structural basis for CP inhibition by two regulators , V-1 and CARMIL proteins . Our findings suggest that CP is not a constitutively active inhibitor of barbed end elongation; rather , the capping activity of CP is fine-tuned for the highly orchestrated assembly of the cellular actin machinery , and the conformational flexibility of CP provides the structural basis for the regulation .
Expression vectors for chicken CPfull and CPΔβC were constructed in pETDuet-1 by PCR , using pET-3d/CP [38] as the template . CP was expressed in E . coli Rosetta2 ( DE3 ) and was purified as described [3] . V-1 ( human ) , expressed in E . coli Rosetta2 ( DE3 ) as a GST-fusion protein , was affinity-purified and the tag was removed . Synthetic peptides derived from CARMIL proteins were obtained from Invitrogen . For crystallization , CP was incubated with a 1 . 2–2 . 0-fold molar excess of V-1 or CARMIL peptides at 4°C for 2 h , followed by gel filtration to purify the complexes . Expression vectors for the GST-CA constructs were prepared from the mouse cDNA clone as previously described [23] . Vectors for GST-CD fragments were constructed by PCR cloning using a human whole brain cDNA library ( Clontech ) as the template . Amplified DNA fragments were cloned into pGEX-6P-1 . GST-fusion proteins were expressed in E . coli Rosetta2 ( DE3 ) cells and affinity-purified using glutathione sepharose resin . Mutations were introduced using a Quikchange mutagenesis kit ( Stratagene ) . Actin was prepared from rabbit skeletal muscle , as previously described [39] , and was further purified by gel filtration chromatography . Pyrene labeled-actin was prepared as described [40] . Spectrin-actin seeds were prepared from rabbit red blood cells , as previously described [41] . Each protein complex , at 8–10 mg/ml in 1 mM DTT and 5 mM Tris-HCl ( pH 8 . 0 ) , was mixed with an equal volume of reservoir solution as follows: 10% PEG4000 , 20% isopropanol , 20 mM EDTA , 0 . 1 M Tris-HCl ( pH 8 . 4 ) for CP/V-1; 12 . 5% PEG400 , 20 mM BaCl2 , 0 . 1 M MES-NaOH ( pH 6 . 0 ) for CPβΔC; 18% PEG400 , 40 mM BaCl2 , 0 . 1 M MES-NaOH ( pH 6 . 0 ) for CP/CA21; 10% PEG400 , 20 mM BaCl2 , 0 . 1 M MES-NaOH ( pH 6 . 5 ) for CP/CD23; and 17 . 5% PEG400 , 30 mM BaCl2 , 0 . 1 M MES-NaOH ( pH 6 . 0 ) for CP/CK23 . The crystals were grown at 20°C by the hanging-drop vapor diffusion method and were cryoprotected with their reservoir solutions supplemented with 20% glycerol ( for CP/V-1 ) or with 35% PEG400 ( for other crystals ) prior to flash-cooling in a cold nitrogen stream . Diffraction data were collected in the BL26B1 beamline at SPring-8 [42] and were processed with HKL2000 [43] . Space groups and cell parameters are listed in Table S1 . Initial phases were determined by molecular replacement with Molrep [44] , using the CP structure as a search model . Model building and refinement were performed with CNS [45] , Refmac [46] , and Coot [47] . Each crystal contains one CP or CP/inhibitor complex in the asymmetric unit . Data collection and refinement statistics are summarized in Table S1 . The barbed end elongation assay from spectrin-actin seeds was performed essentially as previously described [4] . Briefly , G-actin was stored in G-buffer ( 0 . 2 mM CaCl2 , 0 . 2 mM ATP , 0 . 5 mM DTT and 10 mM imidazole , pH 7 . 0 ) . At 90 s prior to polymerization , the Ca2+ was replaced with Mg2+ , by the addition of 1/10 volume of 10 mM EGTA and 1 mM MgCl2 to G-actin . Barbed end elongation was initiated by mixing the solutions in the following order: Mg2+ actin ( 5% pyrene-labeled ) , CP , V-1 or CARMIL protein , a 1/20 volume of 20× polymerization buffer ( 1 M KCl , 20 mM MgCl2 , 20 mM EGTA , 0 . 2 M imidazole , pH 7 . 0 ) and spectrin-actin seeds . Actin polymerization was measured by monitoring the pyrene-actin fluorescence ( excitation 370 nm; emission 410 nm ) at 25°C . The binding affinities of CP for V-1 or CARMIL proteins were evaluated by surface plasmon resonance measurements with Biacore 3000 or Biacore 2000 instruments ( GE Healthcare ) . GST-fusion proteins ( GST-V-1 , GST-CA , or GST-CD ) were immobilized onto a CM5 sensor chip up to 200 RU ( response units; 200 pg/mm2 ) via anti-GST antibodies . CP at various concentrations in running buffer ( 50 mM KCl , 1 mM MgCl2 , 0 . 005% Tween-20 , 10 mM imidazole , pH 7 . 0 ) was perfused over the chip at 20°C , at a flow rate of 20 µl/min . Response curves were obtained by subtracting the background signal generated simultaneously on a control flow cell with immobilized GST . To measure the effect of the CARMIL peptides on the facilitation of CP/V-1 dissociation ( in Figure 11C and 11D ) , we used the “co-inject” mode for successive injections of the peptides followed by CP . Kinetic parameters were determined by fitting the sensorgrams to a simple 1∶1 binding model , using the Bia-evaluation software ( GE Healthcare ) . KD values were obtained from the kinetic rate constants . For several mutant proteins possessing fast dissociation rates for the ligand ( koff >0 . 1 s−1 ) , we measured the amount of bound-CP at the steady state over a wide concentration range . KD values were evaluated by plotting these values against the concentrations of CP . The stable V-1 overexpression transfectant ( V1-69 ) and its mock transfectant ( C-9 ) , established in the PC12D subclone of rat pheochromocytoma cells , were cultured as reported previously [16] . The concentrations of F- and G-actin were measured using an assay kit ( Cytoskeleton ) , as described previously [48] . For subcellular fractionation , the cells were homogenized by sonication in homogenization buffer ( 150 mM NaCl , 2 mM EGTA , 10 mM Tris-HCl , pH 7 . 4 , with protease inhibitors ) . The extracts were centrifuged at 100 , 000 g for 60 min , and the supernatant was designated as the “high speed supernatant” fraction . The pellet was incubated for 30 min in the homogenization buffer supplemented with 0 . 5% Triton X-100 and ultracentrifuged . This supernatant was designated as the “high speed pellet soluble in detergent” fraction , and the “high speed pellet insoluble in detergent” fraction was obtained by further extraction of the pellet in 8 . 3 M urea . The amount of CP in the fractions was determined by Western blotting with an anti-CP β-subunit antibody [21] . For morphological analysis , cells cultured at a density of 5×104 cells per well on the poly-d-lysine-coated culture slides ( BD Biosciences ) for 24 h were fixed by 3 . 7% formaldehyde in PBS and permeabilized with 0 . 1% Triton X-100 in PBS . Fixed cells were pre-incubated with the Image-iT FX signal enhancer ( Invitrogen ) and counter-stained with Alexa Fluor 546-conjugated phalloidin ( Invitrogen ) and Hoechst 33258 ( Dojin ) . The fluorescence images were obtained using Leica microfluorescent system ( AF6500; Leica Microsystems ) . The intrinsic flexibility of CP was examined by the normal mode analysis with the ENM [49] , [50] , [51] . In this model , only the Cα atoms are considered , and a harmonic potential with a single parameter , C , is introduced between all Cα atoms within a cut-off distance , Å . The potential energy of a protein is given aswhere is the vector connecting the i-th and j-th Cα atoms and is that in the crystal structure . The Hessian matrix , whose elements are the second derivatives of the potential energy , was derived and diagonalized , and we obtained the eigenvectors and eigenvalues , representing the normal modes . Since the twisting movements were revealed by comparisons of the crystal structures , we estimated the intrinsic rotations from the lowest frequency mode that corresponds to the largest vibration . As the CP free model structure , we employed the CP structure of the CP/CD23 complex ( i . e . , the CD2 peptide was removed ) . The displacements of each Cα atom were derived from the displacement vector , the eigenvector of the lowest frequency mode scaled by the reciprocal of the eigenvalue . We consider that the set of Cα atoms with small displacements represents the rotation axis . The Cα atoms , whose squares of the displacements were smaller than 2 Å2 , were collected . We found that these Cα atoms could be clearly divided into two groups , and each of them was separately distributed in the α-subunit or the β-subunit ( Table S4 ) . The coordinates of these Cα atoms in each group were evaluated by the principal component analysis , and the first components defined the rotation axes on the α- and β-subunits . In Figure 12 , the axes run on the center of Cα atoms with small displacements . The same analysis was applied to the CP/CD23 complex , with a cut-off displacement of 1 Å2 . The Protein Data Bank accession codes for the crystal structures determined in this study are as follows: CP/V-1 ( 3AAA ) , CPβΔC ( 3AA7 ) , CP/CA21 ( 3AA0 ) , CP/CD23 ( 3AA6 ) , and CP/CK23 ( 3AA1 ) . | Actin is a ubiquitous eukaryotic protein that polymerizes into bidirectional filaments and plays essential roles in a variety of biological processes , including cell division , muscle contraction , neuronal development , and cell motility . The actin capping protein ( CP ) tightly binds to the fast-growing end of the filament ( the barbed end ) to block monomer association and dissociation at this end , thus acting as an important regulator of actin filament dynamics in cells . Using X-ray crystallography , we present the atomic structures of CP in complex with fragments of two inhibitory proteins , V-1 and CARMIL , to compare the modes of action of these two regulators . The structures demonstrate that V-1 directly blocks the actin-binding site of CP , thereby preventing filament capping , whereas CARMIL functions in a very different manner . Detailed comparison of several CP structures revealed that CP has two stable domains that are continuously twisting relative to each other . CARMIL peptides were found to bind across the two domains of CP on a surface distinct from its actin binding sites . We propose that CARMIL peptides attenuate the binding of CP to actin filaments by suppressing the twisting movement required for tight barbed end capping . Our comparative structural studies therefore have revealed substantial insights in the variety of mechanisms by which different actin regulatory factors function . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biophysics",
"cell",
"biology/cytoskeleton"
] | 2010 | Two Distinct Mechanisms for Actin Capping Protein Regulation—Steric and Allosteric Inhibition |
Tanzania is among the Rift Valley fever ( RVF ) epizootic/endemic countries in sub Saharan Africa , where RVF disease outbreaks occur within a range of 3 to 17-year intervals . Detection of Rift Valley fever virus ( RVFV ) antibodies in animals in regions with no previous history of outbreaks raises the question of whether the disease is overlooked due to lack-of effective surveillance systems , or if there are strains of RVFV with low pathogenicity . Furthermore , which vertebrate hosts are involved in the inter-epidemic and inter-epizootic maintenance of RVFV ? In our study region , the Kyela and Morogoro districts in Tanzania , no previous RVF outbreaks have been reported . The study was conducted from June 2014 to October 2015 in the Kyela and Morogoro districts , Tanzania . Samples ( n = 356 ) were retrieved from both the local breed of zebu cattle ( Bos indicus ) and Bos indicus/Bos Taurus cross breed . RVFV antibodies were analyzed by two enzyme-linked immunosorbent assay ( ELISA ) approaches . Initially , samples were analyzed by a RVFV multi-species competition ELISA ( cELISA ) , which detected both RVFV IgG and IgM antibodies . All serum samples that were positive with the cELISA method were specifically analysed for the presence of RVFV IgM antibodies to trace recent infection . A plaque reduction neutralization assay ( PRNT80 ) was performed to determine presence of RVFV neutralizing antibodies in all cELISA positive samples . Overall RVFV seroprevalence rate in cattle by cELISA in both districts was 29 . 2% ( 104 of 356 ) with seroprevalence rates of 33% ( 47/147 ) in the Kyela district and 27% ( 57/209 ) in the Morogoro district . In total , 8 . 4% ( 30/356 ) of all cattle sampled had RVFV IgM antibodies , indicating current disease transmission . When segregated by districts , the IgM antibody seroprevalence was 2 . 0% ( 3/147 ) and 12 . 9% ( 27/209 ) in Kyela and Morogoro districts respectively . When the 104 cELISA positive samples were analyzed by PRNT80 to confirm that RVFV-specific antibodies were present , the majority ( 89% , 93/104 ) had RVFV neutralising antibodies . The results provided evidence of widespread prevalence of RVFV antibody among cattle during an inter-epizootic/inter-epidemic period in Tanzania in regions with no previous history of outbreaks . There is a need for further investigations of RVFV maintenance and transmission in vertebrates and vectors during the long inter-epizootic/inter-epidemic periods .
Rift Valley fever ( RVF ) is a zoonotic disease that causes storm abortions in ruminants [1–3] . The disease leads to introduction of restrictions for international livestock trade from enzootic/endemic countries . The disease imposes a dual impact in that it exacerbates the poverty cycle in livestock-dependent communities , by causing substantial health costs and at the same time affecting negatively the livelihoods of the communities in many sub-Saharan countries where it is enzootic/endemic[4 , 5] . RVF was first reported in early 1930’s in the Eastern Rift Valley province of Kenya causing high rates of abortion in infected sheep [6] . Since then , the Rift Valley fever virus ( RVFV ) has been associated with several periodic disease epidemics and epizootics affecting human and animals in many regions of Africa . Although the virus is enzootic/endemic to sub-Saharan Africa , it has the potential for global spread and has already crossed significant natural geographic barriers such as the Indian Ocean , the Sahara Desert and the Red Sea to reach naive ecologies [7] . Outside Africa , RVF outbreaks were first reported in Saudi Arabia [8] and Yemen [9] in 2000 . This northward spread of RVFV suggests the possibility of the virus being introduced into Europe and North America where several species of mosquitoes competent for viral transmission exist [10] . Recent spatial and temporal analysis of RVF in Tanzania showed that RVF-like disease was reported for the first time in 1930 concurrently with the outbreak in Kenya , with a further ten outbreaks being reported between 1947 and 2007 [7] . In 2006/2007 , there was a massive outbreak with a total of 684 human cases and 234 deaths reported in Kenya , 114 cases with 51 deaths in Somalia and 264 cases with 109 deaths reported in Tanzania [11] . In Tanzania , the 2006/2007 RVF outbreak was widely spread to more than ten regions in the northern , eastern-central and southern parts of the country [7] . In RVFV enzootic/endemic regions , outbreaks occur with 3 to 17 year intervals [12] , which is an average inter-epizootic/inter-epidemic ( IE ) interval of 7 . 9 years [7] . The RVFV maintenance between the long IE periods is not fully understood . Although it has been widely hypothesized that the virus is maintained via transovarially infected Aedes mosquito eggs [12 , 13] , serological evidence suggests that the virus could be maintained through IE circulation in domestic ruminants , wild animals and humans [9 , 14–17] Evidence of RVFV circulating in Tanzania during an IE period has been shown previously in the Kilombero river valley , where various livestock species had RVFV antibodies ( cattle 11 . 03%; sheep 11 . 86% and goats 11 . 37% , respectively ) [14] , although Kilombero river valley was among the sites that experienced the 2006/07 RVF outbreak [7] . Additional surveys in areas without previous history of RVF outbreaks [18] or clinical cases in humans [9] show related prevalence rates in livestock and humans respectively . In addition , the role of mammals as maintenance hosts for RVFV remains largely unknown [19] . Furthermore , the detection of antibodies in areas where no clinical disease has been reported [9 , 18] raises the question of whether the disease is overlooked due to lack-of effective surveillance systems , or whether there are strains of RVFV with low pathogenicity . This study aimed at determining the involvement of non-vaccinated cattle in the IE maintenance and transmission of RVFV in areas with no history of RVF outbreaks in Tanzania .
This study was conducted from June , 2014 to October , 2015 in the Kyela and Morogoro districts , Tanzania . During the 2006/2007 RVF outbreak in Tanzania , ten regions were affected [20] . The Morogoro region was one of the ten regions , but only two districts , the Kilombero and Ulanga districts were affected , while the Morogoro district was not affected [7] . The Mbeya region , where the Kyela district is located , was not affected during the 2006/2007 RVF outbreak in Tanzania [7 , 20] . Kyela is one of the districts in Mbeya region , located in south-western part of the country . Most of the Kyela district is lowland situated in the Great Rift Valley at 505m above sea level , in the flood plains of Lake Nyasa . It receives heavy rains , of about 2000-3000mm per annum and floods are common in March through May . The district has a warm and humid climate , with a mean daily temperature of 23°C . Together with Lake Nyasa , the district also has four large rivers , ( Songwe , Mbaka , Lufilyo , and Kiwira ) , and many streams ( Mkalizi , Kampala , Mgaya , Chiji , Kandete , Masukila , Njisi , and Kubanga ) . Agriculture dominates livelihoods and economic activities of the Kyela district . In addition to rain fed paddy farming , other crops include banana and cocoa cultivation . Other livelihood activities include livestock farming and fishing . Because of the water logging condition , few sheep and goats are kept in the district . Few ( 1–5 ) cattle are kept per household by tethering in communal grazing areas during the day and on the doorsteps of their houses at night for fear of theft , providing an animal reservoir of RVFV in proximity of humans . The Morogoro district is located within the Morogoro region , 200 km east of Dar es Salaam . The annual average rainfall for Morogoro ranges between 500 and 1800 mm with temperatures between 18°C to 28°C . The main occupation of the inhabitants include crop cultivation and livestock keeping and a number of livestock species are kept including cattle , goats , sheep , pigs , camels , donkeys and horses . The livestock production in Morogoro is organised under commercial and traditional sectors . The livestock production systems are pastoralisim , agro-pastoralism and small scale intensive system which is becoming popular as land shortage force many livestock keepers to intensify their production . In the latter system , mainly crossbred animals are kept , and cut and carry system of feeding is practicied . The sampling process involved a two-stage purposive selection of districts and wards based on the findings of the past studies ( 2 ) reporting status of RVF outbreaks in Tanzania . The number of wards was not based on statistical considerations , but on logistic and resource availability . Based on the above , the Kyela and Morogoro districts with no previous history of RVF outbreaks were selected . In both districts , all veterinary officers were consulted to identify wards within each district considered to be at highest risk of RVF occurrence . Criteria used included areas subject to regular flooding , ecological features suitable for mosquito breeding , relatively high concentration of domestic ruminants , proximity to rivers , ponds and lakes . The wards within the districts that were identified with most of these epidemiological characteristics were selected for the study . Within the selected wards , all households keeping domestic ruminants and not having a history of vaccination against RVF were identified using local official veterinary records . The spatial and temporal patterns of RVF outbreaks in Tanzania; 1930 to 2007 [7] , showed that no previous outbreak had occurred in the two study districts . It was further confirmed during the study where , in each district , the veterinary offices were asked for any occurrence of RVF disease and/or outbreak , and history of livestock RVFV vaccination . Furthermore , information on the animal movements into the selected wards was retrieved from the veterinary officials , household heads and herdsmen . Additional information on the sources of replacement heifers was also requested . Wards without inward migration of animals from other areas were selected for the study . The local breed of zebu cattle ( Bos indicus ) and the crosses with exotic breed ( Bos taurus ) were sampled by collecting 5 ml of blood from the jugular vein into plain vacuitaner tubes . The criteria for selection of animals included a history of non-vaccinated status against RVFV , animals born after the 2006/2007 outbreak , calves above 6 months of age , and owners consent to using the animals for study . Herd and individual animal epidemiological data were obtained from the household head and herders as well as through clinical examination . The data collected included the breed , sex and age and feeding practices . In addition , a history of animal movements into the herd and whether the animals were born within the herd or introduced ( moved ) into the herd from another district was recorded . Sampling was based on only those herds with restricted animals without history of movements to high RVF risk areas . The blood samples were collected in vacutainer tubes without an anti-coagulant , labeled and stored in a cooler box with ice packs while in the field . Before blood collection , animals were restrained into the crush or by use of ropes and halters . The blood was allowed to coagulate before serum was separated into a 1 . 5 ml cryovial tube , labeled and stored in a cool box with ice packs until transfer to the laboratory for analysis . Serum samples were stored at -80°C until analysis . Individual animal age was estimated from epidemiological data collected from household heads and herders , and where possible , by review of available records on date of birth and dentition . Records were available from farms keeping crossbred dairy cattle . Dentition was used in determining the age of cattle divided into young or adult , depending on the eruption of the permanent incisors [21] . All cattle that had at least a permanent middle incisor were categorised as adult , while those without were categorised as young . Normally , the permanent incisors in cattle erupt at about 18 months of age and by 24 months they are fully developed . To exclude sampling young animals less than six months old , age was estimated by asking the head of the household , herd boys and other members of the household for the month , season and year of birth . Also , we performed physical observation of animal size and asked if they still were suckling . To avoid sampling animals present during the 2006/2007 RVF outbreak , animals that had initial wear on their incisor teeth ( 5 to 6 years old ) and those which had noticeable wear ( 7 to 8 years old ) were excluded from the study . Breed types were recorded as local or cross-breeds , depending on the body colouration , presence of a hump and horns . Local breeds were shorthorn humped zebu with various body colouration . Sex of the animal and test results were provided in the same data set . Each serum sample was analysed with the commercial Innovative Diagnostic ( ID . vet ) Screen RVF competition multispecies ELISA ( cELISA ) ( IDVet , Montpelier , France ) . The commercial cELISA is based on the recombinant RVFV nucleoprotein and detects both RVFV IgM and IgG antibodies . The cELISA was carried out according to the manufacturer’s instruction . Briefly , 50 μl of the dilution buffer was dispensed into each well of a labelled ELISA plate pre-coated with recombinant RVFV nucleoprotein . Then , 50 μl of the internal positive ( freeze-dried RVFV IgG positive bovine serum supplied by the manufacturer ) and the internal negative control ( supplied by the manufacturer ) were added in duplicates . To the remaining wells , 50 μl of each sample was added . After mixing samples and controls with the TST dilution buffer ( 50 mM Tris/150 mM NaCl/0 . 1% Tween 20 , pH 8 . 0 ) , we incubated at 37°C for 1 hour . The wells were washed three times with washing buffer using a well plate washer ( Thermo Scientific Wellwash Microplate Washer , Waltham , MA USA ) . Next , 100 μl of antinucleoprotein peroxidase ( HRP ) conjugate was added to the wells and the contents of the plate were incubated at room temperature for 30 min , followed by washing three times with 300 μl of wash solution as before to remove excess conjugate . Then , 100 μl of substrate solution 3 , 3' , 5 , 5'-tetramethylbenzidine ( TMB ) was added to each well and the plate was incubated at room temperature for 15 min in the dark . To terminate the reaction 100 μl of 2n Sulphuric acid ( 2NH2SO4 ) stop solution was added to each well . The presence of antibodies to RVFV was detected by lack of a colour change , whereas absence of antibodies to RVFV was detected by a change in substrate colour to blue . The contents of the wells of the microplate were read at a wavelength of 450 nm by a microplate absorbance reader ( Molecular Devices , CA , USA ) . For each cELISA experiment , duplicate internal controls were incorporated . The optical densities ( ODs ) of the control were detected at 450 nm . To verify the reliability and validity of the results obtained from each cELISA test , the average of the ODs of the two negative controls ( NCs ) was > 0 . 7 while the average of the two positive controls divided by the average OD of the NCs was > 0 . 3 . For each sample , the competition percentage was calculated by dividing the OD of the sample by the average OD of the negative control multiplied by 100 ( [ODsample/ODNC] x 100 ) . A sample was considered positive if the value obtained from the formula was ≤ 40% . Any sample with a value of > 50% was considered to be negative , whereas values ranging from 40–50% were considered to be doubtful . The IgM ELISA test was employed for cELISA positive samples only . These samples were analysed with the commercial ID Screen RVF IgM Capture kit ( IDvet , MOntpelier , France ) according to the manufacturer’s instruction . Briefly , 40 μl of the diluent buffer was dispensed into each well of a labelled microwell plate pre-coated with anti-bovine-ovine-caprine IgM polyclonal antibodies . Then , 10 μl of the internal positive control ( freeze-dried anti-RVFV recombinant NP bovine serum supplied by the manufacturer ) and the internal negative control ( supplied by the manufacturer ) were added in duplicates . To the remaining wells , serum samples were added in duplicate and the plate with all samples was incubated at 37°C for 1 hour . The microplate wells were then washed three times with 300 μl by a microplate washer as above . Next , 50 μl of RVFV nucleoprotein or diluent buffer was added and incubated at 37°C for 1 hour . The wells were washed three times followed by addition of 50 μl of anti-RVFV nucleoprotein horseradish peroxidase ( HRP ) conjugate solution to each well and incubation for 1 hour at 37°C . Again , the wells were washed three times as above and 100 μl of the substrate solution , TMB , was added to each well and then incubated for 15 min at room temperature in the dark . Then , 100 μl of stop solution was added to terminate the reaction . The presence of IgM antibodies to RVFV was detected by appearance of blue colouration , which became yellow after addition of the stop solution . The contents of the wells of the microplate were analysed at 450 nm by a microplate absorbance reader ( Molecular Devices , CA , USA ) . For each IgM antibody capture ELISA experiment duplicate internal controls were incorporated . The optical densities ( ODs ) obtained from the samples at 450 nm were validated in accordance with the manufacturer’s instructions as follows: The net OD was calculated: net OD = OD even well-OD odd well The plate was valid if the mean value of the net positive control OD was greater than 0 . 35 and the ratio of the mean values of the net positive and negative control ( absolute value of ODs ) is greater than 3 ( net ODPC/net ODNC > 3 For each sample , the percentage of the ratio of sample and positive control ( s/p% ) was calculated . Samples presenting a S/P percentage ( S/P% ) : All samples that were positive for RVFV antibodies by the cELISA kit were analyzed by PRNT80 . The PRNT80 protocol used was adopted as previously described [22] . The RVFV MP-12 vaccine strain , propagated in Vero-E6 cells , was used in the PRNT assay . Each PRNT assay included the test sera , and a known RVFV antibody positive serum sample and a RVFV antibody negative serum sample from cattle . Each serum sample was diluted in Hanks’ Balanced Salt Solution ( HBSS ) supplemented with one % each of HEPES , penicillin and streptomycin and heat-inactivated fetal bovine serum ( FBS ) . The dilutions sera samples were made in 96 well plates beginning with a 1:5 dilution in the first wells followed by 4-fold serial dilutions of 1:20 , 1:80 , 1:320 , 1:1280 , and 1:5120 in each of subsequent wells . Each diluted serum sample was then mixed with an equal volume of 60–80 plaque-forming units ( PFU ) of MP-12 vaccine virus . The quantification of PFU was confirmed by a plaque assay based on testing a mixture of equal volumes of the 60–80 PFU and HBSS to confirm that the final virus dose ranged from 30–40 PFUs . The antibody positive control consisted of a mixture of equal volume of 60–80 PFU and a 1:10 dilution of antibody positive cattle serum . The antibody negative control consisted of a mixture of equal volume of 60–80 PFU and a 1:10 dilution of RVFV antibody negative cattle serum . The virus/serum dilution mixtures were incubated at 37°C in the absence of CO2 for one hour . Next , 50 μl of the virus/serum dilution mixtures were inoculated onto each of two Vero E6 cell monolayer cultures propagated in 24-well tissue culture plates and incubated for one hour at 37°C and 5% CO2 . Virus mixed with the antibody-positive control serum , was inoculated onto twenty separate Vero E6 cultures . Virus mixed with antibody-negative control serum mixture was inoculated onto four Vero E6 cultures . After incubation for one hour at 37°C with 5% CO2 , each cell culture was overlaid with 0 . 5 ml of a Seakem agarose ( 1% ) with an equal volume of 2X Eagle’s Basal Medium with Earle’s salts ( EBME ) supplemented with 8% FBS and one % penicillin/streptomycin , and Glutamine+8g/l HEPES . After two more days of incubation at 37°C with 5% CO2 , each culture was overlaid with 0 . 5 ml of a mixture of an equal volume of agarose ( 1% ) and 2X EBME supplemented with 5% neutral red , 8% FBS , and penicillin and streptomycin ( 1% ) and Glutamine + 8g/l HEPES and incubated overnight at 37°C with 5% CO2 . The PFUs were counted and recorded for both the controls and cattle serum samples . An 80% reduction in the number of PFUs was used as the endpoint for antibody virus-neutralization titers ( PRNT80 ) . Wells with too high number of PFUs , that were impossible to count at that dilution , were recorded as TNTC ( too numerous to count ) . The data were entered into a Microsoft Excel spreadsheet and imported into STATA version 12 ( Statacorp , College Station , TX , USA ) for cleaning and statistical analysis . Descriptive statistics was carried out followed by univariable analysis to assess initial association between potential risk factors and the outcome variable defined by RVFV seropositivity . The mixed effects logistic regression modelling was used to investigate the association between various potential risk factors and the outcome variable defined by RVFV seropositivity . The models included districts , age , breed , sex and the type of holding . The analysis was conducted in two steps . The statistically significant variables were included in a mixed effects multivariable logistic regression analysis based on a forward variable selection approach , utilising the likelihood ratio statistic and a p-value ≤ 0 . 05 . Because of the differences in the sample sizes and agro-ecological features between Kyela and Morogoro districts , RVFV seropositivity was compared among the wards within the respective district . The Chi-square test was used to compare the RVFV seropositivity among the wards , by using the Rstudio statistical software at p-value ≤ 0 . 05 . During blood collection from cattle , the research team adhered to the generally acceptable ethical standards and strictly followed existing national and international guidelines for minimizing pain and stress to the animals . The study purpose was explained to cattle owners prior to sample collection and upon agreeing to allow samples to be collected from their animals , they provided a written consent form . The study protocol was approved by Research and Publication Committee , College of Veterinary and Biomedical Sciences , Sokoine University of Agriculture , Tanzania . The protocol/permit number assigned by the Institutional Animal Care and Use Committee IACUC/ethics committee Protocol No . SUA/FVM/R . 19 of 17th March 2014 . National or international regulations/guidelines to which animal care and use protocol adhered to: Public health Service Policy on Humane Care and Use of Laboratory Animals and Animal Welfare Regulations .
A total of 356 cattle serum samples were analysed for presence of RVFV antibodies , of which 147 samples were from the Kyela district and 209 samples were from the Morogoro district . The overall seropositivity by cELISA was 29 . 2% ( 104/356 ) and a seroprevalence of 32% ( 47/147 ) and 27% ( 57/209 ) were recorded among animals in Kyela and Morogoro districts respectively . Animals older than 2 years were more likely to be seropositive than animals younger than 2 years ( OR = 0 . 19; p = 0 . 000 ) ( Table 1 ) . Likewise , zebu cattle were more likely to be seropositive than crosses , ( OR = 2 . 5; p = 0 . 000 ) . There were no significant differences between the districts ( OR = 0 . 98; p = 1 . 0 ) and the type of holding ( OR = 1 . 25; p = 0 . 34 ) . In the Morogoro district , the Mikese ward had the highest RVFV seroprevalence at 55 . 3% ( 21/38 ) followed by Magadu at 32% ( 33/103 ) and Mazimbu 4 . 4% ( 3/68 ) . In the Kyela district , the RVFV seroprevalence was 35 . 7% ( 25/70 ) , 40 . 5% 15/37 ) and 17 . 5% ( 7/40 ) in Bujonde , Kajujumele , and Katumba Songwe wards respectively ( Fig 1 , Table 2 ) . There were statistical significant differences in RVFV seroprevalence between the wards in Morogoro district ( p < 0 . 001 ) ( Table 2 ) . However , this was not the case for wards in the Kyela district ( p = 0 . 06 ) ( Table 2 ) . To specifically detect RVFV IgM antibodies , the RVFV antibody positive samples analysed by the cELISA method were subjected to an IgM capture ELISA . Of the 104 analyzed samples , 30 ( 29% ) were positive for RVFV IgM antibodies . In total 8 . 4% ( 30/356 ) of all cattle sampled in the two districts had RVFV IgM antibodies . When segregated by districts , the IgM antibody seroprevalence was 2 . 0% ( 3/147 ) and 12 . 9% ( 27/209 ) in Kyela and Morogoro districts respectively . In the Morogoro district , the RVFV IgM seroprevalence was 11 . 7% ( 12/103 ) , 1 . 5% ( 1/68 ) and 36 . 8% ( 14/38 ) for Magadu , Mazimbu and Mikese wards respectively while in Kyela district , the IgM seroprevalence was 2 . 9% ( 2/70 ) , 0% ( 0/37 ) and 2 . 5% ( 1/40 ) in Bujonde , Kajunjumele and Katumba Songwe wards respectively ( Table 3 ) . Positive samples by cELISA were also analyzed for presence of RVFV neutralising antibody by the PRNT80 assay and 89% ( 93/104 ) of all cELISA-positive samples were PRNT-positive . All ( 47/47 ) cELISA positive samples from the Kyela district contained RVFV neutralising antibody , while 81% ( 46/57 ) of the samples from Morogoro district had neutralising antibody . Antibody titres ranged from 1:10 to 1:10240 and above ( S1 Table ) . Some ELISA positive cattle samples collected from wards in Morogoro district gave titers below 1:10 which was considered negative .
We found IgG and/or IgM antibodies to RVFV in 29 . 2% of cattle sampled during 2014–2015 in Tanzania , from two districts with no RVF outbreaks . All samples were collected during an inter-epizootic/inter-epidemic ( IE ) period from animals born after the large RVF outbreak in East Africa 2006/2007 . The finding of both IgG and IgM positive cattle suggests both long-term persistence of RVFV antibodies and a low level of recent circulation of RVFV . In previous studies from Kilombero , Tanzania and Ijara Kenya the seroprevalence in livestock born after the 2006/2007 outbreak was only 5 . 5% and 13 . 1% respectively , while in a study in Tanzania from 2013 in the Kajunjumele ward in the Kyela region , 25 . 8% had RVFV IgG antibodies [14 , 23 , 24] . Interestingly , we detected a RVFV seroprevalence of 40 . 5% ( 15/37 ) in the Kajunjumele ward from samples collected 2014–2015 , but none of the fifteen seropositive animals were RVFV IgM positive . This suggested that new infections have occurred in the Kajunjumele ward between the previous study , ending August 2013 , and our study , starting June 2014 . This should then have occurred at least 6–8 weeks before our sampling , since IgM antibodies only persist for that time ( 25 ) , but no reported animal or human cases were reported from that region during the period . On the other hand , cattle samples collected from Tanzania during the 2006/2007 outbreak had a seroprevalence of 38 . 7% [25] . The variation in seroprevalence could be explained by time of sampling , new infections , slaughter , removal of seropositive animals , methods used to analyze the samples , as well as the agro-ecological conditions of the study sites . The results reported in this study indicated that domestic cattle from the two studied districts have been exposed to RVFV infection during the IE period and could function as virus amplifiers , although the two study districts have no previous history of RVF outbreaks . The cELISA method detected both IgG and IgM RVFV specific antibodies . The RVFV IgG antibodies are believed to persist in animals for life following infection , and therefore its detection provides a reliable index of previous exposure to RVFV ( 5 , 6 ) , but does not indicate when the animals were infected . The detection of RVFV IgM antibodies indicated that the virus was actively circulating sub-clinically in the both the Kyela and Morogoro districts during the time of sampling , although mainly in the Morogoro district . This is supported by the fact that IgM antibodies persist for only 6 to 8 weeks after initial infection [26] , disappears in 50% of infected animals after 45 days , and are absent in almost 100% of infected animals by 120 days post infection [27] . In the Morogoro district , the Mikese ward had the highest RVFV IgM seroprevalence followed by Magadu and Mazimbu , indicating that RVFV infections have recently occurred in the region and especially in the Mikese ward with 14 RVFV IgM positive cattle out of 38 analyzed . Other studies have also detected RVFV activity in cattle and humans in areas where the disease has never been reported before [12 , 15 , 19 , 28–30] . To summarize , we detected RVFV IgM antibodies in all study wards except Kajunjumele , with Morogoro district having a relatively high IgM seroprevalence compared to Kyela . These findings indicated the presence of active RVFV infection at the time of sampling , during the dry season , at least in the wards examined . Despite the small number of wards and animals tested for IgM , these findings clearly demonstrated the circulation of RVFV during IE periods in non-outbreak areas . It is not clear why the circulating RVFV in these areas did not lead into clinical disease , and the possible mechanisms for the virus maintenance remain to be elucidated . However , possible explanations could be circulation of non-virulent strains of RVFV in these areas or misdiagnosis excluding RVF for other febrile conditions with similar clinical features of fever and abortions . A limitation of the present study was that we unfortunately did not attempt to isolate RVFV from the IgM-positive animals , due to biosafety issues . RVFV is classified as a biosafety level-3 agent and demands biosecurity measures not available during the study . Furthermore , we did not perform any RT-PCR analysis to detect virus RNA . The relatively high RVFV general seroprevalence recorded among cattle in Kyela ( 32% ) and Morogoro ( 27% ) districts could in some part be attributed to the physical characteristics of the respective district . The study site in Kyela is a low-lying area , close to Lake Nyasa , with many swamps and rivers and is subjected to regular flooding during the rainy seasons [9] . The ecology of low altitude and proximity to perennial water bodies were found to be associated with RVFV seropositivity in ruminant herds in Senegal and Madagascar , as well as in humans in Gabon and Tanzania [9 , 31–33] . Such an ecology provides good breeding habitats for mosquito vectors involved in the transmission of the RVFV . Furthermore , a large part of the study site in Kyela is used for wetland paddy cultivation with frequent water logging , suitable for mosquito breeding . In the Kyela district , cattle are usually grazed by tethering in open grassland , communal grazing land , near the wetlands and paddy farms , thus increasing the risk of acquiring RVFV from mosquitoes . On the other hand , the Kyela district is characterized by the abundance of banana and cocoa plantations . One study in Ngorongoro district , Tanzania , trapped more Aedes aegypti ( a vector for RVFV ) in banana and maize farms than in other habitats [34] . Thus , the banana and cocoa plantations in Kyela could form additional breeding habitats for mosquitoes that may transmit the virus to the animals and thus the observed high seroprevalence in this area . Other factors that may contribute to the observed high RVFV seroprevalence include activities that facilitate animal movements such as livestock trade , moving animals to areas with green pastures during the drought season , lending animals among the community members and payment of dowry . Animal movements from high-risk areas could introduce RVFV into naïve animals in new areas [16 , 35] Thus , it is important to carry out studies also in areas found to have high RVFV seroprevalence to better understand the role of animal movements in the dispersal of RVFV and/or its vectors . Such data will be essential for formulation of RVFV control strategies . The observed RVFV transmission hotspots during the sampling period in Magadu and Mikese point to locally existing factors playing a major role in RVFV maintenance and transmission dynamics . Although , entomological surveys were not conducted , the existence of suitable mosquito breeding habitats was evident . The presence of water in farms throughout the year provides suitable habitats for the breeding of RVFV mosquito vectors . Persistent water in aquaculture ponds and waste lagoons close to animal bans and grazing fields at Magadu farm may serve as important breeding habitats for the Aedes mosquito species . The presence of old machinery like tractors , discarded combined harvesters , old automobile tires and water storage containers may serve as water holding places thus providing harbour and breeding habitats for mosquitoes and continuous low-level transmission of RVFV to vertebrate hosts . Older cattle ( >2 years old ) were found to be at a higher risk of having RVFV antibodies than younger cattle ( OR = 0 . 19 , 95% CI ( 0 . 1–0 . 35 ) ) . These findings agree with reports from studies which found higher seroprevalence in older animals [14 , 36–38] . The exotic breeds and their crosses are more susceptible to RVFV infection than local breed which are resistant and well adapted to the environment ( 12 , 13 ) . However , in this study indigenous zebu breed appeared more likely to be RVFV seropositive than crosses ( OR = 2 . 5 , 95% CI ( 1 . 57–4 . 05 ) . Sampled crossbred animals were from dairy farms with frequent use of acaricides to control tick infestation . The acaricides use could possibly prevent cattle from mosquito bites as well ( Mnyone , 2018 , personal communication ) thus reducing the RVFV transmission in these animals . No significant difference between sex or the type of holding was observed . Seroepidemiological studies provides the antibody status in respective species in each area , for the period during which the surveillance was carried out . Thus , continuous surveillance of the antibody prevalence in susceptible species is therefore highly recommended in epizootic/endemic areas .
The results demonstrated widespread prevalence of RVFV antibody among cattle during an inter-epizootic period in regions without previously reported RVF outbreaks . Therefore , it is important for animal health officers in these areas to be aware of the current RVFV circulation so that preventive measures such as vaccination could be implemented . It would be interesting to perform further studies in other similar areas with no history of RVF outbreaks , since it is likely that undetected low-level RVFV is occurring in many places . This would help to identify and target RVF hot spots by control measures , aiming for prevention of RVFV transmission to animals and humans . Future studies could for instance focus on a more comprehensive and inclusive surveillance to identify and characterize RVFV reservoirs and vectors during the IE periods . Longitudinal investigations leading to a better understanding of ongoing RVFV circulation will lead to a better understanding of the IE virus maintenance . | The RVFV maintenance between inter-epizootic/inter-epidemic periods is not fully understood , despite the widely hypothesized belief of maintenance via transovarially infected Aedes mosquito eggs . Increasing serological evidence however , suggests that there could be continuous virus circulation throughout these periods in domestic ruminants , wild animals and humans both in areas with and without known history of RVF outbreaks . In some countries , RVFV antibodies have been demonstrated in livestock raised in areas where no clinical disease has ever been reported . However , in Tanzania , RVFV antibodies in livestock have been demonstrated only in areas with history of RVF outbreaks , raising the question of whether the disease is not present , is overlooked due to lack of effective surveillance systems , or whether there are strains of RVFV with low pathogenicity that do not cause detectable clinical cases in non-outbreak areas . We report here inter-epizootic/inter-epidemic RVFV antibody prevalence in non-vaccinated cattle from areas with no previous RVF outbreak in Tanzania and demonstrate recent virus circulation by detection of IgM antibodies . The differences in RVFV seroprevalence in different study locations suggest local factors that favour the virus amplification and transmission within those areas . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion",
"Conclusion",
"and",
"recommendations"
] | [
"livestock",
"medicine",
"and",
"health",
"sciences",
"rift",
"valley",
"fever",
"virus",
"enzyme-linked",
"immunoassays",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"ruminants",
"pathogens",
"immunology",
"geographical",
"locations",
"microbiology",
"vertebrates",
"tropical",
"diseases",
"animals",
"mammals",
"viruses",
"rift",
"valley",
"fever",
"rna",
"viruses",
"tanzania",
"neglected",
"tropical",
"diseases",
"antibodies",
"immunologic",
"techniques",
"bunyaviruses",
"africa",
"veterinary",
"science",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"veterinary",
"diseases",
"zoonoses",
"proteins",
"medical",
"microbiology",
"microbial",
"pathogens",
"immunoassays",
"agriculture",
"people",
"and",
"places",
"biochemistry",
"eukaryota",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"cattle",
"amniotes",
"bovines",
"organisms"
] | 2018 | Serological evidence of inter-epizootic/inter-epidemic circulation of Rift Valley fever virus in domestic cattle in Kyela and Morogoro, Tanzania |
Virtually all DNA viruses including hepatitis B viruses ( HBV ) replicate their genome inside the nucleus . In non-dividing cells , the genome has to pass through the nuclear pore complexes ( NPCs ) by the aid of nuclear transport receptors as e . g . importin β ( karyopherin ) . Most viruses release their genome in the cytoplasm or at the cytosolic face of the NPC , as the diameter of their capsids exceeds the size of the NPC . The DNA genome of HBV is derived from reverse transcription of an RNA pregenome . Genome maturation occurs in cytosolic capsids and progeny capsids can deliver the genome into the nucleus causing nuclear genome amplification . The karyophilic capsids are small enough to pass the NPC , but nuclear entry of capsids with an immature genome is halted in the nuclear basket on the nuclear side of the NPC , and the genome remains encapsidated . In contrast , capsids with a mature genome enter the basket and consequently liberate the genome . Investigating the difference between immature and mature capsids , we found that mature capsids had to disintegrate in order to leave the nuclear basket . The arrest of a karyophilic cargo at the nuclear pore is a rare phenomenon , which has been described for only very few cellular proteins participating in nuclear entry . We analyzed the interactions causing HBV capsid retention . By pull-down assays and partial siRNA depletion , we showed that HBV capsids directly interact with nucleoporin 153 ( Nup153 ) , an essential protein of the nuclear basket which participates in nuclear transport via importin β . The binding sites of importin β and capsids were shown to overlap but capsid binding was 150-fold stronger . In cellulo experiments using digitonin-permeabilized cells confirmed the interference between capsid binding and nuclear import by importin β . Collectively , our findings describe a unique nuclear import strategy not only for viruses but for all karyophilic cargos .
Most DNA viruses depend on nuclear host factors for their replication . Viruses infecting non-dividing cells have to pass the nuclear envelope through the nuclear pore complexes ( NPCs ) . The NPC is large proteinaceous structure of ∼30 different proteins called nucleoporins ( Nups ) . Due to the eight fold rotational symmetry of the NPC each Nup is present in 8–48 copies , forming a complex of ∼125 MDa . On the cytoplasmic face of the NPC eight fibers extrude from a central ring-like framework , which is embedded in the nuclear envelope . This ring forms openings in the nuclear envelope allowing translocation of cargos with a diameter up to 39 nm [1] . On the karyoplasmic face of the NPC 8 fibers form the cage-like structure of the nuclear basket ( reviewed by [2] ) . NPCs regulate the traffic of proteins and nucleic acids into and out of the nucleus ( reviewed by [3] ) . While small molecules may diffuse between cytoplasm and nucleus karyophilic macromolecules are transported in a complex with soluble nuclear import receptors . It is estimated that ∼1000 transport complexes pass each NPC per second [4] . The best characterized transport receptors belong to the importin ( karyopherin ) β superfamily , comprising importin β , transportin 1 , 2 , transportin SR and exportins . Nuclear import is initiated by binding of the receptors to a signal on the surface of the karyophilic cargo . There is a variety of signals as e . g . M9 domains , interacting with transportins , importin β binding domains and “classical” nuclear localization signals ( NLS ) , which bind to importin β via the adapter molecule importin α . The driving force of nuclear import and export is a gradient of the small GTPase Ran in its GTP-bound form across the nuclear envelope . RanGTP is enriched in the karyoplasm , where it interacts with the transport receptors of the import complex , leading to its dissociation . While the cargo diffuses deeper into the karyoplasm , the RanGTP-receptor complex is exported into the cytoplasm where it dissociates . A key component of nuclear import is Nup153 to which the import complex of cargo and importin ( s ) binds and subsequently dissociates . Nup153 is a 1445 amino acid ( aa ) protein of the nuclear basket [5] , which comprises three domains ( reviewed by [6] ) . The N terminus ( aa 1–670 ) at the nuclear ring of the NPC contains an NPC targeting domain and an RNA binding domain . The N terminus interacts with other proteins of the NPC as Nup107 and Tpr and is required for proper NPC architecture [5] , [7] , [8] . The zinc finger region ( aa 650–880 ) at the distal ring of the NPC facilitates interactions with Ran . The ∼30 FXFG repeat-containing C terminus is part of the hydrophobic meshwork that forms the “sieve” through which karyophilic cargos have to pass [9] . The participation of Nup153 in vital cellular processes makes it difficult to analyze its functions without interfering with cell viability [10] . Viruses with a nuclear phase in their life cycle make use of this import machinery . Best characterized are adeno- , herpes , influenza and the human immune deficiency virus . The latter two viruses disassemble in the cytoplasm and release the genome in complex with karyophilic viral proteins . These complexes fall below the limit of the NPC diameter and pass the pore like cellular macromolecules ( reviewed by [11] ) or by transiently interacting with Nups [12] In contrast the genomes of adenoviruses and herpes viruses remain encapsidated within their capsids . Their diameters of 90 and 120 nm exceed the diameter of the nuclear pore . Adenoviruses bind to the cytoplasmic face of the NPC where they disassemble . Genome translocation through the pore involving viral and cellular karyophilic proteins is not well understood [13] , [14] , [15] , [16] . Herpes virus capsids also bind to the exterior of the NPC using importin β [17] but they open at the penton facing the NPC and inject the DNA through the pore . For both viruses the trigger of genome release is unknown . Similar investigations have been performed in digitonin-permeabilized cells on capsids of the medically important hepatitis B virus ( HBV ) [18] , [19] . Hepatitis B is endemic in large parts of the world . Approximately 350 million people are chronically infected , accounting for 1 million deaths per year . HBV is an enveloped virus comprising an icosaedral capsid , which contains the partially double stranded DNA genome ( relaxed circular , rcDNA ) . The capsid exists in T = 4 ( major form ) and T = 3 symmetry [20] , composed of 240 or 180 copies of the core protein , respectively . The two forms differ in diameter ( 36 and 32 nm ) but functional differences are not known . Entry of the virus into the hepatocyte is not well understood due to low efficiency of the available cell culture systems for HBV infection [21] . Circumstantial evidence however suggests that the capsid enters the cytosol after fusion of the viral envelope with a cellular membrane [22] . In fact lipofection of capsids , which by-passes the rate-limiting natural entry causes productive HBV “infection” of hepatoma cells with in vivo-like efficiency [23] . Like other DNA viruses ( with the exception of baculoviruses [24] ) HBV capsids are transported towards the NPCs using the microtubule transport system [23] . HBV is a pararetrovirus replicating via an RNA pregenome that is transcribed from the nuclear covalently-closed circular form of the viral genome . Consequently the viral rcDNA has to enter the nucleoplasm upon infection where the rcDNA is converted to the covalently-closed circular form . To allow access of the unknown cellular DNA repair factors the viral genome has to be liberated from the capsid either prior to , during or after transport through the NPC . Transport and genome release are obviously highly efficient and well-coordinated , since ∼80% of virions are infection-competent in vivo [25] . After export to the cytoplasm , the RNA pregenome is translated to core protein and the viral polymerase which binds in situ to an encapsidation signal on the pregenome . This complex is specifically encapsidated within the assembling core protein ( reviewed by [26] ) , which forms an immature capsid ( Immat-C ) . The polymerase converts the RNA into rcDNA , which is found in mature capsids ( Mat-C ) . It is worth noting that genome maturation occurs exclusively inside the capsid and is a prerequisite for envelopment of the capsids by the surface proteins for virion formation [27] . Core proteins assemble spontaneously to HBV capsids , e . g . upon expression in E . coli . The first 140 aa of the core protein are essential for assembly and exhibit an ordered structure . Within a resolution limit of 16 Å E . coli-expressed capsids show the same morphology as virion-derived capsids or capsids from infected cells [28] . The arginine-rich C-terminus ( aa 141–185 ) is flexible [29] and exhibits phosphorylation sites for a cellular protein kinase , which has not yet been unequivocally identified . In E . coli-expressed capsids , which contain unspecific E . coli RNA and which are not phosphorylated , the C terminus is localized within the lumen of the capsid [30] . Some steps of genome maturation depend upon core protein phosphorylation , notably pregenome encapsidation and plus strand DNA synthesis [31] , [32] . Both phosphorylation and genome maturation lead to translocation of the core protein C termini , harboring a NLS , to the exterior of the capsids [19] . Consequently , all HBV capsids that have undergone some degree of genome maturation or phosphorylation bind to importin α/β [18] and can be found in the nuclear basket [1] , [19] . Early in infection when sufficient amounts of surface proteins have not yet been synthesized , nuclear entry of progeny genomes increases the number of nuclear HBV DNA copies [33] , which is generally low but can reach numbers of up to 491 per cell in HBV infected patient [34] . Using Digitonin-permeabilized cells – a frequently used system for analysis of nuclear import [35] – it was however shown that Immat-C and in vitro phosphorylated capsids synthesized in E . coli ( P-rC ) failed to exit the nuclear basket and thus do not diffuse deeper into the karyoplasm . In contrast , adding Mat-C to the cells led to the presence of intranuclear capsids and genome release [18] , [19] . The import strategy of HBV capsids seems thus to follow an entirely different strategy than has been shown for other viruses . In particular the arrest of Immat-C in the basket is unique . To date the only examples for cargos with an aborted nuclear import reaction are some Nups that become incorporated into the NPC after cell division and the protein Ubc9 , which partly associates with Nup358 on the cytosolic fibers of the NPC . Evidently , the viral capsids have to interact with the NPCs upon initial infection but during establishment of the infection as well , at which time the nuclear viral DNA becomes amplified . Due to the incomplete understanding of HBV transport and disassembly , the unique strategy and the medical importance of HBV , we evaluated the molecular background of the capsid arrest in the NPC basket . Using in vitro and in cellulo approaches we identified the cellular interaction partner and identified the underlying mechanism responsible for the selective release of mature viral genomes . Collectively , these findings lead to a model of a multi-step , maturation-regulated nuclear entry of the HBV genome .
Abortion of a nuclear import reaction within the nuclear pore must be based on a direct or indirect interaction with proteins of the NPC . The recently observed arrest of Immat-C and P-rC in the nuclear basket thus most likely involves an association with a Nup on the karyoplasmic face of the NPC . According to the current view on the architecture of the NPC candidates would include Nup50 , 54 , 58 , 62 , 93 , 96 , 98 , 107 , 133 , 153 , 160 , Rae1 , Seh1 , Sec13 PBC68 , and Tpr ( summarized by [36] ) . First , we wanted to identify the NPC proteins that are co-precipitated by Immat-C using a nuclear extract of rat liver nuclei . The NPC is composed of tightly interacting mostly hydrophobic proteins requiring denaturating conditions or harsh detergents for separation . As this treatment unfolds proteins we first tested whether refolding restored biological functions . For this purpose , we investigated the importin β binding ability since it is one of the essential functions of Nup153 . Affinity of Nup153 to importin β was shown to be stronger than to other nucleoporins ( Nup62 , Nup214 , Nup358 [37] ) . To test our experimental system , pull-down was performed by incubating recombinant functional importin β with the extract followed by binding of the nucleoporins to solid-phase bound antibody mAb414 . This antibody binds preferentially to the FXFG-repeat containing nucleoporins of vertebrates as e . g . Nup62 , Nup153 , Nup214 and Nup358 [38] , [39] with different efficiency . Figure 1A depicts that importin β became co-precipitated ( lane 1 ) . As the nuclear extract did not contain detectable amounts of importin β ( not shown ) this result implies that the importin β binding activity of the nucleoporin ( s ) was maintained or restored during extraction and renaturation . Binding was specific as antibody-coated beads without the extracted proteins and uncoated beads failed to interact with importin β . Binding occurred in the absence of an importin β-bound cargo . This finding is in accordance with the observation that importin β after its dissociation from the cargo does not diffuse deeper into the karyoplasm but remains bound to the NPC before being exported into the cytoplasm [40] . The nuclear extract was then subjected to co-immune precipitations using Immat-C which are arrested within the nuclear basket . These capsids contain DNA replication intermediates of the viral genome; they interact with the NPCs [19] and can thus be responsible for nuclear genome amplification . For visualization of co-precipitated proteins we used Sypro Red , staining all proteins after SDS PAGE . As depicted in Figure 1B no co-precipitation could be observed in the absence of nuclear extract . Faint bands were observed in the absence of capsids . This indicates some unspecific binding of nuclear proteins to the carrier beads , probably due to nonspecific hydrophobic interactions . Immat-C-driven pull-down however showed the precipitation of one dominant protein , strongly enriched in comparison to the control extract and the precipitation controls . This protein showed an apparent molecular weight of a ∼180 kDa typical for Nup153 [41] , [42] . To confirm the identity of co-precipitated Nup153 we used the antibody mAb414 in Western blot . Figure 1C confirms the Nup153 co-precipitation by Immat-C and provides evidence that neither Nup214 nor Nup358 were co-precipitated . To further confirm specific Nup153 capsid-interaction we preincubated the nuclear extract with Immat-C prior to precipitation via mAb414 and detection of co-precipitated Immat-C . Since antibodies against denaturated core proteins require very large amounts of protein to provide adequate signals we labeled Immat-C by 32P using the protein kinase activity inside the capsids . Phosphoimaging showed that a single protein was labeled having the molecular weight of the core protein of 21 . 5 kDa ( Fig . 1D ) . As depicted in Figure 1E Immat-C could be precipitated by mAb414-bound Nup . No signal was obtained in the absence of nuclear extract or in the absence of mAb414 . To determine whether the Nup153 binding was selective for Immat-C we included different capsid species in co-immune precipitations . We used Mat-C , which enter the nucleus and comprise an rcDNA genome; P-rC , which contain E . coli-RNA and which is arrested in the basket like Immat-C , and capsids that were formed by C-terminally truncated core proteins ( ΔC-rC ) . ΔC-rC do not contain RNA , cannot be phosphorylated , and cannot enter the basket as these capsids do not contain the NLS . All capsids reacted equally well with the anti-capsid antibody used to precipitate them after preincubation with nuclear extract ( not shown ) . Unexpectedly , all capsids were able to precipitate Nup153 as depicted by immune blot using mAb414 ( Fig . 2A ) . The Sypro Red stain of co-precipitated proteins was restricted for the molecular weight of the interaction partners . Proteins smaller ∼75 kDa could not be detected as this part of the SDS PAGE was heavily overloaded with high amounts of bead-bound antibodies and BSA used for saturation of unspecific binding sites . We could have thus missed smaller adapter proteins connecting the capsids with Nup153 . To obtain evidence of direct interaction we replaced the nuclear extract by E . coli-expressed Nup153 that was purified under native conditions . The fusion protein was previously shown to be functional on nuclear import and export after integration into the NPCs of reconstituted Xenopus laevis oocyte nuclei [43] . Figure 2B showed that all capsids precipitated GST-Nup153 . As GST does not bind to the capsids ( not shown ) we conclude that Nup153 interacts directly with the surface of the capsid . The signal was not derived from the capsid preparation since no signal was observed in the absence of GST-Nup153 . Furthermore , GST-Nup153 precipitation was not observed in the absence of the capsids implying that GST-Nup153 did not bind directly to the antibody coated-bead ( Fig . 2C ) . We next set out to determine to which Nup153 domain the capsids bind . We expressed large parts of Nup153 in three fragments ( N: aa 53–272; Z: aa 272–543; C1: aa 618–999 ) as GST fusion proteins . The Nup153 fragments were incubated with the different capsids and co-precipitated by biomagnetic bead-bound anti-capsid antibodies . Co-precipitation of the Nup153 fragments was demonstrated by Western blot using anti-GST antibodies . Figure 2D shows that Mat-C , Immat-C and P-rC failed to precipitate the 50 kDa N terminal part of Nup153 . A strong band at 54 kDa was visible in all samples to which the biomagnetic beads were added . This band was most likely the product of an unspecific binding of the secondary blot antibody to the heavy chain of the antibodies used in precipitation . Using the zinc finger domain Z ( 56 kDa ) no precipitation could be observed ( Fig . 2D ) . However all three capsid species co-precipitated the 68 kDa C1 fragment . Binding was specific as no precipitates could be observed in control reactions without capsids or with capsids but without anti-capsid antibodies on the beads . This Nup153 fragment comprises an importin β-binding domain [44] , [45] thus implying that importin β and capsids compete for Nup153 binding . A fourth His-tagged fragment ( C2: aa 992–1219 ) containing most of the ∼30 FXFG repeats [46] was analyzed by co-precipitation but we observed an unspecific binding to the beads . To circumvent this technical problem we performed retardation gels . We incubated P-rC with His-Nup153 C2 and separated the complex by native agarose gel electrophoresis . The Nup fragment was visualized by anti-His antibodies , the capsid by anti-capsid antibodies . Control experiments were performed using the N and the C1 fragment . Migration of the fragments was determined by anti-GST antibodies . Figure 2E confirmed that the N fragment did not interact with the P-rC as no shift of capsid migration could be observed upon the presence of this Nup153 domain . Nup153 C1 caused a retarded migration of the majority of capsids ( Fig . 2F ) and fragment C2 retarded the migration of all P-rC ( Fig . 2F ) . Although fragment C2 is largely hydrophobic we assume that the capsid binding is not unspecific as the capsids are negatively charged and migrate on native agarose gels as ∼3000 bp linear double stranded DNA fragments . As both fragments C1 and C2 barely overlap we conclude that there is more than one interaction site on Nup153 . In vivo , numerous proteins and nuclear factors pass the NPC simultaneously requiring interaction with Nup153 . As Immat-C and P-rC are arrested in vivo , one should expect that the affinity of the physiological transport complexes to Nup153 is weaker than that of the capsids . We incubated capsids ( P-rC ) immobilized on biomagnetic beads with Nup153 and importin β in different ratios . Figure 3A , lane 1 , shows that no GST-Nup153 bound to the beads in the absence of capsids thus demonstrating the specificity of Nup153 binding . When incubating Nup153 in parallel to different amounts of importin β with the capsids only molar importin β excesses of more than 150-fold with regard to Nup153 prevented Nup153 binding to the capsids . This result thus confirm that capsid and importin β binding sites on Nup153 overlap and show further that the capsid binding was much stronger than the importin β interaction . We next asked whether bound capsids can be displaced from Nup153 by importin β . We preincubated biomagnetic bead-bound P-rC with Nup153 and added importin β after removal of unbound Nup153 . Figure 3B shows that even 6700-fold molar excesses of importin β did not remove Nup153 from the capsid . In order to determine whether the capsid arrest in the basket is mediated by Nup153 we suppressed Nup153 expression in HeLa cells by siRNA . We controlled Nup153 expression in parallel to the expression of Fibrillarin , which is a component of a nucleolar small nuclear ribonucleoprotein ( SnRNP ) involved in ‘house keeping’ for nucleolar integrity [47] . Figure 4A shows that Fibrillarin expression was in fact unaltered but Nup153 reduced by 80% . Suppression was limited because Nup153 is essential for cell viability . After permeabilization of these cells by digitonin a nuclear import assay using P-rC was performed . We visualized Nup153 and capsids using indirect immune fluorescence . Nup153 staining was weaker in 80–90% of the siRNA-treated cells than in non-silenced cells ( Fig . 4B , lower panel ) . The nuclei of these cells were larger than in mock-transfected cells most probably due to the role of Nup153 in mitotic progression [48] . The nucleoporin Nup153 plays separate roles in both early mitotic progression and the resolution of mitosis . In the nuclei of the control cells , P-rC accumulated at the nuclear envelope and did not enter the karyoplasm as demonstrated previously [18] . In contrast , a proportion of capsids were found in the karyoplasm of Nup153 siRNA-transfected cells , ( still image: Fig . 4B upper panel; for videos see Videos S1 , S2 , S3 and S4 ) . Quantification on 20 cells revealed that 18–19% of capsids entered the nucleus while ca . 80% were still arrested at the NE . We next asked how Mat-C proteins could enter the nucleus despite the strong binding to Nup153 . We followed the hypothesis that capsids should remain intact while genome maturation continues but should liberate the genome from the basket once rcDNA is formed . To test this hypothesis we cross-linked the Mat-C subunits , which were 32P-labelled by UV irradiation ( Mat-C UV ) and analyzed their integrity . Figure 5A shows that the cross-linking caused a strong reduced migration of the core protein subunits in SDS PAGE . Most of the protein was retained at the entry site of the SDS PAGE; only traces resulted in a smear >45 kDa . To test whether cross-linking occurred between capsids , which would have caused unsuitable particle aggregates we separated Mat-C UV on native agarose gel and detected them by anti-capsid antibody used before . We observed no difference in migration compared to the P-rC standard ( Fig . 5B ) , demonstrating that UV irradiation only induced bonds between subunits of individual capsids . The result further shows that the UV treatment had not changed the surface charge . We next investigated the transport competence of Mat-C UV in comparison to untreated Mat-C . We injected 1×107 capsids into the cytosolic periphery of 6 Xenopus laevis oocytes and followed the fate of the capsids by electron microscopy . We used Xenopus laevis oocytes sine more NPCs can be analyzed by EM in this system than by using permeabilized cell lines thus giving more reliable results . Combining these two systems and comparing the results is however justified since , as far as is known , nuclear import is identical in both systems [49] , [50] . As control , six Xenopus laevis oocytes were injected with Mat-C . We restricted the incubation time after injection to 1 h , the earliest time point at which significant numbers of capsids arrive at the nuclear pore ( not shown ) . Such a short time was chosen in order to detect possible differences in nuclear entry of the capsids . As shown in Figure 6A both types of capsids entered the nuclear basket as intact particles . We determined the number of capsids that arrived at 68 NPCs ( Mat-C ) and 74 NPCs ( Mat-C UV ) , and determined their location at the NPCs . Figure 6B showed that a similar number of capsids arrived at the nuclear pore indicating that the cross-link neither affected the intracytoplasmic transport capacity nor the interaction with the NPCs . We next analyzed the distribution of the capsids , showing that both capsid species exhibited the same distribution at the NPCs with a majority on the cytoplasmic face . To our experience this dominantly cytoplasmic localization is related to the short incubation time . However , the same proportion of Mat-C and Mat-C UV entered the pore and were found in the nuclear basket , implying that their transport competence was the same . We next analyzed whether Mat-C UV diffuses deeper into the nucleus like untreated Mat-C or remained arrested at the nuclear envelope like Immat-C and P-rC . These assays were performed with digitonin-permeabilized cells since the relatively low amounts of capsids would have been undetectable in Xenopus laevis oocytes . Capsids were detected by indirect immune fluorescence using the same anti-capsid antibody that showed an unchanged reactivity after cross link . Figure 6C shows that Mat-C entered the nucleus as it was previously reported for different permeabilized cells and in living cells [19] , [23] . In contrast Mat-C UV failed to enter the karyoplasm and remained associated at the NPCs and some cytosolic structure . We assume that the cytosolic binding sites are collapsed microtubules as such binding was demonstrated before for digitonin-permeabilized cells [23] . Figures 2 and 3 showed that the capsids bound to a Nup153 domain that participates in importin β binding and that the interaction competes with importin β in vitro . To obtain in situ evidence on Nup153 that is integrated into the NPCs we analyzed whether binding of capsids to Nup153 interfere with the importin β-dependent nuclear import pathway . These experiments were performed with P-rC because the large numbers of capsids required for Nup153 saturation on the permeabilized cells were only available for this type of capsid . We used the human hepatoma cell line HuH-7 cells which is able to synthesize hepatitis B virions after transfection with HBV DNA . We incubated digitonin-permeabilized cells with different amounts of P-rC . Nuclear import into the basket was facilitated by means of the nuclear transport receptors in rabbit reticulocyte lysate . After incubation cells were washed . In the last washing buffer no P-rC was detectable . First we quantified the amount of NPCs and the number of bound capsids . NPC number was calculated from the Nup153 signal obtained by Western blot , which was compared to a dilution series of E . coli-expressed Nup153 ( not shown ) . We determined ∼6000 NPCs per HuH-7 nucleus , which is higher than the ∼2770 NPCs found in HeLa cells [9] . It is however known that the NPC number varies strongly between different cell types ( 18 , 451+/−2 , 336 ( Purkinje cells ) to 402+/−67 ( oligodendrocytes ) [51] . We used P-rC preloaded digitonin-permeabilized cells after washing and added new cytosol together with two fluorescent cargos – Alexa594 NLS-BSA and Alexa647 M9-BSA . NLS-BSA is imported by importin β while M9-BSA uses transportin for nuclear entry . Both cargos comprise the same number of nuclear localization signals , as determined previously [18] . After import reaction , capsids and NPCs were stained by indirect immune fluorescence , and nuclear cargo concentrations were determined semi-quantitatively using confocal laser scan microscopy . Quantification was performed in the equatorial region of the nuclei . A positive control was performed on cells that were preincubated in the absence of capsids but to which the cargos were added in a second step and incubated at 37°C . In the negative control no P-rC was added during the first incubation but the following import reaction with the fluorescent cargos was performed on ice , thus inhibiting active nuclear transport [52] . Figure 7 shows no intranuclear fluorescence in the negative control ( Fig . 7A ) but strong signals for both cargos in the positive control ( Fig . 7B ) . With increasing preload of the nuclei by capsids the import of both cargos became reduced ( Fig . 7C–H ) . At the highest capsid concentration no intranuclear fluorescence could be observed ( Fig . 7H ) . In this sample the average nuclear diameter increased from 200 µm2 in the negative control to 240 µm2 . This observation is in accordance with a “plugging” of the NPCs by the capsids , which does not allow exchange of smaller molecules needed for equilibration of the osmotic pressure between nucleus and reaction mixture . Figures 7B to G show further that the reduction of nuclear import appeared for NLS-BSA at lower preloaded numbers of capsids than for M9-BSA . As no unbound P-rC was present after washing , we can exclude competition between capsid NLS and NLS-BSA for the transport receptors importin α and β . We next quantified the import in 570 nuclei . The mean intranuclear fluorescence in the positive control was taken as 100% . Figure 8 shows scatter plots obtained for each capsid preload . We observed a significant variation between the nuclei in one sample , for instance between 80% and 120% in the positive control . This can be explained by variable nuclear permeability throughout the cell cycle [53] . Moreover , this observation confirms that the nuclei remained intact during the transport reaction as a disruption would have resulted in equal concentrations . The import via transportin and via importin β was correlated in all cells and followed a Gaussian normal distribution . As indicated by the slopes of the regression lines no significant changes were observed at preloads from 0 to 0 . 2 capsids per NPC ( Fig . 8B–D ) . With larger numbers of capsids , the importin-mediated import became greatly reduced ( Fig . 8E; 0 . 7 P-rC/NPC ) or undetectable ( Fig . 8F; 3 . 3 P-rC/NPC ) , while the transportin-mediated pathway remained significantly more active ( p≤10−9 ) . Concentrations ≥3 . 3 capsids/NPC blocked both the NLS-and M9-mediated import , most likely the result of steric hindrance by capsids that got stuck in the channels of the NPCs . Collectively the data however indicate that blocking the capsid binding sites on Nup153 interferes with the importin β-mediated nuclear entry but not with the transportin pathway .
Karyophilic cargos interact transiently with components of the NPC via transport receptors before they are released to the karyoplasm . HBV capsids are an exception as they remain arrested in the nuclear basket . We showed that HBV capsids bound solubilized and renatured rat Nup153 from a nuclear extract with importin binding activity . This is consistent with the high degree of conservation of Nups among different species and the conserved mechanisms of nuclear translocation [54] . It confirms further the observation that Immat-C is arrested at the NPCs of different cell lines [19] . Neither co-purification of Nup98 or Nup160 , which connect Nup153 with the central frame work of the NPC [55] nor of Tpr ( 268 kDa ) , which is attached to the NPC via Nup153 [7] , was observed . This finding indicates that the NPC components became disassembled during purification and did not reassemble upon renaturation . This interpretation is in accordance with previous findings that NPC reconstitution depends on the presence of RanGTP [56] . RanGTP has a low molecular weight of 25 kDa and is removed from the nuclei prior to disintegration of the NPCs . The absence of a 97 kDa band in the co-precipitation further confirms that the binding was not mediated by importin β , which is in accordance with the direct binding observed in co-precipitation of natively purified GST-Nup153 . The latter finding provides further evidence that the binding did not require a protein linker , which may have been below the 75 kDa limit of the Sypro Red stained gel . HBV capsids undergo complex modifications upon genome maturation which are not well understood . Their changes comprise not only the reverse transcription of the encapsidated RNA to rcDNA but also protein phosphorylation [31] , [32] and eventually dephosphorylation . We found that Nup153 was precipitated by Immat-C , containing replication intermediates and possibly empty capsids , and by Mat-C containing rcDNA . This observation suggests that the type of nucleic acid within the capsid has no impact on Nup153 interaction . The observation that E . coli-expressed RNA containing capsids that were phosphorylated in vitro by protein kinase C and empty E . coli-expressed capsids that lacked the RNA-binding and phosphorylated C-terminal domain interacted equally with Nup153 shows that the binding is mediated by the N-terminal assembly domain . It further confirms that importin β , which interacts via importin α with the NLS on the C-terminal domain [18] do not interfer with capsid binding to Nup153 . Searching for the domain on Nup153 , which interacts with the capsids we observed that aa 53–543 of Nup153 did not show any interaction . This part of Nup153 comprises a transportin binding site ( aa 247–290 [57] ) . In contrast aa 618–999 and 992–1219 – both comprising FxFG repeats - interacted with the capsids . As other FxFG repeat comprising Nups were not co-precipitated we conclude that the binding is specific for Nup153 and not based on hydrophobic interactions . To obtain evidence of overlap between importin β and capsid binding sites we performed competition experiments in the presence of excesses of importin β . The strong binding with regard to importin β is consistent with an attachment of the capsids to the FXFG repeats as these sites interact with this import receptor . This strong affinity could explain the mechanism by which the capsids can remain arrested on Nup153 within the cells despite of the 1000 exchange reactions that pass each nuclear pore per second [4] . Partial silencing Nup153 showed that a significant proportion of P-rC entered the nucleus . The incomplete entry is consistent with the high affinity of the capsids to Nup153 , assuming that less than 8 copies of Nup153 are sufficient for capsid arrest . Considering that Nup153-linked Tpr was not precipitated the result thus confirms that no other nucleoporin has a significant effect on the abortion of the capsid entry into the nucleus . Our results showed additionally that the structural part of the capsid caused the interaction , which has a well ordered structure . Within 16 Å resolution no differences could be found between E . coli-expressed capsids , Mat-C and Immat-C [28] although one group reported a hydrophobic pocket on the capsid surface that exists on Mat-C only [58] . We considered structural causes to be an unlikely explanation for the different entry behavior and followed the hypothesis that Immat-C is more stable than Mat-C . Consequently , we assumed that maturation-dependent disintegration of Mat-C leads to capsid subunits which may diffuse from the NPC to the karyoplasm . It was recently shown that capsids may disintegrate to core protein dimers [59] . Irrespective of the T = 3 or T = 4 symmetry of the capsids , the core protein dimers would exceed the number of Nup153 molecules per NPC by a factor of >10 . Supernumerous subunits could diffuse into the karyoplasm where they reassemble [59] . Our nuclear import assays with cross linked capsids confirmed this hypothesis showing that disassembly is a prerequisite for intranuclear capsid formation . These data are also consistent with recent observations that nuclear entry of capsid and genome is independent upon RanGTP [19] , as a dissociation of the import receptors is not required . In order to analyze the functional effects of capsid binding to Nup153 in situ we measured nuclear import via the importin β pathway . Our observation that the NLS-BSA import was impaired to a much greater degree than M9-BSA translocation provides evidence for the concept that transport inhibition was specific by blocking importin β binding sites and was not the result of steric interference . The strong inhibition of the importin β pathway is in accordance with observations of Walther et al . [43] who reported that Nup153 is essential for nuclear entry via importin β but not via transportin . In fact our approach of inhibiting Nup153 function with capsids allows for the first time the confirmation of these data without interfering with the NPC architecture , i . e . Nup50 translocation or Tpr depletion [5] , [7] . Based on our observations we propose a model of nuclear entry for the HBV genome , which strikingly differs from that for any other virus . According to previous data we postulate that the viral genome is transported within the capsid via microtubules to the nuclear periphery [23] . This assumption does not exclude that the capsids undergo a limited disintegration and re-association of some capsid subunits . Such capsid “breathing” is in accordance with in vitro findings [60] , [61] and can explain the observation of polymerase-free hepadnaviral DNA genomes [62] , which are nonetheless precipitable by anti capsid antibodies [63] . After binding to importin α/β the capsids are transported into the nuclear basket ( Fig . 9 ) . This transport requires the DNA minus strand synthesis which is linked to NLS exposure by some core subunits [19] . The import complex binds to Nup153 via importin β , followed by importin α and importin β dissociation , which is mediated by RanGTP . Due to their affinity to Nup153 capsids interact directly with Nup153 . While Mat-C disintegrates , Immat-C remain in the arrested state . In vivo these arrested capsids should not interfere with the viability of the hepatocytes [64] as thousands of capsids would be necessary to significantly block the nuclear pores . In fact , only high level expression of capsids in cell lines causes toxic effects [65] . It is plausible to assume that genome maturation can proceed in Immat-C but experimental evidence is difficult to obtain as genome maturation is slow . At the beginning of infection , when significant amounts of surface proteins have not yet been synthesized , ongoing genome maturation after arrest prevents premature release of the genome into the nucleus . Due to the dependence of the second strand DNA synthesis on the environment within the capsids [66] , premature release of minus strand DNA would lead to a loss of viable virus . Thus , the maturation dependent arrest of HBV capsids is probably an essential step in the life cycle of the virus .
Nuclear proteins were prepared from rat liver nuclei using urea [67] followed by protein refolding during dialysis . Eight gram of rat liver were minced in 16 ml of ice-cold 250 mM sucrose in TKM buffer ( 50 mM Tris-HCl , pH 7 . 5 , 25 mM KCl , 5 mM MgCl2 , protease inhibitor mix ( complete ) ( Roche ) ) . All subsequent preparations were performed on ice . The tissue was homogenized using a motor-driven Teflon pestle with 10 strokes at 1700 rpm . The homogenate was filtered through two layers of a filter ( 59 µm mesh ) . Three ml of homogenate were mixed with 6 ml 2 . 3 M sucrose/TKM buffer and loaded on a 1 ml cushion of 2 . 3 M sucrose/TKM buffer . After centrifugation for 30 minutes at 124 , 000×g at 4°C , the pellet was resuspended in 200 µl TKM buffer . The resuspended nuclei were treated with 4 ml 8 M urea for 1 h at 65°C and subjected to dialysis at 4°C against 1 . 5 l 1×PBS/2 mM DTT ) for 48 h . During the dialysis the buffer was changed four times . The protein concentration was determined by bicinchonic acid ( BCA ) assay . The nuclear proteins were frozen in liquid nitrogen and stored in aliquots at −80°C . This lysate did not contain detectable amounts of importin β , presumably excluding interference of importin β with capsid binding ( not shown ) . GST-Nup153 was expressed using the vector pGEX 2T-hNup153 , which encodes for human wt Nup153 [43] . Expression was performed in E . coli BL21 CodonPlus RIPL ( STRATAGENE ) . Bacteria were grown at 37°C in 2×YT-medium/100 µg/ml ampicillin/30 µg/ml chloramphenicol/ 0 . 2% glucose to an OD600 of 1 . 0 and cooled to 18°C prior to induction of the expression by 1 mM IPTG and incubation for 16 h at 18°C . PMSF was added to 2 mM and the bacteria were chilled for 15 min at 4°C followed by sedimentation at 4000 g for 15 min at 4°C . The pellet was resuspended in PBS/1 mM DTT/1×EDTA-free complete protease inhibitors ( Roche Applied science ) . Bacteria were lysed by sonification on ice . The lysate was cleared 30 min 15000×g at 4°C . One milliliter equilibrated GST-Sepharose High Performance ( GE-Healthcare ) was added to the supernatant and incubated for 1 h at RT followed by o . n . incubation at 4°C . The solution was transferred to a polypropylene column ( BIO-RAD ) . The matrix containing the GST-Nup153 was washed with 30 ml binding-/washing buffer . GST-Nup153 was eluted using 5 ml 20 mM Glutathione/200 mM NaCl/50 mM Tris ( pH 7 . 5 ) /1 mM DTT/1×EDTA-free complete protease inhibitors . The eluate was dialyzed against 200 mM NaCl/50 mM Tris-HCl pH 7 . 5/50% Glycerol/250 mM Sucrose/1 mM DTT for 24 h at 4°C . The buffer was changed three times . GST-Nup153 was stored in aliquots at −80°C . The N terminal human Nup153 fragment was expressed using the vector pGEX-153N ( 53–272 ) , the zinc finger domain using the vector pGEX-153Z ( 272–543 ) and the N terminal half of the C terminus using the vector pGEX-153C1 ( 618–999 ) . Expression was performed using E . coli BL21 ( DE3 ) Lys using the protocol of [68] Bacteria were grown in NZy ( rich ) medium and expression was performed for 3 h at 37°C . The sedimented bacteria were lysed as described above . Purification was performed as described above . The C terminal half of the C terminus ( aa 992–1219 ) was expressed as a His-tagged protein using the vector pET28-153C2 [68] . The His-tagged fragment was expressed by using E . coli BL21 ( DE3 ) Lys in LB/Kanamycin medium . Bacteria were lysed as described above . The precleared lysate was purified by Ni++ agarose ( Quiagen ) using the protocol of the vendor . After elusion , the His-tagged Nup fragment was renatured by dialysis ( 1 h against 1 M urea , 0 . 2 M NaCl , 0 . 1 M NaH2PO4 pH 7 . 6 , 0 . 5 mM PMSF , 1 h dialysis against 0 . 2 M urea , 0 . 2 M NaCl , 0 . 1 M NaH2PO4 pH 7 . 6 , 0 . 5 mM PMSF , 1 h dialysis against 0 . 2 M NaCl , 0 . 1 M NaH2PO4 pH 7 . 6 , 0 . 5 mM PMSF and 1 h against 0 . 2 M NaCl , 0 . 1 M NaH2PO4 pH 7 . 6 ) . P-rC was generated by in vitro phosphorylation of E . coli-derived capsids using protein kinase C and [γ32P] ATP to determine the success of the reaction [69] . E . coli-derived capsids were expressed and prepared as described previously [20] . Quantification of capsids was done by immune blotting of the capsids after native agarose gel electrophoresis [18] . Preparation of Mat-C from virions from the permanently virion-expressing hepatoma cell line HepG2 . 2 . 15 [70] was done according to Rabe et al . [19] . This cell line expresses infectious HBV [70] . Immat-C was purified from the same cell line using the following protocol . Ten 16 cm dishes of HepG2 . 2 . 15 cells were grown in DMEM medium , containing 10% FCS until 80% confluence . The medium was replaced by DMEM/1% FCS and cells were grown for an additional 4 days . Cells were washed twice in PBS , harvested by a rubber policeman and sedimented for 5 min at 200×g and 4°C . Cells were resuspended in 0 . 1% Nonidet P-40/PBS and sonified on ice . Cellular nucleic acids were digested by addition of 20 U/ml DNase I/20 µg/ml RNase A in 15 mM MgCl2 for 15 min at 37°C . This short incubation was chosen as capsid instability may allow entry of nucleases upon longer incubation periods [60] , [61] . The lysate was centrifuged for 20 min at 10 , 000×g . The supernatant was adjusted to 0 . 75% Nonidet P-40/5 mM CaCl2/20 U/ml S7-Nuklease and incubated for further 15 min at 37°C before EDTA was added to 15 mM . The lysate was centrifuged for 10 min at 4°C and 18 , 000 g . The capsids in the supernatant were loaded on a 1 ml 25% ( w/v ) sucrose cushion and sedimented for 2 h at 10°C and 50 , 000 rpm in an SW60 rotor ( Beckman ) . The sediment was resuspended in 500 µl PBS , centrifuged 5 min at 4°C and 12 , 500×g . The capsid-containing supernatant was adjusted to 2 mM DTT in order to prevent disulfide bond formation and stored in aliquots at −80°C . Mat-C were labeled by [γ32P] ATP as described elsewhere [69] . Labeled capsids were exposed 6×8 min to 254 nm UV light on ice ( Stratagene UV Stratalinker; 1 cm distance to the UV source , ∼4000 µW/cm2 ) . Samples withdrawn before and after cross-linking were analyzed by SDS-PAGE with a subsequent exposure to a phosphoimaging screen . Evaluation of a possible inter-capsid cross-link was done by native agarose gel electrophoresis and subsequent immune detection [18] . For immune precipitation 1 . 2×107 anti-rabbit antibody-coated biomagnetic beads ( Invitrogen ) were added to 185 µg anti-HBV capsid antibody ( DAKO ) . The volume was adjusted to 1000 µl by addition of 0 . 1% BSA/PBS and incubated overnight at 4°C on a rotating wheel . Unbound antibodies were removed by washing the beads three times in 0 . 1% BSA/PBS . For inverse immune precipitation , 12 . 5 µg mouse monoclonal antibody 414 ( mAb414 , HISS ) were bound to 1 . 2×107 anti-mouse antibody-coated biomagnetic beads as described for the anti-capsid antibody . For co-immune precipitations of the nucleoporins 100 ng of HBV capsids were preincubated with 75 µg nuclear proteins for 2 h at 37°C in transport buffer ( 20 mM Hepes , pH 7 . 3 , 2 mM MgAc , 110 mM KAc , 5 mM NaAc , 1 mM EGTA , 2 mM DTT , protease inhibitor mix ( complete ) ( Roche ) ) in a final volume of 250 µl . The precipitation mixture was incubated with constant shaking at 37°C overnight . Afterwards the beads were washed three times in 500 µl 0 . 1% BSA/PBS , 1×500 µl 0 . 1% Nonidet P-40/PBS , and transferred into a new cup . After three additional washing steps with 500 µl PBS , the pellet was resuspended in 20 µl 1× loading buffer ( Anamed ) , denatured for 10 min according to the vendor and loaded on the SDS gels . ( Invitrogen and Anamed ) . For detection of total proteins that were co-precipitated Sypro Red staining was performed according the manufacturer . For immune detection the proteins on the gel were transferred to a PVDF membrane ( VWR International ) by electroblotting . The membrane was blocked for 1 h at RT in 5% fat-free milk powder in PBS . For detection of co-precipitated nucleoporins the first antibody ( mAb414 , HISS ) was added at a dilution of 1∶3000 in 5% fat-free milk/PBS for 3 h at RT . After washing three times in 0 . 1% Tween-20/0 . 5% milk/PBS the membrane was incubated for 1 h with a horse radish peroxidase anti-mouse antibody ( 0 . 16 µg/ml; Dianova ) . Detection was performed by ECL ( PerkinElmer ) . 5 ng P-rC and 75 µg Nup153 fragments were incubated in transport buffer ( 20 mM HEPES pH 7 . 3/2 mM Mg acetate/5 mM Na acetate/110 mM K acetate/1 mM EGTA/2 mM DTT/ protease inhibitor [complete , Roche] ) for 2 h at 37°C . 1 . 2×107 anti-GST antibody ( MoBiTec ) -coated biomagnetic beads ( Dynal ) were added and incubated over night at 4°C . The beads were washed 3× with transport buffer including one change of the cups . The beads were incubated for 1 min in PBS/0 . 1% NP-40 , sedimented and washed 4× with PBS . The precipitated proteins were then separated on SDS PAGE and blotted to a PVDF membrane , which was saturated using 5% ( w/v ) milk powder/1×PBS prior to the addition of the first antibody ( anti-GST antibodies , 1∶500 , 2 h , RT ) . The membrane was washed 3×10 min in 1×PBS/0 . 1% Tween 20/0 . 5% milk powder and incubated with a peroxidase-labeled anti-rabbit antibody ( 1∶5000 , 1 h , RT ) . The membrane was washed as described above , followed by an additional 15 min incubation in 1×PBS at RT prior to the visualization by film using ECL . In gel retardation assays the incubation mixture of capsids and Nup153 fragments were separated on 0 . 7% agarose/TAE gels and blotted onto PVDF membranes by capillary blotting [71] . Nup-fragments were detected by anti-GST antibodies or and anti-His antibodies ( dilution 1∶3000 ) as described above , HBV capsids were detected by anti-capsid antibodies ( DAKO , 1∶10 , 000 ) . Two hundred ng P-rC were incubated with 200 ng of GST-Nup153 and different concentrations of importin β for 2 h at 37°C in transport buffer . At the lowest concentration of importin β the ratio was 1 capsid : 28 GST-Nup153 : 33 importin β molecules . The capsids were precipitated by the addition of 1 . 2×107 anti capsid antibody-saturated biomagnetic beads o . n . at 37°C . The beads were washed 3× with 0 . 1% BSA/PBS and once with 0 . 1% Nonidet P-40/PBS . The beads were transferred to a new cup and washed again 3× with PBS before the proteins were separated on a 3–8% SDS PAGE ( Tris acetate gel ) . Co-precipitated GST-Nup153 was detected by Western blot using mAb414 as described above . Alternatively 200 ng of capsids were bound to 1 . 2×107 anti capsid antibody-saturated biomagnetic beads o . n . at 37°C followed by an incubation with 200 ng GST-Nup153 in transport buffer for 2 h at 37°C . After washing , varying amounts of importin β were added for 2 h at 37°C . The beads were then washed as described above and subjected to SDS PAGE and immune blot using mAb414 . The cytosolic microinjection , preparation of the nuclei and electron microscopy are described elsewhere [72] . Six Xenopus laevis oocytes were used per sample . Per oocyte 1×107 Mat-C or Mat-C UV were microinjected . P-rC , available in much higher concentrations than Mat-C , was microinjected in 2×108 particles per oocyte . For quantification the number of NPC with and without capsids as well as their location at the pore were recorded in 50 nm sections . HeLa cells were transfected with small interfering RNA ( siRNA ) ( Dharmacon ) against Nup153 at a final concentration of 10 nM using lipofectamine RNAiMAX ( Invitrogen ) according to the manufacture's instructions . The sequence used corresponded to nucleotide 2593–2615 of human Nup153 ( AAGGCAGACUCUACCAAAUGUTT ) , which has been previously shown to accomplish efficient knock down of Nup153 in HeLa cells [10] . Expression of Nup153 was assessed by labeling Nup153 with a monoclonal antibody , SA1 [73] . Cells were analyzed by Western blot and used to assay nuclear import of P-rC two days post transfection . For control , mock transfection of cells without siRNA was performed . Visualization was performed using enhanced chemoluminescence and autoradiography films at different exposure times . The siRNA transfected cells were then used for transport assays after digitonin-permeabilization . To determine the nuclear import capacity , 25 ng of Mat-C or Mat-C UV were subjected to digitonin-permeabilized HuH-7 cells that were grown on 12 mm collagen-coated cover slips . Growth , permeabilization of the cells and indirect immune stain was described previously [19] . To allow comparison of the results the antibodies were the same as those used for the co-immune precipitations ( anti-capsid [DAKO] , mAb414 ) . As secondary antibodies an Alexa488-labelled goat-anti-rabbit antibody ( Invitrogen ) and a Texas Red-labeled goat-anti-mouse antibody were added ( Dianova ) . Microscopy was performed on a Leica DM IRBE confocal laser scan microscope using the filter settings for FITC , TRITC and Cy5 at a pinhole size of 1 . To study the effect of Nup153 capsid-interaction on the nuclear import of other substrates an in vitro transport assay was performed using a modified protocol of Kann et al . [18] In a first step after permeabilization and washing the NPCs were loaded with a geometrical dilution of P-rC ( 0 , 25–12800 ng per 12 mm cover slip ) for 30 min at 37°C in the presence of RRL , ATP and an ATP-generating system . Afterwards the cover slips were washed 3 times with 500 µl transport buffer/1×EDTA-free complete protease inhibitors/2 mM DTT to remove unbound capsids . In the last wash less than 3 pg of unbound P-rC were present ( <300 pg per 0 . 5 ml washing solution of the last washing step , ∼5 µl remaining fluid estimated ) . The conjugates ( 50 µg/ml ) to be analyzed were added in a second transport reaction with new RRL , ATP and an ATP-generating system for 15 min at 37°C . For analysis of importin-mediated transport we used Alexa594-labelled BSA ( Invitrogen ) linked to the peptide PKKKRKVED that represents the NLS of the SV40T-Ag [74] . The import by transportin was analyzed using Alexa647-labelled BSA conjugated to the M9-domain of hnRNP A1 protein ( YNNQSSNFGPMK ) [75] . Both peptides were linked via a CGGG spacer to the BSA . Analysis of the conjugates by MALDI-TOF MS revealed a comparable conjugation of an average of 19 peptides per BSA molecule . Non-imported conjugates were removed by 3 washes in 500 µl transport buffer . Fixation , blocking reaction and immune stains of NPCs and capsids were done as described previously [18] using the mAB414 antibody or the anti-capsid antibody respectively . As second antibodies we used Alexa532-anti-rabbit and Alexa568-anti-mouse antibodies . Confocal laser scan microscopy was done using the TCS SP5 microscope ( Leica ) equipped with a HCX PLAPO 63×/1 . 4-0 , 6 Oil objective ( Leica ) . Images were taken at a zoom of 2 with a size depicting ∼70 µm pinhole size . The intranuclear area , flanked by the rim-like stain of the NPCs or P-rC stain was selected and the fluorescence signals of the imported cargos were determined ( software LAS AF version 2 . 1 . 0 ) . Calculations were done using Microsoft Excel . Although showing a large linear range absolute fluorophore numbers can be hardly obtained by fluorescence microscopy as the signal depends upon e . g . recording efficiency , filter setting , lens and amplification of the individual dye . To normalize the signals mean values of the positive import reactions were set as 100% . The mean values of nuclei recorded in the negative control at 4°C were set as 0% being only marginally above the background outside the permeabilized cells . Since the evaluated parameters ( total nuclear import , concentration and cell size ) showed a Gaussian normal distribution within each sample a comparison of the signals was done by Students' T-test . HuH-7 cells ( ∼1×106 cells ) from confluent grown 10 cm dish were treated with trypsin , removed by pipetting in 15 ml D-MEM and sedimented at 4°C for 10 min at 200×g . The cells were resuspended in D-MEM and sedimented as described above . This step was repeated before the cells were resuspended in 5 . 5 ml D-MEM/40 µg/ml digitonin . After 5 min at 37°C permeabilization was controlled by microscopy of an aliquot . Cells were sedimented in aliquots that reflect the number of cells per cover slip in transport assays and resuspended in 0 . 5 ml transport buffer/2 mM DTT/PI . This sedimentation and resuspension was repeated twice . The permeabilized cells were resuspended in the transport mixture identical to that in the transport assays using cover slips containing different amounts of P-rC and incubated for 30 min at 37°C . After three washing steps by sedimentation and resuspension in transport buffer/2 mM DTT/1×EDTA-free complete protease inhibitors as above the permeabilized cells in an aliquot were counted . The amount of P-rC was determined as described above by native agarose gel electrophoresis and immune blotting . NPCs were quantified by comparing the amount of cellular Nup153 from digitonin-permeabilized cells with a standard dilution series of E . coli-expressed Nup153 . Cells were grown and harvested and permeabilized as described for “Quantification of nuclear binding of P-rC” . Aliquots containing the same number of cells were denaturated and loaded on an 8% Tris-Glycin SDS PAGE ( Anamed ) . Proteins were transferred onto a PVDF membrane and Nup153 was detected by mAb414 as described above . | Viral capsids facilitate protection of the enclosed viral genome and participate in the intracellular transport of the genome . At the site of replication capsids have to release the genome . The particular factors triggering genome liberation are not well understood . Like other karyophilic cargos , hepatitis B virus ( HBV ) capsids are transported through the nuclear pore using nuclear transport receptors of the importin ß superfamily . Unlike physiological cargos , HBV capsids become arrested within the nuclear basket , which is a filamentous structure on the nuclear side of the nuclear pore . Asking which interaction causes this unique strategy , we found that the capsids bind to a protein of the basket periphery , nucleoporin 153 ( Nup153 ) . The findings were confirmed in situ using digitonin-permeabilized cells that support physiological genome delivery into the nucleus . We observed that HBV capsids bound to Nup153 irrespective of the maturation of the encapsidated genome . But while capsids with an immature genome remained in arrested state , capsids with a mature genome disassembled and released their DNA . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/host",
"invasion",
"and",
"cell",
"entry"
] | 2010 | Nucleoporin 153 Arrests the Nuclear Import of Hepatitis B Virus Capsids in the Nuclear Basket |
Microsporidia are obligate intracellular parasites of most animal groups including humans , but despite their significant economic and medical importance there are major gaps in our understanding of how they exploit infected host cells . We have investigated the evolution , cellular locations and substrate specificities of a family of nucleotide transport ( NTT ) proteins from Trachipleistophora hominis , a microsporidian isolated from an HIV/AIDS patient . Transport proteins are critical to microsporidian success because they compensate for the dramatic loss of metabolic pathways that is a hallmark of the group . Our data demonstrate that the use of plasma membrane-located nucleotide transport proteins ( NTT ) is a key strategy adopted by microsporidians to exploit host cells . Acquisition of an ancestral transporter gene at the base of the microsporidian radiation was followed by lineage-specific events of gene duplication , which in the case of T . hominis has generated four paralogous NTT transporters . All four T . hominis NTT proteins are located predominantly to the plasma membrane of replicating intracellular cells where they can mediate transport at the host-parasite interface . In contrast to published data for Encephalitozoon cuniculi , we found no evidence for the location for any of the T . hominis NTT transporters to its minimal mitochondria ( mitosomes ) , consistent with lineage-specific differences in transporter and mitosome evolution . All of the T . hominis NTTs transported radiolabelled purine nucleotides ( ATP , ADP , GTP and GDP ) when expressed in Escherichia coli , but did not transport radiolabelled pyrimidine nucleotides . Genome analysis suggests that imported purine nucleotides could be used by T . hominis to make all of the critical purine-based building-blocks for DNA and RNA biosynthesis during parasite intracellular replication , as well as providing essential energy for parasite cellular metabolism and protein synthesis .
Microsporidian parasites are highly reduced eukaryotes that have an obligate intracellular lifestyle based upon the exploitation of other eukaryotic cells [1] . The life cycle of microsporidians alternates between a dispersive spore stage that is resistant to environmental stress , and intracellular replicative stages that can only take place inside the cytoplasm of an infected host cell . Despite lineage-specific variations [1] , the general infectious cycle starts with spore germination and the injection of the parasite through a specialised polar tube into the cytoplasm of a suitable host cell . The active vegetative cell ( meront ) then undergoes binary fission , and after several rounds of multiplication , differentiates ( sporogony ) into spores that can exit the host by either cell lysis or exocytosis to infect adjacent cells and tissues or a new host [2] , [3] . Microsporidians are a large group of parasites with over 1200 described species infecting most animal groups including economically important fish , insect pollinators and silkworms [1] , [2] , [4] , [5] . Microsporidians are also increasingly recognised as a significant threat to human health , affecting not only patients with HIV/AIDS but also the young and old in the developing world [6] . A hallmark feature shared by microsporidians and bacterial obligate intracellular pathogens is a dramatic genomic reduction coupled with loss of metabolic pathways during the transition from a free-living to an obligate intracellular lifestyle [7] . Genome analyses suggest that all microsporidians have lost the tricarboxylic acid ( TCA ) cycle and oxidative phosphorylation pathways for ATP production although , with a single exception [5] , [8] , they have retained the pathway for glycolysis [2] , [7] , [9] , [10] , [11] . Published data for Nosema grylli and Trachipleistophora hominis suggest that glycolysis may be mainly active in the spore stage [10] , [12] and hence actively replicating parasites living inside host cells may require an alternative source of ATP . In the case of Encephalitozoon cuniculi this energy gap is potentially filled by the expression of nucleotide transport ( NTT ) proteins on the parasite cell surface , where they can be used to import ATP from the host cytoplasm [7] , [13] . The same type of transport proteins are also used by important , phylogenetically diverse bacterial intracellular pathogens , including Rickettsia and Chlamydia , to import host-generated ATP to support their own reduced metabolism [14] , [15] , [16] , [17] . The broad taxonomic distribution of NTT proteins suggests that intracellular pathogens are using lateral gene transfer to exchange transporter genes [13] , [18] , providing a general strategy for exploiting host cells . Genes for NTT-like transport proteins have been identified in all microsporidian genomes and were recently identified in the genome of the fungal endoparasite Rozella allomyces [11] , [19] . Phylogenomic analyses demonstrate that Rozella and microsporidia share a common ancestor , confirming microsporidia as fungi and suggesting [11] , [19] that the acquisition of NTT transporters was a key step for the foundation of a major clade of endoparasitic fungi . In addition to the loss of mitochondrial ATP-generating pathways , the microsporidians studied so far also lack the enzymes needed for the de novo synthesis of the building blocks of DNA and RNA [7] . Loss of the early steps of purine and pyrimidine biosynthesis , which are costly in terms of ATP , has occurred repeatedly among parasitic protozoa , which have devised a variety of ways of securing and interconverting purines and pyrimidines of host origin [20] . Intracellular bacteria also show loss of pathways for de novo synthesis of purines and pyrimidines . These bacterial pathogens use their NTT proteins to import a range of different nucleotides in addition to ATP , including GTP , UTP and CTP . In the case of Protochlamydia amoebophila , a bacterial symbiont of the protozoan Acanthamoeba , these substrates appear to provide all of the starting materials needed to make DNA and RNA [14] , [15] . Competition studies on the four NTT transporters of E . cuniculi expressed in Escherichia coli indicate that ATP transport is reduced by an excess of some nucleotides , but the actual transport of substrates other than ATP and ADP was not directly investigated [13] . In addition to using its NTT transporters to exploit its host , E . cuniculi also targets an NTT transporter to its highly reduced mitochondrion ( mitosome ) to provide ATP for an organelle that can no longer make its own [13] . Like the other microsporidians for which genome sequences are available [11] , E . cuniculi and T . hominis have lost all genes for members of the mitochondrial carrier family of proteins [7] , [10]: one member of this family is used by canonical mitochondria to transport ATP and ADP [21] . The mitosomes of E . cuniculi contain mitochondrial heat shock protein Hsp70 , which requires ATP for its functions in protein import [22] and Fe/S cluster biosynthesis [23] , [24] . Other microsporidians , including T . hominis [25] , also contain ATP-requiring mitochondrial Hsp70 proteins in their mitosomes , but it is not known if the organelles of these species use NTT transport proteins to import ATP . In the present study we have investigated the evolution , cellular locations and substrate specificities of the nucleotide transport ( NTT ) proteins of T . hominis [10] , a microsporidian that is distantly related to E . cuniculi [26] . Our results demonstrate that the use of surface-located NTT transport proteins is a general strategy adopted by microsporidians to exploit host cells . Acquisition of an ancestral transporter gene at the base of the microsporidian radiation was followed by lineage-specific events of gene duplication , which in the case of T . hominis has generated four paralogous NTT transporters . All four T . hominis NTT proteins are located predominantly to the plasma membrane of replicating parasites . In contrast to E . cuniculi , we found no evidence for a mitosomal location for any of the T . hominis NTT transporters , consistent with lineage-specific differences in transporter and mitosome evolution . All of the T . hominis proteins transported purine nucleotides when expressed in E . coli , but did not transport pyrimidine nucleotides . Analysis of the enzyme repertoire predicted from the T . hominis genome suggests that imported purine nucleotides could be transformed into all of the critical purine-based building-blocks required for DNA and RNA biosynthesis as well as providing essential energy for cellular metabolism and protein synthesis by replicating intracellular parasites .
Nucleotides are the building blocks of DNA and RNA , and also play key roles as signalling molecules and carriers of energy and electrons . They can be made by de novo synthesis pathways in free-living Bacteria , Archaea and eukaryotes [27] . In contrast , the loss of the ability to synthesize nucleotides de novo appears to be a general feature of microsporidia [11] , including T . hominis ( Fig . 1 , Table S1 ) , that is shared with obligate intracellular bacteria such as Chlamydiae and Rickettsiae [15] , [28] . Comparing the manually-curated enzyme complements of T . hominis , E . cuniculi and Nosema ceranae with representative intracellular bacteria ( Fig . 1 , Table S1 ) identified a similar core of enzymes for the transformation of purine and pyrimidine nucleotides between different phosphorylation and oxidation states to meet different metabolic requirements . There are minor differences between microsporidians in the enzymes detected by genome analyses ( Fig . 1 , Table S1 ) , which may reflect differences in the range of substrates that can be used by individual microsporidians . For example , T . hominis is predicted to possess a dCMP deaminase ( EC 3 . 5 . 4 . 12 ) potentially capable of converting dCMP into dUMP , that appears to be missing from the genomes of E . cuniculi and N . ceranae ( Fig . 1 , Table S1 ) . T . hominis also has a gene for uridine kinase ( EC 2 . 7 . 1 . 48 ) which can potentially convert uridine plus ATP into UMP and ADP , that is missing from E . cuniculi and N . ceranae ( Fig . 1 , Table S1 ) . We did not detect any T . hominis , E . cuniculi or N . ceranae enzymes or pathways that could potentially convert between adenine and guanine nucleotides , or between purine and pyrimidine ( cytosine , uracil and thymidine ) nucleotides . This suggests that T . hominis and other microsporidians need to import both types of purine nucleotides and at least one type of pyrimidine nucleotide , or substrates that can be used to make them , to complete DNA and RNA biosynthesis during intracellular replication . The T . hominis genome contains genes for four nucleotide transport ( NTT ) proteins [10] . All four proteins are predicted to contain secondary structure elements typical of characterised NTT proteins [29] , including 11 to 12 predicted alpha-helical transmembrane domains ( TMDs ) ( Figure S1 ) and associated intracellular and extracellular loop regions . Based upon published data it is not possible to predict the range of substrates that can be transported by any particular NTT directly from primary sequence comparisons , although all four E . cuniculi proteins and most of the NTT proteins characterised to date for bacteria are able to transport ATP [13] , [15] , [17] , [28] , [30] , [31] . Four charged residues ( K155 , E245 , E385 and K527 [31] ) are strongly conserved among bacteria , in Rozella allomyces , in most published microsporidian sequences including all four E . cuniculi NTT sequences , and in T . hominis ThNTT2 ( uniprot L7JXU1 ) and ThNTT4 ( uniprot L7JS26 ) ( Figure S1 ) . All four residues were previously shown to be important for the transport mechanism of the Arabidopsis plastid ADP/ATP transporter AATP1 [31] and mutation of K527 also reduced Pi transport by the Protochlamydia amoebophila ADP/ATP transporter NTT1 ( residue K446 in the P . amoebophila sequence [30] ) . The predicted amino acid sequences of ThNTT1 ( uniprot L7JRV4; I155 , N245 , Y385 ) and ThNTT3 ( uniprot L7JTX7; I155 , V245 and Y385 ) have non-conservative changes at three of the four alignment positions , but there are conserved amino acids of the correct identity within 3 or 4 residues in both sequences ( Figure S1 ) . Based upon published information for Arabidopsis [31] and Protochlamydia [15] the conserved lysine ( K527 ) is thought to be important for the transport of nucleoside triphosphates , but not for transport of nucleoside diphosphates . To investigate the evolution of the T . hominis proteins relative to those from other microsporidians , R . allomyces , and bacterial outgroups , we carried out a detailed phylogenetic analysis ( Fig . 2 ) . The common endoparasitic ancestor of R . allomyces and microsporidia is most parsimoniously inferred to have had a single NTT gene [19] . Based upon the absence of any deep symmetrical split in the tree of microsporidian NTTs ( Fig . 2 ) , it appears likely that the common ancestor of microsporidians also possessed a single NTT gene . The variable number of NTT genes detected in the contemporary microsporidian genomes investigated appears to be the product of repeated events of lineage-specific gene duplication . Hence the common ancestor of T . hominis and Vavraia culicis probably had four paralogous NTT genes; by contrast , their close relative Spraguea lophii has six NTT genes [32] . The common ancestor of the three Nematocida isolates had only two genes for NTT transport proteins . The common ancestor of Encephalitozoon and Nosema species probably had four NTT genes , one of which was subsequently lost by Nosema spp . According to classical theory [33] gene duplication can have a number of potential advantages . For example , an increased gene dosage effect could increase the amount of NTT protein produced , or relaxed selection on individual gene copies might allow functional divergence of NTT proteins in terms of their substrate specificities , expression patterns or cellular location . It is interesting to note that T . hominis ThNTT4 is more conserved than the other T . hominis paralogues ( Fig . 2 , Figure S1 ) , and groups closely with related NTT sequences from V . culicis and S . lophii; one possibility is that ThNTT4 carries out the ancestral NTT function for the clade [34] . This protein was also the only ThNTT homologue detected in a recent investigation of the spore proteome of T . hominis [10] . Two of the other T . hominis NTT genes ( ThNTT1 and ThNTT3 ) and their respective V . culicis orthologues are more divergent and have lost or shifted the position of broadly-conserved residues ( Figure S1 ) previously implicated in transport function [31] , suggesting relaxed selection and possibly functional divergence within the T . hominis/V . culicis clade . Bacterial NTT proteins have been studied in greater detail than those of microsporidians and there is evidence that gene duplication events have allowed functional divergence in the transport mechanism and substrate specificities of individual proteins [14] , [15] . The published functional data for the four E . cuniculi NTT proteins demonstrates that they can all transport ATP when expressed in E . coli and , although other substrates have not been directly evaluated for transport , competition experiments with different nucleotides yielded broadly similar inhibition profiles for all four E . cuniculi NTT proteins [13] . The most compelling evidence for functional specialisation affecting E . cuniculi NTT proteins comes from their different cellular locations . Three of the E . cuniculi NTT proteins are located on the surface of the parasite but the fourth ( E . cuniculi EcNTT3 ) is targeted to its mitosomes . Orthologues of E . cuniculi EcNTT3 were also found in E . romaleae , E . hellem and E . intestinalis , but were not detected in Nosema species or other microsporidians . Note that it is unclear whether the cellular localizations and transport specificities of these genes can be transferred to other microsporidian NTTs , because the gene duplications giving rise to the four E . cuniculi NTTs do not date back to the last common ancestor of microsporidia . In particular , the observation that the other three E . cuniculi NTT paralogues are surface-located , a feature shared with bacterial NTT homologues [15] , suggests that the targeting of E . cuniculi EcNTT3 to the mitosome is a derived state that might be restricted to the Encephalitozoon lineage . Computational analyses detected no obvious differences between E . cuniculi EcNTT3 and surface-located E . cuniculi NTT paralogues that might explain the observed differential targeting [13] , and genetic manipulation of microsporidians to identify the specific residues involved is still not possible . Mitosomal targeting in general is not well understood in microsporidia , and even for model mitochondria the precise targeting signals are known for only a subset of organelle proteins [35] , [36] . In order to investigate the locations of the four T . hominis NTT proteins we therefore made specific antibodies and carried out detailed immuno-localisation experiments . The intracellular localisation of the four T . hominis ThNTTs was analysed using quantitative immuno-electron microscopy . Thawed cryo-sections of T . hominis-infected RK cells were labelled with antisera raised against each of the four ThNTTs and the gold-label quantified using methods that ensure precise and unbiased quantification [37] . The specificity of each ThNTT was determined quantitatively in vegetative stages ( meronts ) by assessing the extent to which the specific peptides ( for ThNTT1 , 2 and 3 ) or polypeptide ( for ThNTT4 ) that were used to generate the antisera blocked the individual antibody signals in parallel replicate experiments [38] , [39] ( see Material and Methods and Figure S2 ) . The predominant localisation of all four ThNTTs was in the plasma membrane of the parasite ( Fig . 3 , Figure S2 ) . Specific labelling was virtually absent over the mitosome but was detectable within intracellular membranes ( for ThNTT3 and 4 ) , which were mainly composed of tubulovesicular and cisternal profiles . These compartments may comprise elements of the Golgi complex and endoplasmic reticulum , but the absence of compartment-specific markers for these studies make their exact assignment problematic at present . Nevertheless , it appears possible that the specific signal over these internal membranes represents ThNTTs in transit through the secretory pathway . The antibodies were also used on T . hominis-infected rabbit kidney cells prepared for immunofluorescence analysis ( IFA ) to gain an overview of the ThNTT distribution in replicating parasites . The distribution of staining for antibodies raised for ThNTT1 , ThNTT3 and ThNTT4 demonstrate a localisation on the surface of the growing parasites ( Fig . 4 ) , consistent with the plasma membrane location revealed by the EM data . As illustrated in Fig . 4k , the antibodies against ThNTT4 and ThHsp70 did not give signals for structures inside the developing thick walled spores contained in the sporophorous vesicles ( SPV ) that are a characteristic feature of the T . hominis intracellular lifecycle [40] , [41] . We suspect that this is due to a lack of permeability of the developing spore wall to antibodies because both proteins are detected in spore digests analysed using proteomic methods [10] . The lack of label in the outer envelope of the SPV is consistent with our EM data where no signal was detected for ThNTT4 in the electron-dense outer layer surrounding the parasites ( Fig . 3 ) from which the SPV envelope is thought to originate [41] . We were unable to detect any specific staining in IFA of parasites using the antibody against ThNTT2 despite employing different fixation procedures and making a second polyclonal antibody against segments of several predicted extracellular loops of ThNTT2 as previously described [13] ( Table S3 in Text S1 ) . Our failure to obtain IFA data for ThNTT2 despite successful EM experiments for this protein may reflect differences in sample preparation influencing epitope accessibility: the immuno-EM approach includes opening up the compartments by sectioning whereas IFA involves permeabilization of cell membranes and depends on penetration of the antibody prior to labelling . In summary , we could localise all T . hominis NTTs to the plasma membrane of vegetative cell stages that are growing and replicating inside the host . In contrast to published data for E . cuniculi [13] , there was no evidence for a mitosomal localisation of any of the four T . hominis NTTs . It has been demonstrated that the mitosomes of E . cuniculi and T . hominis contain proteins of the essential Fe/S cluster biosynthesis pathway [23] , [24] , [25] , which in model organisms requires ATP for several steps [24] . It is possible that the mitosomes of T . hominis use other transport proteins to import ATP , but candidates for this role are difficult to predict solely from genome analyses [10] and there is as yet no proteomics data for T . hominis mitosomes . In classical mitochondria , the transport of metabolites across the inner membrane is highly selective in order to maintain the electrochemical proton gradient used for ATP synthesis . Since the mitosomes of T . hominis no longer make ATP it is also possible that selection for an impermeable inner membrane has been sufficiently relaxed to allow passive transport of ATP through the inner membrane translocase ( TIM ) channel [10] of the mitosome protein import pathway . Interestingly , a similar conundrum exists for ATP supply to the mitosome of the extracellular parasite Giardia lamblia [42] . The genome of this parasite lacks genes for mitochondrial ATP generation and mitochondrial carrier family proteins , and it also lacks genes for NTT proteins [43] . Nevertheless , its mitosomes can still make Fe/S clusters [44] , [45] suggesting that ATP is available to support this pathway inside the organelle . To identify the transported substrates of the four T . hominis NTTs we expressed the proteins in Escherichia coli and carried out transport experiments in whole cells [13] with nine different 32P-labeled nucleotides . ThNTT1-4 transported ATP and GTP over background levels measured for E . coli containing the vector only ( Fig . 5A ) . ThNTT1 , 2 and 4 transported GTP with higher rates than ATP , but the differences were not statistically significant; by contrast , ThNTT3 transported ATP slightly faster than GTP ( Fig . 5A ) . The import of ATP and GTP by ThNTTs expressed in the plasma membrane of intracellular T . hominis could provide purine-based substrates for DNA and RNA biosynthesis as well as energy for protein synthesis during parasite replication . 32P-labeled CTP , TTP and UTP were not taken up as the accumulation levels were similar to the E . coli vector-only control . Uptake experiments using radiolabelled 32P-labeled nucleoside diphosphates demonstrated a significant preference for transport of GDP over ADP for all four ThNTTs expressed in E . coli . Import of ADP and GDP would provide substrates for the ATP-activated ribonucleoside diphosphate reductase ( EC 1 . 17 . 4 . 1 ) that provides an essential link between parasite RNA and DNA biosynthesis ( Fig . 1 , Table S1 ) . Based upon our analysis of its genome ( Fig . 1 , Table S1 ) , T . hominis should be able to synthesise all of the purine-based components of DNA and RNA given the import of both adenine and guanine nucleotides . Our analyses suggest that E . cuniculi , N . ceranae ( Fig . 1 , Table S1 ) and potentially other microsporidians will have a similar requirement and capacity . Accumulation of 32P−labeled CDP or UDP in the NTT expressing strains was similar to the E . coli control , showing that these pyrimidine diphosphates were not transported ( Fig . 5B ) . It is clear that the NTTs of T . hominis transport purine nucleotides , but not pyrimidine nucleotides . The apparent absence of genes for enzymes to make pyrimidine nucleotides de novo from the T . hominis , E . cuniculi and N . ceranae genomes suggests that additional , currently unknown transport processes , are needed to complete DNA and RNA biosynthesis during parasite intracellular replication . Microsporidians infect most animal groups , often with devastating consequences for the host animal [1] , [2] , [5] . Given the major loss of genes affecting most metabolic pathways revealed by genome analyses [7] , [11] , surface-located transport proteins are of critical importance for completing the microsporidian life cycle once inside an infected host cell . Consistent with this idea , comparative analyses suggest that expansion of specific transporter families was contemporaneous with loss of metabolic capacity at the origin of the microsporidian radiation [10] , [11] . We have investigated the evolution , intracellular location and substrate specificities of nucleotide transport ( NTT ) proteins , homologues of which are conserved on all microsporidian genomes . Gaps in the predicted microsporidian metabolome suggest that these transporters potentially play essential roles supporting microsporidian DNA and RNA metabolism ( Fig . 1 , Table S1 ) , as well as providing energy for cellular metabolism and protein synthesis for a cellular stage that may no longer make its own [5] . Consistent with predictions from analyses of the T . hominis enzyme repertoire that both types of purine nucleotide must be imported , we detected transport of adenine and guanine nucleotides by all four ThNTTs ( Fig . 5 ) . Further work is now needed to characterise the detailed mechanisms of transport used by the ThNTTs . It has already been demonstrated that the mitosome-located E . cuniculi EcNTT3 is an exchanger of adenine nucleotides exporting ADP in exchange for ATP to supply an organelle unable to make its own ATP [13] . This mechanism ( Class I NTT proteins [15] ) has already been demonstrated for some NTTs of bacterial intracellular pathogens with a reduced energy metabolism - often called energy parasites - including Rickettsia prowazekii and Chlamydia trachomatis [15] . The loss of the ability to make mitochondrial ATP and the apparent down-regulation of glycolytic enzymes in replicating cells of T . hominis [10] suggests that one or more of the T . hominis NTTs might also use this transport mechanism . However , the requirement for net nucleotide import to support DNA and RNA biosynthesis that is predicted by our genome analyses also suggests that at least one of the ThNTTs could mediate a unidirectional proton-energised import of purine nucleotides . This mechanism ( Class II [15] ) has also been described for NTT transporters from intracellular bacteria that lack de novo nucleotide biosynthesis , including Chlamydia trachomatis [14] and Protochlamydia amoebophila [15] . The similarities in the predicted enzyme repertoires of E . cuniculi and N . ceranae suggest that the requirement for host produced ATP and net nucleotide import of both types of purine nucleotides may be a general feature of microsporidians . Although NTT-like proteins in some bacteria [15] have been shown to transport purine and pyrimidine nucleotides , we did not detect any transport of the tested pyrimidine nucleotides by the NTTs of T . hominis under the assay conditions we used ( Fig . 5 ) . It is therefore possible that additional transporters are needed to provide these substrates . One candidate for this function [46] is a conserved family of microsporidian proteins [11] that share significant sequence similarity to E . coli NupG [47] . This is a nucleoside transporter of the major facilitator superfamily of transport proteins that can transport both purine ( adenosine ) and pyrimidine ( uridine ) nucleosides when expressed in E . coli or Xenopus oocytes [47] . Genome analyses ( Fig . 1 , Table S1 ) suggest that any imported uridine could be used by T . hominis to make the pyrimidine nucleotides needed for nucleic acid biosynthesis . The observed pattern of lineage- and even species-specific duplications of NTTs over the microsporidian tree , coupled with differences in their subcellular localization between T . hominis and E . cuniculi , suggests that the role of NTTs in parasite biology has continued to evolve throughout the microsporidian radiation . Previous work on the NTT proteins of Encephalitozoon cuniculi demonstrated that some NTT proteins were located on the surface of parasites inside infected host cells [13] . Here we demonstrate that all four ThNTTs are located in the plasma membrane of replicating T . hominis cells , providing the first detailed evidence for NTT subcellular location . These data suggest that the location of NTT transporters at the host-parasite interface is a general strategy used by microsporidians to exploit host cells and compensate for their own highly reduced metabolism . In contrast to E . cuniculi we found no evidence that any of the ThNTTs were located to the T . hominis mitosome: this feature appears to be a derived feature of mitosome biology that may be restricted to the Encephalitozoon lineage .
All sequences used in this study are provided ( Table S2 in Text S1 ) . Sequences were aligned using muscle ( v3 . 8 . 31 , [48] ) under the default conditions , and divergent sites were removed using trimAl ( v1 . 2rev59 , [49] ) with the “-automated1” function . Bayesian phylogenetic trees were inferred with Phylobayes ( v3 . 3e , [50] ) under the C20 model ( “-catfix C20” ) to account for across-site compositional heterogeneity in the data set . Convergence was assessed by using the bpcomp command to monitor the maximum and average discrepancy in bipartitition frequencies between two independent MCMC chains . The analysis was stopped when the maximum difference dropped below 0 . 1 , as recommended by the authors [50] . The sequences , alignment and treefile have been deposited in Figshare ( http://dx . doi . org/10 . 6084/m9 . figshare . 1104386 ) . Trachipleistophora hominis [40] was grown in RK-13 cells at 37°C in Dulbecco's Modified Eagle Medium ( DMEM ) , containing Kanamycin 100 µg/ml , Penicillin 100 µg/ml , Streptomycin 100 µg/ml , and Fungizone 1 µg/ml . E . coli Rosetta 2 ( DE3 ) ( Novagen ) , BL1-AI , C43 , pLysS , were grown in LB media ( 10 g/l tryptone , 5 g/l yeast extract , 5 g/l NaCl , pH 7 . 5 ) for routine cloning and expression trials . For uptake studies , E . coli Rosetta 2 ( DE3 ) cells were grown in TB media ( 1 . 2 g/l peptone , 24 g/l yeast extract , 72 mM K2HPO4 , 17 mM KH2PO4 and 4 ml/l glycerol ) . E . coli Rosetta 2 ( DE3 ) was routinely grown in media supplemented with 34 µg/ml chloramphenicol , and all strains were grown in media containing 100 µg/ml ampicillin after transformation with the constructs . Cells were grown at 37°C unless indicated otherwise . To obtain antibodies targeting exposed epitopes of these predominantly hydrophobic proteins , we identified two peptide sequences located in predicted surface-exposed loop regions for each ThNTT ( Figure S1 , Table S3 in Text S1 ) . Peptide synthesis , animal immunisation , antisera extraction and affinity purification of all antisera was performed by Agrisera ( Sweden ) . Both peptides for each ThNTT were used for immunisation of the same rabbit , and the affinity-purified antisera were tested for their specificity against E . coli strains expressing the individual ThNTT proteins . The peptide sequences are given in Table S3 in Text S1 . The peptide antibodies for ThNTT3 gave some non-specific binding in IFA experiments and antibodies for ThNTT4 gave a high level of nonspecific background labelling in immuno-electron microscopy , so we designed a second set of antibodies to regions of ThNTT3 or ThNTT4 predicted to form exposed loop regions ( Figure S1 , Table S3 in Text S1 ) . These regions were synthesised ( GenScript Inc . , USA ) as a single gene encoding the polypeptide , cloned into the pQE-40 expression vector ( Qiagen ) and expressed in E . coli M15 [pREP4] cells as single dihydrofolate reductase ( DHFR ) fusion proteins and processed to make rabbit antibodies ( Agrisera , Sweden ) as described previously [13] . Immunofluorescence was performed as described previously [13] , [23] , and microscopy images were captured using a Leica SP2 confocal microscope . Monolayer RK cells ( RK-13 ) were infected with T . hominis and grown to near confluence . The cells were fixed in 0 . 5% glutaraldehyde in 0 . 2 M PIPES buffer ( pH 7 . 2 ) for 15 min at room temperature , then scraped from the culture dish and pelleted ( 15 min at 16 . 000× g ) . The cells were subsequently washed three times with buffer ( 5 min per wash ) and cryoprotected in 2 . 3 M sucrose in PBS overnight at 4°C . Small fragments of the cell pellet were mounted onto specimen carriers and plunge-frozen in liquid nitrogen . Eighty nm thick sections were cut at −100°C ( EM FC7 ultracryomicrotome; Leica , Vienna , Austria ) and mounted on carbon/pioloform-coated EM copper grids ( Agar Scientific , Stansted , UK ) in drops containing equal volumes of pre-mixed 2 . 1 M sucrose and 2% w/v methylcellulose . Prior to labelling , grids were washed in ice-cold distilled water ( 3 times , 5 min each ) followed by PBS at room temperature . The sections were then incubated in 0 . 5% fish skin gelatin ( Sigma Aldrich , Poole , UK ) in PBS , and labelled using rabbit antisera raised against the four T . hominis NTTs , followed by 10 nm protein-A gold ( BBI solutions , Cardiff , UK ) and contrasted using 2% w/v methylcellulose/3% w/v uranyl acetate ( mixed 9∶1 ) . To assess the specificity of labelling , sections were incubated in parallel with antisera which had been pre-mixed with the peptides ( for ThNTT1 , 2 or 3 ) or polypeptide ( for ThNTT4 ) used to generate the antibodies , in order to inhibit specific antibody binding ( peptide-control ) . For this purpose , equal volumes of antisera and the corresponding peptides or polypeptide in PBS were mixed for 30 min at room temperature and were then applied to sections in parallel with the native antisera , which were incubated in PBS under the same conditions . For quantification , labelled sections were sampled systematic uniform random ( SUR; [51] ) in three individual experiments per antibody by taking 16–20 micrographs per sample with a JEOL 1200 EX transmission electron microscope operated at 80 kv using either Ditabis imaging plates ( DITABIS Digital Biomedical Imaging Systems AG , Pforzheim , Germany ) or a GATAN Orius 200 digital camera ( GATAN , Abingdon , Oxon , UK ) . Mitosomes were sampled separately by comprehensive scanning of all parasites within a randomly selected grid square ( 22 to 40 micrographs per sample ) . Tiff files of micrographs were further analysed using Adobe Photoshop CS6 . Square lattice grids were randomly placed on each micrograph and used to estimate the length of membrane profiles of compartments of interest by intersection counting ( grid spacing either 262 , 618 or 914 nm for the plasma membrane and nuclear envelope; 914 nm or 1 . 54 µm for endo-membranes , including endoplasmic reticulum and Golgi as well as every other non-categorized internal membrane apart from nuclear envelope and mitosome membranes ) . Gold particles were categorized as being membrane-associated if the particle was less than 1 particle width away from a membrane profile . The specificity of immunogold labelling was assessed as described previously [38] , [39] . Briefly , the specific labelling density D ( sp ) ( gold per micron ) was estimated by subtracting the labelling density obtained with the peptide-control D ( - ) from the initial ( raw ) density D ( 0 ) . Next , the fraction of the specific labelling F ( sp ) is given by D ( sp ) /D ( 0 ) and F ( sp ) is multiplied with the initial labelling counts over each compartment in order to calculate the specific labelling distribution . The vegetative meront stages of T . hominis could be identified as single or multinucleated cells proliferating in RK cells . Spore stages were distinguished by the presence of a discernible cell wall and/or the formation of the polar tube . The plasma membrane was visible as a smooth membrane profile covered with an electron dense coating in early meront stages and as a more convoluted membrane profile in later meront stages . Endo-membranes were defined as membrane structures inside the cytoplasm including the endoplasmic reticulum , the Golgi and any other membrane compartment excluding the nuclear envelope and mitosomal membranes . Mitosomes were identified as double membrane bound organelles with minor and major axes measuring between 50 and 300 nm . All four full-length T . hominis NTT genes were cloned into the expression vector pET16b ( Novagen ) and the insert was verified by sequencing . NTT2 was inserted between the NdeI and the BamHI sites , and NTT1 , NTT3 and NTT4 were inserted between the XhoI and the Bpu1102I sites; the primer sequences are given in Table S4 in Text S1 . For uptake experiments , E . coli Rosetta 2 ( DE3 ) pLysS cells ( Novagen ) were transformed with recombinant vectors encoding the ThNTT genes , with the empty pET16b vector used as a control . Cells were grown at 37°C to an OD600 of 0 . 5 in Terrific Broth and transporter expression was induced by the addition of 1 mM isopropyl β-d-1-thiogalactopyranoside ( IPTG ) following incubation for 16 hours at 18°C . Cells were harvested by centrifugation ( 6 , 000 g , 5 min . ) , washed twice with PBS ( 8 g/l NaCl , 0 . 2 g/l KCl , 1 . 44 g/l Na2HPO4 , 0 . 24 g/l KH2PO4 , pH 7 . 4 ) , and resuspended in PBS to a final OD600 of 5 . 0 . The cells were kept at 4°C and then pre-incubated for 15 min at 25°C before being used in the uptake assays . Uptake assays with 32P-radiolabeled purine and pyrimidine di- and tri-nucleotides ( Hartmann ) were performed as described previously for 32P-ATP uptake [13] . | Microsporidians are highly reduced obligate intracellular eukaryotic parasites that cause significant disease in humans , animals and commercially relevant insects . Despite their medical and economic interest the mechanisms whereby microsporidians exploit the cells they infect are mainly unknown . We have characterised a conserved family of nucleotide transport proteins that we demonstrate have key roles in parasite biology . Microsporidians cannot synthesize the primary building blocks needed to make DNA and RNA for themselves , so they must import the starting materials from the infected host . We show that the microsporidian Trachipleistophora hominis , originally isolated from an HIV/AIDS patient , may achieve this by using four nucleotide transport proteins located in the plasma membrane of replicating intracellular parasites . In functional assays we demonstrate that all four proteins can transport radiolabelled adenine and guanine nucleotides . Genome analysis suggests that the imported nucleotides could be transformed by T . hominis into all of the critical purine-based building-blocks needed for DNA and RNA biosynthesis during parasite intracellular replication , as well as providing essential energy for parasite cellular metabolism and protein synthesis . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"biology",
"and",
"life",
"sciences"
] | 2014 | Plasma Membrane-Located Purine Nucleotide Transport Proteins Are Key Components for Host Exploitation by Microsporidian Intracellular Parasites |
Co-expression analysis has been employed to predict gene function , identify functional modules , and determine tumor subtypes . Previous co-expression analysis was mainly conducted at bulk tissue level . It is unclear whether co-expression analysis at the single-cell level will provide novel insights into transcriptional regulation . Here we developed a computational approach to compare glioblastoma expression profiles at the single-cell level with those obtained from bulk tumors . We found that the co-expressed genes observed in single cells and bulk tumors have little overlap and show distinct characteristics . The co-expressed genes identified in bulk tumors tend to have similar biological functions , and are enriched for intrachromosomal interactions with synchronized promoter activity . In contrast , single-cell co-expressed genes are enriched for known protein-protein interactions , and are regulated through interchromosomal interactions . Moreover , gene members of some protein complexes are co-expressed only at the bulk level , while those of other complexes are co-expressed at both single-cell and bulk levels . Finally , we identified a set of co-expressed genes that can predict the survival of glioblastoma patients . Our study highlights that comparative analyses of single-cell and bulk gene expression profiles enable us to identify functional modules that are regulated at different levels and hold great translational potential .
Gene expression is often coordinated to carry out cellular activities and biological functions [1] . If the expression levels of two genes rise and fall together across different conditions , they are likely to be members of the same protein complex or participate in the same biological pathways . Therefore , co-expression analysis has been widely used to predict protein-protein interactions ( PPIs ) or annotate functions of uncharacterized genes [2–4] . Built upon co-expression relationships , co-expression networks were often constructed to reveal the functional modules consisting of genes with functional relationships [5–7] . Furthermore , co-expression relationships are often considered to be the consequence of co-regulation that is governed by the same regulatory machinery . Therefore , regulatory elements could be predicted based on the co-expression relationships [8–10] . In addition , co-expression analysis has been applied to cancer biology . For example , co-expressed gene sets could reveal interaction modules in tumor progression [11] , or serve as molecular signatures to classify tumors into different subtypes , which often showed distinct clinical outcomes [12 , 13] . Previous co-expression analyses were mainly conducted at the bulk level in which a large population of cells was profiled as a whole . Recently , single-cell sequencing has emerged as a powerful tool to investigate cellular variability and intratumor heterogeneity [14–16] . However , it remains elusive whether co-expression analysis at the single-cell level will provide novel biological insights into the molecular principles of transcription regulation that would be otherwise hidden at the bulk level . For example , can the same set of co-expressed genes be identified both at the single-cell and bulk levels from the same tissue origin ? Will the comparative co-expression analysis reveal functional modules that are regulated at different levels ? Do the co-expression relationships detected at the single-cell and bulk levels reflect the same regulatory mechanisms ? To address these important questions , we developed a computational approach to perform comparative co-expression analysis between single-cell and bulk samples , and discovered that the majority of the co-expressed gene pairs were unique . Multiple lines of evidence suggest that the discrepancy between the two analyses is not due to technical artifacts . Interestingly , the co-expressed genes in bulk tissues tend to have the same biological functions , while the co-expressed genes in single cells encode proteins that are likely to interact with each other . Strikingly , members in different protein complexes are often predominately connected by one type of co-expression relationships . Furthermore , we find that the co-expression relationships in the single cells and bulk tissues might reflect distinct co-regulatory mechanisms . Interestingly , interchromosomal interactions are highly enriched for single-cell co-expression . Finally , we discover a set of co-expressed genes that can predict the clinical outcome of glioblastoma .
We used glioblastoma as a model system because both single-cell and bulk expression data are available . A dataset of single-cell RNA-seq was obtained from 430 individual cells of five glioblastoma patients [14] . Similarly , gene expression profiles of 120 glioblastomas as bulk tissues were obtained from TCGA consortium [17] . To compare co-expression patterns at single-cell and bulk levels , we calculated Pearson’s correlation coefficients ( R ) of gene expression for all possible gene pairs across the cells ( or tumors ) . Strikingly , the majority ( > 90% ) of co-expressed gene pairs were unique to either single-cell or bulk analysis . For instance , we observed that the expression profiles of two genes , ATP9B and MORC4 , were highly correlated at the single-cell level ( R = 0 . 97 , Fig 1A ) ; the correlation coefficients calculated separately from the five tumors were also consistent ( S1 Fig ) . However , their correlation was not significant at the bulk level ( R = 0 . 04 ) . Conversely , the expression profiles of REST and ROCK2 were found highly correlated at the bulk level ( R = 0 . 85 ) , but not at the single-cell level ( R = 0 . 00051 , Fig 1A and S2 Fig ) . Globally , we separately identified the top 1 , 000 most correlated gene pairs at either single-cell or bulk level and cross-examined whether the same pairs were also correlated at the other level . Surprisingly , only 76 ( 7 . 6% ) of the top 1 , 000 gene pairs are shared between the bulk and single-cell levels ( Fig 1B ) . For example , RPL41 and RPS14 are co-expressed in both single cells ( R = 0 . 75 ) and bulk tissues ( R = 0 . 83 ) ( Fig 1A and S3 Fig ) . However , most co-expressed gene pairs at the single-cell level have no or even negative correlation at the bulk level . Similar pattern was also observed for the top 1 , 000 correlations at the bulk level . It is worthy to note that the observation is not sensitive to the correlation measurement we choose . For example , if maximal information coefficient ( MIC ) , which is able to capture non-linear relationships [18] , was used , a consistent pattern was observed that 96 . 4% co-expressed gene pairs were specific at single-cell or bulk level ( S4 Fig ) . These results suggested that distinct sets of co-expressed gene pairs were yielded at single-cell and bulk levels . Several lines of evidence suggest that the discrepancy in co-expression analysis between bulk and single-cell levels is not due to technical artifacts . First , we checked whether expression correlation was sensitive to the samples chosen for analysis . We randomly partitioned the cells ( or tumors ) into two equal-sized sub-groups and separately calculated corresponding gene expression correlations . The top 1 , 000 co-expressed genes were highly consistent between the two sub-groups ( Fig 1C ) . For example , 524 ( 52 . 4% ) of the top 1 , 000 correlations were shared between the two sub-groups in the single-cell analysis . The remaining 47 . 6% of gene pairs are also highly correlated , even though they were not in the top 1 , 000 . A similar observation was made for bulk-level analysis . This observation suggested that expression correlations were robust and not sensitive to the samples used for calculation . Second , we examined whether the dissociation and processing of single cells introduced measurement errors , which could lead to the discrepancy of co-expression between single-cell and bulk levels . For the five glioblastomas with single-cell expression profiles , we averaged gene expression across the individual cells and then compared the average gene expression profiles with the genuine bulk expression profiles from the same glioblastomas . The comparison showed that the average gene expression was highly correlated with the expression in bulk tissue for each tumor ( Fig 1D and S5 Fig ) . These results suggest that the procedure of isolating and harvesting single cells did not introduce much distortion in expression profiles . Furthermore , in comparison of the expression profiles of the five tumors for single-cell sequencing with the other 120 bulk tumors from TCGA , we found that the five samples were dispersed among the 120 glioblastomas ( Fig 1D ) . This result suggests that the five tumors for single-cell analysis are not characteristically different to the 120 glioblastomas for bulk analysis , and both of the datasets were representative of primary glioblastomas . Third , we explored whether the discrepancy of co-expression patterns between single cells and bulk tissues could be observed in other tissues . Similar analyses were performed using data obtained from prostate cancers . We compared the transcriptome of 122 individual prostate cancer cells with those of 398 bulk prostate cancers from TCGA [19] . The results showed that only 4% of the top 1 , 000 correlations were shared between single-cell and bulk levels ( S6 Fig ) . Taken together , all of the above analyses suggest that the observation of distinct co-expressed gene pairs in single cells and bulk tissues was valid , and not due to technical artifacts . In order to dissect the biological roles of the co-expressed genes at the single-cell level , we classified the co-expressed genes into three groups: single-cell specific , bulk specific , and shared at both levels ( S7 Fig and see Materials and Methods for the details ) . In brief , we compared the distributions of expression correlation coefficients from real and randomly shuffled expression profiles to identify the thresholds of significantly positive correlations at single-cell or bulk levels . Using the obtained thresholds , 5 , 303 , 107 , 851 , and 12 , 584 gene pairs were classified as single-cell specific , bulk specific , and shared co-expressed gene pairs , respectively ( S8 Fig ) . Next , we attempted to discover distinct characteristics of these three groups of co-expressed genes . We first checked whether protein products of the co-expressed genes were enriched for known PPIs . By surveying the PPI networks of the BioGRID database [20] using the corresponding proteins of those co-expressed genes , we found that bulk specific co-expressed genes were slightly enriched for PPIs . Specifically , the protein products of 591 ( 0 . 55% ) of the 107 , 851 co-expressed gene pairs specific to the bulk tissues have known PPI relationships , while only 0 . 34% was expected for randomized gene pairs ( P = 3 . 8E-91 , student’s t-test ) . In contrast , PPIs were much more enriched in single-cell specific co-expressed genes ( 90 of 5 , 303 , 1 . 7% ) , which was a 5-fold enrichment compared to the expectation ( P = 2 . 0E-247 , student’s t-test ) ( Fig 2A ) . Strikingly , we observed that 1 , 167 of 12 , 584 ( 9 . 3% ) shared co-expressed genes have PPIs ( Fig 2A ) , a 27-fold enrichment compared to the expectation ( P < 1 . 0E-500 , student’s t-test ) . The enrichment was not due to relatively high correlation coefficients in the shared group . The same trend was also observed if we compared the three groups at the same range of correlation coefficients ( Fig 2B ) . Furthermore , the enrichment for PPIs in co-expressed genes increased with the degree of correlation coefficients , suggesting the fidelity of the relationships between the co-expressed genes and PPIs . Surprisingly , we observed that the three classes of co-expressions were not homogeneously distributed among annotated protein complexes . Instead , different protein complexes were enriched in different classes of co-expressions . Members of many protein complexes are co-expressed at bulk level , such as proteasome and CDC5L complex ( Fig 2C ) . However , members in other complexes ( e . g . condensing II ) are co-expressed in both single cells and bulk tissues ( Fig 2C ) . Perhaps the most striking examples are cytoplasmic and mitochondrial ribosomes . Of 1 , 816 co-expressed gene pairs that belong to the cytoplasmic ribosomal complexes , 1 , 791 ( 98 . 6% ) were co-expressed at both single-cell and bulk levels . In contrast , among 329 co-expressed genes of the mitochondrial ribosomal complexes , all of them are bulk specific ( Fig 2C ) . These results suggest that the synchronized expression of members in protein complexes is governed through different types of co-expression relationships , reflecting distinct regulatory mechanisms . We next examined whether co-expressed genes tend to share similar biological functions . To this end , we calculated the semantic similarity of the biological process ( BP ) terms of gene ontology ( GO ) [21] between two genes using GOSemSim [22] . Our analyses demonstrated that the shared and bulk specific co-expressed gene pairs tend to have similar biological functions . Specifically , the fractions of shared and bulk specific co-expressed genes having the same functions were 4 . 6 and 1 . 5-fold higher than the expectation , respectively ( Fig 3A ) . In contrast , the single-cell specific co-expressed genes were not enriched for function similarity ( 0 . 997-fold , Fig 3A ) . Nevertheless , gene pairs with the highest correlation coefficients ( R > 0 . 4 ) at the single-cell level were also enriched for function similarity ( Fig 3B ) . The three groups of co-expressed gene pairs are enriched for different biological functions . Again , we checked the biological functions associated with the genes from the top 1 , 000 shared , single-cell specific , or bulk specific co-expression pairs . For single-cell specific co-expressed genes , GO terms including biological adhesion , and regulation of apoptosis were enriched ( Fig 3C and S9 Fig ) . The shared co-expressed genes were associated with translational elongation , and oxidative phosphorylation . It is also interesting to note that these genes are also enriched in neurodegenerative diseases , such as Parkinson’s disease , given the neuronal origin of glioblastomas ( Fig 3C and S9 Fig ) . The bulk specific co-expressed genes were significantly associated with oxidative phosphorylation and neurodegenerative diseases . These results further demonstrated that the different functional modules were associated with different types of co-expressed genes . To determine whether the underlying regulatory mechanism of co-expression at single-cell level is different from those at the bulk level , we analyzed the possible regulatory relationships for the three groups of co-expressed genes . We examined two distinct and complementary mechanisms for co-expression ( Fig 4A ) . First , we tested whether the co-expressed genes tend to have synchronized activity of their cis-regulatory elements across different physiological conditions . Second , we checked whether the cis-regulatory elements governing each pair of co-expressed genes more likely have physical contact in the three-dimensional nuclear space . We computed the accessibility of gene promoters annotated by DNase I hypersensitive sites ( DHSs ) and corresponding DHS signal correlations across 125 human cell types and tissues [23] . Our analysis revealed that shared and bulk specific co-expressed gene pairs had significantly higher DHS correlation than the random expectation . For example , RPL9 and RPS6 belong to the shared co-expressed gene group , and the accessibility of their promoters was perfectly synchronized across the 125 cell types ( R = 0 . 98 , Fig 4B ) . Similarly , the accessibility of another pair , FRYL and RAPGEF6 , bulk specific co-expressed genes , was also highly correlated ( R = 0 . 98 ) . Overall , the highest peak of the distribution of DHS correlation for shared and bulk specific co-expressed gene pairs were located at 0 . 81 and 0 . 79 , respectively ( Fig 4C ) . In contrast , the correlation coefficient for single-cell specific co-expressed genes was much lower than the other two groups ( P < 1 . 0E-300 , student’s t-test ) . For example , DHS signal of two genes , GIS and GSS , was not correlated ( R = 0 . 068 , Fig 4B ) . The correlation coefficients of single-cell specific genes were much broader distributed , with the highest peak located at 0 . 12 ( Fig 4C ) . We then calculated the probability that two genes physically interact with each other based on chromatin interaction data [24] . In IMR90 cell lines , we discovered that the single-cell specific and bulk specific co-expressed genes were more likely to have physical interactions than expectation ( Fig 4D ) . In contrast , the shared co-expressed genes were not enriched for chromatin interactions ( Fig 4D ) . The same observation was confirmed in an independent cell line of hESC ( S10 Fig ) . Our results demonstrated that the datasets obtained from bulk tissues ( e . g . DHS and chromatin interactions ) could partially explain the co-expression at bulk and single-cell levels , and different types of co-expressions might be regulated by different mechanisms . Previous co-expression studies at the bulk level have shown that genes within the same topological domain were more likely to interact with each other [24] . Here we asked whether a pair of co-expressed genes resided on the same chromosome or even within the same topological domain . Interestingly , for bulk specific co-expressed genes , we observed that 16 . 9% were found on the same chromosome , whereas only 5 . 3% of single-cell specific co-expressed genes were encoded on the same chromosome , which was almost the same as randomly selected gene pairs ( average 5 . 4% , Fig 5A ) . If we only focused on the top 1 , 000 highest co-expressed gene pairs , the difference between two levels became even more significant , 47 . 5% and 5 . 5% of bulk and single-cell specific genes were located in the same chromosome , respectively ( Fig 5B ) . We further asked to what degree the intrachromosomal co-expressed genes were from the same topological domain [24] . Our analysis revealed that 3–6% of shared and bulk specific intrachromosomal co-expressed gene pairs were located at the same topological domain ( Fig 5C ) . By contrast , no single-cell specific gene pairs were from the same topological domain . When we separated the co-expressed genes based on whether they were encoded on the same chromosomes , we found that the interchromosomal chromatin interactions were enriched for single-cell specific co-expressed genes ( Fig 5D ) . This result suggests that many co-expressed genes in single cells were co-regulated through interchromosomal interactions , by which the cis-regulatory elements of genes were physically connected and co-regulated by common regulators such as enhancers ( Fig 4A ) . Recent studies demonstrated that network-based classification approaches provided more power in prediction of clinical outcomes than individual genes [25–27] . We searched the subnetworks within the three types of co-expression networks to identify a set of co-expressed genes that could stratify patients with most significantly different survival time . We classified 120 patients with RNA sequencing from TCGA into two groups based on the expression profiles of genes within each subnetwork ( or combination of subnetworks ) and compared the survival rates between the two groups ( Fig 6A ) . By examining all subnetworks and the combination of the subnetworks , we discovered a combination that achieved the best separation of patient survival rates , which consisted of 4 shared and 2 single-cell specific co-expressed genes ( Fig 6B ) . The two groups of patients were well separated based on the silhouette plot ( S11 Fig ) . The survival rates of the two groups were significantly different ( P = 3 . 9E-4 , log-rank test , FDR < 0 . 1 , Fig 6C ) . As comparison , we performed the same analysis to bulk co-expressed genes , but no subnetwork was found to classify the patients with significant difference in survival rates ( S12 Fig ) , suggesting that single-cell expression profiles help to improve the prognosis of glioblastoma . Furthermore , we classified the patients into four subtypes according to TCGA classification scheme [28] , and their survival rates were not significantly different ( S13 Fig ) . To confirm the classification power of co-expressed genes , we tested our gene signature using an independent set of 101 glioblastomas whose expressions were profiled using microarray from TCGA . The validation indicated that six-gene signature could significantly stratify poor and favorable survival of the patients ( P = 0 . 013 , log-rank test , Fig 6D ) . These results suggest that the co-expressed gene signature has a great potential to predict patient survival .
Our analysis revealed distinct characteristics for the co-expressed genes at single-cell and bulk levels . The stark difference between the two levels suggests that the single-cell expression profiles provide novel biological insights when they are compared with bulk expression profiles . Interestingly , the DHS and chromatin interaction datasets obtained from bulk tissues could partially explain the co-expression at single-cell level . Nevertheless , we are fully aware of the difference of gene regulation between bulk and single-cell levels . For example , two bulk co-expressed genes could have the same accessibility of regulators in their promoters , whereas the regulation of the two genes at single-cell level is independent to each other and could result in un-correlated accessibilities of the promoters ( Fig 4A ) . If we could deconvolute the signal from the bulk tissues or obtain the datasets on gene regulation at single-cell level , we expect to obtain stronger connection between co-expression and co-regulation . Although a few DHS or ChIP-seq datasets at single-cell level are available [29–31] , the data quality is still not optimal ( e . g . low sequencing depth ) . One interesting observation is that majority of the single-cell co-expressed genes are located in different chromosomes , in line with a recent observation that co-expressed odorant receptor genes was not restricted to single chromosome at single-cell level [32] . While the current chromatin interaction analyses are mainly focused on intrachromosomal interactions [33 , 34] , our analysis suggests that interchromosomal interactions are of biological interests . In our analysis , a set of six co-expressed genes was used to stratify glioblastoma patients into two groups with significantly different survival . Although these genes were selected without prior knowledge of cancer biology , the genes are relevant to glioblastomas . For example , PLXNA4 ( plexin A4 ) has been shown to promote tumor angiogenesis and progression of glioblastoma cells [35] . Similarly , NRXN3 ( Neurexin 3 ) was involved neuron cell-cell adhesion and glioma cell migration [36] . Gene RAB3GAP2 ( RAB3 GTPase activating non-catalytic protein subunit 2 ) was implicated in neurodevelopment and Warburg Micro syndrome [37] , whereas PJA2 ( praja ring finger ubiquitin ligase 2 ) degraded MOB1 to support glioblastoma growth [38] . Moreover , both GDI2 ( GDP dissociation inhibitor 2 ) and C21orf33 ( chromosome 21 open reading frame 33 ) was dysregulated in fetal Down syndrome brain [39 , 40] . All the genes were related to glioblastoma or neural diseases , suggesting their underlying function in tumorigenesis and progression of glioblastoma .
Single cell expression datasets were obtained from references [14 , 19] . For glioblastoma , 430 individual cells from 5 patients were sequenced for gene expression . For prostate cancer , 122 cells from 22 patients were sequenced . Bulk expression datasets were obtained from The Cancer Genome Atlas ( TCGA , https://tcga-data . nci . nih . gov/tcga/ ) . In total , 120 glioblastomas and 398 prostate adenocarcinomas were measured by RNA sequencing at the bulk level . For bulk expression profile , we excluded the genes whose average expression was below 100 RPKM ( Reads Per Kilobase per Million mapped reads ) . For single-cell gene expression , we excluded the genes if the expression levels across over two-thirds individual cells were equal to zero . Only the genes that were measured at both single-cell and bulk levels were included for further analysis . In total , 4 , 837 and 4 , 722 genes were analyzed for glioblastoma and prostate adenocarcinomas , respectively . We performed log2-transformation for RPKM . In order to avoid 0 value for invalid log2-transformation , we added 1 to RPKM value . We then performed global centralization by subtracting corresponding average expression across tissues or cells . Quantile normalization of the expression was further conducted across all samples . All analyses were performed in R platform ( http://www . r-project . org/ ) . Pairwise correlations for all genes were calculated using Pearson correlation coefficient ( R ) . The formula is as follows R=∑i=1n ( xi−x¯ ) ( yi−y¯ ) ∑i=1n ( xi−x¯ ) 2∑i=1n ( yi−y¯ ) 2 where x , y are gene pairs , and n is the sample size . All gene pairs were ranked according to R values . The hierarchical clustering of expression profiles took Pearson’s correlation coefficient as similarity measurement , and used complete linkage . Similarly , we also used MIC ( maximal information coefficient ) to measure expression correlation of gene pairs [18] . We then classified the co-expressed genes into three groups: bulk specific , single-cell specific , and shared . Since the distributions of correlation coefficients are quite different between single cells and bulk tissues , we could not choose a uniform cutoff to define the positive correlation . Instead , we developed a shuffled-expression-based algorithm to determine the cutoffs for single-cell and bulk expression separately . Firstly , we shuffled the expression for each gene across the samples , and generated a corresponding distribution of correlation coefficients . We then set the correlation coefficient at the top percentage of 10−6 as cutoff for positive correlation . After setting the cumulative probability of no correlation in random distribution to 0 . 3 for each side around zero correlation , we obtained the cutoffs of no correlation . The criteria for positive correlation and no correlation are very stringent here because we want to make sure the selected groups of gene pairs are indeed bulk specific or single-cell specific . Those positively correlated gene pairs at both single-cell and bulk levels were assigned to the group of shared co-expressed genes . Single-cell specific co-expressions were those gene pairs with positive correlation at the single-cell level but no correlation at the bulk level . Similarly , those gene pairs with positive correlation at the bulk level but with no correlation at the single-cell level were assigned to bulk specific co-expressed genes . In order to associate gene co-expressions with protein-protein interactions ( PPIs ) , we downloaded PPIs from BioGrid [20] . We calculated the fraction of co-expressions with PPIs in each type of co-expressed genes . Meanwhile , we generated one thousand sets of 1 , 000 pairs of genes randomly selected from all gene pairs as control gene pair sets . Each set of control gene pairs were associated with PPIs as well . To calculate the proportion for different ranges of expression correlations , we divided co-expressed genes into equal-interval groups with 0 . 1 bin size of the correlations . The components of protein complexes were from CORUM database [41] . All shared and specific co-expressions were mapped to each protein complexes . The layout and view of co-expression network of protein complexes were performed in Cytoscape [42] . We used R package GOSemSim to calculate the semantic similarity of the biological process ( BP ) terms of gene ontology ( GO ) [22] between two genes . If similarity value of gene pair ≥ 0 . 5 , the genes were called with GO similarity . Based on this criterion , we calculated the percentage of co-expressed gene pairs and randomly selected gene pairs with GO similarity . To identify the enriched GO terms , we each chose the top 1 , 000 co-expressed gene pairs from three groups of co-expressions , respectively . We obtained 129 unique genes from top 1 , 000 shared co-expressions . After excluding the genes were overlapped between shared and single-cell specific co-expressions , we obtained 319 single-cell specific genes . Besides , 640 genes were unique to bulk specific co-expressions . These three groups of genes were separately performed function enrichment analysis through DAVID software [43] . According to the enriched functions , co-expression networks of top 1 , 000 correlations were organized into different modules . The genes were assigned to the most significant module if they were enriched in multiple functional modules . DNase I hypersensitive sites ( DHSs ) in 125 human cells and tissues were downloaded from ENCODE project [23] . DHSs within the promoter regions ( upstream 1 , 000 base pairs relative to transcription start sites ( TSSs ) ) were associated to genes . If no DHS peaks were found within the promoter regions , the intensity of DHSs of genes was assigned to zero . We then calculated DHS correlations of gene pairs across 125 cell types . To identify chromatin interaction of co-expressed genes , we used Hi-C data from previous publication [24] . The DNA regions across upstream 5 , 000 , gene body , and downstream 5 , 000 were used to identify whether gene pairs have chromatin interaction . Chromosomal relationships of co-expressed gene pairs were plotted using Circus [44] . Topological domains in genome were reported by a previous study [24] . According to the locations of TSSs , gene pairs were determined whether they were located in the same chromosome or topological domain . We constructed three networks separately from single-cell specific , bulk specific and shared co-expressions . Using ‘Fast Modularity’ software [45] , we then determined 56 , 42 , and 15 dense subnetworks within these three co-expression networks , which reflect the functionally related gene groups . For each subnetwork , we performed hierarchical clustering of patients based on the bulk expression levels of the genes within the subnetwork . The patients were classified into two groups according to the clustering and then compared of their survival using the Kaplan-Meier method [46] . The significance of differential survival between two groups of patients was assessed with a log-rank test . After testing all the subnetworks , 7 shared , 1 single-cell specific and 1 bulk specific subnetworks were found to be able to separate the patients with significantly different survival rates ( P < 0 . 05 , log-rank test ) . We then further examined the combination of at most three significant subnetworks using the same procedure and discovered one combination with the best performance for tumor prognosis . We then estimated the false discovery rate ( FDR ) using Benjamini and Hochberg approach [47] . The quality of partition of patients was assessed through silhouette graph [48] . In TCGA , another 123 glioblastomas were measured using microarray platform , of which 22 samples were also profiled with RNA sequencing . In order to make the expression levels comparable between microarray samples and sequencing samples , we used one of patients ( TCGA-06-0156 ) , which was measured both by RNA sequencing and microarray , for normalization . After log2-transformation of sequencing data , expression profile of each patient subtracted the average expression of TCGA-06-0156 from RNA sequencing . Similarly , expression profiles measured by microarray also subtracted the average expression of microarray-measured TCGA-06-0156 . Using the gene signature obtained from the 120 glioblastomas , we then predicted the class of additional 101 microarray-measured glioblastomas ( a validation set ) through a nearest shrunken centroid [49] . | With the development of single-cell sequencing , an increasing number of biological insights were revealed at the single-cell resolution . Here we integrated the expression profiles from single cells and bulk tissues to discover that a majority of gene pairs were specifically co-expressed at single-cell and bulk levels . Our comparative analysis reveals co-expressed functional modules at different levels , and suggests a distinct regulatory mechanism in which single-cell co-expressed genes are regulated through physical interactions from different chromosomes . Moreover , we found a set of co-expressed genes to predict patient survival . This study suggests that single-cell and bulk co-expression analysis could provide novel biological insights and great clinical potential . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"gene",
"regulation",
"blastomas",
"cancers",
"and",
"neoplasms",
"oncology",
"neurological",
"tumors",
"genome",
"analysis",
"epigenetics",
"chromatin",
"genomics",
"chromosome",
"biology",
"proteins",
"gene",
"expression",
"chromosome",
"pairs",
"gene",
"ontologies",
"biochemistry",
"protein",
"complexes",
"cell",
"biology",
"neurology",
"genetics",
"biology",
"and",
"life",
"sciences",
"glioblastoma",
"multiforme",
"computational",
"biology",
"chromosomes"
] | 2016 | Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes |
The Mediator complex provides an interface between gene-specific regulatory proteins and the general transcription machinery including RNA polymerase II ( RNAP II ) . The complex has a modular architecture ( Head , Middle , and Tail ) and cryoelectron microscopy analysis suggested that it undergoes dramatic conformational changes upon interactions with activators and RNAP II . These rearrangements have been proposed to play a role in the assembly of the preinitiation complex and also to contribute to the regulatory mechanism of Mediator . In analogy to many regulatory and transcriptional proteins , we reasoned that Mediator might also utilize intrinsically disordered regions ( IDRs ) to facilitate structural transitions and transmit transcriptional signals . Indeed , a high prevalence of IDRs was found in various subunits of Mediator from both Saccharomyces cerevisiae and Homo sapiens , especially in the Tail and the Middle modules . The level of disorder increases from yeast to man , although in both organisms it significantly exceeds that of multiprotein complexes of a similar size . IDRs can contribute to Mediator's function in three different ways: they can individually serve as target sites for multiple partners having distinctive structures; they can act as malleable linkers connecting globular domains that impart modular functionality on the complex; and they can also facilitate assembly and disassembly of complexes in response to regulatory signals . Short segments of IDRs , termed molecular recognition features ( MoRFs ) distinguished by a high protein–protein interaction propensity , were identified in 16 and 19 subunits of the yeast and human Mediator , respectively . In Saccharomyces cerevisiae , the functional roles of 11 MoRFs have been experimentally verified , and those in the Med8/Med18/Med20 and Med7/Med21 complexes were structurally confirmed . Although the Saccharomyces cerevisiae and Homo sapiens Mediator sequences are only weakly conserved , the arrangements of the disordered regions and their embedded interaction sites are quite similar in the two organisms . All of these data suggest an integral role for intrinsic disorder in Mediator's function .
The Mediator complex is a gigantic ( 1 MDa ) multi-protein complex that plays a number of essential roles in eukaryotic gene regulation [1] . It functions as a co-activator , a co-repressor as well as a general transcription factor by transmitting information from the regulatory factors bound at enhancers to the RNAP II transcription machinery [1] , [2] . Mediator is recruited by promoter- and/or enhancer-bound activators [3] followed by association of general transcription factors and RNAP II with the promoter in vivo [4] , [5] ( Figure 1 ) . Mediator dissociates from RNAP II after initiation , and remains attached to the promoter [6] , [7] providing a pre-formed scaffold for the reinitiation [8] . Interactions with RNAP II and regulatory proteins induce dramatic conformational changes in Mediator [9] , [10] . Activator induced specific rearrangements in Mediator expose cryptic RNAP II binding site and modulate the assembly of the pre-initiation complex ( PIC ) [11] , [12] . This suggests that activators/repressors regulate transcription by altering the structure of the RNAP II holoenzyme . These conformational changes were thus proposed to underlie the regulatory mechanism of Mediator [13] . Mediator consists of 20–30 subunits that are organized in a modular fashion , with Head , Middle , and Tail regions [14] ( Figure 1 ) . The Tail can serve as the main target for activators/repressors [15] . The Med9 submodule of the Middle may connect the regulatory signals to the Head [16] , which could in turn interact directly with RNAP-TFIIF for pre-initiation complex formation [17] . The Middle also receives repression signals from the CDK module , which dissociates prior to transcription [18] . The functions of the individual subunits however , are rather obscure apart from the reported kinase activity of the Cdk8 [19] and the histone acetyltransferase activity of the Med5 [20] , which are non-essential for Mediator's function . Mediator protein sequences are highly variable with the exception of a few subunits [21] . The majority of the subunits have no apparent domains , not even the expected domains for chromatin modification such as chromo [22] or bromo domains [23] ( Y . T . unpublished data ) . Nevertheless , based on cryo-electron microscopy , the overall structural organisation of several eukaryotic Mediator complexes is similar [24] . The low sequence conservation of Mediator proteins and the absence of known globular domains suggest the presence of disordered regions in Mediator . Such disordered regions might be responsible for similar structural characteristics in different organisms observed in EM studies [24] despite the lack of sequence conservation . IDRs can contribute to Mediator's function in three different ways: they can provide flexible target sites that can adapt to different partners with variable architectures; they can act as malleable linkers connecting globular domains that impart modular functionality on the complex; and they can also facilitate assembly and disassembly of complexes in response to regulatory signals . To understand whether IDRs play a role in transcription regulation of the Mediator , 340 sequences of 30 subunits were collected ( Table S1 ) and their tendencies for intrinsic disorder were predicted using bioinformatics approaches [25] , [26] . Out of the 27 eukaryotic organisms Saccharomyces cerevisiae and Homo sapiens sequences were analyzed in detail and the results were corroborated using all available sequences ( shown in the Supporting Information , Figures S1 , S2 , S3 , S4 , S5 and S6 ) . The estimated level of disorder increases from yeast to man and in both organisms the propensity of disordered regions substantially exceeds that of signaling proteins and also that of multi-protein complexes of similar size . Subunits that interact with activators/repressors or function in regulatory signal transfer , located mostly in the Tail and Middle modules , are most abundant in IDRs . Overall , 43 sites for protein-protein interactions were predicted in 16 subunits in Saccharomyces cerevisiae and 79 sites in 19 subunits in Homo sapiens Mediator . In yeast , 11 of the predicted molecular recognition features ( MoRFs ) overlap with experimentally detected binding sites or post-translational modification sites , out of which those in Med7/Med21 [27] and Med8/Med18/Med20 [28] complexes have been structurally confirmed . The arrangement of ordered/disordered regions and location of disordered interaction sites are similar in Saccharomyces cerevisiae and Homo sapiens , although sequences of IDRs are only weakly conserved . All these results suggest that Mediator functions as a malleable machine in transcription regulation with an integral role for intrinsically disordered regions for the gene-specific regulatory functions .
Preference of Mediator proteins for intrinsic disorder was assessed by two independent bioinformatics approaches: PONDR-VSL1 that is a support vector machine algorithm [25] and IUPred that utilizes statistical inter-residue potentials [26] . Disorder predictions for Mediator proteins were carried out by both techniques at the amino acid level using sequences of individual proteins and the disorder scores were averaged over the entire sequence . As the two prediction methods provided consensus results , in the following only those obtained by the IUPred algorithm will be detailed . A preponderance of intrinsic disorder ( average disorder above the 0 . 5 threshold value ) was found in 4 and 6 out of 25 subunits in Saccharomyces cerevisiae and Homo sapiens , respectively ( Figure 2 ) . In addition , Med9 ( in yeast ) and Med4 ( in man ) have a level of disorder that is comparable to the disordered proteins assembled in the DisProt database [29] . These proteins likely lack a well-defined tertiary structure in the free form , but can partly or fully fold upon interacting with their partners [30] . The inherent flexibility of these subunits however , can contribute to structural organisation and molecular interactions of the complex . Overall , the levels of disorder ( as averaged over all subunits ) are higher in man than in yeast , suggesting an increase in the propensity or length of disordered regions . In Saccharomyces cerevisiae the Tail is most enriched in subunits with preference for intrinsic disorder ( Med2 , Med3 , Med15 ) , while in Homo sapiens the Middle module appears to be most abundant in malleable proteins ( Med1 , Med9 , Med19 , Med26 ) . In the Head only Med8 is predicted to be disordered in Homo sapiens . Disorder scores averaged over sequences from all available organisms also indicate large variations in some subunits ( please note , that in this case the number of sequences/subunits differ; Figure S1 ) . This might implicate functional changes of various Mediator proteins during evolution . The amino acid compositions of Mediator proteins in Saccharomyces cerevisiae and Homo sapiens are also incompatible with a folded structure [31] ( Figure 3 ) , although they exhibit some variations . As compared to globular proteins , yeast and human Mediator proteins are depleted in hydrophobic ( I , L , V ) , aromatic ( W , Y , F ) and C residues ( designated as order-promoting ) ; and enriched in polar ( Q , N , T , S ) , charged ( E , D ) and structure-breaking ( P ) residues ( designated as disorder-promoting ) . Such a composition resembles the general characteristics of intrinsically disordered proteins [32] . Various subunits , like the Med4 and Med15 are abundant in potential post-translational modification sites ( S and T ) that are preferably embedded in disordered regions [33] . Generally disordered polyQ and polyN regions frequently appear in various subunits , such as Med1 , Med9 , Med10 , Med12 and Cdk8 ( Figure S2 ) . The Q-rich region in Med15 in Saccharomyces cerevisiae for example is involved in glucocorticoid receptor transactivity [34] . The propensity of Q-rich regions also increases from yeast to man . Repeat expansion may contribute to rapid evolutionary changes of Mediator proteins and may have created linkers between globular segments [35] . Intrinsically disordered regions of any length have been observed to be involved in biological functions , but those of 30 residues or longer have been especially well studied [36] . The function of these regions are diverse but are frequently related to molecular recognition [37] . IDRs are usually exploited for regulatory purposes as 66±5% of cell-signaling proteins [38] , and 90% of transcription factors were predicted to contain IDRs ( longer than 30 aa ) [39] , [40] . In Saccharomyces cerevisiae 80% of Mediator subunits have predicted IDRs equal to or longer than 30 residues , and 24% have IDRs above 100 residues in length [25] ( Figure S3 ) . In Homo sapiens , IDRs longer than 30 and 100 residues appear in 75% and 32% of Mediator proteins , respectively ( Figure S3 ) . This suggests that the length of IDRs increased from yeast to man . The number of disordered segments is also higher in the human complex than in the yeast complex ( Figure 4 ) . This is mostly due to the discrepancy in the number of IDRs in the Middle . This module is the most abundant in disordered regions in Homo sapiens . In the Head the propensity of IDRs is also slightly higher ( below 70 residues in length ) in man than in yeast . In Saccharomyces cerevisiae , disordered regions are preferably located in the Tail , some exceeding 100 residues in length . Along these lines , the longest IDRs in yeast are found in Med2 ( 334 ) , Med3 ( 256 ) , Med15 ( 263 ) of the Tail , whereas in human Mediator , Med1 ( 645 ) , Med9 ( 241 ) , Med26 ( 261 ) of the Middle are equipped with the longest IDRs ( Figure 5 and Table S2 ) . Med13 of the CDK appears to have a long IDR in both organisms: 226 and 162 in yeast and human , respectively . Large multi-protein complexes generally take advantage of the plasticity of their components; i . e . , the population of intrinsically disordered segments increases with complex size [41] . Multi-protein complexes of 11–100 proteins fulfilling various functions , have IDR propensity with median value of 12% , which estimates the percentage of disorder required to assemble a complex of a given size . The percentage of amino acids in IDRs is 32% and 33% in yeast and human Mediator , respectively ( Figure S4 ) , and these values considerably exceed those obtained for other complexes of similar size . One possibility is that the Mediator IDRs perform additional ( eg . , regulatory ) tasks besides the self-assembly of the complex . Indeed , the level of disorder in Mediator is even higher than in signaling proteins ( Figure S3 ) . Molecular recognition by IDRs is achieved by short , distinguishable segments , such as preformed elements [42] , molecular recognition features [43] , primary contact sites [44] and linear motifs [45] , [46] . Preformed elements [42] and molecular recognition features [43] are predisposed to fold upon binding , and this reduces the entropy penalty of the recognition process . Primary contact sites [44] or linear motifs [45] are usually short , exposed segments that facilitate formation of highly specific interactions . In general all these recognition sites have higher local hydrophobicity than their environment and often exhibit transient secondary structure [46] . In Saccharomyces cerevisiae and Homo sapiens Mediators , we focused on those recognition sites that are biased for an α-helical conformation , termed α-MoRFs . These segments fold onto an α-helix in the bound form and can be predicted from the irregularities in computed disorder patterns using a neural network algorithm with 0 . 87±0 . 08 accuracy [47] . A prototypical example of an α-MoRF is the short α-helical segment in the disordered transactivator domain of p53 that mediates binding to Mdm2 [48] , [49] . Multiple , tandem binding sites can be found in the BRCA1 protein that serve a scaffold function [50] . In yeast , predictions indicate the presence of 43 α-MoRFs in total , distributed over 16 subunits ( Table 1 ) . Some subunits have multiple α-MoRF regions , with Med15 of the Tail ( 11 α-MoRFs ) and Med13 of the CDK module ( 6 α-MoRFs ) in yeast having the largest numbers of these regions . In accord with the increased level of disorder , 79 interaction sites were identified in 19 subunits in Homo sapiens ( Table S2 ) . Most interaction sites were located in Med3 of the Tail ( 18 α-MoRFs ) and Med1 of the Middle ( 14 α-MoRFs ) and Med13 of the CDK ( 8 α-MoRFs ) . The predicted α-MoRFs in Saccharomyces cerevisiae , which may serve as potential target sites for protein-protein interactions or for post-translational modifications , were compared to experimentally verified binding sites reported in literature or assembled in protein-protein interaction databases . So far 11 out of the of the 43 predicted α-MoRFs in yeast have been experimentally corroborated ( Table 1 ) . For example , the α-MoRF encompassing residues 333–350 of Med3 likely corresponds to the Gcn4 target site [51] , while the α-MoRF 195–212 predicted in Med7 serves as a contact site with Med10 [52] . Specific mutation sites in Med17 at the interaction sites with the Middle and Tail modules [2] ( and Y . T . unpublished data ) also coincide with the identified MoRFs . The region 116–255 of Med15 that interacts with Gal4 [53] contains two predicted α-MoRFs . The 261–351 segment of Med15 that is responsible for transcriptional activation of glucocorticoid receptor also contains one α-MoRF that matches the observed interaction site [34] . The region 396–655 of Med13 contains 3 predicted α-MoRFs and has been observed to contact various partners: Caf1 , Crc4 , Not2 as well as Cdk8 [54] . The predicted phosphorylation site at T237 in Med4 , which might play role in enhancement of RNAP CTD phosphorylation by TFIIH [55] , matches the experimentally determined position . In the case of Med7 and Med8 , the available crystal structures of the Med7/Med21 [27] and the Med8/Med18/Med20 [28] complexes can be used for structural validation of α-MoRFs ( Figure 6 ) . The Med7/Med21 heterodimer serves as a hinge that was proposed to be responsible for large scale changes in the Mediator's structure [27] . In the complex three α -helices of Med7 were observed that constitute a coiled-coil . The predicted α-MoRF 195–212 is located at the C terminal end of α3 that makes contacts with α3 helical region of Med21 . In accord with its predicted increase in flexibility , this segment has elevated B-factors in the bound form . Of course the elevated B-factor values might simply stem from its terminal location . The C-terminal fragment encompassing residues 193–210 of Med8 , which was predicted as an α-MoRF , adopts an α-helical conformation in the Med8/Med18/Med20 complex [28] . While 27 residues of Med8 were used for crystallization , only 16 were observed in the complex , indicating the presistance of disorder even in bound form . This segment is embedded in a larger disordered region , encompassing the linker between the C and N terminal of Med8 . This linker exhibits enhanced sensitivity to proteolytic digestion in the free protein corroborating its disordered state . This region was shown to be essential for transcription in vivo by harboring elongin B and C [56] . An independent argument for the functional importance of the predicted α-MoRFs in 6 subunits ( Med7 , Med9 , Med10 , Med11 , Med15 , Med17 , cf . Table 1 ) is underscored by their overlap with helical regions that have been proposed to be highly conserved from yeast to man [21] . IDRs in homologous proteins often exhibit remote sequence relationships . The functioning of IDRs likely relies on their biased amino acid composition and their short motifs [43] , [44] , [46] , the latter of which enables a rapid evolution of IDRs [57] , [58] . Hence , the presence of IDRs might account for the weak sequence conservation of Mediator proteins despite their similar functions or architectures [14] , [24] . As anticipated , a remarkable difference between the sequence conservation of disordered and ordered regions were also seen in Saccharomyces cerevisiae and Homo sapiens Mediators ( Figure 7 ) . This distinction can also be observed if Mediator subunits from all available organisms are aligned ( Figure S5 ) . In contrast to the sequence behaviors , the propensities of order and disorder promoting amino acids in IDRs were found to be highly conserved ( Figure S5 ) . Recently we introduced a method to assess the conservation of IDRs based on the arrangements of ordered and disordered segments , as predicted by the IUPred algorithm , in different sequences [59] . This can be evaluated at the level of residues , i . e . , by computing the percentage of residues designated as ordered or disordered at the same position in sequence alignments . On the average 74 . 5% of residues are located in regions with the same character ( disordered or ordered ) in Saccharomyces cerevisiae and Homo sapiens ( Figure S6 ) . Alternatively , the overlap between ordered and disordered segments in different sequences can be measured by adopting the accuracy measures of secondary structure predictions [59] , [60] . In this case the arrangement of ordered/disordered segments in different sequences is compared to each other in terms of the persistence of their location in different organisms . The overlap between the patterns of ordered/disordered regions in yeast and human Mediator is 73 . 2% . This value significantly exceeds the corresponding value determined from randomized sequences with the same amino acid composition ( Figure 8 ) . Thus it appears that , in contrast to the sequences themselves , the arrangements ( patterns ) of disordered regions are conserved in different organisms , providing a further support for their functional importance .
Transcriptional control requires an intimate interplay between the enhancer- and repressor-bound factors and the basal transcription machinery . In eukaryotic organisms large co-activators , such as the Mediator complex [1] or CBP/p300 [61] are responsible for transducing regulatory information to the core apparatus and link chromatin remodeling to m-RNA synthesis . The mechanism by which these large assemblies impart versatility and specificity on transcription regulation however , remains to be uncovered . It has been proposed that dramatic conformational changes that occur upon interactions with regulatory proteins [10]–[13] as well as with RNAP II [9] could serve as a basis of the Mediator's control mechanism [13] . Such large-scale structural rearrangements could be facilitated by highly flexible/malleable segments that can serve as molecular “hinges” [10] . Furthermore , based on the abundance of intrinsically disordered proteins in signaling [36] , we reason that the signal transducer function of Mediator is also intertwined with IDRs . IDRs mediating specific , transient interactions were observed at various checkpoints of transcription [62] , like in histone tails [63] , transactivator domains of transcription factors [64] and the C-terminal domain of RNAP II [65] . In this study , bioinformatics approaches were employed to assess the preference of Mediator proteins for intrinsic disorder , focusing on the comparison of Saccharomyces cerevisiae and Homo sapiens Mediator complexes . Various subunits , located mostly in the Middle ( Med1 , Med9 , Med19 , Med26 ) in human and in the Tail ( Med2 , Med3 , Med15 ) in yeast are predicted to be enriched in disordered regions ( Figure 2 and Figure 4 ) . As the level of disorder in these proteins is higher than that of proteins assembling into other complexes of similar size , IDRs are likely exploited for additional , regulatory functions besides facilitating the self-assembly of the complex . Along these lines , the propensity of disordered regions in both yeast and human Mediator exceed that in signaling proteins . Results obtained on all available Mediator sequences ( 340 ) presented in Supporting Information ( Figures S1 , S2 , S3 , S4 , S5 and S6 ) also corroborate the results obtained on the two organisms emphasized here . Because the predictions were performed on individual sequences , we cannot exclude the possibility that regions predicted to be intrinsically disordered adopt a well-folded structure upon interacting with other Mediator subunits or with regulatory proteins . Electron microscopy results however indicate the pliability of the complex at low ionic strength ( Francisco Asturias , private communication ) that argues against the complete loss of disordered state in the Mediator complex . An independent argument comes from the structure-function analysis of complexes of intrinsically disordered proteins . In many cases IDRs were found to remain disordered even bound to their partners and yet critically affect binding affinity or specificity [66] . In these ‘fuzzy’ complexes IDRs interact via short segments , while the embedding regions may remain structurally variable . To probe if IDRs are utilized for macromolecular communication , sites of protein-protein interactions were predicted in disordered regions and are biased for an α-helical conformation . In total 43 α-MoRFs were identified in yeast Mediator , with 79 α-MoRFs in human Mediator . The roles of α-MoRFs as protein-protein interaction sites is also suggested by the overlap of the predicted and experimentally observed binding regions . For example , in Saccharomyces cerevisiae 11 α-MoRFs were predicted in Med15 of the Tail that is likely to be the main sensor for regulatory proteins , while 6 α-MoRFs in Med13 of CDK is embedded in a region that hosts various trancriptional proteins ( Table 1 ) . Overall , the functional importance of 11 predicted α-MoRFs either as interaction sites or post-translational modification sites have been experimentally confirmed in yeast . In the cases of the Med7/Med21 [27] and the Med8/Med18/Med20 [28] complexes , structural data corroborate the role of the predicted α-MoRFs as recognition sites that adopt an α-helical structure in the bound state . Although less experimental data are available for human Mediator , 5 α-MoRFs predicted in Med1 fall into regions interacting with various transcriptional proteins ( Table S2 ) . For example , the N-terminal 306 residues of Med1 is involved in the transactivator function of BRCA1 [67] , while the 433–803 region ( with 4 predicted α-MoRFs ) hosts the nuclear receptor LXRb and KIF1a [68] . So how does intrinsic disorder contribute to the function of Mediator ? IDRs represent an ensemble of conformations [69] that imparts extreme flexibility onto the complex . In response to regulatory signals IDRs can adopt different conformations [70] and thereby induce functional transitions . In this way they could contribute to the observed pleomorphism of Mediator . IDRs with multiple binding sites indicated by the MoRFs may provide a scaffold-like function and thereby can be important to organize the complex . IDRs can also serve as malleable linkers between globular domains and may underlie modular functionality of the Mediator complex that enable it to interpret different combinations of transcriptional inputs [71] . IDRs can also facilitate assembly/disassembly of large complexes [37] , for example association of Mediator with TFIID triggers assembly of the PIC . IDRs can be involved in complex signaling events [72] due to their adaptability . The same IDR can accommodate different partners [73] that may exert different , even opposite outcomes on transcription [74] . For example , the disordered N-terminal region of Med3 can host both Gcn4 and Tup1 proteins [51] , or the C-terminal 100 residues of Med19 are involved in both transcriptional activation and repression [75] . IDRs are also preferred environments for post-translational modification sites [33] that provide a further regulatory tool for the Mediator complex ( cf . T237 in Med4 [55] ) . The presence of disordered regions also highlight an evolutionary aspect of Mediator's function . We observe that the propensity of disordered regions as well as the number of embedded interaction sites increases from yeast to man . This not only argues for an integral role of IDRs in Mediator's function , but may explain why the human Mediator is capable of processing a significantly larger number of regulatory signals ( eg . the number of transcription factors increase by one order of magnitude from yeast to man [76] ) . Even if IDRs are conserved , as it was demonstrated by their similar arrangements in Saccharomyces cerevisiae and Homo sapiens their sequences are tolerant to substantial changes as long as the amino acid composition is biased for disorder [58] , [66] . Only sequences of short segments that serve as recognition sites need to be restrained , as seen in case of 6 α-MoRFs [21] . On the other hand it is very easy to turn on and off the functionalities carried by these short motifs [45] . In conclusion , we propose that conserved intrinsically disordered regions contribute to the gene-specific regulatory function of the Mediator . IDRs with weak sequence restraints can provide an evolutionarily economic solution for the Mediator to handle a steadily increasing amount of complex regulatory signals . These results argue for the functional conservation of the Mediator and may account for the evolution of its regulation complexity .
Mediator protein sequences were extracted from the UniProt and NCBI databases using a large number of Mediator subunit names . Overall 556 sequences were identified out of which the redundant ones above 90% identity were removed by the CD-hit program [77] . In addition , a PSI-BLAST [78] search was performed using the 196 sequences from 10 organisms in the reference [21] . All resulting sequences were assembled in the MED_ALSEQ database that contained 340 sequences of 30 Mediator subunits derived from 27 eukaryotic organisms ( Table S1 ) . The corresponding randomized sequences ( 50 times each ) were collected in the MED_ALRAN database . As a nomenclature for the Mediator subunits we adopted the unified convention proposed in reference [79] . Med19 and Med26 was assigned to the Middle module according to the reference [80] . Intrinsic disorder preferences of sequences in the MED_ALSEQ and MED_ALRAN databases were predicted at amino acid level using the IUPred ( http://iupred . enzim . hu ) [26] and PONDR VSL1 [25] algorithms . Intrinsically disordered segments were defined as regions with more than 30 subsequent residues with predicted disorder above 0 . 5 , allowing a maximum of 3 residue long ordered gaps . MoRFs were computed using the reported algorithm [47] . Likely phosphorylation sites were identified using the DisPhos program [33] . The fractional difference is calculated as ( CX−Cordered set ) /Cordered set , where CX is the averaged content of a given amino acid in a protein set and Cordered set is the corresponding averaged content in a set of ordered proteins from the PDB . Due to the presence of low-complexity regions , an iterative PSI-BLAST [78] based profile generation algorithm was performed to align full-length sequences of Mediator proteins [59] . Groups of homologous sequences were defined based on mutual sequence similarity ( below the treshold of E = 10−5 ) between all members of the group . The final multiple alignment was generated by the CLUSTALW algorithm [81] using the BLAST profiles extracted from sequence groups . The performance of the alignment as compared to previous alignments [21] , [27] are presented in Tables S3 and S4 . The sequence conservation of the Mediator proteins was evaluated comparing individual amino acid types ( AAcons ) using a simple Sum-of-Pairs ( SP ) score formula [82] . The score was 1 if identical residue was present in each positions of the alignment , otherwise it was 0 and these scores were averaged over the entire sequence . Similarity between patterns of disordered and ordered regions was assessed using accuracy measures of secondary structure predictions [59] , [60] . The overlap between ordered and disordered motifs ( excluding gap positions ) at residue level ( Q ) was characterized by the accuracy matrix defined as Q2 = 100 ( MOO+MDD ) /N , where MOO and MDD are the number of positions associated with the same motif type . Overlap between the segments were computed aswhere S1 and S2 stand for segments in two distinct sequences , respectively , minov ( S1; S2 ) is the length of the overlap between S1 and S2 , maxov ( S1; S2 ) is the total extent of S1 and S2 in the given conformational state and len ( S1 ) is the length of the segment in the reference sequence . δ ( S1; S2 ) is the minimum of [ ( maxov ( S1; S2 ) –minov ( S1; S2 ) ; minov ( S1; S2 ) ; int ( len ( S1 ) /2 ) ; int ( len ( S2 ) /2 ) ] . The normalization factor N is given by the number of residues in conformational state i and the second summation runs over all M conformational states . Q and SOV values obtained for each possible pair within a given group of aligned sequences were averaged . The significance of the results was probed against the overlap values computed on the MED_ALRAN database . | Intrinsically disordered proteins/regions do not adopt well-defined three dimensional structures; instead , they function as conformational ensembles . They are distinguished in molecular recognition and involved in various regulatory processes . Several components in the transcription machinery–for example , the transactivator domains of transcription factors–are disordered . Mediator , which is a large complex that transduces regulatory information from activators/repressors to the core apparatus , was found to contain a preponderance of intrinsically disordered regions in its various subunits . Such disordered regions are commonly involved in conformational changes coupled to functional transitions , in protein–protein interactions , or in posttranslational modifications . Several such predicted recognition sites were in good agreement with experimental data . Intrinsically disordered regions illuminate a novel aspect of Mediator's regulation and could explain its versatility and specificity in handling transcriptional signals . Their integral role in Mediator function is further underscored by the conserved arrangements of ordered/disordered segments and of the embedded interaction sites . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"computational",
"biology/macromolecular",
"sequence",
"analysis",
"computational",
"biology/sequence",
"motif",
"analysis",
"computational",
"biology/transcriptional",
"regulation"
] | 2008 | Malleable Machines in Transcription Regulation: The Mediator Complex |
Mutations in the cystic fibrosis transmembrane conductance regulator ( CFTR ) gene cause cystic fibrosis ( CF ) and are associated with congenital bilateral absence of the vas deferens ( CBAVD ) , which is the major cause of infertility in male patients with CF . However , most Taiwanese patients with CBAVD do not carry major CFTR mutations . Some patients have a single copy deletion of the solute carrier family 9 isoform 3 ( SLC9A3 ) gene . SLC9A3 is a Na+/H+ exchanger , and depleted Slc9a3 in male mice causes infertility due to the abnormal dilated lumen of the rete testis and efferent ductules . Furthermore , SLC9A3 interacts with CFTR in the pancreatic duct and functions as a genetic modifier of CF . However , SLC9A3 function and its relation to CFTR expression in the male reproductive tract in vivo remain elusive . In the present study , we found that CFTR expression was dramatically decreased in the epididymis and vas deferens of Slc9a3 knockout mice . Adult Slc9a3-/- mice showed not only significantly decreased epididymis and vas deferens weight but also increased testis weight . Furthermore , Slc9a3-/- mice developed obstructive azoospermia because of abnormal abundant secretions and calcification in the lumen of the reproductive tract . Ultrastructural analysis of the epithelium in Slc9a3–/–epididymis and vas deferens displayed disorganized and reduced number of stereocilia and numerous secretory apparatuses . Our data revealed that interdependence between SLC9A3 and CFTR is critical for maintaining a precise microenvironment in the epithelial cytoarchitecture of the male reproductive tract . The Slc9a3-deficient mice with impaired male excurrent ducts in this study provide proof for our clinical findings that some Taiwanese of CBAVD carry SLC9A3 deletion but without major CFTR mutations .
Cystic fibrosis ( CF ) , characterized by mutations in transmembrane conductance regulator ( CFTR ) gene , is the most common autosomal recessive disorder in Caucasians [1–4] . CFTR is an apical membrane Cl- channel and is responsible for anion secretion in the lungs , pancreas , male reproductive tract , and other epithelial cells . Loss of CFTR activity causes dehydration of the apical membrane and impairs the clearance of mucus from the respiratory tract [5] . Most patients with congenital bilateral absence of the vas deferens ( CBAVD ) have mutations in and/or susceptible variants of the 5T allele in intron 8 of CFTR [6–8] . Up to 78%–82% of genetic mutations or variants of CFTR have been detected in CBAVD patients from different countries [6 , 8–13] . Abnormal atrophy of the tissue structure of the vas deferens and the corpus and cauda epididymis is the major cause of male infertility in patients with CBAVD [14 , 15] . However , most Taiwanese patients with CBAVD do not carry CFTR mutations , and this is consistent with the low incidence of CF in Asian populations including Taiwan [16] . We previously performed genome-wide mapping of copy-number variations through oligonucleotide array-based comparative genomic hybridization ( CGH ) and identified loss of solute carrier family 9 isoform 3 ( SLC9A3 ) allele in Taiwanese men with CBAVD ( in two of seven Taiwanese men with CBAVD ) [17] . SLC9A3 , a Na+/H+ exchanger , is expressed in the apical membranes of epididymal , vas deferens , renal proximal tubule , and intestinal epithelium [18–22] . Zhou et al . indicated that SLC9A3 is also expressed in the nonciliated cells of the efferent duct , which connects the testis and epididymis , and that Slc9a3-/- male mice are infertile because of the abnormal dilated lumen of the rete testis and efferent ductules [23] . However , the role of SLC9A3 in the epididymis and vas deferens remain to be clarified . Another well-known function of SLC9A3 is regulation of ion homeostasis in the intestine and colon . SLC9A3 is mainly involved in the transepithelial absorption of Na+ and water and often functionally couples with the Cl-/HCO3- exchanger [24] . In one previous study , Slc9a3-/- mice showed elevated intestinal fluid and diarrhea because of decreased Na+ and HCO3- absorption [21] . Ahn et al . was the first to demonstrate that SLC9A3 interacts with the C-terminal PDZ motif of CFTR in PS120 cells [25] . In that study , SLC9A3 and CFTR were colocalised in the pancreatic duct of wild-type ( WT ) mice and SLC9A3 expression decreased by 53% in the pancreatic duct of homozygous △F508 mutation ( △F/△F ) Cftr mice . This reciprocal interaction between SLC9A3 and CFTR is regulated by sodium–hydrogen exchange regulatory cofactor 2 in a renal epithelial cell line [26] . Furthermore , loss of SLC9A3 activity increases survival and reduces the occurrence of intestinal obstructions in Cftr-/- mice because it rescues the dehydration induced by impaired CFTR function in the intestinal epithelium [27] . Genetic studies have also supported the clinical association between SLC9A3 and CF . Single nucleotide polymorphisms in SLC9A3 in children with CF are significantly associated with two clinical manifestations , the early infection of Pseudomonas aeruginosa and worsened pulmonary function [28] . Genome-wide association studies have indicated that genetic variants of SLC6A14 , SLC26A9 , and SLC9A3 in patients with CF ( n = 3 , 763 ) increased susceptibility to early meconium ileus [29 , 30] . Furthermore , five CF-modifier loci , including SLC9A3 , were associated with lung disease severity in 6 , 365 patients with CF [31] . These comprehensive studies highlight the critical associations between genetic variants of SLC9A3 and clinical indices such as the penetrance of the phenotype and age of onset in patients with CF . Although previous studies have indicated that SLC9A3 is associated with CFTR and significantly affects the severity of CF-related diseases , the direct connection between SLC9A3 and CF-related diseases in vivo is unclear . In the present study , we found that Slc9a3 deficiency in mice induced CBAVD-like phenotypes .
Most Caucasian patients with CBAVD show genetic mutations or variants of CFTR [6] . However , genes associated with CBAVD in Asian and Taiwanese populations are unclear [16 , 32] . Our previous large-scale genetic screening suggested that SLC9A3 is a high-potential candidate gene for CBAVD [17] . SLC9A3 and CFTR are coexpressed in the pancreatic duct , and the amount of SLC9A3 was shown to be reduced in △F/△F Cftr mice [25] . In our results , Slc9a3-/- mice were completely infertile compared with age-matched WT and heterozygous mice ( Table 1 ) . Therefore , we evaluated whether CFTR expression in Slc9a3-/- mice was reduced and contributed to the sterility . In contrast to our expectations , CFTR expression was drastically reduced in the caput ( 95 . 2% ± 1 . 3 ) and cauda ( 85 . 7% ± 6 . 6 ) epididymis and vas deferens ( 90 . 4% ± 4 . 1 ) ( Fig 1 ) . These findings suggested that reduced CFTR expression was responsible for the reproductive tract pathology in Slc9a3-/- mice . Evaluating the causes of reduced CFTR expression and infertility in Slc9a3-/- males first requires the precise localization of SLC9A3 . Until now , information on the localization of SLC9A3 in different regions of the epididymis and vas deferens in mice has been incomplete . Several studies have shown that SLC9A3 is expressed in the apical region of nonciliated cells of the efferent ducts in species including humans , mice , rats , hamsters , and roosters [19 , 22 , 23 , 33–38] . Although the expression of SLC9A3 was detected in the principal cells of the epididymis except distal cauda epididymis and vas deferens in rats [22] , additional details regarding SLC9A3 expression in mice remains to be revealed . We further verified the SLC9A3 localization in the epididymis and vas deferens of mice ( Fig 2 ) . The specificity of anti-SLC9A3 antibody used in our study was confirmed through immunofluorescence staining on the epididymal sections of two Slc9a3–/–mice ( S1 Fig ) , and no signal was observed along the stereocilia in the epididymal epithelia of Slc9a3-/- mice ( S1A and S1B Fig ) . SLC9A3 localization in the efferent ducts was identical to that in previous studies ( Fig 2A ) [23 , 35] . In the epididymis of 2-month-old WT males , SLC9A3 was localized in the apical stereocilia of the corpus and cauda epididymal epithelia ( Fig 2C and 2D ) . SLC9A3 signals were also detected along the stereocilia on the vas deferens epithelium ( Fig 2E , enlarged box ) . By contrast , SLC9A3 was more widely distributed in the caput epididymal and vas deferens epithelia ( Fig 2B and 2E ) . The SLC9A3 localization ( Fig 2 ) corresponds to the significantly reduced expression of CFTR in Slc9a3-deficient epididymis and vas deferens ( Fig 1 ) . We speculate that the interaction between CFTR and SLC9A3 may be critical for protein stability in male excurrent ducts . Ten 2-month-old Slc9a3-/- and WT male mice were analyzed to evaluate the pathology of the reproductive system . Slc9a3-deficient mice displayed increased testis size [3 . 59 ± 0 . 08 ( WT ) vs . 5 . 46 ± 0 . 17 ( Slc9a3-/- ) ] . By contrast , the weight of the epididymis [1 . 18 ± 0 . 02 ( WT ) vs . 0 . 85 ± 0 . 02 ( Slc9a3-/- ) ] and vas deferens [0 . 46 ± 0 . 01 ( WT ) vs . 0 . 41 ± 0 . 01 ( Slc9a3-/- ) ] were reduced ( Fig 3 ) . We suggested that an obstruction probably occurred in the reproductive ducts of Slc9a3-/- males and were similar to those observed in knockout ( cf/cf ) Cftr mice [39] . To determine the effect of SLC9A3 deficiency on infertility , we first analyzed testicular sections of WT and Slc9a3-/- mice of various ages to determine the course of progressive changes . The structures of the interstitial tissue and seminiferous tubules in WT testis were integrally organized , and germ cells showed regular arrangement and complete development ( Fig 4A and 4B ) . The composition of the germ cell population was comparable between 2-month-old WT and Slc9a3-/- mice , but the organization of germ cells in the Slc9a3-/- mice was slightly disordered ( Fig 4C and 4D ) . However , the number of each germ-cell type was slightly lower in 4 months-old Slc9a3-/- males [Fig 4E and 4F; S2 Fig , the number of elongating spermatids: 144 . 25 ± 28 . 95 ( WT ) vs . 102 . 86 ± 26 . 9 ( Slc9a3-/— 4 months ) ] . Some testicular lumens of 6-month-old Slc9a3-/- males had undergone atrophy and displayed moderate-to-severe hypospermatogenesis ( Fig 4G and 4H ) . Efferent ductules , one of the factors underlying the Slc9a3-/- testicular histopathology , are the connective bridge between the rete testis and epididymis and are involved in the reabsorption of approximately 90% of luminal fluid from the testes [40] . SLC9A3 was expressed on the epithelium of efferent ductules ( Fig 2A ) . Compared with age-matched WT mice ( Fig 5A and 5B ) , the luminal diameters of the efferent ductules in 4-month-old Slc9a3-/- mice ( Fig 5C and 5D ) were wider . This phenotype was consistent with previous study by Zhou et al . [23] . Moreover , unexpected calcification was observed in the efferent ductules of Slc9a3-/- mice at the age of 4 months ( Fig 5C and 5D ) . According to these data , we speculated that the testes of Slc9a3-/- mice underwent atrophy , probably because of back pressure from fluid accumulation caused by the obstruction and dysfunction of the efferent ducts [41–43] . Sterility in patients with CF is due to the loss of the vas deferens and corpus and cauda epididymis [6] . We found that CFTR expression and organ weight were significantly decreased in the epididymis and vas deferens of Slc9a3-/- mice ( Figs 1 and 3 ) . To characterize the physiological significance of SLC9A3 in the epididymis and vas deferens , we analyzed the histological changes in Slc9a3-/- males of various ages . The lumen was filled with spermatozoa throughout the entire epididymis in WT mice ( Fig 6A and 6B represent the caput epididymis; Fig 7A and 7B represent the cauda epididymis ) . In 2-month-old Slc9a3-/- males , a reduced number of spermatozoa was observed in a few ducts of the caput epididymis ( Fig 6C and 6D and S3 Fig ) . Recognizable spermatozoa were nearly absent in the lumen of the caput epididymis of >2-month-old Slc9a3-/- males . Moreover , the level of abnormal secretions was augmented in the lumens of the caput epididymis with an increase in age of Slc9a3-/- mice ( Fig 6E–6H ) . Similar phenotypes were occurred in the cauda epididymis of Slc9a3-/- mice and were also found to progressively deteriorate with age ( Fig 7 ) . Compared with the caput epididymis of Slc9a3-/- mice , the elevated secretions were significantly more severe in the cauda epididymis . The vas deferens of Slc9a3-/- males also displayed elevated secretions ( Fig 8 ) . Spermatozoa were not transported from the epididymis into the vas deferens in 2-month-old Slc9a3-/- mice ( S3E and S3F Fig ) . Aberrant secreted materials were observed in the entire vas deferens , beginning from the age of 2 month . ( Fig 8 and S4 Fig ) . The epithelia of excurrent ducts are actively involved in the maintenance of proper luminal milieu through mechanisms including secretion and absorption [44] . Epididymal and vas deferens epithelium are lined by nonmotile stereocilia . These membrane-extended structures substantially increase the surface area and absorptive and secretive capacities of epithelial cells [44] . Because abundant secretions were existed in the excurrent ducts of Slc9a3-/- mice , we further examined the ultrastructure of the epithelium in these organs by transmission electron microscopy . The amount of stereocilia was dramatically fewer in the Slc9a3-/- caput epididymis ( Fig 9A and 9B , arrows ) . Moreover , the stereocilia in the Slc9a3-/- cauda epididymis was less organized and fewer in number . Vesicles , a type of secretory apparatus , were more abundant in the epithelium of the Slc9a3-/- cauda epididymis ( Fig 9C and 9D , light blue arrows ) . Distinct morphological changes were also observed in the vas deferens of Slc9a3-/- mice . Stereocilia were less prominent and disturbed ( Fig 9E and 9F ) . Hence , the less well-ordered and reduced number of stereocilia in the epithelium of the epididymis and vas deferens and more abundant secretory probably account for the chaotic secretion in the excurrent ducts of Slc9a3-/- mice .
Zhou et al . indicated that the luminal diameter of the efferent ductules in 90–120-day-old Slc9a3-/- mice was dilated [23] . In our results , we further identified unexpected calcification in the lumen of the efferent ductules in Slc9a3-/- mice at the age of 4 months . The expression of SLC9A3 is abundant in the apical membrane of the epithelium in the gastrointestinal tract and kidneys [45] . Pan et al . indicate that SLC9A3 plays a critical role not only in Na absorption but also in Ca2+ homeostasis [46] . In that study , Slc9a3-/- mice exhibited reduced Ca2+ reabsorption in proximal tubules . SLC9A3-deficient mice also displayed lower intestinal Ca2+ absorption . Both of these contributed to the hypomineralized bones in Slc9a3-/- mice . We suggest that SLC9A3 might participate in the regulation of Ca2+ homeostasis in the efferent ductules . SLC9A3 deficiency may have led to an imbalanced Ca2+ concentration and further depositing in the lumen of the efferent ductules as the Slc9a3-/- mice aged . Because the efferent ductules are the bridge between the testis and epididymis in mice and they participate in the reabsorption of approximately 90% of luminal fluid from the testes , this obstruction in the efferent ductules caused the fluid accumulation and formation of back pressure to the testis of Slc9a3-/- mice [40–43] . This may be the one of the main reasons for testicular atrophy and obstructed azoospermia in Slc9a3-/- mice . Mammalian spermatozoa are developed in the testis and undergo concentration and maturation in the seminiferous tubules , efferent ducts , epididymis , and vas deferens [38] . Precise regulation of pH and ion homeostasis is critical for luminal milieu [47] . SLC9A1 , SLC9A2 , SLC9A3 , and SLC9A5 , the Na+/H+ exchangers , are critical motional proteins in these processes [44 , 48] . SLC9A1–SLC9A3 are expressed in the efferent ducts and epididymis , and SLC9A1 and SLC9A5 are expressed in mature spermatozoa [44] . Loss of only Slc9a1 or Slc9a3 in male mice induces infertility . Slc9a1-null mice show severely decreased sperm motility because of disturbed intracellular pH in sperms [49] . SLC9A3 cooperates with ion channels such as CFTR and NBC1 and regulates H+ secretion and HCO3- reabsorption in WT mice [50] . We found that Slc9a3-/- mice displayed obstructed azoospermia-like phenotypes , which may be partially contributed to by decreased CFTR expression , similar to that observed in knockout ( cf/cf ) Cftr mice [39] . In addition , the dilated efferent ducts of Slc9a3-/- mice were abnormally calcified . We suggest that aberrant secretion and calcification are likely due to the dysregulation of ion homeostasis and improper pH caused by SLC9A3 deficiency . Ahn et al . indicated that SLC9A3 colocalized and interacted with CFTR in PS120 cells and mice pancreatic ducts [25] . In that study , the reduced SLC9A3 levels ( 53% ) and activity in the pancreatic ducts of △F/△F Cftr mice were evaluated . The authors proposed that CFTR forms a complex with SLC9A3 and EBP53 to increase the stability of SLC9A3 . In the present study , the markedly reduced CFTR expression in the epididymis and vas deferens caused by SLC9A3 deficiency was more deteriorated ( 85 . 7%–95 . 2% , Fig 1 ) . This is consistent with and even more severe than that reported in the previous study [25] . We suggest that the obstructed azoospermia-like phenotypes in Slc9a3-/- mice were attributable to both SLC9A3 deficiency and reduced CFTR expression . The homozygous △F508 mutation ( △F/△F ) and knockout ( cf/cf ) Cftr mice , which show reduced male fertility , are two major strains of genetically modified mice with Cftr mutations [39 , 51] . In these strains , the development and morphology of the epididymis and vas deferens are normal before 20 days of age . At 40–44 days , spermatozoa are present in the testes and epididymis of △F/△F and cf/cf mice , but the lumen of the vas deferens contains abnormal secretions instead of spermatozoa and is narrower . These mouse strains are less fertile than the control mice , with only one in three cf/cf male mice being fertile . In the present study , we found that Slc9a3-/- mice displayed similar but worse phenotypes including impaired spermatogenesis and less spermatozoa and severe aberrant secretions in the epididymis . Moreover , these results are consistent with the deteriorated levels of CFTR in Slc9a3-/- mice reported in a previous study ( Fig 1 ) [25] . Large-scale genetic studies have indicated that genetic variants of SLC9A3 are associated with early infection , lung infection severity , and susceptibility to meconium ileus in patients with CF [28–31] . However , the association between CBAVD , a mild phenotype of CF , and SLC9A3 is unclear . Recently , we detected the loss of a SLC9A3 copy in Taiwanese patients with CBAVD by performing CGH and real-time PCR [17] . In the present study , we determined the detailed reproductive phenotypes , which are similar to the defects of cf/cf mice , in Slc9a3-/- mice . These findings indicate a direct association between SLC9A3 and CBAVD in vivo . Because the phenotypes of deficiency or different mutations of CFTR in mouse models are quite distinct from those in swine models or humans [14 , 15 , 39 , 52] , the pathogenesis in male excurrent ducts caused by SLC9A3 loss in different species should also be dissimilar . The association between SLC9A3 function and CBAVD must be clarified by collecting more CBAVD patients and performing clinical examinations of the epididymis and vas deferens in future .
The animal use protocol were reviewed and approved by the Institutional Animal Care and Use Committee ( IACUC ) of Fu Jen Catholic University ( Approval number: A10064 ) . The animal experiments were performed according to the international guidelines and regulations . FVB . 129 ( Cg ) -Slc9a3tm1Ges/J mice were purchased from Jackson Laboratory . The genotype of each male mouse was assayed by extracting genomic DNA from the tail and by performing PCR . The primers used in genotyping were as follows: F1 ( 5ʹ-CATACAACATAGGACTAGCC-3' ) , R1 ( 5ʹ-CACTACTAGTCAGGCACTCT-3' ) , and R2 ( 5ʹ-CACTACTAGTCAGGCACTCT-3' ) . The primer ratio of F1 , R1 , and R2 was 2:1:1 . More than 10 mice of each genotype were sacrificed at 2 months of age by anesthesia with isoflurane , and their organs , including the testes , epididymis , and vas deferens , were collected and weighed . The fertility and fecundity of WT and Slc9a3-/- mice were compared by placing 2-month-old males of each genotype with two WT female mice and by counting the number of pups from each pregnancy . Briefly , the organ tissues were homogenized in a lysis buffer [20 mM Tris/HCl ( pH 8 ) , 150 mM NaCl , 5 mM MgCl2 , 0 . 5% Triton-X 100 , 10% glycerol , and a protease inhibitor cocktail] and total protein extractions were heated for 5 min at 37°C before SDS-PAGE [53] . Antibodies against CFTR ( ab2784; dilution , 1:3 , 000; Abcam , Cambridge , MA , USA ) and actin ( A5441; dilution , 1:20 , 000; Sigma-Aldrich , St Louis , MO , USA ) were used and detected by chemiluminescence . Density was quantified using Image J software ( National Institutes of Health , Bethesda , USA ) . WT and Slc9a3-/- mice were sacrificed at 2 , 4 , and 6 months of age , and their organs were collected . The testes were fixed in Bouin’s solution ( Sigma-Aldrich ) , and the epididymis and vas deferens were fixed in PBS containing 4% paraformaldehyde . Next , the tissues were processed for embedding in paraffin wax . Sections of these paraffin-embedded tissues were stained with hematoxylin and eosin ( H&E ) for histological analysis . For immunofluorescence staining , the dewaxed sections were boiled with 0 . 1 M sodium citrate buffer ( pH 6 . 0 ) for antigen retrieval . Sections were incubated overnight at 4°C with diluted primary antibodies including anti-SLC9A3 antibody ( ab95299; Abcam , Cambridge , MA , USA ) and anti-pan-keratin ( 4545; Cell Signaling , Beverly , MA , USA ) . Primary antibodies were detected with Alexa Fluor 488 and Alexa Fluor 568 fluorescent secondary antibodies ( Invitrogen , Carlsbad , CA , USA ) and followed by DAPI staining and mounted with Dako mounting medium . The acrosome was stained with lectin peanut agglutinin ( L-32458; Invitrogen , Carlsbad , CA , USA ) . In all the experiments , at least three age-matched WT and Slc9a3-/- mice were analyzed . Parts of the caput and cauda epididymis and vas deferens from 2-month-old mice were excised and immediately fixed with 4% paraformaldehyde and 0 . 1% glutaraldehyde overnight at 4°C . Next , the tissues were rinsed with 0 . 1 M phosphate buffer ( pH 7 . 2 ) and treated with 1% osmium tetroxide at room temperature for 2 hours . After being rinsed with phosphate buffer again , the tissues were gradually dehydrated by series of increasing concentrations of ethanol . The tissues were then embedded with Spurr’s resin kit ( cat-14300; EMS ) overnight at room temperature . The embedded tissues were sectioned into 75-nm-thick sections by using an ultramicrotome ( EM UC7 , Leica Microsystems , Wetzlar , Germany ) and mounted on copper grids . Ultramicrographs were acquired using a transmission electron microscope ( JEM-1400; JEOL ) at 100 Kva . All results were obtained from experiments performed in triplicate ( at least ) and are presented as the mean ± SEM . Data were analyzed with a Student’s t test to determine the significance between two groups . Differences with a p value of <0 . 05 were considered statistically significant . | Cystic fibrosis ( CF ) is the most common inherited life-threatening disease in Caucasians . The most well-known cause of CF is a genetic defect in CFTR , an apical membrane chloride and bicarbonate channel . The symptoms of CF include defects in the respiratory , digestive , and male reproductive systems . Most male patients with CF are infertile due to congenital bilateral absence of the vas deferens ( CBAVD ) , which leads to obstructive azoospermia . Nevertheless , Taiwanese patients with CBAVD do not carry the common mutations of CFTR found in Caucasians . We have identified a potential candidate , SLC9A3 , of which a single copy is lost in Taiwanese patients with CBAVD . In addition to the previously reported role of SLC9A3 in the digestive system and efferent ductules , we now report that the SLC9A3 deficiency causes obstructive azoospermia and impairs the epithelial structure of the reproductive tract . Loss of SLC9A3 also leads to dramatic reduced expression of CFTR in the reproductive tract . We suggest that the interplay between SLC9A3 and CFTR is responsible for CF-related infertility . Thus , we have characterized a potential critical player in the pathogenesis of CBAVD and provide a new diagnostic candidate for Asian patients with CBAVD . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"urology",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"male",
"infertility",
"genetic",
"diseases",
"fibrosis",
"pulmonology",
"germ",
"cells",
"animal",
"models",
"physiological",
"processes",
"developmental",
"biology",
"model",
"organisms",
"cystic",
"fibrosis",
"experimental",
"organism",
"systems",
"epididymis",
"sperm",
"research",
"and",
"analysis",
"methods",
"autosomal",
"recessive",
"diseases",
"animal",
"cells",
"biological",
"tissue",
"mouse",
"models",
"infertility",
"clinical",
"genetics",
"anatomy",
"cell",
"biology",
"physiology",
"secretion",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"genetics",
"human",
"genetics",
"genital",
"anatomy"
] | 2017 | Loss of SLC9A3 decreases CFTR protein and causes obstructed azoospermia in mice |
Infection of host cells by pathogenic microbes triggers signal transduction pathways leading to a multitude of host cell responses including actin cytoskeletal re-arrangements and transcriptional programs . The diarrheagenic pathogens Enteropathogenic E . coli ( EPEC ) and the related Enterohemorrhagic E . coli ( EHEC ) subvert the host-cell actin cytoskeleton to form attaching and effacing lesions on the surface of intestinal epithelial cells by injecting effector proteins via a type III secretion system . Here we use a MAL translocation assay to establish the effect of bacterial pathogens on host cell signaling to transcription factor activation . MAL is a cofactor of Serum response factor ( SRF ) , a transcription factor with important roles in the regulation of the actin cytoskeleton . We show that EPEC induces nuclear accumulation of MAL-GFP . The translocated intimin receptor is essential for this process and phosphorylation of Tyrosine residues 454 and 474 is important . Using an expression screen we identify FLRT3 , C22orf28 and TESK1 as novel activators of SRF . Importantly we demonstrate that ABRA ( actin-binding Rho-activating protein , also known as STARS ) is necessary for EPEC-induced nuclear accumulation of MAL and the novel SRF activator FLRT3 , is a component of this pathway . We further demonstrate that ABRA is important for structural maintenance of EPEC pedestals . Our results uncover novel components in pathogen-activated cytoskeleton signalling to MAL activation .
Infection of host cells by pathogenic microbes triggers signal transduction pathways leading to a multitude of host cell responses including actin cytoskeletal re-arrangements and transcriptional programs . This is achieved via the delivery of virulence factors directly into target cells [1] . Often structurally divergent , these effector proteins mimic eukaryotic functions [2] and are usually delivered into the host-cell cytosol by needle-like , type III ( T3SS ) , type IV ( T4SS ) and type VI ( T6SS ) secretion systems [3] . These secretion systems are large multi-protein complexes that span the entire cell envelope . More than 25 species of Gram-negative bacteria have a Type III secretion system [4] . Many of the T3SS secreted bacterial virulence factors seem to fall into two general classes: 1 ) those that indirectly subvert actin dynamics by modulating the host-cell machinery involved in actin organization , or 2 ) those that directly bind actin [3] . Although the types of virulence factors introduced by various organisms differ , there is a shared theme of the subversion of nucleation promoting factors directly or indirectly via Rho , Rac or Cdc42 . Bacterial pathogens can manipulate a host-cell's cytoskeleton to attach , invade and/or move in the cell . A conserved strategy involves manipulating F-actin by modulating or mimicking G proteins in the host cell . Among transcription factors , Globular ( G ) -actin to Filamentous ( F ) -actin changes are sensed by serum response factor ( SRF ) . SRF is a widely expressed transcription factor that controls the expression of many immediate early , muscle-specific and cytoskeletal genes [5] , [6] . The activity of SRF is primarily controlled by its interaction with signal-regulated or tissue-specific regulatory cofactors . Two families of signal-regulated cofactors have been identified: the ternary complex factor ( TCF ) family , which are activated by mitogen activated protein ( MAP ) kinase phosphorylation [7] , and the myocardin-related transcription factors ( MRTFs ) . The MRTFs include Myocardin , MAL ( also known as MRTF-A , BSAC or MKL1 ) and MRTF-B ( also called MKL2 or MAL16 ) . Rho-family GTPases and monomeric actin regulate the activity of MAL and MRTF-B [8] , [9] . Rho family-mediated changes in actin dynamics are sensed by MAL , which contains G-actin-binding RPEL motifs at the N-terminus . Stimulation of Rho family-GTPases releases MAL from an inhibitory complex with G-actin and strongly activates SRF-regulated transcription [9] , [10] . When overexpressed in heterologous systems , a number of wild type proteins involved in RhoGTPase signalling to actin dynamics , including Cdc42 , Rac and VASP can activate SRF [8] , [11] . However , these results have not been explored in the context of a potential link between bacterial pathogenesis and SRF mediated transcriptional programs . Furthermore understanding actin biology in the context of pathogen triggers also offers insight into regulation of the actin machinery in the host cell . This has previously proven to be a very successful avenue , especially for the study of bacterial factors targeting host cell GTPases . Both cellular and microbiological approaches have brought great insight into the bacterial infection process and host physiology [12] , [13] . We developed a screen to identify both bacterial and host-cell factors important for pathogenesis . We use MAL-GFP translocation to establish the effect of bacterial pathogens on actin-mediated , host cell signalling to transcription factors and identify novel host cell factors involved in the maintenance of the EPEC pedestal . Here we report that EPEC infection induces nuclear accumulation of MAL-GFP and subsequent transcription of SRF target genes , in a manner dependent on pedestal formation . The translocated EPEC effector Tir is essential , as is phosphorylation of Tir by host cell kinases . We show that the host gene ABRA ( also known as STARS ) , is necessary for MAL translocation and that FLRT3 is a novel SRF activator that functions as a signalling intermediary between the pedestal and nucleus .
SRF activation through the co-factor MAL requires Rho-mediated actin signalling [8] . G-actin binds directly to MAL [9]; extracellular stimuli activate cellular GTPases ( Rho , Rac and CDC42 ) driving actin polymerization and altering the G-/F-actin ratio . This releases MAL , allowing it to accumulate in the nucleus , form a complex with SRF and drive transcription . In many cell types MAL is predominantly cytoplasmic and accumulates in the nucleus only upon stimulation to activate target genes [9] , [14]–[16] . Using MAL nuclear accumulation as a readout we developed a microscopy-based screen for SRF activation in epithelial cells ( Figure 1A ) . To test the effects of bacterial infection on the regulation of the actin cytoskeleton , we screened a panel of gastrointestinal tract-associated bacterial pathogens including Enteropathogenic E . coli ( EPEC ) , Adherent Invasive E . coli ( AIEC ) , Salmonella enterica serovar Typhimurium ( S . Typhimurium ) , and a non-pathogenic E . coli K12 for the ability to induce the nuclear accumulation of the SRF co-factor MAL . COS-7 cells were transfected with MAL-GFP . After 18 hours , the transfected cells were serum starved for a further 24 hours and then infected in DMEM containing 0 . 3% Foetal Calf Serum ( FCS ) with bacteria for a total of 5 hours . Following infection , cells were washed , fixed and stained for immunofluorescence . We found that EPEC , but not AIEC , K12 or S . Typhimurium could induce robust nuclear accumulation of MAL-GFP ( Figure 1A ) . The percentage of cells exhibiting nuclear localization and both cytoplasmic and nuclear ( C/N ) localization of MAL-GFP increased significantly from 9 . 19%±1 . 09% to 53 . 94%±4 . 21% , and from 13%±1 . 36% to 25 . 23±4 . 02% respectively , when compared to the uninfected 0 . 3% serum control ( Figure 1B ) . The nuclear localization induced by EPEC was less efficient than that of the 15% serum control ( Figure 1A and B ) . These data suggest that nuclear localisation of MAL is specific to EPEC infection and not merely a general response to host/pathogen interaction or actin-mediated invasion events . MAL ( MRTF-A ) is a well-described cofactor for Serum Response Factor ( SRF ) . We wanted to confirm that the EPEC-induced nuclear localization of MAL-GFP was actually associated with SRF . To test this we transfected COS-7 cells with siRNA targeting SRF or a non-targeting control siRNA ( Invitrogen ) , and determined the knockdown efficiency by quantitative RT-PCR ( Figure 2A ) . Infection of SRF-knockdown cells with EPEC resulted in a significant reduction in nuclear accumulation of MAL-GFP to 13 . 39%±1 . 61% compared to infection of wild type or non-targeting siRNA transfected COS-7 cells 45 . 38%±5 . 5% and 39 . 04%±5 . 39% respectively ( Figure 2B and C ) . It is likely that SRF knockdown affects cytoskeletal gene expression , which in turn affects MAL localization . To confirm that MAL functions as a coactivator of SRF during EPEC infection we measured the expression levels of known SRF target genes at 3 , 5 and 8 hours post infection ( Figure S1 ) . Of those tested Cdc42ep3 ( CDC42 effector protein ) , ARHGDIB ( Rho GDP dissociation inhibitor ( GDI ) beta ) , Acta2 ( Alpha actin 2 ) , Egr2 ( Early growth response 2 ) , IL-6 ( Interleukin 6 ) and Vav3 ( vav 1 guanine nucleotide exchange factor ) , were induced by EPEC infection but not by infection with EPEC Δtir ( Figures 3 and S1 ) . Fyn , Rsu1 and c-fos were not activated by EPEC infection during the timepoints measured . This data supports the hypothesis that nuclear MAL functions as an SRF cofactor during EPEC infection . Given the relationship between SRF and actin , and the actin cytoskeleton rearrangements induced by pedestal formation , we hypothesized that pedestal formation would be necessary to induce the observed nuclear accumulation of MAL-GFP . To test this hypothesis we infected COS-7 cells with EPEC Δtir , which are unable to build actin pedestals [17] . In COS-7 cells infected with EPEC Δtir , MAL-GFP remained predominantly cytosolic , with 77 . 5% of cells±1 . 75% , displaying no significant difference to the 0 . 3% FCS control 75 . 7%±1 . 46% ( Figure 4A and B ) . To confirm that this loss of phenotype was due solely to the lack of Tir , we infected COS-7 cells with EPEC Δtir rescued with a plasmid carrying Tir ( pTir [17] ) . COS-7 infected with EPEC Δtir/pTIR efficiently rescued the MAL-GFP nuclear accumulation phenotype , with 57 . 38%±1 . 73% of cells exhibiting nuclear localization of MAL-GFP ( Figure 4A and B ) . Similar to COS-7 cells infected with wild-type EPEC ( 60 . 08%±1 . 03% nuclear ) . Therefore the formation of the F-actin-rich pedestal is clearly necessary for EPEC induced MAL-GFP accumulation in the nucleus . This is supported by the fact that no SRF target genes were induced by infection with EPEC Δtir ( Figure 3 ) . Among all the secreted EPEC effector proteins , only Tir is involved in signalling host cells to generate actin pedestals [18] . Phosphorylated Tir Y474 binds the adapter protein Nck to recruit N-WASP [17] , while phosphorylated Y454 stimulates a lower efficiency Nck-independent pathway [19] . To determine if the activation of SRF by pedestal formation was dependent on a specific pathway , i . e . Nck dependent or independent , we infected MAL-GFP expressing COS-7 cells with EPEC Δtir strains rescued with pTIR Y474F , Y454F or Y474F/Y454F mutants and determined the percentage of cells displaying cytosolic or nuclear localization of MAL-GFP . Infecting cells with EPEC Δtir expressing either TirY454F or TirY474F significantly reduced the percentage of cells showing nuclear accumulation of MAL-GFP to 21 . 2%±0 . 6% ( p = 3 . 96*10−5 ) and 23 . 04%±6 . 8% ( p = 2 . 9*10−5 ) respectively , relative to those infected with wild-type EPEC ( 51 . 9%±2 . 9% , Figure 4C and D ) . The double mutant decreased the percentage of cells with nuclear MAL-GFP further , to a level not significantly different from the EPEC Δtir control , 14 . 86%±1 . 26% and 11 . 39%±1 . 7% respectively , which suggests that stimulation of actin assembly by Tir is crucial for MAL-GFP nuclear accumulation in response to EPEC infection . To further understand the Tir requirements for EPEC induced MAL-GFP translocation we expressed a plasma membrane-targeted construct containing the intimin-binding extracellular loop and the COOH-terminal cytoplasmic domain of Tir ( TirMC ) , or a similar plasma membrane-targeted Tir construct in which Tyr474 had been mutated to phenylalanine ( TirMC ( Y474F ) ) [18] . These constructs were clustered at the plasma membrane by infecting cells with EPEC Δtir , which cannot induce MAL-GFP translocation . Clustering the COOH terminus of Tir beneath the plasma membrane is sufficient to drive actin pedestal formation [18] . TirMC or TirMC ( Y474F ) expressing cells were identified by anti-HA fluorescence . 68 . 2%±6 . 17% of cells expressing TirMC displayed a nuclear localization of MAL-GFP after 5 hours of infection with EPEC Δtir ( Figure S2 ) . In contrast , nuclear localization of MAL-GFP was significantly reduced to 27 . 9%±4 . 03% ( p = 0 . 000225 ) in cells expressing TirMC ( Y474F ) following infection with EPEC Δtir ( Figure S2 ) . These results suggest that the pathway of activation is unimportant , but rather the act of building and maintaining the pedestal is necessary to activate SRF . To test the hypothesis that EPEC-induced nuclear accumulation of MAL is driven by infection-driven changes in G∶F-actin ratios within the host cell , we quantified the G- and F-actin in EPEC infected cells relative to uninfected cells . Cells were extracted with a Triton X-100 lysis buffer ( see materials and methods ) and separated into 100 , 000-g supernatant and pellet fractions . Under these conditions G-actin is found in the supernatant and F-actin in the pellet . As shown in figure 4E , at timepoints early in the infection , consistent with the kinetics of pedestal formation in tissue culture cells , we could detect an average 2 . 3-fold increase in F-actin in EPEC infected cells ( Figure 4E ) . Together these data demonstrate that pedestal formation can alter G∶F-actin ratios in infected cells and that pedestal formation is necessary for accumulation of MAL-GFP in the nucleus . EPEC and EHEC induce attaching and effacing lesions by different signalling mechanisms . Whereas EPEC Tir is the only translocated EPEC effector required to trigger pedestal formation , EHEC translocates two effectors , Tir ( EHEC ) and EspFu ( also known as Tccp ) to generate pedestals in an Nck-independent manner [18] , [20] . We reasoned that if the act of building pedestals was enough to activate SRF , then TirEHEC+EspFU would be commensurable to TirEPEC in inducing MAL-GFP nuclear accumulation . As such , we tested to see if TirEHEC could rescue the EPEC Δtir phenotype . COS-7 cells expressing MAL-GFP were infected with EPEC Δtir exogenously expressing TirEHEC ( KC12 ) or TirEHEC+EspFU ( KC12/pEspFU ) [21] . Post-infection , the cells were fixed , stained and MAL-GFP localization determined by fluorescence microscopy . Under these conditions 39 . 9%±4 . 56% of cells infected with KC12/pEspFU exhibited a nuclear localization of MAL-GFP compared to 26 . 9±2 . 06% of cells infected with KC12 ( Figure 5A and B ) . The nuclear localization of MAL-GFP induced by KC12 was significantly reduced compared to cells infected with wild-type EPEC ( 48 . 4%±4 . 6% ) . Recent studies have demonstrated that the I-BAR family protein insulin receptor tyrosine kinase substrate ( IRTKS ) is central to EHEC pedestal formation , forming a ternary complex with TirEHEC , pEspFU and N-WASP necessary for pedestal formation [22] , [23] . We tested whether IRTKS was necessary for MAL-GFP translocation in the TirEHEC rescue system . We first confirmed the ability of three siRNAs to knockdown IRTKS in COS-7 cells . siRNA B reproducibly gave the best knockdown ( Figure 5C ) . We tested the ability of EPEC KC12/pEspFU to induce nuclear accumulation of MAL-GFP in the IRTKS knockdown cells . Knockdown of IRTKS significantly reduced the ability of KC12/pEspFU to induce nuclear accumulation of MAL-GFP from 30 . 5% of cells in the control to 15 . 9%±1 . 79% in knockdown cells ( Figure 5D ) . These data are consistent with significant actin-rearrangement induced by pedestal formation , being central to the nuclear accumulation of MAL-GFP . KC12 are inefficient builders of actin pedestals [21] and , under these conditions , cause very little nuclear translocation of MAL-GFP . However , the rescue expressing TirEHEC and EspFu , the two EHEC effectors required for robust pedestal formation , induces nuclear accumulation of MAL-GFP comparable to wild type EPEC . Secondly IRTKS has been shown to be necessary for efficient pedestal formation by EHEC [22] , [23] . In the TirEHEC rescue system used here , knockdown of IRTKS led to reduced pedestal formation and a subsequent lack of MAL-GFP nuclear accumulation and activation of SRF . To identify host factors involved in the nuclear translocation of MAL-GFP we tested the ability of a number of known or putative actin binding proteins to induce nuclear translocation of MAL-GFP . Candidate expression plasmids were cotransfected with MAL-GFP into COS-7 cells and the cellular localization of MAL-GFP was determined by fluorescence microscopy . We defined the minimum cut-off point for activation as a 2-fold increase over the vector only control . Both ABRA and SRF were able to significantly induce the nuclear accumulation of MAL-GFP ( Figures 6a and S3 ) . 79 . 9%±6 . 04% of cells overexpressing ABRA exhibited nuclear localization of MAL-GFP and 87 . 05%±1 . 14% of cells overexpressing SRF displayed nuclear localization of MAL-GFP , an 8-fold increase over the vector only control . In addition we , identified three novel genes that could induce nuclear accumulation of MAL-GFP by overexpression . These genes are FLRT3 ( 32 . 2% nuclear ) , TESK1 ( 54 . 3% nuclear ) and C22orf 28 ( 35 . 59% nuclear , Figures 6A and S3 ) . ABRA is an actin binding protein that can induce nuclear accumulation of MAL-GFP and activate SRF [24] . C22orf28 ( also known as HSPC117 or FAAP in mice ) is a cell adhesion protein with Ankyrin repeats , that interacts with vinculin and talin [25] . TESK1 ( testis-specific kinase 1 ) is a LIM kinase-related serine/threonine kinase that has been shown to influence actin organization via its ability to phosphorylate cofillin [26] . FLRT3 ( Fibronectin leucine rich transmembrane protein 3 ) is a putative type I transmembrane protein containing 10 leucine-rich repeats , a fibronectin type III domain , and an intracellular tail . It has been implicated in neurite outgrowth [27] and cell adhesion [28] and has a predicted SRF binding site in its promoter . Furthermore we have recently demonstrated that Flrt3 is induced by bacterial infection [29] . To assess the significance of the overexpression screen hits for EPEC induced activation of SRF , we determined the ability of EPEC to induce MAL-GFP translocation in the absence of each protein individually . Knockdown efficiency was first established for the three candidate genes FLRT3 , TESK1 and C22orf28 ( Figure 6B ) . While C22orf28 and TESK1 had no effect , knockdown of ABRA or FLRT3 significantly reduced MAL-GFP nuclear translocation induced by EPEC to 20 . 6%±3 . 2% and 16 . 9±11 . 16% respectively ( Figure 6C ) . This indicates that ABRA and FLRT3 are both required for EPEC-induced translocation of MAL-GFP to the nucleus . Furthermore we confirmed the ability of ABRA and FLRT3 to activate an SRE-luciferase reporter in the absence of serum ( Figure 6D ) . Both epitope tagged and untagged constructs of ABRA and FLRT could induce transcriptional activity of a luciferase gene under the control of the serum response element . Together these results demonstrate that ABRA and FLRT3 are components of the pathway involved in EPEC induced signaling to SRF . As both ABRA and FLRT3 can induce nuclear accumulation of MAL-GFP and are required for EPEC-induced nuclear accumulation of MAL-GFP ( Figures 6 ) , we sought to undertake an epistasis analysis of ABRA and FLRT3 . We tested to see if FLRT3 knockdown would inhibit ABRA-induced translocation of MAL-GFP . We found that ABRA-induced nuclear accumulation of MAL-GFP was significantly reduced to 45 . 4%±7 . 6% of cells in the FLRT3 knockdown cells compared to 75 . 7%±3 . 3% in the wild type control ( Figure 6E and S2C ) . In the reciprocal experiment , ABRA knockdown had no effect on FLRT3 induced nuclear accumulation of MAL-GFP ( Figure S4 ) . These findings are consistent with FLRT3 functioning downstream of ABRA but upstream of MAL . Surprisingly FLRT3 siRNA reduces ABRA-induced MAL nuclear localization with or without serum induction under these conditions , whereas FLRT3 siRNA alone has no effect on serum induced MAL nuclear localization ( Figure S2C ) . It therefore appears that the combination of ABRA overexpression and FLRT3 knockdown can block serum induction of MAL . We next sought to establish the cellular localization of each of the candidate proteins during EPEC infection to determine their involvement in pedestal formation ( Figure 7 and S5 ) . ABRA colocalized with F-actin and was enriched in the EPEC pedestal ( Figure 7A , arrowheads ) , while SRF was always localized to the nucleus ( Figure S3 ) . FLRT3 localized to the plasma membrane and was enriched at pedestal sites ( Figure 7A ) . With ABRA localizing to the EPEC pedestal and knockdown inhibiting MAL-GFP nuclear accumulation , we questioned whether loss of ABRA would also affect EPEC pedestal morphology . Although pedestals associated with single bacteria appeared normal , pedestals associated with micro colonies of EPEC appeared unstructured ( Figure 7B and S6 ) , taking on an appearance akin to a ruffle . This suggests that maintenance of discreet pedestals is lost in the ABRA knockdown , with single pedestals merging into one large structure . We therefore suggest that ABRA is necessary for proper pedestal formation , which in turn , is necessary for SRF activation .
Recent studies have suggested a connection between pathogen mediated actin re-organization and serum response factor ( SRF ) transcriptional programs [11] . We screened a panel of gastrointestinal tract-associated pathogens for the ability to induce nuclear accumulation of MAL-GFP . Surprisingly only EPEC caused a significant change in MAL-GFP localization under the infection conditions tested . We suspected S . Typhimurium would have some effect on MAL-GFP localization . It has been shown that S . Typhimurium induces actin ruffles during entry and activates host cell Rho-GTPases [30] , [31] . However , unlike EPEC infection , Salmonellae rapidly return the host cell cytoskeleton to its resting state following engulfment , via the action of the effector protein SptP [32] . Perhaps this down-modulation of actin polymerization by S . Typhimurium is sufficient to stifle the activation of SRF , whereas the prolonged actin remodelling induced by EPEC infection is not . We confirmed that MAL translocation correlates with upregulation of SRF target genes during EPEC infection ( Figure 3 and S1 ) . EPEC infection selectively activates SRF target genes , most significantly EGR2 and IL-6 , but also CDC42EP3 , ARHGDIB , ACTA2 and VAV3 , relative to uninfected controls . None of these genes were activated by EPEC Δtir infection . CDC42EP3 , ARHGDIB , and VAV3 are all involved in Rho , Rac or Cdc42-mediated signalling and are consistent with the Rho dependent pathway of MAL translocation and SRF activation [7] . Whether upregulation of these genes is required for pedestal formation or pathogen survival , or is a natural consequence of pedestal formation is unclear at this time , but warrants further study . EGR2 is an immediate-early , zinc finger transcription factor with two serum response elements in its 5′ flanking sequence [33] . EGR2 can be activated by a number of infectious agents including viruses ( Human T-cell Leukemia virus type 1 ) , bacteria , and parasites ( Toxoplasma gondii ) [34]–[36] . Interestingly in T . gondii infection EGR2 induction was dependent on rhoptry secretion , a process analogous to secretion of proteins into a host cell by the bacterial type III secretion system [36] . Likewise , we find the secreted protein Tir to be essential for EPEC-induced activation of EGR2 . In other infections EGR2 expression is often accompanied by EGR1 and c-FOS . Under our experimental conditions the expression of EGR1 and c-FOS was not induced . This may suggest that this is an EPEC-specific response rather than a general innate pathogen response . It is clear that host signalling pathways are activated in response to many infectious agents , suggesting they are functioning in innate immunity . Although IL-6 is a well-known SRF target [10] , [37] its expression can be induced by a number of bacteria [38] , it is possible therefore , that IL-6 may function as an innate sentinel in this context . The fact that none of these genes were induced by infection with EPEC Δtir demonstrates that pedestal formation is fundamental to this signalling cascade . Tir is an essential effector for the assembly of F-actin pedestals . Following secretion , Tir inserts into the host cell membrane , presenting an extracellular domain that binds the bacterial surface protein intimin [39] . The C-terminal region of TirEHEC is phosphorylated at Tyr474 by host-cell kinases [40] in a manner similar to host receptor phosphorylation [41] , [42] . Phosphorylated Y474 and its flanking residues bind Nck via its SH2 domain [17] , [43] . Nck subsequently recruits and activates N-WASP stimulating ARP2/3 driven F-actin assembly . In addition , TirEPEC can promote weak actin polymerization in an Nck-independent manner via phosphorylation of Tir residue Y454 [19] . In this report we show that Tir is essential for EPEC-induced MAL-GFP nuclear accumulation and subsequent transcriptional activation of selective SRF target genes . Infection of epithelial cells with EPEC Δtir does not induce MAL-GFP nuclear accumulation , but this phenotype is rescued by the exogenous expression of Tir ( Figure 4A ) . This is consistent with actin rearrangement driven by pedestal formation being key for SRF activation rather than a translocated effector activating SRF directly . In further support of this idea TirEHEC+pEspFU could also rescue the EPEC Δtir phenotype ( Figure 5 ) . TirEHEC is functionally divergent from TirEPEC [17] , [44] , [45] . TirEHEC lacks a residue equivalent to Tyr474 [40] , is not tyrosine phosphorylated in cells [46] and does not bind Nck [43] . To efficiently form actin pedestals EHEC requires a second translocated effector EspFU ( TccP ) [20] , [21] . EspFU is recruited indirectly to Tir by IRTKS [22] , [23] , where it can than activate N-WASP which results in actin polymerization . Although the initial signalling methods used to recruit and activate host cell nucleation factors between the related pathogens are different , the net result is the same . Likewise , single mutations of either TirEPEC Y454 or Y474 to non-phosphorylatable phenylalanines drastically reduced the nuclear accumulation of MAL-GFP to similar levels ( Figures 4C and S2 ) , suggesting that Nck dependent or independent activation of N-WASP is irrelevant to EPEC-induced MAL-GFP nuclear accumulation . In addition , knockdown of SRF reduced EPEC-induced MAL-GFP accumulation in the nucleus to near uninfected levels ( Figure 2 ) . This is likely the result of altered cytoskeletal gene expression , resulting from the loss of SRF . In order to identify the host signaling cascades that are co-opted by bacterial virulence factors to regulate the cytoskeleton , we sought a scheme to identify genes generally employed in mammalian cytoskeleton control . We picked known and putative actin-associated or regulatory genes and tested their ability to induce nuclear accumulation of MAL-GFP . Novel genes inducing MAL-GFP nuclear accumulation with probable involvement in actin-cytoskeletal rearrangement were then evaluated for involvement in host-pathogen interactions . We identified FLRT3 , TESK1 and C22orf28 as novel inducers of MAL nuclear accumulation and confirmed the involvement of FLRT3 in EPEC induced MAL translocation by siRNA ( Figure 6 ) . Overexpression of ABRA induced nuclear accumulation of MAL-GFP consistent with published data for the Murine homologue STARS [14] , [24] . Knockdown of ABRA significantly decreased EPEC induced accumulation of MAL-GFP in the nucleus , suggesting that ABRA is a necessary component in the signaling pathway . In addition we found ABRA was enriched in EPEC pedestals and that ABRA knockdown adversely affected pedestal morphology . STARS has been shown to activate SRF and stabilize the F-actin cytoskeleton in a RhoA dependent manner , with the carboxy terminal being sufficient and necessary to activate SRF and bind actin [24] . The pedestal phenotype observed in ABRA knockdown cells is consistent with ABRA stabilizing the F-actin cytoskeleton in this context ( Figure 7B ) . Loss of this stabilization function in microcolonies leads to the dissolution of discreet pedestals and results in a structure more similar to a ruffle . Under our experimental conditions overexpression of SRF also resulted in the nuclear accumulation of MAL-GFP . The specific reason for this is currently unclear . Currently the prevailing hypothesis states that MAL continually shuttles between the cytoplasm and the nucleus . Perhaps MAL has a higher binding affinity for SRF than G-actin , and upon entering the nucleus , preferentially complexes with SRF and is retained in the nucleus . Transcription of SRF is controlled by SRF its self [47] , the overexpression of SRF may be interpreted by the cell as activation of the pathway , leading to an upregulation of SRF target genes and subsequent decrease in G-actin . These are just two potential hypotheses that may not be mutually exclusive , but warrant further study . Of the proteins identified in this study ABRA and FLRT3 localized to the EPEC pedestal ( Figure 7 ) , and were both necessary for EPEC-induced translocation of MAL-GFP ( Figure 6 ) . Epistasis analysis showed that knockdown of FLRT3 could significantly reduce ABRA-induced nuclear accumulation of MAL-GFP , but ABRA knockdown had no effect on FLRT3-induced nuclear accumulation of MAL-GFP ( Figure 6E and S4 ) . This places FLRT3 downstream of ABRA and identifies it as an intermediary protein from pedestal to nucleus . Based on this data we hypothesize a new model ( Figure 8 ) , where EPEC-induced remodeling of the actin cytoskeleton , via Tir , activates SRF in an ABRA and FLRT3 dependent manner . Our findings therefore reveal a novel mechanism for pathogen-induced activation of a host transcription factor . They shed light on the relationship between ABRA and SRF and identify FLRT3 as a new component of this signalling pathway .
COS-7 cells were obtained from ATCC and routinely cultured in DMEM supplemented with 10% iron supplemented foetal calf serum ( Hyclone , USA ) and 40 µg/ml gentamycin sulphate . EPEC strains carrying tir deletions and complementation plasmids have been described previously [17] . S . Typhimurium SL1344 DsRed2 was given by Dr . H . C . Reinecker . MAL was amplified from a mouse cDNA template by PCR using a forward primer introducing a XhoI site: 5′ CTCGAGATGCCGCCTTTGAAAAGCCCC 3′; and a reverse primer introducing a SacII site: 5′ CCGCGGCAAGCAGGAATCCCAGTGGAG 3′ . The resulting product was ligated into pEGFP-N1 ( Clontech ) . To generate constructs for mammalian expression of ABRA , SRF , FLRT3 , TESK1and C22orf28 the coding sequences were amplified from cDNA clones in pCMV-SPORT6 obtained from Open Biosystems . Coding sequences were amplified using the primers in Table 1 . After digestion with the appropriate restriction enzymes , the coding sequence was subcloned into N-terminally tagged pCMV-3xFlag or -3xMyc vectors derived from the pCMV-Myc vector ( Clontech , catalog no . 631604 ) . COS-7 cells were plated onto 18 mm glass coverslips in 12-well plates at a density of 4×104 cells per well . After 24 h cells were transfected in antibiotic-free medium with MAL-GFP plus additional myc- or Flag- tagged constructs ( in a modified pCMV vector , Clontech , USA ) , where noted , at a 1∶1 ratio , using GeneJuice ( Novagen , UK ) , according to the manufacturers instructions . 18 h post-transfection cells were washed twice in PBS and incubated in DMEM 0 . 3% FCS for a further 18 h , prior to bacterial infections . COS-7 cells were plated onto 18 mm glass coverslips in 12-well plates at a density of 4×104 cells per well . After 24 h , 20 pmol of modified RNA oligoduplexes ( Stealth RNAi; Invitrogen ) , were transfected into each well using X-tremeGENE ( Roche ) , according to the manufacturers instructions . siRNA silencing sequences are shown in Table 2 . Cells were serum starved 48 h post-transfection as described above and infected , fixed and stained as described below . RNA extraction was performed by using an RNeasy kit ( Qiagen ) in accordance with the manufacturer's instructions . 500 ng of total RNA was reverse-transcribed using an iScript cDNA synthesis kit ( Bio-Rad ) . The gene expression reported is representative of three independent experiments . Real-time quantitative PCR was performed in triplicate in a Bio-Rad iCycler thermal cycler equipped with an iQ5 optical module using the iQ SYBR Green Super Mix ( Bio-Rad ) . In brief , 100 ng of reverse-transcribed cDNA was used for each PCR with forward and reverse primers at 250 nM . The thermal cycling conditions were 4 min at 95°C , followed by 40 cycles at 94°C for 15 s and 59°C for 1 min . Values were normalized to that of GAPDH . All PCR products were analyzed on a 2% agarose gel to verify the correct size of the amplicons . RT-PCR primer sequences are shown in Table 3 . For EPEC , AIEC and E . coli K12 infections were performed as previously described [21] with slight modifications to normalise infection conditions between the different strains . Briefly , colonies were seeded from fresh agar plates into 3 mls of LB broth with relevant antibiotics and grown with agitation at 37°C overnight . Cultures were then diluted 1∶1000 in DMEM containing 0 . 3% foetal calf serum and 1 ml added to each well of a 12-well plate . Plates were incubated at 37°C , 5% C02 for 5 hours . S . Typhimurium infections were performed as described [48] , with slight modifications to extend the infection time to the same duration as the EPEC infections . Briefly , SL1344 colonies from fresh agar plates were grown in LB broth plus 100 µg/ml ampicillin with agitation at 37°C overnight . Cultures were diluted 1∶33 and grown for a further 4 hours . Infections were performed using 1∶1000 dilutions of these sub-cultures , yielding a multiplicity of infection of 1∶10 . Infections were allowed to proceed for 30–40 minutes at 37°C , 5% C02 , then washed twice in DMEM+100 µg/ml Gentamycin to remove external bacteria and incubated for a further 4 . 5 hours at 37°C , 5% C02 . Following infection , transfected cells were washed in PBS and fixed in 4% formaldehyde solution in PBS for 15 min . Cells were then permeabilised in 0 . 1% Triton-X 100 in PBS for 2 min , blocked with 10% donkey serum for 15 min and stained using appropriate antibodies for 1 h . Primary antibodies used were anti-Flag ( Sigma Aldrich ) , anti-HA ( Covance , USA ) and anti-myc 9E10 ( Covance , USA ) . The secondary antibody was Alexa488 or Alexa568-conjugated donkey anti-mouse ( Jackson Immunoresearch ) . Actin was stained with Alexa568 or Alexa488-conjugated phalloidin ( Invitrogen ) , DNA was labelled with DAPI ( Invitrogen ) . Following staining coverslips were washed three times in PBS and mounted in ProLong Gold antifade reagent ( Invitrogen ) . Cells were imaged using a Leica SP5 confocal microscope or a Ziess Axioplan widefield microscope . Expression constructs in pCMV-SPORT6 were obtained from Open Biosystems . COS-7 cells were transfected with 250 ng of MAL-GFP and 250 ng of expression construct or an empty vector control . Eighteen hours post transfection the cells were washed twice with PBS and incubated in DMEM 0 . 3% FCS for a further 24 hours . Cells were washed in PBS and fixed in 4% formaldehyde solution in PBS for 15 min , and co-stained with DAPI ( Invitrogen ) . Cellular localization of MAL-GFP was determined by epifluorescence microscopy . Data are the means ± standard deviation of 3 experiments . A minimum of 150 transfected cells were counted for each condition of each experiment . COS-7 cells were transfected with 50 ng of SRE-luciferase reporter plasmid [8] , 1 ng of renilla luciferase ( Promega ) and either 500 ng of Flag-ABRA/untagged pCMV-ABRA or 150 ng FLRT3-Flag/pCMV-FLRT3 . Controls were transfected with the appropriate empty vector . 8 hours post transfection cells were washed twice in PBS and resuspended in DMEM containing 0 . 3% FCS . 18 hours later cells were lysed in passive lysis buffer ( Promega ) and luciferase activities were measured with a Glomax 20/20 luminometer ( Promega ) . G-∶F-actin ratios were quantified using a G-actin/F-actin In vivo assay kit ( Cytoskeleton ) , in accordance with the manufacturers guidelines . Cells were infected as described above . 3 . 5 hours post infection samples were washed once in PBS , scraped and lysed with a bent 21 gauge needle in LAS2 lysis buffer . F-actin was then separated from G-actin by centrifugation at 100 , 000×g for 60 min at 37°C . The F-actin-containing pellet was resuspended in LAS2 buffer containing 2 µM cytochalasin D at a volume equivalent to the G-actin-containing supernatant volume . The resuspended F-actin pellet was kept on ice for 60 min with mixing by pipette every 15 min to dissociate F-actin . The F-actin and G-actin preparations were then assayed for protein . Equal amounts of protein were separated by 10% SDS-PAGE and detected by blotting with anti-actin . Band intensities were quantified with Odyssey application software ( LI-COR ) . The following are the Entrez IDs ( http://www . ncbi . nlm . nih . gov/ ) for the genes discussed in this article . | Many significant immune diseases are caused by bacterial pathogens that deliver effector proteins into their host . The pathogen uses these proteins to subvert the hosts' normal cytosolic defense in a way that services the pathogen . It is therefore important to understand the normal processes of a cell and how they are affected by bacterial infection . We have established the effect of bacteria on host cell signalling to the transcription factor serum response factor . Serum response factor is a widely expressed transcription factor that controls the expression of many important genes . We show that Enteropathogenic E . coli infection can activate serum response factor and that the effector protein Tir is essential for this activation . Furthermore , we identify new genes that are important in this infection-induced activation and show that they are important in maintaining structures necessary for Enteropathogenic E . coli infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"cell",
"biology",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"molecular",
"biology/transcription",
"initiation",
"and",
"activation",
"cell",
"biology/cytoskeleton"
] | 2011 | Bacterial and Host Determinants of MAL Activation upon EPEC Infection: The Roles of Tir, ABRA, and FLRT3 |
The FoxA family of pioneer transcription factors regulates hepatitis B virus ( HBV ) transcription , and hence viral replication . Hepatocyte-specific FoxA-deficiency in the HBV transgenic mouse model of chronic infection prevents the transcription of the viral DNA genome as a result of the failure of the developmentally controlled conversion of 5-methylcytosine residues to cytosine during postnatal hepatic maturation . These observations suggest that pioneer transcription factors such as FoxA , which mark genes for expression at subsequent developmental steps in the cellular differentiation program , mediate their effects by reversing the DNA methylation status of their target genes to permit their ensuing expression when the appropriate tissue-specific transcription factor combinations arise during development . Furthermore , as the FoxA-deficient HBV transgenic mice are viable , the specific developmental timing , abundance and isoform type of pioneer factor expression must permit all essential liver gene expression to occur at a level sufficient to support adequate liver function . This implies that pioneer transcription factors can recognize and mark their target genes in distinct developmental manners dependent upon , at least in part , the concentration and affinity of FoxA for its binding sites within enhancer and promoter regulatory sequence elements . This selective marking of cellular genes for expression by the FoxA pioneer factor compared to HBV may offer the opportunity for the specific silencing of HBV gene expression and hence the resolution of chronic HBV infections which are responsible for approximately one million deaths worldwide annually due to liver cirrhosis and hepatocellular carcinoma .
Hepatitis B virus ( HBV ) is a small enveloped DNA virus that infects the hepatocytes of human and ape liver [1] . Infection is generally considered to be restricted to hepatocytes by the viral receptor ( s ) which appears to include the sodium taurocholate cotransporting polypeptide ( NTCP ) [2] . Upon infection , the viral capsid transports the HBV 3 . 2kbp partially double-stranded DNA genome to the nucleus where it is converted into covalently closed circular ( CCC ) DNA [1 , 3] . HBV CCC DNA appears to be highly stable and is resistant to current antiviral therapies used for the treatment of chronic viral infections [4–8] . It serves as the template for the transcription of the HBV 3 . 5kb , 2 . 4kb , 2 . 1kb and 0 . 7kb transcripts which are translated into the viral gene products , hepatitis B e antigen ( HBeAg ) , core antigen ( HBcAg ) , reverse transcriptase/DNA polymerase , surface antigen ( HBsAg ) and X-gene product ( HBxAg ) [1] . The viral polymerase binds to the HBV 3 . 5kb pregenomic RNA and this ribonucleoprotein particle is subsequently encapsidated by the core antigen polypeptide [9 , 10] . Inside this immature core particle , the viral pregenomic RNA is reverse transcribed into genomic DNA generating a mature core particle [11 , 12] . These mature particles can cycle genomic DNA back to the nucleus to increase the pool of nuclear HBV CCC DNA or bind surface antigen within the endoplasmic reticulum ( ER ) membrane and bud into the lumen of the ER [13] . Virus particles are subsequently transported through the Golgi apparatus and secreted from the cell into the circulation by vesicle trafficking [1 , 14 , 15] . Viral tropism is also determined by the liver-specific expression of the HBV genomic DNA [16–18] . At birth in HBV transgenic mice , HBV DNA is not transcribed and hence replication is absent from neonatal hepatocytes [19] . As the hepatocytes complete their postnatal differentiation program , liver-enriched transcription factor abundance increases [20] activating HBV transcription and replication which reach their maximal levels around the time of weaning [19 , 21] . A number of liver-enriched transcription factors including the nuclear receptors , hepatocyte nuclear factor 4 ( HNF4 ) , retinoid X receptor ( RXR ) , peroxisome proliferator-activated receptor ( PPAR ) , farnesoid X receptor ( FXR ) , liver receptor homolog 1 ( LRH1 ) , estrogen related receptor ( ERR ) , the homeobox factor , hepatocyte nuclear factor 1 ( HNF1 ) , the basic-leucine zipper factors , CCAAT-enhancer-binding proteins ( C/EBP ) and the winged-helix transcription factors , forkhead box protein A/hepatocyte nuclear factor 3 ( FoxA/HNF3 ) bind to the HBV enhancer and promoter regulatory sequence elements [17 , 22 , 23] . The nuclear receptors governing HBV transcription have been shown to be a major determinant of viral tropism [16 , 24] . In contrast , the importance of the other liver-enriched transcription factors in the regulation of HBV transcription and replication is unclear [16] . Interestingly , the FoxA proteins are pioneer transcription factors which bind to and mark cellular genes for expression at later stages during development [25–28] . In particular , FoxA has been shown to bind and mark the albumin enhancer and α-fetoprotein promoter for subsequent gene expression during liver development and definitive endoderm differentiation , respectively [25 , 29 , 30] . As HBV transcription is developmentally restricted with detectable viral biosynthesis occurring shortly after birth in the neonatal liver [19 , 21] , it was of interest to determine the importance of FoxA in this process and whether its role as a pioneer transcription factor contributed to HBV RNA synthesis during hepatocyte maturation . Pioneer transcription factors bind to their target genes at an early stage in tissue development and modulate chromatin structure permitting gene expression at a later developmental stage [25 , 26 , 29] . Subsequent binding of additional transcription factors to these accessible enhancer and promoter regulatory sequence elements leads to the recruitment of coactivators , the mediator complex , the general transcription factors and RNA polymerase II generating a functional preinitiation complex which directs transcription initiation and gene expression [29 , 31 , 32] . In tissues that do not express the required pioneer factor , the target genes for these factors are not marked early in development and may never be expressed in these tissues presumably due to a generally repressive chromatin environment . The mechanism ( s ) of tissue specific silencing of pioneer factor target genes in the tissues that do not express these factors is poorly defined . However , it appears that DNA methylation of cytosine residues to 5-methylcytosine may contribute to this process as the regulatory regions of these genes are often hypomethylated in cells and tissues where they are or may be expressed and hypermethylated in tissues where these genes are silent [33–38] . Direct evidence for a link between pioneer factor control of tissue-specific developmental gene expression and regulatory region methylation status is limited [36 , 37] as deletion of pioneer factors such as FoxA leads to the loss of the gene expression profile essential for normal tissue development and hence is generally lethal [26] . In this study , the effect of FoxA-deficiency on the liver-specific expression of the viral genome was investigated in the HBV transgenic mouse model of chronic infection [21] . FoxA-deficiency resulted in the loss of HBV transcription and replication , essentially leading to a “cure” of the chronic viral infection . The deficiency in FoxA was associated with the methylation of the HBV genomic DNA indicating directly that the developmental expression of the FoxA pioneer factors is essential to prevent the epigenetic silencing of the viral genome . Developmental studies demonstrated that HBV genomic DNA was fully methylated at birth when viral transcription is absent . This indicates that FoxA mediates , directly or indirectly , the postnatal demethylation of the viral DNA leading to its subsequent expression during the process of hepatocyte maturation . Additionally , the residual FoxA3 synthesis in these mice was sufficient to mark and support the expression of the set of hepatocyte-specific genes which are dependent upon this pioneer factor such that liver maturation occurred and viable animals were produced . This indicates that there are different developmental expression requirements for appropriately marking essential hepatocyte-specific cellular genes compared to HBV genomic DNA with this specific pioneer factor which ultimately can influence their subsequent expression during cellular differentiation . This may be a general mechanism of action of pioneer factors and suggests the concentration of the pioneer factor ( s ) combined with the affinity and number of binding sites in enhancer and promoter sequences may determine the stage of development and differentiation when tissue-specific genes are marked for subsequent expression . Failure to correctly mark genes for expression by the pioneer factor ( s ) may ultimately result in their silencing by DNA methylation .
HBVFoxA2fl/flAlbCre ( + ) , HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) and HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) transgenic mice were viable and displayed no overt phenotype [47] . In contrast , neonatal and adult HBVFoxA1fl/flFoxA2fl/flFoxA3-/-AlbCre ( + ) transgenic mice were not observed . The levels of the FoxA RNAs in the livers of the wildtype HBV transgenic mice ( HBVFoxA2fl/flAlbCre ( - ) , HBVFoxA1fl/flFoxA2fl/flAlbCre ( - ) and HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) ) were compared with the levels of the FoxA RNAs in the FoxA-deleted HBV transgenic mice ( HBVFoxA2fl/flAlbCre ( + ) , HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) and HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) ( Fig 1A–1C ) . FoxA1 RNA was reduced by 300–1700 fold in the HBV transgenic mice with any FoxA1fl/flAlbCre ( + ) genotype ( Fig 1A ) . FoxA2 RNA was reduced by more than 200-fold in the HBV transgenic mice with any FoxA2fl/flAlbCre ( + ) genotype ( Fig 1B ) . FoxA3 RNA was increased by approximately 2-fold in all the HBV transgenic mice with any FoxA2fl/flAlbCre ( + ) genotype indicating that the loss of FoxA2 expression resulted in a modest increase in FoxA3 expression ( Fig 1B and 1C ) presumably reflecting the coordinated transcriptional regulation of the FoxA factor abundances during development . In contrast , FoxO1 RNA levels although somewhat variable appeared to be relatively insensitive to changes in FoxA factor abundance ( Fig 1D ) . As note previously for the FoxA1fl/flFoxA2fl/flAlbCre ( + ) mice [47] , none of the mutant mice in this study showed significant changes in blood biochemistry , including levels of alkaline phosphatase , alanine aminotransferase ( ALT ) , aspartate aminotransferase ( AST ) , bilirubin , blood urea nitrogen , creatine , glucose , triglyceride , albumin ( Alb ) , total protein ( TP ) , globulin ( TP-Alb ) , Alb:globulin ratio , and triacylglycerol , with the exception of cholesterol which was approximately 2-fold lower in the HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) , 114±20 mg/dl; HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , 60±7 mg/dl , p = 0 . 01 , n = 3 ) , compared with the corresponding controls . In contrast to the FoxA1fl/flFoxA2fl/flAlbCre ( + ) mice [47] , the HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mice displayed hepatomegaly ( Fig 1E ) . The livers of these mice were approximately 1 . 7–1 . 9 fold larger than their AlbCre negative wildtype controls . The liver-specific FoxA2-null and FoxA1:FoxA2-null HBV transgenic mice ( FoxA2fl/flAlbCre ( + ) and FoxA1fl/flFoxA2fl/flAlbCre ( + ) , respectively ) displayed a relatively modest , 1 . 2–2 . 5 fold , reduction in the level of serum HBeAg ( Fig 1F ) . However the HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mice showed dramatically reduced levels of serum HBeAg , approximately 5–10 fold lower than their wildtype controls ( Fig 1F ) . As HBeAg is translated from the HBV 3 . 5kb precore RNA [48] , these observations suggest that the FoxA-deficiency in these HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mice is associated with a marked decrease in the synthesis of the HBV 3 . 5kb precore RNA . HBV transgenic mice that lacked liver-specific expression of FoxA2 ( HBVFoxA2fl/flAlbCre ( + ) ) , FoxA1 plus FoxA2 ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) , or FoxA1 plus FoxA2 with reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) were examined for their steady state levels of HBV transcripts by analysis of total liver RNA ( Fig 2 ) . The steady state levels of the HBV 3 . 5kb and 2 . 1kb transcripts in the livers of the HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , were greatly diminished relative to the controls ( Fig 2 ) . The liver-specific deletion of FoxA2 or FoxA1 plus FoxA2 ( HBVFoxA2fl/flAlbCre ( + ) and HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) mice , respectively ) reduced the levels of the HBV 3 . 5kb transcripts to a relatively modest extent , 1 . 4–2 . 5 fold , by RNA filter hybridization ( Fig 2B ) and RT-qPCR analysis ( Fig 2C ) . These observations are consistent with the observed reduction in serum HBeAg ( Fig 1F ) and suggests that the lack of FoxA1 and FoxA2 modestly reduced the level of the HBV 3 . 5kb precore RNA that encodes the HBeAg [48] . HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , displayed a 4–8 fold reduction in the HBV 3 . 5kb RNA by RNA filter hybridization analysis ( Fig 2B ) and a 8–16 fold reduction by RT-qPCR analysis ( Fig 2C ) . These reductions in HBV 3 . 5kb RNA are consistent with the observe reduction in serum HBeAg seen in these FoxA-deficient mice , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ( Fig 1F ) . HBV transgenic mice that lacked liver-specific expression of FoxA2 ( HBVFoxA2fl/flAlbCre ( + ) ) , FoxA1 plus FoxA2 ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) , or FoxA1 plus FoxA2 with reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) were examined for their steady state levels of HBV replication intermediates by analysis of total liver DNA ( Fig 3 ) . HBV DNA replication intermediates were undetectable in the livers of the HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) ( Fig 3 ) . The liver-specific deletion of FoxA2 or FoxA1 plus FoxA2 ( HBVFoxA2fl/flAlbCre ( + ) and HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) mice , respectively ) reduced the levels of the HBV replication intermediates by 3–5 fold by DNA filter hybridization ( Fig 3B ) . These decreases in replication intermediates were slightly greater than those observed for the HBV 3 . 5kb RNA but they are consistent with previous findings which suggest that viral DNA synthesis can be sensitive to small changes in HBV transcription [4 , 49 , 50] . HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , failed to displayed any detectable HBV replication intermediates ( Fig 3B ) despite expressing low levels of HBV 3 . 5kb RNA ( Fig 2B and 2C ) . These observations suggest that either the level of RNA synthesis is insufficient to support HBV DNA synthesis in this model of chronic HBV infection or viral replication is being suppressed at both the transcriptional and posttranscriptional levels as has been suggested previously [51] . Immunohistochemical analysis of the livers of HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , display greatly reduced HBcAg staining within the liver lobule ( Fig 4D ) compared to control mice ( Fig 4A ) . Indeed , weak cytoplasmic and limited nuclear staining is observed in only a very limited number of hepatocytes located close to the central vein ( Fig 4D ) . These findings are consistent with the reductions in HBV RNA and DNA synthesis observed in these mice ( Figs 2 and 3 ) . Previous histological analysis of FoxA1:FoxA2-null mice ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) had demonstrated that their livers displayed dilated , disorganized , and expanded bile ducts due to cholangiocyte proliferation which is characteristic of biliary hyperplasia [47] . Additionally , these proliferating bile ducts were surrounded by extracellular matrix which was comprised of collagen bundles which is typical of liver fibrosis [47] . Hematoxylin and eosin staining of livers from HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , also displayed similar biliary hyperplasia which was more severe than that seen in the FoxA1:FoxA2-null mice , HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ( Figs 4B , 4E and 5 ) . Similarly , the fibrosis observed by trichrome staining of the livers from the HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) was more severe than that seen in the FoxA1:FoxA2-null mice ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) , consistent with mild to moderate , bridging portal fibrosis ( Figs 4C , 4F and 5 ) . The extensive biliary epithelial cell proliferation and fibrosis alone , in the absence of any observable hepatocyte damage as measured by serum ALT and AST , appear to explain the noted increase in liver size associated with the HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mouse ( Fig 1E ) . No differences in liver tissue histology were apparent between HBV transgenic and non-transgenic mice of the same FoxA genotype indicating that HBV biosynthesis did not contribute to the observed liver phenotypes . Histological analysis of the livers of HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , indicated that these mice displayed biliary hyperplasia and fibrosis ( Fig 4 ) which appeared to be more severe than had previously been reported for FoxA1:FoxA2-null mice , HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) [47] . To more precisely address this issue , total liver RNA was analyzed by RT-qPCR to evaluate cytokine transcript levels , stellate cell activation and biliary epithelial proliferation ( Fig 5 ) . In all cases , FoxA2-null HBV transgenic mice ( HBVFoxA2fl/flAlbCre ( + ) ) did not display any significant differences from control mice . In contrast , FoxA1:FoxA2-null mice ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) showed an intermediate phenotype between the control and the HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) . FoxA1:FoxA2-null mice ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) displayed a 2–4 fold increase in TNFα and 2OAS ( a marker for type I interferon synthesis ) mRNA compared to the controls ( Fig 5A and 5B ) . HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) displayed a 4–24 fold increase in the cytokine transcripts ( Fig 5A and 5B ) . As these cytokines can post-transcriptionally reduce HBV replication intermediate levels [52–54] , these observations may account for the differences in the reduction of HBV RNA and DNA in the HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mice . IL6 mRNA levels displayed very modest changes in the various FoxA-deficient mice ( Fig 5C ) . In contrast , TGFβ1 , 2 and 3 levels were dramatically induced in the HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) whereas the induction was much less dramatic in the FoxA1:FoxA2-null mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) ( Fig 5D–5F ) . Similar quantitative differences were apparent for stellate cell activation as reflected in the induction of αSMA and Col1A1 mRNAs ( Fig 5G and 5H ) and biliary epithelial cell proliferation as seen by the induction of CK19 and 20 mRNAs ( Fig 5I and 5J ) . These observations are all consistent with HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) displaying a greater level of cytokine-mediate fibrosis and biliary hyperplasia than the FoxA1:FoxA2-null HBV transgenic mice ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) ( Fig 5 ) . Despite the FoxA-deficiency , mutant mice displayed the same level of RNA for key transcription factors ( HNF1α , HNF1β , PPARα , HNF4α , FXRα and LRH1 ) know to govern HBV biosynthesis as were observed in the control mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) relative to HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ; fold difference = 0 . 8–1 . 3 , p>0 . 2 , n = 4 to 9 ) . These observation suggest that transcription factor abundance within the liver does not explain the observed loss of HBV biosynthesis within the FoxA-deficient mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) As adult HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , display fibrosis and biliary hyperplasia ( Figs 4 and 5 ) , it was of interest to determine when this phenotype became apparent during postnatal development and what effect it might have on HBV RNA synthesis ( Fig 6 ) . Initially , the level of HBV transcription throughout postnatal development was determined by RNA filter hybridization analysis and RT-qPCR analysis ( Fig 6 ) . As observed with adult mice ( Fig 2 ) , HBV transcription was reduced 3–7 fold in the HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) compared with the controls throughout postnatal development . To address the timing of the postnatal alterations associated with FoxA deficiency , total liver RNA was analyzed by RT-qPCR to evaluate cytokine transcript levels , stellate cell activation and biliary epithelial proliferation at 1 , 2 , 3 , 4 and 7 weeks of age in wildtype HBV transgenic mice , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) , and HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ( Fig 7 ) . The complete loss of FoxA1 and FoxA2 RNA was apparent in the livers of 1 week old HBV transgenic mice expressing only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) ( Fig 7A and 7B ) . By 4 weeks , FoxA3 RNA levels were higher in the FoxA-deficient mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) as compared with the wildtype mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) ( Fig 7C ) . For the markers of inflammation , biliary epithelial cells proliferation and fibrosis , no significant differences in mRNA levels were observed at 1 or 2 weeks of age between control and FoxA-deficient mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) and HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , respectively ) indicating that major differences in these processes were not apparent until the FoxA-deficient mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) were 3 weeks old . This suggests that the elevated levels of all these markers which are apparent during this neonatal stage of liver development are a normal part of the process of neonatal liver growth ( Fig 7D–7K ) . In contrast , after 2 weeks of age the levels of cytokine RNAs ( Fig 7D–7I ) , biliary epithelial cells proliferation markers ( Fig 7K ) and fibrosis associated genes ( Fig 7J ) increase in the FoxA-deficient mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) whereas their levels generally decrease as liver growth slows in the wildtype mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) ) . As HBV transcription is already decreased in the 1 and 2 week old FoxA-deficient HBV transgenic mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) relative to the controls ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) ) ( Fig 6 ) , it appears that FoxA-deficiency alone is sufficient to reduce viral transcription . FoxA-deficiency in the HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , is associated with a dramatic developmental reduction in HBV RNA and DNA synthesis ( Figs 2 , 3 , 4 and 6 ) . As FoxA is a pioneer transcription factor responsible for marking liver-specific genes for expression later in hepatocyte maturation and DNA methylation contributes to tissue specific gene expression , it was of interest to see if FoxA-deficiency was affecting HBV gene expression by altering the developmental pattern of viral DNA methylation . Consequently , bisulfite genomic sequencing of the HBV transgene DNA from wild-type and FoxA gene deleted HBV transgenic mice was performed to determine the role of FoxA in the regulation of viral DNA methylation ( Fig 8 ) . Regardless of FoxA status , the CpG island located between HBV nucleotide coordinates 1066–1773 ( %GC = 54 . 9 , Observed CpG/Expected CpG = 0 . 85 , Length = 708bp ) was hypomethylated ( Fig 8A and 8B ) [55] . This region encompasses the enhancer 1/X-gene promoter and enhancer 2/core gene promoter regions and is flanked by two FoxA binding sites [17] . The percentage of DNA methylation increases progressively from approximately nucleotide coordinate 2000–3182 which includes the large and major surface antigen promoter and three additional FoxA binding sites [40 , 41] . The percentage of DNA methylation is at its highest and most consistent levels on all genomes from approximately nucleotide coordinate 1–706 ( Fig 8A and 8B ) . Importantly , the average level of HBV DNA methylation between nucleotide coordinates 1–706 for all 16 mice examined in this analysis correlates ( R2 = 0 . 91 ) strongly with the level of serum HBeAg ( Fig 8C ) suggesting that this region , at a minimum , plays an important role in governing the level of HBV 3 . 5kb precore RNA synthesis and hence HBeAg production . Given that this region of the viral genome has not previously been identified as playing a role in governing HBV RNA synthesis , it appears that this may represent a previously unknown in vivo mode of HBV transcriptional regulation . Furthermore , it is apparent that the inhibition of HBV transcription that is apparent in the HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , correlates with the very high level of methylation ( 93–95% ) observed in this region of the genome suggesting that DNA methylation resulting from FoxA-deficiency is associated with the observed loss of viral transcription and replication intermediates ( Figs 2 and 3 ) . Interestingly , the control , FoxA2-null and FoxA1:Fox2-null mice ( HBVFoxA2fl/flAlbCre ( + ) and HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) , respectively ) display a range of DNA methylation levels between 20–80% which is consistent with the variation in serum HBeAg ( Fig 8C ) and appears to be consistent with the number of hepatocytes expressing HBcAg by immunohistochemistry ( Fig 4 ) . To address this issue , the distribution of DNA methylation sites within the three HBV DNA amplicons that contained the largest number of CpG sites were evaluated for the control , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( - ) , and HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ( Fig 9 ) . The HBV DNA amplicon spanning nucleotide coordinates 341–711 contains 11 CpG sites ( Fig 9A and 9B ) . The percentage of DNA sequences that were hypermethylated ( fully methylated or unmethylated at only a single site ) correlated with the overall level of methylation of this sequence at each position . This indicates that regardless of the origin of the HBV transgene DNA , it was either completely ( or almost completely ) methylated or completely ( or almost completely ) unmethylated ( Fig 9A and 9B ) . Virtually none of the sequences displayed an intermediate level of DNA methylation . This is consistent with the suggestion that this region of the genome is unmethylated in the hepatocytes that are expressing the HBV transgene DNA and hence supporting viral biosynthesis but extensively methylated in the hepatocytes that are not expressing the HBV transgene DNA and hence are not supporting viral biosynthesis . These observations are consistent with previous immunohistochemical analysis of HBcAg in HBV transgenic mouse livers [21 , 49 , 50] . The HBV DNA amplicon spanning nucleotide coordinates 1215–1629 contains 38 CpG sites ( Fig 9C and 9D ) . This HBV transgene DNA amplicon is located within the CpG island and as such is hypomethylated [56] . Methylation of any CpG sequence within this region was very rare as might be expected ( Fig 8 ) so almost all sequences were unmethylated with only a very limited number of singly methylated CpG sequence . The HBV DNA amplicon spanning nucleotide coordinates 2131–2441 contains 14 CpG sites ( Fig 9E and 9F ) . As noted for the HBV DNA amplicon spanning nucleotide coordinates 341–711 , the HBV transgene DNA was either hypo- or hypermethylated with up to 3 CpG sites being methylated or unmethylated , respectively , within any single amplicon sequence . This observation is again consistent with the suggestion that the HBV transgene DNA is either almost completely methylated or completely unmethylated within this region of the genome ( Fig 9E and 9F ) . Partial methylation of the HBV transgene DNA is essentially absent suggesting that methylation of one CpG site enhances the probability that adjacent CpG sites will also be methylated . In the transgenic mouse model of chronic HBV infection , HBV transcription occurs in a limited number of tissues including the liver and kidney [21 , 57] . In contrast , HBV transcription is essentially absent from other tissues including the muscle , spleen , lung and brain [21 , 57] . To evaluate the role of methylation in the tissue specific expression of the HBV transgene , the level of HBV transgene DNA methylation was evaluated in these tissues ( Fig 10 ) . Regardless of the tissue being examined , the CpG island located between HBV nucleotide coordinates 1066–1773 was hypomethylated ( Fig 10A and 10B ) . Similar to the findings in the livers of the HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ( Fig 8 ) , where viral transcription was limited , the regions of the HBV genome spanning nucleotide 2000–3182 and 1–706 were hypermethylated in muscle , spleen , lung and brain ( Fig 10A and 10B ) consistent with a role for DNA methylation in the inhibition of viral transcription in these tissues . Of note , these same regions of the viral genome were only partially methylated in liver and kidney , the tissues that supports relatively abundant HBV transcription [21 , 57] . As HBV biosynthesis is essentially absent in wildtype neonates [19] , it was of interest to investigate the methylation status of HBV genomic DNA from the livers of new born mice ( Fig 10C ) . The methylation profile of the HBV genomic DNA from these wildtype neonates was essentially the same as the livers of HBV transgenic mice expressing only reduced levels of FoxA3 , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ( Fig 8 ) and the transgene DNA from muscle , spleen , lung and brain ( Fig 10A and 10B ) . Furthermore , the HBV DNA amplicon spanning nucleotide coordinates 341–711 , 1215–1629 and 2264–2474 ( Fig 11 ) demonstrate that the HBV transgene DNA is either hypo- or hypermethylated within any singe amplicon sequence . This observation is again consistent with the suggestion that the HBV transgene DNA is either almost completely methylated or completely unmethylated within these regions of the genome regardless of the origin of the HBV transgene DNA ( Fig 11 ) . In addition , the level of methylation within the region spanning nucleotide coordinates 341–711 correlates with the level of HBV transcription in the various tissues and at distinct postnatal periods of liver development . This suggests that FoxA may govern the developmental expression of the HBV genome by modulating the level of viral DNA methylation throughout hepatocyte maturation .
The epigenetic modification of eukaryotic genomic DNA by methylation of cytosine residues in the context of CpG sites has been characterized in some detail throughout development [58 , 59] . During gametogenesis and in the pre-implantation embryo , DNA is demethylated and subsequently remethylated as the process of early development proceeds through the initial stages of cellular proliferation and germ cell layer differentiation [58 , 59] . Appropriate methylation of genomic DNA during development is essential for viability with the loss of DNA methyltransferase ( DNMT ) activity in mice being associated with an embryonic or postnatal lethal phenotype [60 , 61] . Furthermore , it is apparent that when cells differentiate into tissue-specific cell types , the patterns of RNA expression observed in these cells negatively correlates to a significant degree with the methylation status of the DNA sequence elements in proximity to the associated gene [35] . Hypomethylation of regulatory DNA sequence elements , including enhancer , promoter and intragenic sequences , are generally associated with expressed genes whereas hypermethylation of regulatory DNA sequence elements is primarily associated with non-expressed genes [35 , 62] . However , detailed mechanistic insights into the developmental processes leading to these general patterns of gene expression and DNA methylation is currently quite limited [36 , 37 , 62] . Pioneer transcription factors are critical determinants of tissue specific gene expression [26] . Pioneer transcription factors bind to the regulatory regions of genes and mark them for expression at a later stage of development [29] . Pioneer factors can bind to chromatin in the context of nucleosomes , leading to a more relaxed conformation which permits gene expression to occur once additional transcription factors bind to the enhancer and promoter regions of these genes at later developmental stages [25] . However , it is not clear why the absence of pioneer factor binding to non-target genes leads to the complete failure of gene expression at subsequence stages of cellular differentiation . It does suggest that the genes that are not marked by pioneer factors in a specific cell type must , somehow , be identified in a manner which prevents their subsequent activation regardless of the expression of additional transcription factors at later stages in development and/or differentiation . Processing these genes into heterochromatin would be one route that would ensure the silencing of these genes and this might be aided by the hypermethylation of the regions of the chromosomes containing the relevant genes in the appropriate cells of any particular tissue [63] . However , cell-type specific deletion of pioneer factors which are essential for the correct gene expression patterns to mediate cellular differentiation is not possible because of the essential nature of their target genes for functional tissue development [26] . Consequently there is only limited understanding of the mechanism ( s ) of pioneer factor function and the consequences of their loss in the tissues where they play a major role in tissue development . Therefore a non-essential target gene which is highly dependent on pioneer factor expression for its developmental expression may be informative if conditions exist where the target gene but not the essential cellular genes are completely dependent on a specific regulated pattern of pioneer transcription factor expression . The HBV genome contains seven binding sites for the FoxA pioneer transcription factor and all four of the viral promoters are regulated by FoxA in cell culture [39–41] . Furthermore , FoxA modulates HBV transcription and replication in non-hepatoma cells complemented with nuclear receptors [16 , 42] . Therefore it was of interest to determine the role of this pioneer factor in the regulation of HBV transcription and replication in vivo using the HBV transgenic mouse model of chronic viral infection in the presence and absence of various degrees of FoxA-deficiency . Consequently , liver-specific FoxA2-null , liver-specific FoxA1-null:FoxA2-null and liver-specific FoxA1:FoxA2-null:FoxA3-heterozygous HBV transgenic mice ( HBVFoxA2fl/flAlbCre ( + ) , HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) and HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , respectively ) , were generated and characterized . The most dramatic phenotype was associated with the liver-specific FoxA1:FoxA2-null:FoxA3-heterozygous HBV transgenic mice , HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , which express only reduced levels of FoxA3 ( Fig 1A–1C ) . These mice have enlarged livers ( Fig 1E ) but very low levels of serum HBeAg ( Fig 1F ) . They have reduced levels of viral RNA and DNA in their livers ( Figs 2 and 3 ) , which explains the low level of serum HBeAg and indicates that FoxA is essential for the in vivo expression of the HBV genomic DNA . Immunohistochemical analysis of the livers of these mice revealed dramatically reduced levels of HBcAg ( Fig 4A and 4D ) , which accounts for the complete loss of viral DNA replication intermediates ( Fig 3 ) . Furthermore , these livers displayed biliary hyperplasia and fibrosis to a greater extent than previously reported in the liver-specific FoxA1:FoxA2-null mice ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) ) ( Figs 4B , 4C , 4E , 4F and 5 ) [47] . The adult HBV transgenic mice which express only reduced levels of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) also displayed elevated levels of transcripts for a number of cytokines including TNFα , 2OAS as an indicator of type I interferon expression , IL6 and TGFβ ( Fig 5A–5F ) which may have influenced the levels of HBV replication intermediates by post-transcriptional mechanisms [51] . To address this issue , viral transcription was assessed in the livers of neonatal HBV transgenic mice between the ages of one to four weeks ( Fig 6 ) . Differences in cytokine transcript levels were not apparent until mice were 3 weeks of age ( Fig 7 ) whereas FoxA-deficiency ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) was associated with reduced HBV transcription at all developmental stages examined ( Fig 6 ) . These observations indicate that FoxA is essential for HBV transcription throughout postnatal liver development . Methylation analysis of HBV transgene DNA from adult mice demonstrated that FoxA-deficiency ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) increased viral epigenetic modification at CpG sites within the region spanning nucleotide coordinates 1–706 ( and to a less extent 2000–3182 ) such that it was approaching 100% ( Fig 8 ) . The level of CpG site methylation within the region from 1–706 correlated with the level of serum HBeAg ( Fig 8C ) and was consistent with the immunohistochemical distribution of HBcAg within the liver lobules of these mice ( Fig 4A and 4D ) . These observations suggested that the HBV transgene DNA within the hepatocytes expressing HBV transcripts and replicating virus were essentially unmethylated whereas the hepatocytes failing to support HBV transcription and replication possessed HBV transgene DNA which was hypermethylated . This appears to be the case in all mice as the frequency distribution of DNA methylation was essentially all or none within the HBV DNA amplicons spanning nucleotide coordinates 341-711and 2131–2441 ( Fig 9 ) . Similar analysis of neonatal livers isolated from 0 . 5 day old wild type HBV transgenic mice and additional adult tissues ( Fig 10 ) also indicated that only adult kidney and liver tissues which were capable of supporting viral transcription contained a fraction of cells which were hypomethylated within the HBV DNA amplicons spanning nucleotide coordinates 341-711and 2264–2474 ( Fig 11 ) . Together , these data indicate that FoxA is required to mark HBV genomic DNA for transcription by protecting it from DNA methylation or mediating its demethylation during the postnatal stages of liver development ( Fig 12 ) . Indeed , the observation that neonatal HBV transgene DNA from wildtype mice is essentially 100% methylated in the HBV DNA amplicons spanning nucleotide coordinates 341-711and 2264–2474 ( Fig 11 ) but completely unmethylated in a fraction of hepatocytes in adult mice ( Fig 11 ) indicates that the HBV transgene DNA must lose it 5-methylcytosine residues as a result of neonatal hepatocyte proliferation or maturation . This implies that the developmental binding of FoxA to HBV recognition sequences must either prevent the recruitment of DNMTs to the viral transgene DNA or actively recruit ten eleven translocation ( TET ) methylcytosine dioxygenases [64] . In the former case , DNA methylation is lost by subsequent genomic DNA replication associated with hepatocyte proliferation and in the latter demethylation is an active process leading to the removal of 5-methylcytosine by its oxidation to 5-hydroxymethylcytosine , 5-formylcytosine and 5-carboxylmethylcytosine followed by base-excision repair [65] . Alternatively , active FoxA-dependent DNA demethylation might involve deamination of 5-methylcytosine to thymine by activation-induced deaminase ( AID ) or apolipoprotein B mRNA-editing enzyme , catalytic polypeptides ( APOBEC ) followed by base-excision repair ( BER ) [66] . The kinetics of the loss of 5-methylcytosine during postnatal liver development should indicate if the process is active or passive and what , if any , role the TET enzymes or single- and double-strand DNA break repair pathways play in this process . If TET or BER are involved in HBV DNA demethylation , TET or BER inhibitors could potentially be considered as antiviral therapies for both neonatal infections and the treatment of chronic HBV infections in adults although secondary effects of such approaches would need careful evaluation . This study demonstrates that FoxA is essential for postnatal HBV transcription and replication whereas under the specific conditions of limited FoxA3 expression seen in this transgenic mouse model ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) , cellular gene expression remains sufficient to support viability and essentially normal hepatocyte function as reflected by serum chemistry with the exception of circulating cholesterol which is decreased about two-fold in these mice . Regardless , this indicates that limited postnatal FoxA3 expression is sufficient to support liver function and normal growth in the HBV transgenic mice . In contrast , the complete absence of FoxA expression in the liver is incompatible with viability . This indicates that limiting abundance of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) can supply the pioneer function necessary for liver-specific gene expression necessary for essentially normal hepatocyte differentiation whereas it is insufficient for HBV transcription and replication due to its failure to protect the HBV transgene DNA from comprehensive region specific CpG site methylation . These observations imply that the pioneer factor function of FoxA are distinct for different genes and likely depend on FoxA concentrations through liver development in combination with the number and affinity of the FoxA recognition sequences associated with the genes it regulates through liver specification and differentiation ( Fig 12 ) . Physiologically normal levels of FoxA expression during development result in the expression of HBV transcription and replication within a day of birth [19] . At birth , HBV transgene DNA is fully methylated ( Figs 10 and 11 ) . This suggests that immediately after birth , FoxA directly or indirectly inhibits methylation of the HBV genome as the hepatocytes proliferate and differentiate with liver growth . This may be achieved by FoxA directly or indirectly recruiting TET or BER proteins leading to the active demethylation of the HBV transgene DNA [64 , 66] . Alternatively , FoxA may directly or indirectly prevent the recruitment of DNMTs to the HBV transgene DNA and passive demethylation may occur through multiple rounds of cellular DNA replication and cell division ( Fig 12A ) . In the presence of a limiting abundance of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) in the hepatocytes , this fails to occur and DNA methylation is preserved through cellular DNA replication and methylation of the hemi-methylated HBV transgene DNA by the maintenance DNA methyltransferase , DNMT1 ( Fig 12B ) . Hypermethylated HBV DNA is transcriptionally inactive in natural infection , HBV transgenic mice and in cell culture [67–69] . Under normal cellular conditions , liver-specific genes are marked by FoxA during hepatocyte specification and differentiation and expressed in the liver at various developmental stages depending on the specific gene being considered ( Fig 12C ) [26 , 29] . In contrast to the situation with the HBV transgene DNA , limiting abundances of FoxA3 ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) are sufficient to maintain the pioneer function of this transcription factor and establish levels of liver-specific gene expression which are at least sufficient to support essentially normal hepatocyte function for the lifetime of the mouse ( Fig 12D ) . These observation indicate that probably both the level of FoxA expression and the affinity of pioneer factor binding to its cognate recognition sequence within the regulatory region of the enhancers and promoters of the liver-specific genes it regulates determines whether or not FoxA can mark a specific gene for expression later in the developmental program . Also as a consequence of differences in affinity of FoxA for its cognate recognition sequence and the increased expression of FoxA throughout liver development [20] , it is likely that different FoxA target genes are marked for subsequent expression at different stages of hepatocyte maturation . Indeed consistent with this suggestion , examples of hepatocyte-expressed FoxA-regulated genes including serum albumin ( Alb ) , apolipoprotein AI ( apoAI ) and cytosolic phosphoenolpyruvate carboxykinase ( PEPCK ) are demethylated at distinct stages of liver maturation [25 , 36 , 70–74] . Furthermore , HBV may be particularly sensitive to FoxA-deficiency due to the high number of sites within the viral genome . Ultimately , it appears that limiting amounts of FoxA3 in HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mice leads to its failure to mark the HBV transgene DNA resulting in its hypermethylation and loss of transcriptional competence . This failure to appropriately developmentally mark the hypermethylated HBV transgene DNA with the FoxA pioneer transcription factor may lead to the HBV transgene DNA being incorporated into a repressive chromatin environment such as heterochromatin . In such a chromatin environment , the liver-enriched transcription factors present within the hepatocytes would not be able to access the regulatory elements within the viral genome resulting in the loss of HBV biosynthesis as observed in the HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) mice . Interestingly , both the FoxA2-null mice and the FoxA-deficient HBV transgenic mice ( HBVFoxA1fl/flFoxA2fl/flAlbCre ( + ) and HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) , respectively ) are sensitive to bile acid toxicity [47 , 75] . This is consistent with the observation that reduced levels of FoxA2 are associated with reduced levels of certain bile acid transporters and bile acid modifying activities [75] . As seen for HBcAg , HBV RNA and DNA , viral expression is observed adjacent to the central vein in zone 3 of the hepatic lobule ( Fig 4 ) [21] . This suggests the possibility that there may be a gradient of FoxA activity within the developing mouse hepatic lobule restricting viral biosynthesis to this region of the liver by methylating and silencing the viral transgene DNA in the hepatocytes distal to the central vein . FoxA-deficiency may drastically reduce the zone where FoxA activity is sufficient to prevent the hypermethylation of the HBV transgene DNA ( Figs 4 , 8 and 9 ) . A similar situation might also be occurring with the expression of FoxA target genes encoding bile acid modifying and transporter proteins but probably to a more modest extent than seen for HBV expression [75] . In the wild type mice there is sufficient FoxA across the liver lobule to support expression of the bile acid modifiers and transporters at a level which can prevent bile acid toxicity . In contrast the zone of hepatocytes may become more restricted when FoxA is limiting , resulting in overt toxicity when exposed to high concentrations of bile acids due to the restricted number of hepatocytes able to correctly transport these metabolites [75] , presumably due to the increased methylation of the FoxA target genes encoding bile acid modifying and transporter genes . Similar , mechanisms involving liver-specific pioneer factors may be responsible for the zonal expression of a number of other liver-specific gene products . Of major clinical importance , most HBV infections worldwide occur from mother to child at birth due to mixing of maternal and fetal blood [76 , 77] . In the HBV transgenic mouse model of chronic viral infection , viral biosynthesis does not occur in the liver until after birth and increases throughout postnatal maturation due to increased HBV transcription which parallels the increase in liver-enriched transcription factor abundance associated with hepatocyte terminal differentiation [19–21] . As the situation is likely to be similar in man , the transient therapeutic reduction in the level of FoxA in neonates infected with HBV may lead to the irreversible methylation and transcriptional inactivation of the covalently closed circular HBV DNA ( CCC DNA ) generated from the initial mother to child infection at birth . Given the viability and essentially normal blood chemistry observed in the adult FoxA-deficient HBV transgenic mice ( HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre ( + ) ) , the transient reduction in the level of FoxA in neonates is unlikely to have any long term health consequences while eliminating viral biosynthesis . A similar approach may also be beneficial in the treatment of adult HBV chronic carries . Additionally , a more detailed understanding of the process by which FoxA deficiency eliminates HBV biosynthesis may lead to the identification of additional therapeutic targets , such as the DNMT , TET and BER enzymes , which when modulated appropriately may lead to the selective methylation of HBV CCC DNA and its transcriptional silencing . If successful , this approach could lead to the eradication of the viral replication intermediate , HBV CCC DNA , which is resistant to all current therapies . A potential caveat regarding these observations arises from the differences in the transcriptional templates utilized in the HBV transgenic mouse model and natural infection in man . In the former , the template is a terminal-redundant chromosomally integrated copy of the viral genome whereas in natural infection it is HBV CCC DNA . Whether the nature of the transcriptional template or the developmental timing of natural infection affects the methylation status of the viral DNA under conditions of FoxA deficiency requires additional investigation . Irrespective of these caveats , these studies clearly demonstrate that FoxA mediates the developmental demethylation of the HBV genomic DNA leading to viral transcription and replication in the HBV transgenic mouse model of chronic HBV infection . These effects of FoxA on HBV transcription are independent of the expression of cytokines responsible for biliary epithelial cell proliferation or stellate cell activation during liver development .
The production and characterization of the HBV transgenic mouse lineage 1 . 3 . 32 has been described ( 5 ) . These HBV transgenic mice contain a single copy of the terminally redundant , 1 . 3-genome length copy of the HBVayw genome integrated into the mouse chromosomal DNA . High levels of HBV replication occur in the livers of these mice . The mice used in the breeding experiments were homozygous for the HBV transgene and were maintained on the SV129 genetic background ( 9 ) . The production and characterization of the floxed FoxA1 ( FoxA1fl/fl ) , floxed FoxA2 ( FoxA2fl/fl ) , FoxA3-null and albumin Cre ( lineage B6 . Cg-Tg ( Alb-cre ) 21Mgn/J , Jackson Laboratory ) transgene ( AlbCre ) mice has been described [47 , 75 , 78–82] . The FoxA1fl/flCre , FoxA2fl/flCre and FoxA3-null mice do not express FoxA1 , FoxA2 and FoxA3 ( also called hepatocyte nuclear factor 3α , 3β and 3γ or HNF3α , HNF3β and HNF3γ ) , respectively , in the liver ( and extrahepatic tissues in the case of the FoxA3-null mice ) after Cre-mediated excision of the FoxA1 and FoxA2 gene sequences , respectively , located between the loxP sites . The mice used in the breeding experiments were homozygous for their respective alleles and maintained on either the C57BL6;129J ( FoxA1fl/fl and FoxA2fl/fl ) or SV129 ( FoxA3-null and AlbCre ) genetic backgrounds [19 , 46 , 47 , 81 , 83] . HBV transgenic mice ( lineage 1 . 3 . 32 ) were bred with mice carrying the floxed FoxA1 ( FoxA1fl/fl ) , floxed FoxA2 ( FoxA2fl/fl ) , FoxA3-null ( FoxA3-/- ) alleles and the albumin Cre ( lineage B6 . Cg-Tg ( Alb-cre ) 21Mgn/J , Jackson Laboratory ) transgene ( AlbCre ) to generate HBVFoxA2fl/flAlbCre , HBVFoxA1fl/flFoxA2fl/flAlbCre and HBVFoxA1fl/flFoxA2fl/flFoxA3+/-AlbCre transgenic mice . Littermate HBVFoxA2fl/fl , HBVFoxA1fl/flFoxA2fl/fl and HBVFoxA1fl/flFoxA2fl/flFoxA3+/- transgenic mice without the AlbCre transgene were used as controls . Mice were screened for the HBV transgene , the FoxA1fl/fl , FoxA2fl/fl , FoxA3-null alleles and the AlbCre transgene by polymerase chain reaction ( PCR ) analysis of tail DNA . Tail DNA was prepared by incubating 1 cm of tail in 500 μl of 100 mM Tris hydrochloride ( pH 8 . 0 ) , 200 mM NaCl , 5 mM EDTA , 0 . 2% ( wt/vol ) SDS containing 100 μg/ml Proteinase K for 16 to 20 hours at 55°C . Samples were centrifuged at 14 , 000 rpm in an Eppendorf 5417C microcentrifuge for 10 minutes and the supernatant was precipitated with 500 μl of isopropanol . DNA was pelleted by centrifugation at 14 , 000 rpm in an Eppendorf 5417C microcentrifuge for 10 minutes and subsequently dissolved in 100 μl of 5 mM Tris hydrochloride ( pH 8 . 0 ) , 1 mM EDTA . The HBV transgene was identified by PCR analysis using the oligonucleotides , 5’-TCGATACCTGAACCTTTACCCCGTTGCCCG-3’ ( oligo XpHNF4-1 , HBV coordinates 1133 to 1159 ) and 5’-TCGAATTGCTGAGAGTCCAAGAGTCCTCTT-3’ ( oligo CpHNF4-2 , HBV coordinates 1683 to 1658 ) , and 1 μl of tail DNA . A PCR product of 551 bp indicated the presence of the HBV transgene . The FoxA1 wild type and floxed alleles were identified by PCR analysis using the oligonucleotides , 5’-CTGTGGATTATGTTCCTGATC-3’ ( oligo FoxA1F ) and 5’-GTGTCAGGATG CCTATCTGGT-3’ ( oligo FoxA1R ) , and 1 μl of tail DNA . A PCR product of 290 bp indicated the wild type FoxA1 allele whereas a PCR product of 480 bp indicated the floxed FoxA1 allele . The FoxA2 wild type and floxed alleles were identified by PCR analysis using the oligonucleotides , 5’-CCCCTGAGTTGGCGGTGGT-3’ ( oligo FoxA2F ) and 5’-TTGCTCACGGAAGAGTAGCC-3’ ( oligo FoxA2R ) , and 1 μl of tail DNA . A PCR product of 290 bp indicated the wild type FoxA2 allele whereas a PCR product of 450 bp indicated the floxed FoxA2 allele . The FoxA3 wild type and null alleles were identified by PCR analysis using the oligonucleotides , 5’-GGCAGTGCTTCCGGGTATGTA-3’ ( oligo FoxA3F ) , 5’-GGGAAGAGGTCCATGATCCAT-3’ ( oligo FoxA3R ) , and 5’-CAAAGCGCCATTCGCCATTCA-3’ ( oligo FoxA3LacZ-3 ) , and 1 μl of tail DNA . A PCR product of 196 bp indicated the wild type FoxA3 allele whereas a PCR product of 290 bp indicated the null FoxA3 allele . The AlbCre transgene was identified by PCR analysis using the oligonucleotides , 5’-CCAGCTAAACATGCTTCATCGTCG-3’ ( oligo CRE-1 ) and 5’-ATTCTCCCACCGTCAGTACGTGAG-3’ ( oligo CRE-2 ) , and 1 μl of tail DNA . A PCR product of 300 base pairs indicated the presence of the Cre transgene . The samples were subjected to 42 amplification cycles involving denaturation at 94°C for 1 minute , annealing at 55°C for 1 minute , and extension from the primers at 72°C for 2 minutes . The 20 μl reaction conditions used were as described by the manufacturer ( Gene Choice ) and contained 2 units of Taq DNA polymerase . HBV transgenic mice were fed normal rodent chow and water was available ad libitum . Mice were sacrificed and liver tissue was frozen in liquid nitrogen and stored at –70°C prior to DNA and RNA extraction . All animal experiments were Institutional Animal Care and Use Committee ( IACUC ) approved and performed according to institutional guidelines with University of Illinois at Chicago Institutional Biosafety and Animal Care Committee approval ( ACC Number: 14–025 ) . All animal procedures were performed in the College of Medicine Research Building at the UIC and adhere to the policies of the NIH Office of Laboratory Animal Welfare ( OLAW ) , the standards of the Animal Welfare Act , the Public Health Service Policy , and the Guide for the Care and Use of Laboratory Animals . Total DNA and RNA were isolated from liver of HBV transgenic mice as described ( 8 , 46 ) . Protein-free DNA was isolated in an identical manner to the total DNA except the proteinase K digestion was omitted [49] . DNA ( Southern ) filter hybridization analyses were performed using 20 μg of HindIII digested DNA ( 46 ) . Filters were probed with 32P-labeled HBVayw genomic DNA ( 10 ) to detect HBV sequences . RNA ( Northern ) filter hybridization analyses were performed using 10 μg of total cellular RNA as described ( 46 ) . Filters were probed with 32P-labeled HBVayw genomic DNA to detect HBV sequences and mouse glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) cDNA to detect the GAPDH transcript used as an internal control ( 45 ) . Filter hybridization analyses were quantified by phosphorimaging using a Packard Cyclone Storage Phosphor System . Reverse transcription-quantitative polymerase chain reaction ( RT-qPCR ) was used to measure the relative levels of FoxA1 , FoxA2 , FoxA3 , FoxO1 , tumor necrosis factor α ( TNFα ) , 2’ , 5’-oligoadenylate synthase ( 2OAS ) , interleukin 6 ( IL6 ) , transforming growth factor β1 ( TGFβ1 ) , TGFβ2 , TGFβ3 , alpha smooth muscle actin ( αSMA ) , collagen 1A1 ( Col1A1 ) , cytokeratin 19 ( CK19 ) , CK20 and HBV 3 . 5kb transcript levels in mouse liver RNA . After DNase I treatment , 1 μg of RNA was used for cDNA synthesis using the TaqMan reverse transcription reagents ( Applied Biosystems , Foster City , CA ) , followed by real-time PCR quantification using SYBR Green and an Applied Biosystems 7300 real-time thermocycler ( Applied Biosystems ) . Thermal cycling consisted of an initial denaturation step for 10 min at 95°C followed by 40 cycles of denaturation ( 15 sec at 95°C ) and annealing/extension ( 1 min at 60°C ) . The relative FoxA1 , FoxA2 , FoxA3 , FoxO1 , TNFα , 2OAS , IL6 , TGFβ1 , TGFβ2 , TGFβ3 , αSMA , Col1A1 , CK19 , CK20 and HBV 3 . 5kb RNA expression levels were estimated using the ΔΔCt method with normalization to mouse GAPDH RNA [84] . The PCR primers used were 5’-AAGATGGAAGGGCATGAGAG-3’ ( mouse FoxA1 sense primer ) , 5’-CCAGGCCGGAGTTCA-3’ ( mouse FoxA1 antisense primer ) , 5’-GGCCAGCGAGTTAAAGTAT-3’ ( mouse FoxA2 sense primer ) , 5’-TGTTGCTCACGGAAGAGTAG-3’ ( mouse FoxA2 antisense primer ) , 5’-ATGACCTGGCCGAGTGGA-3’ ( mouse FoxA3 sense primer ) , 5’-ATGGTGGGCACAGGATTCAC-3’ ( mouse FoxA3 antisense primer ) , 5’-GCTGCAATGGCTATGGTAGGA-3’ ( mouse FoxO1 sense primer ) , 5’-GTCACAGTCCAAGCGCTCAAT-3’ ( mouse FoxO1 antisense primer ) , 5’-CATCTTCTCAAAATTCGAGTGACAA-3’ ( mouse TNFα sense primer ) , 5’-TGGGAGTAGACAAGGTACAACCC-3’ ( mouse TNFα antisense primer ) [85] , 5’-GAAACTTCATTCAAACCCGGCCCA-3’ ( mouse 2OAS sense primer ) , 5’-CCGGAAGCCTTCAGCAATGTCAAA-3’ ( mouse 2OAS antisense primer ) , 5’-CTCTGCAAGAGACTTCCATCCAGT-3’ ( mouse IL6 sense primer ) , 5’-GAAGTAGGGAAGGCCGTGG-3’ ( mouse IL6 antisense primer ) , 5’-ATTCCTGGCGTTACCTTGG-3’ ( mouse TGFβ1 sense primer ) , 5’-CCTGTATTCCGTCTCCTTGG-3’ ( mouse TGFβ1 antisense primer ) , 5’-GAGCGGAGCGACGAGGAG-3’ ( mouse TGFβ2 sense primer ) , 5’-TGTAGAAAGTGGGCGGGATGG-3’ ( mouse TGFβ2 antisense primer ) , 5’-ATGGTGGTGAAGTCGTGTAAG-3’ ( mouse TGFβ3 sense primer ) , 5’-GTGAGGTCTGTCGCTTTGG-3’ ( mouse TGFβ3 antisense primer ) , 5’-GTCCCAGACATCAGGGAGTAA-3’ ( mouse αSMA sense primer ) , 5’-TCGGATACTTCAGCGTCAGGA-3’ ( mouse αSMA antisense primer ) , 5’-GCTCCTCTTAGGGGCCACT-3’ ( mouse Col1A1 sense primer ) , 5’-CCACGTCTCACCATTGGGG-3’ ( mouse Col1A1 antisense primer ) , 5’-ACTTGCGCGACAAGATTC-3’ ( mouse CK19 sense primer ) , 5’-AACTTGGTTCTGAAGTCATCTGC-3’ ( mouse CK19 antisense primer ) , 5’-GCACAGATTAAAGAGCTGCAAA-3’ ( mouse CK20 sense primer ) , 5’-GTCCTCTGCAGCCAGCTTAG-3’ ( mouse CK20 antisense primer ) , 5’-GCCCCTATCCTATCAACACTTCCGG-3’ ( HBV 3 . 5kb RNA sense primer , coordinates 2311 to 2335 ) , 5’-TTCGTCTGCGAGGCGAGGGA-3’ ( HBV 3 . 5kb RNA antisense primer , coordinates 2401 to 2382 ) , 5’-TCTGGAAAGCTGTGGCGTG-3’ ( mouse GAPDH sense primer ) , and 5’-CCAGTGAGCTTCCCGTTCAG-3’ ( mouse GAPDH antisense primer ) [46] , respectively . HBeAg analysis was performed using 2 μl of mouse serum and the HBe enzyme linked immunosorbent assay as described by the manufacturer ( Epitope Diagnostics ) . The level of antigen was determined in the linear range of the assay . Liver tissue samples were fixed in sodium phosphate-buffered formalin ( Fisher Scientific ) , embedded in paraffin , sectioned ( 5 μm ) , and stained with hematoxylin and eosin ( H&E ) or trichrome ( TC ) ( Electron Microscopy Sciences ) . Immunohistochemical detection of HBcAg in paraffin-embedded mouse liver sections was performed using a polyclonal rabbit anti-HBcAg primary antiserum ( Dako ) and a horseradish peroxidase-conjugated goat anti-rabbit immunoglobulin G F ( ab’ ) 2 fragment secondary antiserum ( Sigma ) [21] . The antibody coated slides were subsequently incubated with 3 , 3’-diaminobenzidine ( Sigma ) and counterstained with Mayer’s hematoxylin ( Sigma ) . Bisulfite treatment of protein-free genomic DNA for methylation analysis was performed using the EZ DNA Methylation-Lightning Kit ( D5030; Zymo Research , Inc . , Irvine , CA , USA ) according to the manufacturer’s instructions . The 99 CpG sequences in the HBV DNA genome were targeted using PCR amplification of the bisulfite-treated DNA , followed by sequencing of the amplicons on an Illumina MiSeq instrument . Preparation of DNA for high-throughput amplicon sequencing was performed in two PCR steps in a protocol termed “targeted amplicon sequencing ( TAS ) ” as described previously [86 , 87] . Thirteen HBV primer pairs targeting 95 of the 99 CpG sites were used . The primer pairs targeting the bisulfite converted HBV DNA were ( i ) 5’-ACACTGACGACATGGTTCTACACAATACCTAAACCTTTACCC-3’ ( oligo CS1FP1; HBV nucleotide coordinates 1131–1150 ) and 5’-TACGGTAGCAGAGACTTGGTCTGTTTTAGTTAGTGGGGGT-3’ ( oligo CS2RP1; HBV coordinates 1214–1197 ) , ( ii ) 5’-ACACTGACGACATGGTTCTACACAAACTTTCACTTTCTC-3’ ( oligo CS1FP1a; HBV coordinates 1086–1102 ) and 5’-TACGGTAGCAGAGACTTGGTCTGTTGATGGTTTATGATTAA ( oligo CS2RP1a; HBV coordinates 1233–1215 ) , ( iii ) 5’-ACACTGACGACATGGTTCTACACCCCCACTAACTAAAAC-3’ ( oligo CS1FP2; HBV nucleotide coordinates 1198–1214 ) and 5’- TACGGTAGCAGAGACTTGGTCTGGGTAATATTTGGTGG-3’ ( oligo CS2RP2; HBV nucleotide coordinates 1645–1630 ) , ( iv ) 5’- ACACTGACGACATGGTTCTACATTAAACTCTCAACAATATCA-3’ ( oligo CS1FP3; HBV nucleotide coordinates 1668–1687 ) and 5’-TACGGTAGCAGAGACTTGGTCTAAGTTATTTAAGGTATAGTTTG-3’ ( oligoCS2RP3; HBV nucleotide coordinates 1896–1875 ) , ( v ) 5’-ACACTGACGACATGGTTCTACAGTGGTTTTGGGGTATGG-3’ ( oligo CS1FP4; HBV nucleotide coordinates 1890–1906 ) and 5’- TACGGTAGCAGAGACTTGGTCTCAAATTAACACCCACCC-3’ ( oligo CS2RP4; HBV nucleotide coordinates 2130–2114 ) , ( vi ) 5’-ACACTGACGACATGGTTCTACAGGGTGGGTGTTAATTTG-3’ ( oligo CS1FP5; HBV nucleotide coordinates 2114–2130 ) and 5’-TACGGTAGCAGAGACTTGGTCTCCAAAAAATACTAACATTAAAAT-3’ ( oligo CS2RP5; HBV nucleotide coordinates 2464–2442 ) , ( vii ) 5’- ACACTGACGACATGGTTCTACAAGTTATAGAGTATTTGGTGT-3’ ( oligo CS1FP5a; HBV nucleotide coordinates 2244–2263 ) and 5’-TACGGTAGCAGAGACTTGGTCTCCCAATAAAATTCCCCA-3’ ( oligo CS2RP5a; HBV nucleotide coordinates 2491–2475 ) , ( viii ) 5’- ACACTGACGACATGGTTCTACAGATTGTAATTGATTATGTTTG -3’ ( oligo CS1FP6; HBV nucleotide coordinates 2625–2645 ) and 5’- TACGGTAGCAGAGACTTGGTCTCCATACTATAAATCTTATTCCC-3’ ( oligo CS2RP6; HBV nucleotide coordinates 2832–2853 ) , ( ix ) 5’-ACACTGACGACATGGTTCTACAGGGAATAAGATTTATAGTATGG ( oligo CS1FP7;HBV nucleotide coordinates 2832–2853 ) and 5’-TACGGTAGCAGAGACTTGGTCTTAAACCTAAAAACTCCACC-3’ ( oligo CS2RP7; HBV nucleotide coordinates 3061–3043 , ( x ) 5’-ACACTGACGACATGGTTCTACAAATCAAAAAAACAACCTACC-3’ ( oligo CS1FP8; HBV nucleotide coordinates 3115–3134 ) and 5’-TACGGTAGCAGAGACTTGGTCTGTGAGTGATTGGAGGT-3’ ( oligo CS2RP8; HBV nucleotide coordinates 340–325 ) , ( xi ) 5’-ACACTGACGACATGGTTCTACAACCTCCAATCACTCAC-3’ ( oligo CS1FP9; HBV nucleotide coordinates 325–340 ) and 5’-TACGGTAGCAGAGACTTGGTCTGTTAAATAGTGGGGGAAAG-3’ ( oligo CS2RP9; HBV nucleotide coordinates 730–712 ) , ( xii ) 5’-ACACTGACGACATGGTTCTACAGGATGATGTGGTATTGG-3’ ( oligo CS1FP10; HBV nucleotide coordinates 743–759 ) and 5’-TACGGTAGCAGAGACTTGGTCTCAAAACCCAAAAAACCCAC-3’ ( oligo CS2RP10; HBV nucleotide coordinates 1020–1002 ) , and ( xiii ) 5’- ACACTGACGACATGGTTCTACATTGATGTTTTTGTATGTA-3’ ( oligo CS1FP11; HBV nucleotide coordinates 1053–1070 ) and 5’-TACGGTAGCAGAGACTTGGTCTATAACCAAACCCCAACC-3’ ( oligo CS2RP11; HBV nucleotide coordinates 1222–1206 ) . These primers contained linker sequences ( underlined ) at the 5’ ends of the oligonucleotides , termed common sequences ( CS1 and CS2 ) . These PCR reactions were performed using ZymoTaq PreMix according to the manufacturer’s instructions ( Zymo Research ) . PCR amplification involved 10 minutes denaturation at 95°C , followed by 40 cycles of 95°C for 30 s , 50°C for 40 s , 72°C for 60 s . Finally , a seven 7 minute incubation at 72°C was performed . The HBV amplicons were subsequently subjected to a second stage PCR reaction ( AccuPrime SuperMix II , Life Technologies ) , used to incorporate unique barcodes and sequencing adapters . The PCR primers used were 5’- AATGATACGGCGACCACCGAGATCTACACTGACGACATGGTTCTACA-3’ ( oligo PE1CS1 ) and 5’-CAAGCAGAAGACGGCATACGAGATNNNNNNNNNNTACGGTAGCAGAGACTTGGTCT-3’ ( oligo PE2BCCS2 ) . The unique 10 base sample-specific barcodes are indicated with bold Ns and represent a subset of 384 unique sequences ( Fluidigm ) . PCR amplification involved 5 minutes denaturation at 95°C , followed by 8 cycles of 95°C for 30 s , 60°C for 30 s , 68°C for 30 s . Finally , a seven minute incubation at 68°C was performed . Pooled libraries were sequenced using an Illumina MiSeq instrument and data were analyzed using the Casava1 . 8 pipeline . Sequencing was performed using MiSeq V2 chemistry , with paired-end 2x250 base reads , employing Fluidigm custom sequencing primers . Raw paired-end sequence data were merged without trimming using the software package PEAR [88] . Merged , trimmed reads were sub-sampled to 2 , 500 sequences per sample , and mapped to the HBV reference DNA sequence [89] . Greater than 99 . 9% of reads mapped to the reference sequence . Unmethylated CpG sites were converted to UpG ( TpG upon PCR amplification ) by bisulfite treatment and the percent methylation at any nucleotide coordinate was determined by the ratio of C residues ( derived from 5-methylcytosine ) to C+T residues ( derived from 5-methylcytosine plus cytosine , respectively ) at any position within the HBV genomic DNA sequence . In addition , the methylation status of each read was assessed individually by counting the number of methylated CpG sites observed within that read . The distribution of methylation levels per read allows for the characterization of the cellular heterogeneity in the methylation status at each amplicon . | This study demonstrates the connection between FoxA expression and gene silencing by DNA methylation in vivo during liver maturation . Insufficient FoxA expression results in selective developmentally regulated hepatitis B virus ( HBV ) silencing by DNA methylation . To our knowledge , this is the first in vivo demonstration that pioneer factors such as FoxA function by mediating the developmental demethylation of their target genes , leading to their tissue specific gene expression . Furthermore , our results strongly imply that the marking of cellular target genes for subsequent transcription later in development is dependent upon the level and timing of FoxA expression plus its affinity for its target sequences within enhancer and promoter regions . Consequently , these findings suggest that the appropriate control of FoxA activity during development could lead to the transcriptional inactivation of nuclear HBV covalently closed circular DNA by methylation and hence resolution of chronic HBV infection . This represents a clinical goal that current therapies are unable to attain , and hence suggests a potential route to a cure for this chronic infection which kills approximately 1 million individuals annually . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"liver",
"gene",
"regulation",
"regulatory",
"proteins",
"dna-binding",
"proteins",
"dna",
"transcription",
"animal",
"models",
"model",
"organisms",
"dna",
"replication",
"experimental",
"organism",
"systems",
"transcription",
"factors",
"epigenetics",
"dna",
"mammalian",
"genomics",
"dna",
"methylation",
"chromatin",
"research",
"and",
"analysis",
"methods",
"chromosome",
"biology",
"proteins",
"animal",
"cells",
"gene",
"expression",
"hepatocytes",
"chromatin",
"modification",
"mouse",
"models",
"dna",
"modification",
"animal",
"genomics",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"anatomy",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"genomics"
] | 2017 | Hepatic deficiency of the pioneer transcription factor FoxA restricts hepatitis B virus biosynthesis by the developmental regulation of viral DNA methylation |
Chromosome missegregation in germ cells is an important cause of unexplained infertility , miscarriages , and congenital birth defects in humans . However , the molecular defects that lead to production of aneuploid gametes are largely unknown . Cdc20 , the activating subunit of the anaphase-promoting complex/cyclosome ( APC/C ) , initiates sister-chromatid separation by ordering the destruction of two key anaphase inhibitors , cyclin B1 and securin , at the transition from metaphase to anaphase . The physiological significance and full repertoire of functions of mammalian Cdc20 are unclear at present , mainly because of the essential nature of this protein in cell cycle progression . To bypass this problem we generated hypomorphic mice that express low amounts of Cdc20 . These mice are healthy and have a normal lifespan , but females produce either no or very few offspring , despite normal folliculogenesis and fertilization rates . When mated with wild-type males , hypomorphic females yield nearly normal numbers of fertilized eggs , but as these embryos develop , they become malformed and rarely reach the blastocyst stage . In exploring the underlying mechanism , we uncover that the vast majority of these embryos have abnormal chromosome numbers , primarily due to chromosome lagging and chromosome misalignment during meiosis I in the oocyte . Furthermore , cyclin B1 , cyclin A2 , and securin are inefficiently degraded in metaphase I; and anaphase I onset is markedly delayed . These results demonstrate that the physiologically effective threshold level of Cdc20 is high for female meiosis I and identify Cdc20 hypomorphism as a mechanism for chromosome missegregation and formation of aneuploid gametes .
Mitotic checkpoint genes are believed to be prime targets for deregulation in human infertility [1] . The mitotic checkpoint constitutes an intricate molecular network that ensures accurate chromosome segregation by coordinating metaphase-to-anaphase progression with the establishment of bipolar spindle attachment and metaphase plate alignment of all mitotic chromosome pairs [2] . At early stages of mitosis , various mitotic checkpoint proteins , including members of the Bub and Mad protein families , concentrate at unattached kinetochores to generate a diffusible signal that inhibits the anaphase-promoting complex or cyclosome ( APC/C ) , a large E3 ubiquitin ligase that drives metaphase-to-anaphase transition by catalyzing the ubiquitination and degradation of cyclin B1 and securin [3] . Although the exact composition of the inhibitory signal remains a major subject of investigation , it is believed to contain Bub3-bound BubR1 and Mad2 that has been primed by kinetochore-associated Mad1-Mad2 to stably interact with the APC/C activating subunit Cdc20 [4] , [5] , [6] . Upon attachment and alignment of the last chromosome pair , the inhibitory signal is quenched and APC/C activated through release of Cdc20 inhibition , triggering the ubiquitination and destruction of cyclin B1 and securin . Separase , a protease that is held in an inactive state by securin and cyclin B1/Cdk1 , is then allowed to cleave the Scc1 subunit of the cohesin complex that holds sister chomatids together , inducing the physical separation of sister chromatids by spindle forces [7] , [8] . A thorough assessment of the role of mitotic checkpoint genes in gametogenesis and infertility has not been possible because complete inactivation of mammalian mitotic checkpoint genes invariably disrupts the chromosome segregation process so severely that cells cannot survive [2] , [9] . In vitro studies of primary mouse oocytes in which key mitotic checkpoint proteins were depleted by morpholinos or RNA interference have pointed to an importance of several mitotic checkpoint proteins during the first meiotic division . For instance , sustained prophase I arrest of primary oocytes depends on stabilization of the Cdc20-related APC/C coactivator Cdh1 by BubR1 [10] . BubR1 retains control of Cdh1 stability after hormone-induced resumption of meiosis , thereby allowing APC/CCdh1-mediated securin degradation and progression through prometaphase I . Interestingly , BubR1 protein levels have been shown to decline in ovary and testis as normal mice age , which combined with the observation that mutant mice with low amounts of BubR1 are infertile , has led to speculation that BubR1 might be a key determinant of age-related meiotic errors in germ cells [11] . While APC/CCdh1 regulates early meiotic events in mice [10] , [12] , Cdc20 knockdown experiments in primary oocytes indicate that APC/CCdc20 is active in late meiosis I [10] , where it is responsible for driving oocytes into anaphase via the destruction of cyclin B1and securin , much like mitosis in somatic cells [13] . Coordination of APC/CCdc20 activation with proper kinetochore-microtubule attachment in meiosis I is dependent on the mitotic checkpoint proteins Mad2 and Bub1 , as depletion or expression of dominant-negative mutants of these proteins in primary mouse oocytes causes chromosome missegregation [14] , [15] , [16] , [17] . Whereas the depletion studies in primary mouse oocytes identify Cdc20 and Cdh1 as critical regulators of the first meiotic division , testing whether the functions unveiled in vitro operate in vivo remains an important challenge . Furthermore , it remains unknown whether Cdc20 and Cdh1 are also important for male meiosis I or stages of male and female gametogenesis other than meiosis I . Importantly , for Cdc20 and Cdh1 to be candidate infertility genes , one would expect their dysfunction to reduce fertility without compromising overall health and viability . Addressing these issues has been hampered by the embryonic lethality caused by inactivation of Cdh1 and Cdc20 in mice , with Cdh1-null embryos dying at mid-gestation due to placental defects [18] , [19] and Cdc20-null embryos at the two-cell stage due to permanent metaphase arrest [18] . In the present study , we bypassed the problem of early embryonic lethality of Cdc20 knockout mice by generating mutant mouse strains in which the dose of Cdc20 is reduced in graded fashion , enabling us to examine the physiological relevance of this APC/C cofactor . Our findings reveal that the threshold for pathophysiology is lowest in the female germline . We demonstrate that while both mitotic and meiotic divisions of male and female germ cells are characterized by inaccurate chromosome segregation and aneuploidization , only female meiosis I is so severely affected that almost exclusively aneuploid mature eggs are generated . We show that these eggs fertilize normally , but that the resulting zygotes die after the first few embryogenic divisions .
A series of mutant mouse strains in which expression of Cdc20 is gradually reduced was generated by using various combinations of wild-type ( Cdc20+ ) , hypomorphic ( Cdc20H ) and knockout ( Cdc20− ) alleles ( Figure 1A–1D ) . The Cdc20H allele was produced by targeted insertion of a neomycin phosphotransferase II ( neo ) gene cassette into the third intron of the Cdc20 gene ( Figure 1A ) . The neo gene contains a cryptic exon with stop codons in all three reading frames , thereby considerably reducing the amount of wild-type protein produced by targeted allele [11] , [20] , [21] , [22] . The Cdc20− allele was from gene trap mouse embryonic stem ( ES ) cell clone XE368 ( Figure 1B ) . Previously , it has been shown that this gene trap allele is the equivalent of a null allele and that embryos that are homozygous for this allele arrest and die at the two-cell stage of development [23] . In contrast , Cdc20+/H , Cdc20+/− , Cdc20H/H and Cdc20−/H mice were viable and had no overt phenotypes . Western blot analysis demonstrated that Cdc20+/H , Cdc20+/− , Cdc20H/H and Cdc20−/H ovary and testes had a graded reduction of Cdc20 protein ( Figure 1E and 1F ) . Western blot analysis of spleen , bone marrow , and mouse embryonic fibroblast extracts of Cdc20+/+ and Cdc20−/H mice suggested that the observed Cdc20 protein reductions are universal , irrespective of tissue or cell type ( Figure 1G , and data not shown ) . While establishing cohorts of Cdc20 mutant mice for long-term observation , we noticed that Cdc20−/H females yielded little or no offspring , which prompted us to measure the impact of graded reduction in Cdc20 expression on female fertility . Two-month-old Cdc20+/+ , Cdc20+/H , Cdc20+/− , Cdc20H/H and Cdc20−/H mice were bred to Cdc20+/+ males of the same age and the number of litters and pups produced per female was recorded for three months . Despite normal copulation rates ( Figure 2A ) , Cdc20−/H females produced on average about 4-fold fewer litters than females of the other genotypes ( Figure 2B ) , while the average number of pups was about 15-fold lower ( Figure 2C ) . Notably , of the seven Cdc20−/H females in the study , four failed to produce any offspring ( Figure 2D ) . Only Cdc20+/− and Cdc20+/H embryos can be produced by Cdc20−/H females bred to Cdc20+/+ males . Importantly , pups of these genotypes were produced at normal rates when Cdc20+/− , Cdc20+/H and Cdc20H/H females were bred to Cdc20+/+ males ( Figure 2C and 2D ) , indicating that the failure of Cdc20−/H females to produce offspring with Cdc20+/+ males was not due to the genotype of the embryos produced . Together , the above data demonstrate that Cdc20−/H females are either infertile or severely subfertile . The Cdc20 threshold level for fertility problems is remarkably sharp because Cdc20H/H females , which produce slightly more Cdc20 than Cdc20−/H females , have normal fertility ( Figure 2A–2D ) . Ten of 10 Cdc20−/H males were fertile and produced on average 7 pups per litter ( data not shown ) , indicating that gametogenesis in male mice has a lower dependence on Cdc20 than the female reproductive system . To study how Cdc20 deficiency impedes female fertility , we screened hematoxylin-eosin ovary sections of sexually mature Cdc20−/H females for overt defects in oogenesis . However , no apparent morphological differences were found ( Figure 2E ) . Cdc20−/H and Cdc20+/+ ovary sections contained similar amounts of primordial , primary , secondary and antral follicles , as well as similar numbers of mature oocytes and corpora lutea ( Figure 2E and 2F ) . These data indicated that the fertility problem of Cdc20−/H females is not due to a failure to produce , mature or ovulate oocytes . To explore preimplantation embryonic development , Cdc20−/H and Cdc20+/+ females were naturally mated with Cdc20+/+ males and embryos were collected at day 3 . 5 of development ( E3 . 5 ) . While 93% of embryos collected from Cdc20+/+ females were at the expected blastocyst stage , only 15% of Cdc20−/H females had reached this stage ( Figure 3A and 3B ) . The remaining embryos were either in the one- to four-cell stage or completely degenerated . Notably , the total number of embryos produced by Cdc20+/+ and Cdc20−/H females was the same ( Figure 3B ) , indicating Cdc20−/H females had normal fertilization rates and were capable of ovulating normal numbers of mature oocytes . Furthermore , the number of normal blastocysts produced by Cdc20−/H females is similar to the number of live born pups these females produce , indicating that embryos that attain the blastocyst stage were capable of developing into healthy animals . The above data indicated that the majority of eggs produced by Cdc20−/H females stop proliferating after the first cell divisions of the preimplantation period . To confirm this and to characterize preimplantation embryo development , we collected one-cell stage embryos from Cdc20+/+ and Cdc20−/H females crossed with Cdc20+/+ males and monitored their development in vitro . As expected , most embryos from Cdc20+/+ females developed to the blastocyst stage within four days ( Figure 3C and 3D ) . In contrast , none of the embryos from Cdc20−/H females developed beyond the 4-cell stage , with the majority of embryos remaining at the one cell stage . This growth phenotype is remarkably different from that of Cdc20−/− embryos , which typically arrest in metaphase at the two-cell stage due to inability to degrade cyclin B1 and securin in the absence of Cdc20 [23] . Importantly , one cell stage embryos from Cdc20−/H females are either Cdc20+/− or Cdc20+/H . Embryos of these genotypes show normal survival rates when derived from Cdc20+/− and Cdc20+/H females and Cdc20+/+ males ( see Figure 2B and 2C ) . Together , these data suggested that the early death of the embryos produced by Cdc20−/H females is due to defects introduced during oogenesis . We hypothesized that Cdc20 hypomorphism promotes chromosome missegregation during oogenesis , resulting in production of aneuploid embryos that fail to thrive . To test this idea , we collected one-cell stage embryos from Cdc20+/+ , Cdc20H/H and Cdc20−/H females mated with Cdc20+/+ males and prepared metaphase spreads for chromosome counts . We found that 11% of embryos from Cdc20+/+ females were aneuploid compared to 27% and 78% of embryos from Cdc20H/H and Cdc20−/H females , respectively ( Figure 4A ) . Aneuploidy was strongly biased toward loss of chromosomes , irrespective of Cdc20 genotype . Importantly , nearly 30% of aneuploid embryos from Cdc20−/H females had 14 to 19 extra chromosomes ( Figure 4A and 4D ) . We noted that these embryos contained a very high proportion of chromosome pairs ( Figure 4D ) , which suggested that they originated from mature oocytes that had failed to complete meiosis II after fertilization . Next , we determined whether Cdc20 hypomorphism also leads to erroneous chromosome segregation at earlier stages of oogenesis . During embryogenesis , primordial germ cells migrate to the developing gonad to form oogonia , which expand in number through a series of mitotic divisions before differentiating into primary oocytes that arrest in prophase of meiosis I . To determine whether the early mitotic divisions might contribute to the aneuploidy seen in fertilized eggs , primary oocytes were harvested from ovaries of Cdc20+/+ , Cdc20H/H and Cdc20−/H females . In mice , primary oocytes normally have 20 paired chromosomes , called bivalents . Primary oocytes from Cdc20+/+ and Cdc20H/H females had abnormal numbers of bivalents in 10% and 13% of spreads , respectively ( Figure 4B ) . In contrast , primary oocytes from Cdc20−/H females had considerably more aneuploidy , with 29% of spreads showing abnormal numbers of bivalents ( Figure 4B and 4E ) . These spreads showed no evidence of precocious separation of bivalents , indicating that formation of chiasmata was intact at low Cdc20 levels . Although Cdc20 insufficiency causes aneuploidy during the early mitotic divisions of oogenesis , aneuploidy rates of primary oocytes were substantially lower than those of fertilized eggs . To explore whether additional aneuploidy occurred during meiosis I , we prepared metaphase spreads from secondary oocytes of Cdc20+/+ , Cdc20H/H and Cdc20−/H females and counted chromosomes . We found that aneuploidy rates of secondary oocytes from Cdc20+/+ and Cdc20H/H females increased modestly to 23% and 22% , respectively ( Figure 4C ) . This verified that the level of Cdc20 protein in oocytes from Cdc20H/H females was enough to let the chromosomes separate correctly at meiosis I . In contrast , a much more dramatic increase was recorded for secondary oocytes from Cdc20−/H females , with 90% of spreads showing numerical chromosome abnormalities ( Figure 4C and 4F ) . To obtain direct evidence for chromosome missegregation during the first meiotic division of Cdc20 insufficient oocytes , we monitored chromosome movements of Cdc20+/+ and Cdc20−/H primary oocytes during meiosis I using time-lapse fluorescence imaging ( Figure 5A ) . To visualize chromosomes we injected in vitro transcribed H2B-mRFP mRNA into the oocytes . In this setup , oocytes from Cdc20−/H females displayed much higher rates of chromosome missegregation than oocytes from Cdc20+/+ females ( Figure 5B ) . The two types of errors that were observed are congression failure and chromosome lagging , of which the latter defect was clearly most frequent . Particularly , chromosome lagging incidents involving three or more lagging chromosomes occurred at much higher rates in Cdc20−/H oocytes ( Figure 5B and 5C , and Video S1 and Video S2 ) . Thus , consistent with our chromosome counts on secondary oocytes , chromosome segregation errors during meiosis I contribute considerably to the infertility phenotype of Cdc20−/H females . Orderly progression of oocytes through meiosis I is controlled by the APC/C , which prompted us to examine whether timing of meiosis I is deregulated at low Cdc20 levels . Cdc20+/+ and Cdc20−/H oocytes were injected with H2B-mRFP mRNA and observed by time-lapse microscopy while executing meiosis I . We found that the time from germinal vesicle breakdown ( GVBD ) to metaphase was similar in Cdc20+/+ and Cdc20−/H oocytes ( Figure 6A and 6B ) , which is consistent with the notion that Cdh1 functions as the primary ACP/C activator during the early stages of meiosis I [12] . However , the average time from metaphase entry to anaphase onset was about two times longer in Cdc20−/H oocytes than in Cdc20+/+ oocytes ( Figure 6A and 6B ) . This delay was unlikely to be due to chromosome segregation errors as oocytes with misaligned or lagging chromosomes were excluded from the analysis . Consistent with delayed metaphase progression , PBE extrusion was markedly delayed in Cdc20−/H oocytes ( Figure 6C ) . Taken together , these data indicate that the timing of metaphase I is subject to deregulation when the amount of Cdc20 protein is limited . To explore the mechanism underlying the chromosome missegregation phenotype of Cdc20−/H primary oocytes , we measured the rate of degradation of two key APC/CCdc20 substrates , cyclin B1 and securin [12] . In the first set of experiments , we injected Cdc20−/H and Cdc20+/+ primary oocytes with mRNA encoding cyclin B1-EGFP and monitored the degradation of fluorescent protein by live-cell imaging . Oocytes were coinjected with H2B-mRFP mRNA to accurately assess the timing of cyclin B1-EGFP degradation . As illustrated in Figure 7A and 7B , Cdc20+/+ oocytes degraded most of their cyclin B1-EGFP during late prometaphase and early metaphase . Cdc20−/H oocytes entered metaphase I around the same time as Cdc20+/+ oocytes . However , they did so with relatively high cyclin B1-EGFP protein levels and completed substrate degradation ∼2 h later than Cdc20+/+ oocytes . To confirm that cyclin B1 degradation was delayed , we used indirect immunofluorescence to measure endogenously expressed cyclin B1 levels of Cdc20+/+ and Cdc20−/H oocytes in metaphase I . As shown in Figure 7C and 7D , cyclin B1 levels were indeed higher in Cdc20−/H oocytes than in Cdc20+/+ oocytes . Importantly , these oocytes also showed elevated levels of phosphorylated Cdk substrates ( Figure 7C and 7D ) , suggesting that the rise in cyclin B1 expression resulted in increased cyclin B1-Cdk1 activity in metaphase I . Next , we coinjected securin-EYFP [24] and H2B-mRFP mRNA into Cdc20−/H and Cdc20+/+ primary oocytes . We noticed that expression of securin-EYFP protein markedly inhibited PBE even in Cdc20+/+ oocytes ( data not shown ) , but were able to control this problem by reducing the concentration of the injected securin-EYFP mRNA . In Cdc20+/+ oocytes , onset of securin-EYFP degradation typically coincided with metaphase entry and then rapidly progressed until anaphase onset ( Figure 8 ) . In Cdc20−/H oocytes , however , securin-EYFP protein degradation did not start until mid metaphase . Degradation not only started later , but was also less efficient , resulting in anaphase entry with higher than normal levels of securin-EYFP . In a recent study , McGuinness et al . demonstrated that the timing of cyclin A2 degradation in primary oocytes is similar to that of securin [17] , which is surprising given that mitotic cells fully degrade this cyclin in prometaphase . In light of these findings , we wanted to examine whether the degradation of cyclin A2 was impaired in Cdc20−/H oocytes . As for securin-EYFP , cyclin A2-EGFP inhibited PBE in Cdc20+/+ oocytes when expressed at high levels ( data not shown ) , but again we were able to control this problem by injecting low amounts of transcript . Consistent with the earlier data [17] , Cdc20+/+ primary oocytes rapidly destroyed cyclin A2-EGFP in metaphase I ( Figure 9 ) . In contrast , both the onset and the rate of cyclin A2-EGFP were substantially reduced in Cdc20−/H oocytes . Strikingly , Cdc20−/H oocytes again entered anaphase I with higher substrate levels than Cdc20+/+ oocytes . Taken together , the above data demonstrate that multiple APC/C substrates are inefficiently degraded when Cdc20 levels are low , raising the possibility that persistent cyclin-CDK activity in metaphase I might underlie , at least in part , the chromosome missegregation phenotype of Cdc20−/H oocytes . It is conceivable that delayed cyclin and securin degradation impairs separase activation , and therefore proper cleavage of cohesin along chromosome arms of bivalents prior to anaphase onset . To test for this possibility , we collected Cdc20−/H and Cdc20+/+ primary oocytes , cultured them in vitro until they arrested in metaphase II and then stained chromosomes for the presence of Rec8 , a meiosis specific component of the cohesin complex [25] , [26] . While Rec8 staining was readily detectable along chromosome arms of metaphase I chromosomes , no such staining was detectable in metaphase II oocytes , irrespective of Cdc20 genotype ( Figure S1 ) , implying that Cdc20−/H oocytes generated sufficient separase activity for complete cleavage of Rec8 . Furthermore , core mitotic checkpoint proteins that are involved in kinetochore assembly , kinetochore-microtubule and/or spindle assembly checkpoint activation , such as Bub1 , BubR1 , and Mad2 , were normally localized at kinetochores of Cdc20−/H primary oocytes ( Figure S2 ) . Cdc20−/H males appeared to have normal fertility , predicting that male meiosis I is much less sensitive to Cdc20 hypomorphism . To verify this , we prepared chromosome spreads of testicular cell suspensions from Cdc20+/+ and Cdc20−/H mice and performed chromosome counts on secondary spermatocytes . Although aneuploidy was 5-fold higher at low than at normal Cdc20 levels ( Figure 10A ) , secondary spermatocytes of Cdc20−/H males had much lower aneuploidy rates than secondary oocytes of Cdc20−/H females ( 19% versus 90% ) . Chromosome counts on primary spermatocytes revealed a 4-fold increase in aneuploidy due to Cdc20 hypomorphism , with 12% of spreads showing abnormal numbers of bivalents ( Figure 10B ) , suggesting that the mitotic divisions that spermatogonia have to undergo to produce primary spermatocytes are error prone at low Cdc20 levels . The rather modest increase in aneuploidy from 12% to 19% as primary spermatocytes develop into secondary spermatocytes underscores that the fidelity of male meiosis I remains quite high at low Cdc20 levels . Furthermore , histology and apoptosis rates were normal in testis of Cdc20−/H males , as judged by hematoxylin and eosin ( H/E ) and TUNEL staining of testis sections , respectively ( Figure 10C–10E ) .
By generating a series of mice with graded reduction in Cdc20 levels , we discovered a remarkably sharp threshold for Cdc20 expression in female germ cells below which chromosome segregation errors occur at high frequency , leading to production of aneuploid eggs that are fertilization competent but fail to progress beyond the first few embryonic divisions . On the other hand , low Cdc20 levels are well tolerated by somatic tissues and have no overt impact on the overall health and life expectancy of mice . These findings raise the intriguing possibility that hypomorphic Cdc20 alleles may be responsible for unexplained fertility problems in otherwise healthy women . Because aneuploidy has been associated with reduced cell growth and survival [27] , [28] , one might have predicted that oogenesis would be severely disrupted in Cdc20 hypomorphic mice . Surprisingly , however , we did not observe significant alterations in the number and morphology of follicles and corpora lutea in these mice . These findings suggest that cellular pathways that might inhibit cell proliferation or induce cell death in response to chromosome missegregation are either not active in female germ cells or require a higher threshold for activation than in somatic cells [29] . Our finding that folliculogenesis was unperturbed was also unexpected in light of studies showing that depletion of Cdc20 from primary oocytes by a morpholino causes metaphase I arrest [12] . For somatic cells it has been estimated that metaphase arrest requires a 20-fold or higher reduction in cellular Cdc20 levels [30] . We suspect that morpholino treatment reaches this level of reduction , whereas Cdc20 hypomorphism does not . In systematically karyotyping primary and secondary oocytes and fertilized eggs , we discovered that Cdc20 hypomorphism promotes aneuploidization at different stages of oogenesis , involving both mitotic and meiotic divisions . The highest increase in aneuploidy , however , occurred in the first meiotic division . The most prominent segregation errors that we observed during meiosis I are chromosome misalignment and chromosome lagging . Previous studies in HeLa and Ptk1 cells uncovered that cyclin A2 overexpression causes chromosome misalignment [31] , suggesting that alignment defects in Cdc20 hypomorphic oocytes might be related to their inability to destroy cyclin A2 in a timely fashion . Resolution of chiasmata requires removal of cohesin from chromosome arms , which involves cleavage of the cohesin subunit Rec8 by separase [32] . In turn , activation of separase requires APC/C-mediated degradation of securin and cyclin B , both of which are delayed in Cdc20 hypomorphic primary oocytes . Thus , it is possible that Cdc20 hypomorphic oocytes do not have enough APC/C activity to fully activate separase and properly resolve chiasmata , thereby prompting chromosome lagging and aneuploidization . Arguing against this explanation is the fact that chromosome spreads of Cdc20 hypomorphic metaphase II oocytes did not contain any bivalents or chromosomes that stained positive for Rec8 along chromosome arms . Alternatively , chromosome lagging in Cdc20 hypomorphic oocytes might be caused by microtubule-kinetochore attachment defects [33] , [34] . For instance , delayed degradation of cyclin B1 ( or other APC/C substrates ) might promote such defects by disrupting key components of the mechanisms that establish syntelic attachment or that correct merotelic or amphitelic attachments . We found that two mitotic checkpoint proteins required for proper microtubule-chromosome attachment , Bub1 and BubR1 , were normally localized at kinetochores of Cdc20 hypomorphic oocytes . However , it should be emphasized that microtubule-kinetochore attachment is a complex process requiring many different proteins , any of which could be deregulated in our mutant oocytes . Interestingly , in somatic cells , a small fraction of Cdc20 accumulates at kinetochores during mitosis [35] , which raises the possibility that Cdc20 might have a more direct role in establishing proper microtubule-kinetochore attachments . Our finding that a significant percentage of Cdc20−/H primary oocytes were already aneuploid before resuming meiosis I suggests that the mitotic divisions by which primordial germ cells develop into primary oocytes are prone to chromosome missegregation when Cdc20 levels are below a certain threshold . Due to technical limitations , it was not possible to verify this experimentally . The precise impact of Cdc20 hypomorphism on meiosis II is difficult to decipher , largely because nearly all oocytes are aneuploid after meiosis I . However , the presence of a sizeable amount of near triploid fertilized eggs strongly suggests that Cdc20 insufficiency can cause failure of maternal sister chromatids to separate during meiosis II , although we note that it cannot be excluded that the preexisting aneuploidy rather than the low Cdc20 levels drive the separation defect . It could be argued that embryos from Cdc20−/H females bred to Cdc20+/+ males might fail to thrive due to a potential lack of Cdh1 expression in the early embryos , rendering embryonic mitotic divisions particularly dependent on Cdc20 . However , this explanation seems unlikely because Cdh1 has been shown to be expressed in two-cell stage mouse embryos [23] . Furthermore , it should be considered that the embryos from Cdc20−/H females bred to Cdc20+/+ males that fail to thrive had either Cdc20+/− or Cdc20+/H genotypes . Embryos of both these genotypes show normal survival rates when derived from Cdc20+/− and Cdc20+/H females crossed with Cdc20+/+ males ( Figure 2B and 2C ) , further supporting the idea that aneuploidy acquired during oogenesis is largely responsible for the early death of embryos from Cdc20−/H females . An intriguing finding was that the fertility problems of Cdc20−/H mice are restricted to females , even though our analysis of aneuploidy in primary and secondary spermatocytes demonstrated that mitotic and meiotic divisions of male germ cells are prone to aneuploidy . However , the key difference between males and females that probably accounts for their distinct fertilities is that the rate of aneuploidization during meiosis I is substantially higher in females than in males . Why might female meiosis I be more sensitive to Cdc20 hypomorphism ? A recent study of mouse oocytes suggests that mammals have a unique mechanism for control of meiosis I in that they require APC/CCdh1 activity for progression through prometaphase I [12] . Cdc20 is targeted for destruction by this early APC/C activity and needs to be re-synthesized during metaphase I to enable anaphase onset . It is possible that Cdc20 destruction in prometaphase I only occurs in females , perhaps creating a higher degree of Cdc20 insufficiency in oocytes than in spermatocytes .
The gene targeting procedure used to produce the hypomorphic Cdc20 allele ( H ) was as previously described [22] . To generate the targeting construct , Cdc20 gene fragments of 3 . 9 kb ( spanning exons 1–3 ) and 4 . 7 kb ( spanning exons 4–10 ) were PCR amplified from 129Sv/E genomic DNA and cloned into HindIII-XbaI and SalI-NotI sites of pNTKV1901 ( Stratagene ) . The targeting construct was linearized with NotI and electroporated into TL1 129Sv/E ES cells . Transfectants were selected in 350 µg/ml G418 and 0 . 2 µM FIAU , and expanded for Southern blot analysis using a 710 bp 3′ external probe on EcoR1-digested genomic ES cell DNA . This probe was amplified by PCR from 129Sv/E genomic DNA using the following primers: 5′-CATGGCTGGTTTGGGAGAGAATGC TG-3′ and 5′-CACAACACAGTTCATCTTCCCAGTG-3′ . Chimeric mice were produced by microinjection of targeted ES cell clones with 40 chromosomes into C57BL/6 blastocysts . Chimeric males were mated with C57BL/6 females and germline transmission of the Cdc20H allele was verified by PCR analysis of tail DNA from pups with a agouti coat color . The Cdc20− allele used in our studies was derived from gene trap ES clone XE368 ( purchased from BayGenomics ) . The following primer combinations were used for PCR genotyping of mice used in our studies: primers a ( 5′-CAGAAAGCCTGGTCTCTCAACCTG-3′ ) and b ( 5′-CACAGTAGTCATTCCGGATT TCGGG-3′ ) for Cdc20+; primers b and c ( 5′-TCCATTGCTCAGCGGTGCTG -3′ ) for Cdc20H; and primers d ( 5′-GTATCCAACCATGGCCAAGGTGGCTGAG-3′ ) and e ( 5′-TATACGAAGTTATCGATCTGCGATCTGC-3′ ) for Cdc20− . All mouse experiments were conducted after approval of the Mayo Clinic Committee on Animal Care and Use . All mice in the study were of a 129Sv/E x C57BL/6 mixed genetic background . Female fertility was measured by breeding 2-month-old females of various Cdc20 genotypes to 2- to 3-month-old wild-type males for a 3-month period . During this period , we recorded , for each female , the number of vaginal plugs ( to determine whether females showed normal mating behavior ) , the number of litters produced , and the amount of pups delivered . Histological evaluation of testes and ovaries were as previously described [11] . Follicles and corpora lutea were counted in five ovary sections of each mouse . Follicle classification was according to Pedersen and Peters [36] . TUNEL staining was done on 5 µm testis sections using an in situ cell death detection kit from Roche . Western blot analysis was performed as described earlier [37] . Extracts of MEFs , splenocytes , and bone marrow were prepared in PBS containing 0 . 1% NP40 , 10% glycerol and complete protease inhibitor cocktail ( Roche ) . Extracts were centrifuged at 20 , 000 g for 15 min ( 4°C ) , and supernatants collected for electrophoresis . Quantitation of relative Cdc20 protein levels in Cdc20+/H , Cdc20+/− , Cdc20H/H and Cdc20−/H testis and ovary , and Cdc20−/H MEFs , spleen , and bone marrow was done as previously described [38] . Briefly , Cdc20 western blot signals obtained with rabbit Cdc20 antibody from Santa Cruz ( SC-8358 ) , were quantified using ImageJ software ( http://rsbweb . nih . gov ) and normalized to background and β-actin ( Sigma A5441 ) or α-tubulin ( Sigma , T-9026 ) signals . Values obtained were normalized to those of corresponding wild-type tissues and MEFs , where wild-type signals were set at 100 . Normalized signal values were converted to percent protein using the graph of Figure S3 . Relative Cdc20 protein amounts represent the average of at least two independent samples . Indirect immunofluorescence was performed as previously described [37] , [39] . Immunofluorescence images were captured using a Carl Zeiss LSM 510 laser-scanning microscope with a c-Apochromat 100× oil immersion objective . Fluorescent signals from cyclin B1 and P- ( Ser ) CDKs substrate labelings were quantitated using ImageJ software . The mean fluorescence intensity was determined after background subtraction of images transformed to 8 bits grayscale . The following primary antibodies were used: cyclin B1 ( Calbiochem , PC-133 ) , P- ( Ser ) CDKs substrate ( Cell Signaling , #2324 ) , BubR1 ( 1-350 ) [11] , human anti-centromere antibody ( Antibodies Inc , 15-235-0001 ) , Bub1 ( 25-165 ) [28] , Mad2 ( polyclonal anti-mouse full-length Mad2 antibodies generated in a rabbit ) , and Rec8 ( kindly provided by Dr . J . Lee [25] ) . Primary oocytes were isolated from ovaries of 3- to 4-week-old Cdc20+/+ and Cdc20−/H mice as described [40] , and cultured in micro-drops of G-1 v5 plus medium ( Vitrolife ) under embryo-tested paraffin oil ( Vitrolife ) . In case primary oocytes were used in mRNA microinjection experiments , 50 µg/ml dibutyryl cyclic AMP ( dbcAMP ) was added to the G-1 v5 plus medium to inhibit GVBD . To obtain secondary oocytes , 3- to 4-week-old Cdc20+/+ and Cdc20−/H females were injected with pregnant mare serum gonadotropin ( PMSG; 5 IU/mouse , Sigma G4527 ) and 46 h later with human chorionic gonadotropin ( hCG; 5 IU/mouse , Sigma C0684 ) . Eighteen h after the hCG injection , ovaries were collected and secondary oocytes harvested from oviducts . Metaphase II-arrested oocytes for Rec8 immunostaining experiments were prepared by culturing primary oocytes from ovaries of 3- to 4-week-old Cdc20+/+ and Cdc20−/H mice in G-1 v5 plus medium until they arrested in metaphase . Fertilized eggs were produced by mating 6- to 12 week-old Cdc20+/+ and Cdc20−/H females with Cdc20+/+ males . The next morning , one-cell stage embryos were harvested from oviducts and freed of cumulus cells as described [41] . Embryo culturing was done in micro-drops of G-1 v5 plus medium as described [42] . Embryos were photographed daily from day E0 . 5 to E4 . 5 . For chromosome counts on oocytes and one-cell stage embryos , the procedure of Tarkowski [43] was followed . Briefly , freshly harvested secondary oocytes and fertilized eggs were cultured for 20 h at 37°C in medium containing 0 . 5 µg/ml colcemid , incubated in 1% sodium citrate for 20 min at RT and transferred to glass slides . Ethyl alcohol and glacial acetic fixative ( 3∶1 ) was dropped on the zygotes and secondary oocytes three times . Air-dried slides were Giemsa stained and chromosomes counted using a light microscope with a 100× objective . Primary oocytes were collected and cultured in micro-drop cultures of G-1 v5 plus medium . Upon GVBD , primary oocytes were harvested and chromosome spreads prepared . For chromosome counts on spermatocytes , testes were collected and minced between two microscope slides . Released cells were suspended in 5 ml PBS , centrifuged at 1 , 000 rpm for 5 min , resuspended 5 ml 0 . 075 M KCl , and incubated at RT for 30 min . Cells were fixed in Carnoy's solution , washed , and finally resuspended in 0 . 5 ml fixative . Twenty-five µl aliquots were dropped onto pre-wetted microscope slides and chromosomes were stained with Giemsa . To measure the accuracy of chromosome segregation during meiosis I , chromosome movements of primary oocytes were followed by time-lapse microscopy . To this end , H2B-mRFP mRNA was produced by in vitro transcription using the T3 mMESSAGE mMACHINE kit ( Ambion Inc ) . Using a Femtojet microinjector ( Eppendorf ) , GV-stage primary oocytes were microinjected with 5–10 picoliter of mRNA solution containing 0 . 5 µg/ml H2B-mRFP mRNA [44] . Injected oocytes were allowed to recover for 30 min in micro-drops of M2 medium containing 50 µg/ml dbcAMP and then transferred to 35 mm glass-bottomed culture dishes ( MatTek Corporation ) containing G-1 v5 plus medium without dbcAMP to induce GVBD . Chromosome movements were followed using a Zeiss Axio Observer Z1 system with CO2 Module S , TempModule S , Heating Unit XL , Pln 40x/0 . 6 Ph2 DICIII objective , AxioCam MRm camera , and AxioVision 4 . 6 software [45] . The temperature of the imaging medium was kept at 37°C . Images were collected at interframe intervals of 20 min . To analyze timing of meiosis I , the time intervals from GVBD to prometaphase , prometaphase to metaphase , and metaphase to anaphase were measured . Importantly , only H2B-mRFP mRNA-injected Cdc20+/+ and Cdc20−/H oocytes progressing through meiosis I without any chromosome segregation errors were included in our timing analysis . To determine polar body extrusion rates , Cdc20+/+ and Cdc20−/H oocytes were collected and monitored by differential interference contrast ( DIC ) time-lapse microscopy as they progressed through meiosis I . To analyze the degradation kinetics of mitotic cyclins and securin , coding sequences for cyclin B1-EGFP , securin-EYFP and cyclin A2-EGFP were cloned into pBluescript RN3 or pMDL2 [46] , and mRNAs were produced by in vitro transcription as described above . GV-stage primary oocytes were microinjected with 5–10 picoliter of mRNA solutions containing 0 . 5 µg/ml H2B-mRFP +0 . 5 µg/ml cyclin B1-EGFP , 0 . 1 µg/ml H2B-mRFP +0 . 1 µg/ml securin-YFP , or 0 . 1 µg/ml H2B-mRFP +0 . 1 µg/ml cyclin A2-EGFP . Injected oocytes were allowed to recover for 30 min in micro-drops of M2 medium containing 50 µg/ml dbcAMP and then transferred to 35 mm glass-bottomed culture dishes . Time-lapse microscopy was initiated 1 or 2 h after GVBD to allow for expression of fluorescent protein-tagged APC/C substrates . Images were collected at interframe intervals of 20 min . Quantification of fluorescence levels was as follows . For each oocyte and for each time point , images detecting mRFP , EGFP/EYFP , and DIC were acquired . Time-lapse images were then exported as grayscale “avi” uncompressed files . Videos were opened using ImageJ using avi reader plugin . DIC images were used to highlight the area occupied by the oocyte using the freehand tool in ImageJ . The highlighted area was moved to the corresponding EGFP/EYFP image and the mean fluorescence intensity within this area measured after background subtraction . Mean fluorescence intensities were expressed in arbitrary units . The value of time zero ( the fluorescence intensity for the first image acquired ) was considered 100% and the subsequent time-lapse intensities were normalized against it . Excel T-TEST software was used for statistical analyses . | Aneuploidy , an abnormal number of chromosomes , is a common defect in sperm and egg cells that is responsible for human infertility , miscarriage , and congenital birth defects . Although these developmental outcomes are prevalent in human reproduction , little is known about the molecular defects that may cause aneuploidy in germ cells . In this study , we identify Cdc20 , a critical activator of the APC/C E3 ubiquitin ligase that initiates sister chromosome separation by ordering the destruction of cyclin B1 and securin , as a female infertility gene . We show that female mice with low amounts of Cdc20 have normal fitness but almost exclusively produce aneuploid embryos that fail to thrive and die early in development . The aneuploidy primarily results from chromosome segregation errors in primary oocytes that may be caused by inefficient APC/C-mediated destruction of mitotic cyclins and securin during metaphase I . Thus , our studies reveal that primary oocytes are highly dependent on Cdc20 for accurate chromosome segregation and raise the possibility that Cdc20 insufficiency may be a cause of infertility in otherwise healthy women . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology/germ",
"cells",
"cell",
"biology/cell",
"growth",
"and",
"division",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"genetics",
"and",
"genomics/chromosome",
"biology"
] | 2010 | Cdc20 Is Critical for Meiosis I and Fertility of Female Mice |
Taenia solium ( T . solium ) cysticercosis remains a neglected zoonotic disease in India . The current study was planned to estimate the prevalence of T . solium porcine cysticercosis in the Punjab state of India , to compare this prevalence with the disease prevalence in pigs reared outside Punjab and to assess the distribution of the parasite in pig carcasses . Two slaughter shops were selected in each of the 22 districts of Punjab . Pigs slaughtered on the day/s of inspection were post-mortem inspected to identify the presence of T . solium cysts . Estimated true prevalence was estimated by taking into account the diagnostic sensitivity ( 38% ) and specificity ( 100% ) of post-mortem inspection using the Rogan-Gladen estimator . Positive carcasses were purchased and brought to the laboratory to assess the tissue distribution of T . solium cysts and to conduct PCR targeting large subunit rRNA gene , internal transcribed spacer 1 gene , ITS1 gene and Cytochrome oxidase I gene . The selected PCR products were submitted for sequencing and phylogenetic analyses were performed . We contacted 71 shop owners to achieve a sample of 44 shops for the study . We inspected 642 pigs reared in Punjab and 450 imported from other states at these slaughter shops . In addition , we sampled 40 pigs from an abattoir located in the state capital . Of the 642 pigs reared in Punjab , 9 had T . solium cysts with an apparent prevalence of 1·40% ( 95% CI: 0·74% , 2·64% ) and the estimated true prevalence of 3 . 69% ( 95% CI: 1·95% , 6·95% ) . Pigs imported from outside the state had a significantly higher prevalence ( odds ratio: 2·58; 95% CI: 1·12 , 5·98; p-value: 0·026 ) as 15 of the 450 imported pigs were positive ( apparent prevalence: 3 . 33%; 95% CI: 2 . 03% , 5 . 43%; estimated true prevalence: 8 . 77%; 95% CI: 5 . 34% , 14 . 28% ) . None of samples was positive from the pigs sampled at the abattoir in the state capital . The PCR confirmed T . solium cysts from all the 24 positive samples . We counted a median of 897 ( range 526–1964 ) cysts per infected pig from the 19 infected pig carcasses inspected . The phylogenetic tree based on the alignment of partial cytochrome oxidase 1 sequences indicated all positive samples to be clustered with the T . solium Asian genotype . The analysis did not indicate the presence of T . asiatica in the slaughter pigs . Despite the underestimation of the prevalence due to missing mildly-infected carcasses , low participation and lack of representative sampling , the presence of heavily infected carcasses containing viable cysts , particularly those imported from outside the state , indicates that T . solium cysticercosis is an important food safety concern for pork consumers in Punjab , India . Measures should be taken to reduce the disease prevalence in pigs to reduce the disease burden in the public .
Taenia solium ( T . solium ) cysticercosis is an important disease affecting pigs . The pigs become infected after ingesting eggs or gravid proglottis released in the environment after contamination from the faeces of infected human beings . T . solium cysticercosis is endemic in pigs in India [1] and has been reported from several regions of India [2 , 3 , 4 , 5 , 6] . For example , a sero-prevalence of 11 . 6% of porcine cysticercal antigens was reported from Tamil Nadu , India [7] . Despite recent gains in the understanding of the nature and the prevalence of the disease , and successes in health interventions [8] , T . solium cysticercosis is still endemic and affects mainly poor people in the resource-limited countries [9] . Taenia solium human neurocysticercosis has been reported from the human population from many regions of India such as Bihar , Orissa , Uttar Pradesh and Punjab but is rare in Kashmir because of its majority Muslim population [10] . Recently , we demonstrated that human neurocysticercosis-associated active epilepsy results in 2·10 million ( 95% uncertainty interval 0·99–4·10 million ) disability-adjusted life years per annum in India [11] . A thorough inspection of slaughter pigs for T . solium cysticercosis could help break the life cycle of this parasite . The formal postmortem-inspection at slaughter commonly relies on visual inspection of predilection sites such as heart , diaphragm , masseters , tongue , neck , shoulder , intercostal and abdominal muscles [12] . Exploring other overlooked muscular regions or organs as predilection sites is essential to supplement the current post-mortem inspection procedures . Tongue test was reported to have 70% sensitivity and 100% specificity of in the detection of porcine cysticercosis [13] . Lightowlers et al . ( 2015 ) [14] estimated that slicing of the heart , tongue and masticatory muscles at a thickness of approximately 3 mm had a diagnostic sensitivity of approximately 80% in lightly infected animals and recommended tissue dissection as a highly specific and relatively low-cost method for diagnosis of porcine cysticercosis . Compared to tongue examination , ultrasonography has been found to more sensitive ( 100% versus 91% ) but less specific ( 90% versus 98% ) , although these differences were not statistically significant [15] . There is very limited information about the prevalence and distribution of T . solium porcine cysticercosis in the state . A study found the disease prevalence to be 4·23% [2] . However , that study was conducted only in four districts of the state . We are not aware of any study that provided prevalence estimates from other districts or estimated the tissue distribution of the cysts in the state . Further , it is known that scavenging and feral pigs are captured from other states and brought for slaughter and sale in the state . There is anecdotal evidence that these pigs are highly infested with cysts but there is no objective evidence for this . Therefore , the current study aimed to ( a ) estimate the prevalence of T . solium porcine cysticercosis by collecting samples from throughout the state , ( b ) assess the distribution of cysts in pig carcasses , and ( c ) compare the disease prevalence between pigs reared in the state and those imported from outside the state .
The Institutional Ethics Committee , Guru Angad Dev Veterinary & Animal Sciences University , Ludhiana did not have any objection to the study as samples were to be collected from slaughtered pigs ( Approval number IAEC/2015/97-129 ) . This study was carried out from August 2016 to July 2017 . Punjab is an agrarian state of Northern India ( Latitude of 30°4'N and Longitude 75° 5' E ) consisting of 22 districts , with a human population of over 27 million [16] and a pig population of more than 32 000 [17] . Most of the pigs are owned by small-holders belonging to low-income groups . As per the official data , 12 240 pigs were slaughtered during the year 2014 [17] . Although no objective data are available , the authors estimate that more than 80% of the pigs are slaughtered in small slaughter shops . The state does not have a pig abattoir except one in the state capital . The target population consisted of pigs slaughtered in Punjab , India . The study population consisted of pigs slaughtered in the 44 pig slaughter shops representing all 22 districts ( 2 per district ) of Punjab . The sample size was calculated using Statulator [18] to be 547 to estimate the prevalence with 95% confidence , a design effect of 1 . 5 and 2% margin of error and assuming an expected prevalence of 4% based on a previous study [2] . The sample size increased to 730 when a design effect of 2 was applied . We assumed the pig population to be 32000 for these estimates . The sample size was also calculated to be 393 to detect the disease in the state if present at a prevalence of 2% or above with 95% confidence , assuming the pig population to be 32000 and type I error of 5% , diagnostic sensitivity of 38% and a perfect specificity [19] . Note that this sample size would not be sufficient to demonstrate freedom of disease from each district . Two slaughter shops were selected in each of the 22 districts of Punjab . A sample of 10–75 pigs slaughtered on the day/s of inspection was post-mortem inspected to identify the presence of T . solium cysts at each slaughter shop . Pig population and number of pigs inspected for T . solium cysticercosis in different districts of the state have been described in S1 Table . In addition , pigs imported from outside the state by the slaughter shop owners were selected , if available on the day of sampling . Finally , 40 pigs slaughtered at an abattoir in the capital of the state were also sampled . Although the selection of pigs at the shops/abattoir was not random , we tried to ensure that the pigs are loosely representative of the target population and did not select more than two pigs from a pig owner or a batch ( pigs from unknown owners sold by a middleman at the slaughter shop ) . At the time of post-mortem inspection , the masseter and pterygoid muscles , diaphragm , tongue and heart muscles were visually examined , palpated and incised at least twice with long and parallel incisions [2 , 20] . The remaining carcass was visually inspected for the presence of cysts . The viable cysts from all infected carcasses were stored in 70% ethanol for the molecular analysis . The viable cysts had cyst walls containing larval cestode with fluid filled bladder and an invaginated scolex [2] . Caseous cysts were considered degenerated cysticercii unless another etiology was evident [2] . The infected pig carcasses were purchased and transported to the laboratory in biohazard bags for further examination . The distribution of T . solium cysts in different muscles and organs of the pigs were assessed [21] . The visceral organs were separated , and the remaining carcass was longitudinally cut into two equal parts . The visceral organs along with half the carcass were sliced and the cysts were counted in all the visceral organs and one of the two equally divided half-carcasses . The degenerative and viable cysts were separately counted in the selected muscles and organs . The viable cysts from all the infected carcasses were stored in 70% ethanol for molecular analysis . We explored diaphragm , tongue , hyoid muscle , Biceps femoris , abdominal muscles , Serratus Ventralis , Longissmus dorsi , Intertransversus lumborum , Triceps Brachhi , internal and external masseter muscles . In addition , visceral organs such as spleen , liver , lungs , heart , kidney , brain and oesophagus were also explored . Median cysts per muscle/organ were divided by the respective average muscle/organ weight ( gm ) to estimate the number of median cysts per gram in the infected muscles/organs . One cyst per carcass was homogenised in a sterile pestle and mortar and 30 mg of the cyst material was used for the extraction . DNA extraction was carried out using HiPurA mammalian genomic purification spin kit ( Himedia ) as per manufacturer’s instructions . The eluted DNA was stored at—20°C till further use . The published oligonucleotide primer sequences were used for the PCR amplification ( Table 1 ) and were synthesized by Eurofins Pvt . Ltd . The PCR reaction was carried out in a total reaction volume of 25μl containing 12·5μl master mix ( GoTaq green Promega ) , 1μl each of forward and reverse primers ( 10 pmol ) , 5·5μl nuclease free water and 5μl DNA template ( 20–100 ng ) . The cycling condition for uniplex PCR was pre-denaturation and polymerase activation step at 94°C for 2 min , with 35 amplification cycles ( denaturation at 94°C for 30 sec , annealing for large subunit rRNA gene and internal transcribed spacer 1 gene at 60°C , ITS1 gene at 56°C and cytochrome oxidase I gene at 50°C for 30 sec and elongation at 72°C for 1 min ) and a final elongation step at 72°C for 5 min in Master cycler Pro ( Eppendorf , T-Gradient ) thermal cycler . The morphologically confirmed T . solium cysts were used as positive control ( s ) . The DNA extraction control and no template controls were used as negative controls . The 10μl of amplified product was subjected to the 1 . 5% agaorse gel electrophoresis and gel documentation . Due to suitability of the mitochondrial genes to compare polymorphisms in T . solium and to establish phylogenetic trees for related Taenia species ( T . solium , T . saginata and T . asiatica ) , the positive amplicons using JB3 and JB4 . 5 primers against mitochondrial cytochrome oxidase 1 gene were purified and sequenced . DNA sequencing was performed in both directions by AgriGenome , Kerala , India . Sequence chromatograms were analysed using the Bioedit , ClustalW and Mega 6·0 computer software programmes . Sequences were matched using NCBI BLAST software , aligned and compared with previously published sequences of T . solium ( Gene Bank accession numbers AF360870·1 , FM958310·1 , GU097653·1 , EF076752·1 , FN995658·1 , and FN995666·1 ) using Mega 6 . 0 computer software . The sequences were also compared with T . saginata ( Gene Bank accession number AB533172·1 ) and T . asiatica ( Gene Bank accession number AB107236·1 ) . Echinococcus granulosus ( Gene Bank accession number FJ608752 . 1 ) and E . multilocularis ( Gene Bank accession number AB461420·1 ) were used as an out group . Distance-based analysis was conducted and a tree was constructed using the neighbour-joining algorithm and Mega 6·0 software . The apparent and estimated true prevalence of porcine cysticercosis was estimated with 95% confidence interval ( CI ) using Epi Tools [22 , 23] . Univariable logistic regression analyses were performed to assess the effect of age , sex and pig rearing status on the disease prevalence using a binary outcome variable ( cysts present: 1/0 ) . For tissue distribution , the median number of T . solium cysts from all the positive pigs were estimated from the total number of cysts counted from muscles and visceral organs of all the 19 pig carcasses . The Wilcoxon rank sum test was used to compare the number of cysts present in male and female pigs as the distribution of cyst counts was right skewed and therefore , the assumptions of the parametric 2-sample t-test were invalid . However , the difference in viable and degenerative cysts within a carcass was approximately normally distributed , and therefore , the paired t-test was used to test if the mean difference was significantly different from zero . Fisher’s exact test ( one tailed ) was conducted to compare the disease prevalence between formal abattoir and slaughter shop inspected pigs from Punjab . All the analyses were conducted in R-statistical program unless indicated otherwise ( R statistical package version 3 . 4 . 0 , R Development Core Team ( 2015 ) , http://www . r-project . org ) .
We contacted 71 slaughter shop owners of which 44 consented to participate in the study ( response rate = 61% ) . We inspected 642 pigs reared in Punjab , of which 9 had T . solium cysts with an apparent prevalence of 1·40% ( 95% CI: 0·74% , 2·64% ) and the estimated true prevalence of 3·69% ( 95% CI: 1·95% , 6·95% ) . Pigs imported from outside the state ( n = 450 ) were selected from four districts , namely Ludhiana ( 138 ) , Jalandhar ( 256 ) , Firozpur ( 21 ) and Patiala ( 35 ) , of which 15 were positive indicating an apparent prevalence of 3·33% ( 95% CI: 2·03% , 5·43% ) and the estimated true prevalence of 8·77% ( 95% CI: 5·34%-14·28% ) . Pigs imported from outside had a significantly higher prevalence ( odds ratio: 2·58; 95% CI: 1·12 , 5·98; p-value: 0·026 ) than the pigs reared in the state . None of samples was positive from the pigs sampled from the abattoir in the state capital . Data analysis revealed high disease prevalence from the slaughter shops as compared to the formal abattoir , although the association was not statistically significant ( p-value = 0·58 ) . Detailed information on the district-wise apparent and estimated true prevalence ( 95% CI ) is presented in Table 2 . The disease was recorded only from four districts of the state . The polymerase chain reaction confirmed the T . solium cysts and revealed the amplicon sizes of 286 bp , 420 bp , 1150 bp and 333 bp from all the 24 positive samples targeting large subunit rRNA gene , cytochrome oxidase I gene , internal transcribed spacer 1 gene and the diagnostic antigen Ts14 gene , respectively . The PCR did not show any reaction with the negative controls . The phylogenetic tree based on the alignment of partial cytochrome oxidase 1 sequences indicated that all positive samples were found to be clustered with the T . solium Asian genotype ( Fig 1 ) . The analysis did not indicate the presence of T . asiatica in the inspected pigs . The three cytochrome oxidase 1 gene T . solium partial coding sequences had an alignment score of 99% among themselves and 95–99% with the previously reported [2] T . solium sequences from Punjab . The sequence identity of all the sequences from this study was in the range of 97–99% with the published T . solium sequences in the database having accession numbers AF360870 . 1 , FM958310 . 1 , GU097653 . 1 , EF076752 . 1 , FN995658 . 1 , and FN995666 . 1 . All the three isolates from this study showed 88% similarity with the sequences of T . sagianta from China ( AB107247 ) and Mongolia ( AB271695 ) whereas as no significant similarity was found with Taenia asiatica ( AB107236 ) . The explanatory variable ‘pig rearing area’ was significantly associated with the outcome variable ( OR 3·0; 95% CI 1·12 , 5·98; p < 0·01 ) . The prevalence was not significantly different in different age ( OR 1·2; 95% CI 0·5 , 3·3; p = 0·7 ) or sex ( OR 0·6; 95% CI 0·3 , 1·5; p-value = 0·3 ) groups . Multivariable analyses were not conducted as only one explanatory variable was significant in univariable analyses . Of the 24 positive pig carcasses , 19 were purchased and further examined in the laboratory . The carcasses weighed 40 . 7 kg on the average ( range 36–50 kg ) and contained a median of 897 ( range 526–1964 ) cysts per infected pig carcass . Of these , a median of 280 cysts ( range 160–720 ) were viable and 575 ( range 350–1244 ) were degenerative . The mean cyst count was significantly different between degenerative and viable cysts ( 291; 95% CI 231–351 ) ( p-value: < 0 . 001 ) . However , the median number of cysts was not significantly different between male ( 947 ) and female ( 815 ) pigs . Maximum median numbers of cysts were counted in triceps brachi muscle ( median 99; range 48–782 cysts ) , serratus ventralis ( 93; 21–334 ) followed by inter transversus muscle ( median 89; 45–231 ) and the external masseter muscle ( median 63; 36–194 ) . The cyst burden was highly variable between organs and muscle groups . High loads were observed in the masseter , forelimb ( s ) and hind limb ( s ) muscles whereas the low counts were observed in the tongue , oesophagus , brain , heart and hyoid muscles . The median numbers of cysts in all the positive carcasses has been presented in Table 3 .
The current study re-confirms that T . solium cysticercosis is endemic in pigs in the Punjab state of India . We estimated a true prevalence of 3·7% ( 95% CI: 1·95% , 6·95% ) in pigs reared in the state . A previous study also reported an apparent prevalence of 4·23% ( 95% CI: 2·8 , 6·3 ) from the selected areas in the state [2] . Higher apparent prevalence of 5·14% has been reported from Uttar Pradesh [24] and 9 . 5% from the Assam state of India [5] . Many factors such as geographic area , rearing practices and pig owners’ hygienic practices play important role in the occurrence of porcine cysticercosis [25] . Therefore , the geographical distribution of infection varies in different states of India [26] . The endemic nature of the parasite demands implementation of control measures in the affected areas . The prevalence estimate in this study is likely to be an under-estimate of the actual prevalence . A low response rate ( 61% ) could have caused a selection bias as only the shop owners receiving relatively healthier pigs might have agreed to participate in the study . Additionally , only two shops per district were selected . This might have led to an underestimation of disease prevalence as it is possible that those butchers who were aware of a high rate of infection among their carcasses were also more likely to have refused permission for the study for fear of the legal consequences or fear of loss of income . Further , the pigs were inspected with the inspection procedure reported to have a diagnostic sensitivity and specificity of 38% and 100% , respectively [21] . Although false positive detections were unlikely , it is quite likely that we could have missed a number of positive carcasses and only detected heavily infected pigs , further leading to an under-estimation of the disease prevalence . Slicing of the heart , tongue and masticatory muscles at a thickness of approximately 3 mm has been reported to have a diagnostic sensitivity of approximately 80% in lightly infected animals and therefore this method is recommended to be used in the future research [14] . Availability of a highly specific and sensitive serologic test could also overcome this limitation in the future . The use of battery of tests such as a combination of both serological and post-mortem inspection could improve diagnostic sensitivity in the future research . The disease was detected from four of the 22 districts in the state . We calculated sample size for estimating the disease prevalence in the state and to detect the disease if it is present at more than 2% level . However , the sample size was not sufficient to detect the disease in districts . Therefore , we cannot be sure that the districts from where the carcasses were found to be negative for cysticercosis do not have the disease . Further studies by calculating sample size sufficient to detect the disease at a district or sub-district level should be conducted to detect the disease or to demonstrate freedom from the disease in these districts . The selection of slaughter shops from within districts was not random due to a lack of a database of the numbers and types of pig slaughter shops and an absence of animal identification systems in the state . This could have biased our results but for the first time we selected samples from all districts of the state and tried to ensure representative selection of pig carcasses at pig shops . Further , some slaughter shop owners declined to sell infected pig carcasses for the study due to fear of any legal action which contributed to the low number of carcasses in the tissue distribution study ( only 19 out of 24 infected whole carcasses could be inspected to assess tissue distribution ) . Interestingly , none of the pigs from the formal abattoir was found to be positive . This could be due to the reason that only healthier pigs are submitted for slaughter to formal abattoirs . This suggests that meat inspection in formal abattoirs alone is not sufficient to control the disease . Therefore , inspection should also be conducted in unorganised slaughter shops . Pigs imported from outside the state had significantly higher prevalence than those reared in the state suggesting that they are a greater risk for the spread of T . solium cysticercosis in the state . The anecdotal information received from the slaughter shop owners revealed that most of these pigs are scavenging or feral pigs brought from other states and sold at discounted rates . Scavenging of pigs has been reported to be an important risk for porcine cysticercosis [2] . Similarly , feral pigs have been reported to be an important source for infectious diseases [27] . We recommend that all pigs imported from outside the state must be inspected for T . solium cysticercosis during slaughter in the state . Additionally , an import restriction such as tongue inspection or serological testing of all these pigs at the port of entry is also recommended . We detected high cyst loads in the infected pig carcasses . High cyst load of 76–80340 cysts in pig carcasses has also been reported from Tanzania [28] . Similarly , approximately 20% of the carcasses contained more than 200 cysts and 93% of the infected carcasses had viable cysts in an endemic focus in Nepal [29] . This indicates that high cyst loads are not uncommon in the endemic areas . Presence of heavy cyst load in infected carcasses undoubtedly raises a public health concern as it increases the number and risk of infected pork servings consumed by the pork consumers in the state . The cysticerci were recovered from all important muscles such as diaphragm , tongue , hyoid muscle , biceps femoris , abdominal muscles , serratus ventralis , longissmus dorsi , intertransversus lumborum , triceps brachii , external masseter and internal masseter muscles . The cysts were also recorded from heart , brain , and oesophagus , whereas no cyst was recovered from the kidneys , liver , spleen and lungs . Similar observations have also been recorded in Tanzania and Nigeria [28 , 30] . Phylogenetic analysis revealed the presence of Asian genotypes of T . solium from all of the samples . Molecular detection and phylogenetic analysis were performed on one cyst per infected pig . Therefore , the presence of species other than T . solium could not be ruled out . However , none of the pigs examined to determine the tissue distribution had Taenia cysts in their livers , suggesting the absence of T . asiatica in the infected pigs . The Asian genotype of T . solium has been reported from many other Asian countries , including China , Thailand and Indonesia [31] . Many genotyping studies have targeted the mitochondrial cytochrome oxidase 1 gene and have reported African/Latin American genotypes of T . solium from East African countries [32] . Taenia asiatica and T . solium ( African/Latin American genotype ) were not detected in the current study . The choice of diagnostic test for the diagnosis of T . solium cysticercosis in pigs is an important issue . We performed post-mortem inspection of slaughter pigs in the current study . The carcass examination is a highly specific method and can readily differentiate between viable and degenerative cysts . However , it reduces carcass quality and the diagnostic sensitivity of carcass examination is low barring the whole carcass examinations . A serological test could reduce labour , costs involved and maintain carcass quality but most of the available serological tests also show a lower sensitivity in rural pigs having a low cyst burden [20 , 33 , 34] . In the absence of reliable serological test , we believe our choice of post-mortem inspection to be appropriate in the current situation . Many risk factors such as scavenging of pigs , deworming and vaccination status of pigs , presence or absence of latrines in the households , farmer’s level of education , household size , annual family income , habit of consuming of raw pork and hand-washing after using the toilet , and human taeniosis status of a pig farmer that may be associated with the infection in pigs could not be evaluated due to the lack of information of farm level factors in this study . Therefore , further studies involving farms should be conducted to investigate farm level risk factors . The study found an estimated true prevalence of 3 . 69% in domestic and 8 . 77% in imported pigs slaughtered in the Punjab state of India . Many factors such as underestimation of the prevalence due to missing of mildly-infected carcasses , low participation and lack of representative sampling , and a low sample size to molecularly differentiate Taenia species undermined the outcome of this study . However , absence of cysts in liver of the infected pigs and presence of heavily infected carcasses containing viable cysts indicates that T . solium cysticercosis is an important food safety concern in Punjab and should be tackled as a priority . Pigs slaughtered at slaughter shops should be regularly examined to ensure that the pork is safe for human consumption . Special attention should be paid to the scavenging and feral pigs imported for slaughter and sale in the state . A disease control policy should be developed and implemented using a One Health approach to control the disease both in pigs and the public . | Taenia solium cysticercosis is a neglected zoonosis and severely affects pork production and public health in India . The current study was conducted to estimate the prevalence and distribution of T . solium porcine cysticercosis in the Punjab state of India and to compare the disease prevalence in pigs reared within and outside Punjab . Overall , 24 of the 1132 inspected pigs had viable cysts . Imported pigs had significantly higher prevalence than the pigs produced locally . The analysis did not indicate the presence of T . asiatica in the slaughter pigs . The study confirms the endemic nature of T . solium cysticercosis in Punjab state of India and provides suggestions about reducing the disease burden in pigs and the public . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"tropical",
"diseases",
"geographical",
"locations",
"vertebrates",
"india",
"parasitic",
"diseases",
"animals",
"animal",
"slaughter",
"mammals",
"tongue",
"neglected",
"tropical",
"diseases",
"animal",
"management",
"cardiac",
"muscles",
"digestive",
"system",
"swine",
"musculoskeletal",
"system",
"epidemiology",
"abdominal",
"muscles",
"muscles",
"agriculture",
"people",
"and",
"places",
"helminth",
"infections",
"eukaryota",
"mouth",
"asia",
"anatomy",
"cysticercosis",
"biology",
"and",
"life",
"sciences",
"amniotes",
"organisms"
] | 2018 | Prevalence and distribution of Taenia solium cysticercosis in naturally infected pigs in Punjab, India |
Bacteria engage in contact-dependent activities to coordinate cellular activities that aid their survival . Cells of Myxococcus xanthus move over surfaces by means of type IV pili and gliding motility . Upon direct contact , cells physically exchange outer membrane ( OM ) lipoproteins , and this transfer can rescue motility in mutants lacking lipoproteins required for motility . The mechanism of gliding motility and its stimulation by transferred OM lipoproteins remain poorly characterized . We investigated the function of CglC , GltB , GltA and GltC , all of which are required for gliding . We demonstrate that CglC is an OM lipoprotein , GltB and GltA are integral OM β-barrel proteins , and GltC is a soluble periplasmic protein . GltB and GltA are mutually stabilizing , and both are required to stabilize GltC , whereas CglC accumulate independently of GltB , GltA and GltC . Consistently , purified GltB , GltA and GltC proteins interact in all pair-wise combinations . Using active fluorescently-tagged fusion proteins , we demonstrate that GltB , GltA and GltC are integral components of the gliding motility complex . Incorporation of GltB and GltA into this complex depends on CglC and GltC as well as on the cytoplasmic AglZ protein and the inner membrane protein AglQ , both of which are components of the gliding motility complex . Conversely , incorporation of AglZ and AglQ into the gliding motility complex depends on CglC , GltB , GltA and GltC . Remarkably , physical transfer of the OM lipoprotein CglC to a ΔcglC recipient stimulates assembly of the gliding motility complex in the recipient likely by facilitating the OM integration of GltB and GltA . These data provide evidence that the gliding motility complex in M . xanthus includes OM proteins and suggest that this complex extends from the cytoplasm across the cell envelope to the OM . These data add assembly of gliding motility complexes in M . xanthus to the growing list of contact-dependent activities in bacteria .
Bacteria interact extensively within and between species to coordinate cellular activities or efficiently compete . These interactions rely on diffusible factors or on direct cell-to-cell contacts [1 , 2] . Contact-dependent interactions include transfer of DNA or proteins by type IV secretion systems , killing involving the delivery of toxins by the type VI secretion systems , contact-dependent growth inhibition involving two-partner secretion systems , and stimulation of motility in Myxococcus xanthus [3–5] . Here , we focused on understanding the contact-dependent mechanism underlying stimulation of gliding motility in M . xanthus . Bacterial motility facilitates a wide variety of processes including virulence , biofilm formation and development [6] . Bacteria have at least three mechanisms for motility on surfaces . Rotating flagella propel bacterial cells to bring about translocation [7] . Type IV pili undergo cycles of extension , adhesion to a substratum and retraction to bring about motility [8] . Bacterial cells can also move over surfaces without the aid of flagella and type IV pili by a mechanism referred to as gliding motility [9] . Bacterial flagella are homologous structures present in Gram-positive as well as–negative bacteria , mechanistically fairly well-understood and consists of four parts that together span from the cytoplasm , across the cell envelope to the cell surface [7] . Type IV pili are similarly widespread homologous structures and the machinery underlying type IV pili function also spans from the cytoplasm to the cell surface [8] . Gliding motility is present in Gram-positive as well as in -negative bacteria [9] . In contrast to the homologies observed for flagella and type IV pili systems , the machineries for gliding motility are non-homologous suggesting that gliding motility has evolved independently several times [6 , 9] and mechanistically , gliding motility is poorly understood . M . xanthus is a rod-shaped , Gram-negative bacterium that has two genetically distinct motility systems that allow translocation on solid surfaces in the direction of the long axis of a cell [10] . One system depends on type IV and is often referred to as S-motility [10 , 11] . Gliding motility , often referred to as A-motility in M . xanthus , allows movement of single cells [10] . The force for gliding is generated along the cell length [12–14] . Consistently , several proteins that are required for gliding motility have been shown using fluorescently-tagged fusion proteins to localize to clusters that are distributed along the length of the ventral side of the cell [13 , 15–18] . Traction force is generated at the position of these clusters supporting the notion that these clusters represent gliding motility complexes [13] . The gliding motility complexes assemble at the leading cell pole , remain stationary with respect to the substratum as a cell is moving , and disassemble as they approach the lagging cell pole . During gliding M . xanthus cells deposit slime trails of unknown composition and the motility complex have been suggested to attach to the substratum via the slime [19] . Numerous proteins involved in gliding motility have been described [13 , 17 , 18 , 20–22] . Genetic and cytological evidence suggests that gliding motility is driven by a protein complex that spans part or all of the cell envelope . This complex includes the AglR , AglQ and AglS proteins , which are homologs of MotA/TolQ/ExbB ( AglR ) and MotB/TolR/ExbD ( AglQ and AglS ) and form a proton channel in the inner membrane ( IM ) [13 , 18] . AglQ and AglR have been shown directly to localize to the clusters of motility complexes [13 , 23] ( Fig 1 ) . Additional proteins that localize to the cytoplasm , IM , periplasm or outer membrane ( OM ) have been suggested to be components of the gliding motility complex . These proteins include the 11 GltA-K proteins that are encoded by two gene clusters ( Fig 1 ) and among which the eight GltA-H are paralogs of the NfsA-H proteins that are important for spore formation [17 , 24–27] . The NfsA-H proteins have been suggested to form a complex that spans the cell envelope and with protein localizing to the IM , periplasm and OM . Similarly , the GltA-K proteins have been suggested to form a complex that would span from the cytoplasm to the OM [17] . Several of the proteins required for gliding motility have been shown to interact based on pull down experiments [15 , 17] ( Fig 1 ) . However , only the cytoplasmic AglZ [16] , CglF ( GltF ) , which is reported to be a periplasmic as well as an IM protein , and GltD , which is reported to localize to the cytoplasm and periplasm , have been shown to localize to the clusters of motility complexes along the cell length [15 , 17] ( Fig 1 ) . GltD and AglR have also been reported to localize to a rotating helical structure [18 , 23] . Accordingly , two models for the gliding machinery in M . xanthus have been proposed . In both models , motility complexes are distributed along the length of the ventral side of the cell . In the focal adhesion model , the motility complex spans from the cytoplasm over the IM , periplasm , OM to the cell surface where it connects to the substratum via the slime and generate traction force [13 , 17 , 19] . In the helical rotor model , the motility complex spans the IM and periplasm and move on a rotating helix . These complexes are hypothesized to slow down close to the substratum in that way forming clusters and causing a deformation of the cell surface . These deformations , in turn , generate drag forces between the cell and the substratum [18 , 23] . An analysis of the biophysical properties of cell-substrate interactions during gliding , provided evidence in favor of a focal adhesion model [28] . A prediction from the focal adhesion complex model is that the gliding motility complex includes proteins that localize to the OM . Even though several proteins that are essential for gliding motility have been predicted or directly shown to localize to the OM [17 , 29 , 31 , 32] ( Fig 1 ) , none of these proteins have been shown to be part of the gliding motility complex . Interestingly , several gliding motility mutants including cglC mutants can be transiently stimulated to move by gliding upon direct contact with cells that are wild-type ( WT ) for the corresponding genes [33] . This process , termed stimulation , depends on the contact-dependent physical transfer of OM lipoproteins between cells [29 , 34–36] in a process that may involve fusion of the OMs of donor and recipient [36 , 37] . Currently , it is not known how transfer of lipoproteins required for gliding results in stimulation of gliding in M . xanthus . Interestingly , in the type IV pili system of M . xanthus , lack of the Tgl lipoprotein results in a defect in type IV pili-dependent motility [33] . Type IV pili-dependent motility can be transiently restored in a tgl mutant by the contact-dependent physical transfer of Tgl from a tgl+ donor [29 , 33] . In the recipient cells , transferred Tgl stimulates multimer formation by the PilQ secretin in the OM thereby allowing assembly of the type IV pili machinery [29 , 38 , 39] . Here , we identify the first OM components of the gliding machinery . We demonstrate that GltB and GltA are OM β-barrel proteins that form a subcomplex with the periplasmic GltC protein and that this subcomplex is an integral part of the gliding motility complex . Moreover , our data suggest that the OM lipoprotein CglC stimulates gliding by facilitating the OM integration of GltB and GltA . Also , our data demonstrate that assembly of the gliding motility complex depends on CglC , GltB , GltA and GltC as well as on AglZ in the cytoplasm and AglQ in the IM suggesting a non-hierarchical assembly pathway for the motility complex . Intriguingly , upon stimulation of a ΔcglC recipient by a cglC+ donor , CglC instigates the assembly of the gliding motility complex in the recipient likely by facilitating the integration of GltB and GltA into the OM in the recipient . Therefore , transferred CglC directly stimulates assembly of the gliding motility complex .
While studying the RomR response regulator that is required for full function of both motility systems in M . xanthus [40 , 41] , we isolated miniHimar insertion mutations that caused defects in gliding motility . Two of these insertions mapped to MXAN_2540 and MXAN_2541 ( Fig 2A ) . Previously , MXAN_2538 , MXAN_2541 and MXAN_2542 were identified in transposon mutagenesis screens and suggested to be important for gliding motility [21 , 22] . Luciano et al . [17] showed that in-frame deletions of MXAN_2538 , MXAN_2539 , MXAN_2540 or MXAN_2541 caused gliding motility defects and named these genes gltK , gltB , gltA and gltC , respectively ( Fig 1 ) . Because MXAN_2538 was recently shown to be identical to cglC [20] , we investigated these genes in more detail to gain insight into how CglC may stimulate gliding motility . To verify the results of the transposon mutagenesis screen and previously published data , in-frame deletions in each of the five genes from MXAN_2538-MXAN_2542 were generated in the fully motile WT strain DK1622 . Motility assays using DK1622 and verified mutants for gliding ( DK1217 ) and type IV pili-dependent motility ( DK1300 ) as controls , demonstrated that WT as well as the five in-frame deletion mutants formed the rafts characteristic of type IV pili-dependent motility on 0 . 5% agar , which is favorable to type IV pili-dependent motility only [42] , whereas DK1300 as expected did not form these rafts ( Fig 2B ) . On 1 . 5% agar , which is favorable to gliding motility only , DK1622 displayed the single cells and slime trails characteristic of gliding motility at the edge of the colony . In contrast , neither the DK1217 control strain nor the mutants with in-frame deletions of MXAN_2538 , MXAN_2539 , MXAN_2540 or MXAN_2541 did . The in-frame deletion of MXAN_2542 did not have any effect on gliding motility . In agreement with these findings , it was recently reported using a Tn5 transposon insertion in MXAN_2542 that this gene is not essential for gliding [20] . Consistently , we found that MXAN_2539 , MXAN_2540 or MXAN_2541 were transcribed as a single polycistronic mRNA that did not include MXAN_2542 using reverse transcription-PCR on total RNA isolated from exponentially growing WT cells ( S1 Fig ) . Henceforth , the cglC , gltB , gltA and gltC nomenclature is used for MXAN_2538-_2541 . CglC , GltB , GltA and GltC bear the hallmarks indicative of cell envelope localization: CglC contains a type II lipoprotein signal peptide followed by a region that does not contain known domains ( Fig 2E ) in agreement with previous analyses [20] . GltB , GltA and GltC all contain a type I signal peptide . GltB and GltA are paralogs and have a domain homologous to the OM β-barrel domain of OmpA from Escherichia coli ( S2 Fig ) suggesting that GltB and GltA are integral OM proteins . GltC contains multiple TPR repeats that are frequently observed in proteins in multi-protein assemblies . Moreover , the GltB and GltA paralogs NfsB and NfsA are also predicted to be integral OM β-barrel proteins and localize to the OM when expressed in E . coli [25] . Also , the GltC paralog NfsC associates with the OM when expressed in E . coli [25] . To confirm that the gliding motility defects caused by the in-frame deletions of cglC , gltB , gltA and gltC were due to the lack of the corresponding protein and not a polar effect on downstream genes , we carried out complementation experiments in which cglC was cloned downstream of its native promoter and gltB , gltA and gltC separately cloned downstream of the promoter of the gltBAC operon ( Fig 2A ) . The corresponding plasmids were integrated ectopically at the phage Mx8 attB site in the relevant mutants . The gliding defect in all four mutants was complemented by the ectopic copy of the corresponding WT gene ( Fig 2B and 2C ) . Immunoblot analyses demonstrated that CglC and GltC accumulated at lower levels in the complementation strains than in WT ( Fig 2D ) . In contrast , GltB and GltA accumulated at higher levels in the complementation strains than in WT despite using the same promoter for expression of gltB , gltA and gltC . In complementation strains in which cglC , gltB , gltA and gltC were expressed from the pilA promoter full complementation was also observed ( Fig 2B and 2C ) . In these four strains , CglC and GltA accumulated at higher levels than in WT and GltB and GltC accumulated at levels similar to those in WT ( Fig 2D ) . Thus , we conclude that CglC , GltB , GltA and GltC are required for gliding motility . To determine the subcellular localization of CglC , GltB , GltA and GltC , fractionation experiments were performed in which total cell extract , the soluble fraction enriched for cytoplasmic and periplasmic proteins , the membrane fraction enriched for IM and OM proteins , and OM vesicles ( OMVs ) were isolated from exponentially growing M . xanthus cells . As shown in Fig 3A , the control proteins multimeric PilQ in the OM [39] , PilC in the IM [43] and PilB in the cytoplasm [44] fractionated as expected , confirming the successful fractionation . CglC , GltB and GltA fractionated with the membrane fraction as well as with OMVs . GltC fractionated with the soluble fraction and was neither detected in the membrane fraction nor in OMVs . In combination with the sequence analyses , we conclude that CglC is an OM lipoprotein , GltB and GltA are integral OM β-barrel proteins , and GltC is a soluble periplasmic protein . These findings are in agreement with the observation that the GltB and GltA paralogs NfsB and NfsA localize to the OM when expressed in E . coli [25] . The GltC paralog NfsC also fractionates with the OM in E . coli and has been suggested to associate with the OM [25] . Importantly , GltC and NfsC only share 19%/35% identity/similarity supporting the notion that these two proteins may interact differently with the OM . OM lipoproteins are commonly assumed to face towards the periplasm; however , several OM lipoproteins that are exposed on the cell surface have been identified [45–48] . To determine the orientation of CglC in the OM , intact WT M . xanthus cells were treated with limited amounts of Proteinase K . The OM β-barrel protein Oar [32 , 49 , 50] and the PilQ multimer in the OM were readily degraded ( Fig 3B ) while Tgl , which is an OM lipoprotein that faces the periplasm [51] , and the IM protein PilC [43] showed little degradation in this assay . Importantly , CglC also only showed little degradation . Thus , we conclude that CglC is facing the periplasm . To analyze whether CglC , GltB , GltA or GltC may interact directly , we systematically determined the accumulation of each protein in the absence of each individual other protein using immunoblot analysis . CglC accumulated at WT levels in the absence of GltB , GltA or GltC and the absence of CglC did not affect the accumulation of GltB , GltA or GltC ( Fig 4A and 4B ) . In the absence of GltB protein , GltA and GltC did not accumulate ( Fig 4A and 4B ) . In the absence of GltA protein , GltB and GltC did not accumulate ( Fig 4A and 4B ) . In the absence of GltC protein , GltB and GltA accumulated as in WT . These effects on protein accumulation were not caused by polar effects of the in-frame deletions because each of the in-frame deletion mutants was complemented by an ectopic copy of the relevant WT gene ( Fig 2B and 2C and 2D ) . Additionally , the protein stability effects were not caused by the lack of gliding motility because GltB , GltA and GltC accumulated in the ΔcglC mutant . Similarly , GltB and GltA were stable in the absence of AglZ and AglQ ( see below ) . We conclude that the two integral OM proteins GltB and GltA mutually stabilize each other as well as the periplasmic protein GltC suggesting that these three proteins interact directly . Supporting this conclusion , NfsA and NfsB are also mutually stabilizing and both are important for NfsC stability [24] . In the M . xanthus type IV pili system , the OM lipoprotein Tgl functions as a pilotin to stimulate multimer formation by the PilQ secretin in the OM [29] . To assess whether the OM lipoprotein CglC is important for the OM integration of GltB and GltA , we isolated the soluble fraction , the membrane fraction and OMVs from WT and ΔcglC cells and analyzed for the subcellular localization of GltB and GltA . In the absence of CglC protein , GltB and GltA accumulated in the membrane fraction ( Fig 3C , left panel ) ; however , the accumulation of GltB and GltA in the OMVs was significantly reduced ( Fig 3C , right panel ) . Because GltB and GltA accumulate at WT levels in the ΔcglC mutant ( Fig 4A and 4B ) , these data strongly suggest that CglC is important for integration of GltB and GltA into the OM and that GltB and GltA accumulate in the IM in the absence of CglC . To test for direct interactions , soluble tagged variants of CglC , GltB , GltA and GltC without their signal peptides were expressed and purified under native conditions from E . coli , i . e . MalE-CglC20-172 , MalE-GltB20-275 , MalE-GltA22-256 , GltC25-673-His6 and GST-GltB20-275 . Surprisingly , the OM β-barrel proteins GltB20-275 and GltA22-256 fused to MalE and also to GST in the case of GltB20-275 were soluble whereas C-terminally His6–tagged GltB20-275 and N-terminally His6-tagged GltA20-256 were not soluble suggesting that the MalE and GST tags help to maintain GltB20-275 and GltA22-256 solubility . To test for direct interactions , each MalE-tagged protein as well as MalE without an attached protein was incubated with an equal amount of GltC25-673-His6 . Subsequently , the protein mixtures were loaded on an amylose-coupled matrix , bound proteins eluted by addition of maltose , separated by SDS-PAGE and analyzed by immunoblotting . In these experiments , GltC25-673-His6 specifically bound to the amylose matrix in the presence of MalE-GltB20-275 and MalE-GltA22-256 but not in the presence of MalE-CglC20-172 or MalE ( Fig 5A and 5C ) . In protein mixtures containing GST-GltB20-275 and MalE-CglC20-172 , MalE-GltA22-256 or MalE , GST-GltB20-275 bound to the amylose matrix in the presence of MalE-GltA22-256 but not in the presence of MalE-CglC20-172 or MalE ( Fig 5B ) . Therefore , GltB and GltA and GltC interact directly in all three pair-wise combinations whereas no interaction was observed between MalE-CglC20-172 and GltC25-673-His6 or GST-GltB20-275 ( Fig 5C ) . These observations are in agreement with the in vivo protein stability experiments ( Fig 4A and 4B ) . Because CglC20-172 and GltA22-256 could only be purified as soluble proteins when fused to MalE , we were unable to test for direct interactions between CglC and GltA . Our inability to detect an interaction between CglC and GltB despite the observation that CglC is important for OM integration of GltB and GltA indicate that such interactions may only be transient , that CglC interacts primarily with GltA , or this effect of CglC is indirect . To determine if CglC , GltB , GltA and GltC are components of the gliding motility complexes , we generated fluorescent proteins in which mCherry was fused to the C-terminus of the four proteins and then expressed under the control of the native promoter from plasmids integrated at the Mx8 attB site . The ectopic expression of the relevant fusion proteins restored the motility defects in the ΔgltB , ΔgltA and ΔgltC mutants and were not negative dominant in a WT background ( S3A Fig ) , demonstrating that the fusion proteins are active . In the case of CglC-mCherry , we observed that the full-length fusion protein was degraded and therefore this construct was not considered further . GltB-mCherry ( S3B Fig ) and GltA-mCherry ( S3C Fig ) accumulated at levels comparable to those of the native proteins whereas GltC-mCherry accumulated at reduced levels ( S3D Fig ) . Expression of GltB-mCherry in the ΔgltB background restored accumulation of GltA and GltC ( S3F and S3G Fig ) . Similarly , expression of GltA-mCherry in the ΔgltA background restored accumulation of GltB and GltC ( S3E and S3G Fig ) . As opposed to the native proteins ( Fig 4A and 4B ) , GltB-mCherry accumulated at WT levels in the ΔgltA background ( S3B Fig ) and GltA-mCherry accumulated at WT levels in the ΔgltB background ( S3C Fig ) suggesting that the mCherry tag protects GltB and GltA from proteolytic degradation in the absence of GltA and GltB , respectively . In the WT background , GltB-mCherry localized to multiple clusters along the cell length in the majority of cells and in the ΔgltB background 48% of cells contained these clusters ( Fig 6A ) suggesting that GltB-mCherry is more efficiently incorporated into these clusters in the presence of native GltB . GltA-mCherry localized to multiple clusters along the cell length in the majority of cells in the WT as well as in the ΔgltA background ( Fig 6A ) . GltC-mCherry also localized to multiple clusters along the cell length and did so more efficiently in the ΔgltC background ( Fig 6A ) . In microscopy z-stacks of these cells , the GltB-mCherry , GltA-mCherry and GltC-mCherry clusters were only visible when the focal plane was focused close to the substratum ( Fig 6A ) . In time-lapse fluorescence microscopy , all three proteins behaved similarly and formed fixed clusters that remained stationary with respect to the substratum as cells moved , assembled towards the leading cell pole , and disassembled towards the lagging cell pole ( Fig 6B ) suggesting that all three proteins are incorporated into the gliding motility complex . To further establish that GltB , GltA and GltC are integral components of the gliding motility complex , we generated strains to localize GltB or GltA in parallel with AglZ , which localizes to the leading cell pole and to the clusters of gliding motility complexes [16] . As shown in Fig 6C and 6D and S4 Fig , GltB-mCherry as well as GltA-mCherry colocalized with AglZ-YFP to clusters along the cell length in both snapshot analyses and time-lapse analyses but did not colocalize with AglZ-AFP at the leading cell pole . From these analyses and because GltB , GltA and GltC interact directly , we conclude that GltB , GltA and GltC are components of the gliding motility complex . Next , we investigated the localization of GltB and GltA in the absence of other proteins required for gliding ( Fig 7 ) . Localization of GltB-mCherry to clusters along the cell length was strongly reduced in the absence of GltA , GltC , AglZ and AglQ and instead GltB-mCherry localized along the entire cell circumference suggesting that the fusion protein is homogeneously dispersed in the OM . GltA-mCherry localization to the clusters along the cell length was also dependent on GltB , GltC , AglZ and AglQ and in the absence of any one of these proteins GltA-mCherry also mostly localized along the entire cell circumference suggesting that the fusion protein is also homogeneously dispersed in the OM . GltB-mCherry and GltA-mCherry also mostly localized along the cell periphery in the absence of CglC , which our data suggest is important for OM integration of GltB and GltA , supporting the notion that in the absence of CglC , GltB and GltA are homogeneously dispersed in the IM . These observations confirm that GltB and GltA in the clusters along the cell length are incorporated into gliding motility complexes . Because native GltB and GltA ( S5A Fig ) , as well as the GltB-mCherry and GltA-mCherry fusions ( S5B Fig ) , accumulate as in WT in the absence of AglZ or AglQ , we conclude that incorporation of GltB and GltA , and by implication GltC , into the gliding motility complex depends on proteins that localize to the cytoplasm , IM , periplasm and OM . To determine whether the incorporation of AglZ and AglQ into the gliding motility complex also depends on CglC , GltB , GltA and GltC , active AglZ-YFP and AglQ-mCherry fusions were expressed from their native sites in the four individual mutant backgrounds . As reported previously [16] , AglZ-YFP localized to multiple clusters along the cell length in the majority of the cells as well as to a cell pole in the WT background ( Fig 8 ) . In contrast , in the four mutant backgrounds , AglZ-YFP predominantly formed a single large cluster that localized to a cell pole or somewhere along the cell length . These results were similar to those obtained with AglQ-mCherry , which also localize to multiple clusters along the cell length and a cell pole in the WT background [13] ( Fig 8 ) . In the absence of CglC , GltB , GltA or GltC , AglQ-mCherry mostly localized diffusely to the cell periphery as well as in a single cluster at a cell pole in the case of the ΔgltB , ΔgltA and ΔgltC mutants . In all four mutant backgrounds , the two fusion proteins accumulated as in WT ( S6 Fig ) . We conclude that proteins that localize to the periplasm and OM are important for incorporation of AglZ as well as AglQ into the gliding motility complex . Because CglC is important for the incorporation of GltB , GltA , AglZ , AglQ and likely also GltC into the gliding motility complexes , we hypothesized that transfer of CglC from a cglC+ donor to a ΔcglC recipient instigates the assembly of intact motility complexes . To test this hypothesis , we performed stimulation assays in which the non-motile cglC+ strain DK6204 , which cannot be stimulated to move , served as a CglC donor as described [20] and five different ΔcglC strains served as recipients . DK6204 and the five ΔcglC strains had defects in gliding motility and had no single cells and slime trails at the colony edges ( Fig 9A ) . Importantly , when the donor was mixed with a recipient , single cells and slime trails were readily observed at the colony edge , thus , verifying stimulation of the ΔcglC strains . As expected , GltB-mCherry , GltA-mCherry and AglQ-mCherry in the ΔcglC recipients mostly localized to the cell periphery in the absence of the donor ( Fig 9B ) . Strikingly , all three proteins in the ΔcglC recipients in the presence of the donor strain localized in multiple clusters along the cell length in the majority of cells ( Fig 9B ) . AglZ-YFP in the ΔcglC recipient in the absence of the donor mostly localized to a single cluster at a pole and also shifted towards formation of clusters along the cell length in the presence of the donor in the majority of cells ( Fig 9B ) . Thus , during stimulation CglC from a donor brings about the assembly of intact and functional motility complexes in the recipient .
The machinery in M . xanthus that generates gliding motility has been hypothesized to span parts of the cell envelope or even the entire cell envelope . We report the functional characterization of the four proteins CglC , GltB , GltA and GltC all of which are required for gliding motility . We show that CglC is an OM lipoprotein that is facing towards the periplasm , GltB and GltA are integral OM β-barrel proteins , and GltC is a soluble periplasmic protein . The gliding motility machinery in M . xanthus is localized in clusters along the cell length . The localization of proteins required for gliding motility to such clusters is used as a readout for their incorporation into the motility machinery . We show that GltB , GltA and GltC localize to clusters along the cell length demonstrating that they are components of the gliding motility machinery . This conclusion is corroborated by several lines of evidence . First , GltB , GltA and GltC interact directly in all pair-wise combinations . Second , GltB and GltA colocalize with AglZ . Third , the localization of GltB and GltA to these clusters , in addition to being mutually dependent , was not only dependent on CglC and GltC but also on AglQ and AglZ that are bona fide components of the gliding motility machinery . Conversely , localization of AglZ and AglQ to these clusters , and therefore their incorporation into the gliding motility complex , was also dependent on CglC , GltB , GltA and GltC . In total , we conclude that GltB , GltA and GltC are integral components of the gliding motility machinery . Previously identified components of the gliding motility machinery localize to the cytoplasm , IM and periplasm [13 , 15–18 , 23] ( Fig 1 ) . The identification of GltB and GltA as OM components of the gliding motility complex has implications for the understanding of the structure of this complex . The data presented are consistent with a focal adhesion model where the GltB/GltA/GltC subcomplex connects to proteins in the periplasm , IM and cytoplasm . Therefore , we assert that the gliding motility complex spans from the cytoplasm , over the IM and periplasm to the OM similarly to the machineries that drive flagella rotation and type IV pili extension/retraction . Notably , the motility complexes after assembly at the leading cell pole remain stationary with respect to the substratum as cells glide , therefore , this structural model of the gliding motility complex implies that these complexes “move through the peptidoglycan” [28] . None of the many proteins that are required for gliding motility contain peptidoglycan hydrolyzing or synthesizing domains . So , it remains an open question how the motility complexes “move through the peptidoglycan” . Similarly , it remains to be shown how the GltB/GltA/GltC subcomplex connect to the remaining proteins of the gliding machinery . However , several proteins including GltG and GltJ required for gliding motility have large periplasmic regions that could connect to the GltB/GltA/GltC subcomplex ( Fig 1 ) . GltB and GltA mutually stabilize each other as well as GltC . Thus , if the GltB/GltA complex does not form , then these proteins and GltC become unstable . In the absence of AglZ or AglQ , GltB and GltA are not incorporated into the gliding motility complex , however , both proteins accumulate . The stability of GltB and GltA in the absence of AglZ or AglQ implies that GltB , GltA and GltC interact directly to form a subcomplex in the OM and periplasm under these conditions . In the absence of AglZ or AglQ , this subcomplex is dispersedly localized throughout the OM/periplasm and in the presence of AglQ and AglZ , this subcomplex is incorporated into the gliding motility complex . In the absence of GltB , GltA or GltC as well as in the absence of proteins predicted to localize to the cytoplasm , IM or periplasm [15 , 17] AglZ and AglQ mostly localize to a single cell pole suggesting that these two proteins engage in the formation of polarly localized subcomplexes in the absence of other components of the gliding machinery . Taken together these observations indicate that each component regardless of its cytoplasmic , IM , periplasmic or OM localization is crucial for the assembly of the gliding motility complex . This assembly may proceed via the connection of subcomplexes that are otherwise localized dispersedly to the OM/periplasm and IM/cytoplasm . This is in contrast to the assembly pathways of other motility complexes , where the assembly process essentially proceeds in a linear inside-out manner in the case of flagella systems [7] and in a linear outside-in manner in the case of type IV pili [38 , 52] . The role of CglC as an integral part of the gliding machinery remains unclear . In the case of the type IV pili machinery in M . xanthus , the OM lipoprotein Tgl functions as a pilotin and is important for assembly of the multimeric form of the PilQ secretin in the OM [29] . Because multimeric PilQ in the OM functions as an assembly platform for the remaining type IV pili machinery , lack of Tgl indirectly interferes with assembly of this entire machinery [38] . Similarly , the data presented here suggest that the sole function of CglC may be to facilitate the OM integration of GltB and GltA thereby allowing the assembly of the entire gliding motility machinery . In 1977 Hodgkin and Kaiser observed that several gliding motility mutants including cglC mutants could be transiently stimulated to glide upon contact with a donor that was WT for the mutant gene [33] in a process that depends on the transfer of OM lipoproteins from the donor to the recipient [29] . A type II signal sequence that targets a protein to the OM is necessary and sufficient for transfer of that protein between cells [34 , 36] . The function of the transferred proteins and how this transfer results in stimulation of gliding has remained enigmatic . Here , we showed that in a ΔcglC mutant , the motility complexes do not assemble . However , upon stimulation , GltB , GltA , AglZ and AglQ localized to clusters along the cell length demonstrating that after stimulation , the gliding motility complexes assemble . Because our data suggest that CglC facilitates the OM incorporation of GltB and GltA , we propose that CglC provided from a donor to a recipient facilitates the OM incorporation of GltB and GltA in the recipient , thereby , allowing the remaining components of the machinery to assemble into functional gliding motility complexes . This is in striking analogy to the type IV pili system of M . xanthus in which the Tgl lipoprotein is transferred from a donor to a recipient and stimulates multimer formation of the PilQ secretin in the OM thereby allowing assembly of the type IV pili machinery [29 , 33 , 38] . The direct cell contacts required for OM lipoprotein transfer in M . xanthus depends on the cell surface receptor TraA [35 , 53] . TraA is highly polymorphic and transfer of OM lipoproteins depends on identical TraA proteins present in donor and recipient . Thus , the TraA polymorphism confers upon M . xanthus cells the ability to discriminate between self and non-self ensuring that OM lipoproteins are only shared with kin [53] . Recently , the molecular mechanisms underlying cell-cell contact-dependent activities in bacteria have started to be unraveled . The data presented here adds to this list by demonstrating that gliding motility can be stimulated in a contact-dependent manner by transfer of a protein that stimulates assembly of the gliding motility complexes .
DK1622 was used as the WT M . xanthus strain and all M . xanthus strains used are derivatives of DK1622 . M . xanthus strains used are listed in Table 1 . Plasmids are listed in S1 Table . All plasmids were verified by sequencing . M . xanthus cells were grown at 32°C in 1% CTT broth [33] and on CTT agar plates supplemented with 1 . 5% agar . Kanamycin ( 50 μg/ml ) or oxytetracycline ( 10 μg/ml ) was added when appropriate . Plasmids were introduced into M . xanthus by electroporation . Site-specific integration of plasmids at the Mx8 attB site on the chromosome was confirmed by PCR . In frame deletions and gene replacements were generated as described [54 , 55] . The transposon miniHimar ( Kan ) on the plasmid pMiniHimar , which is a non-replicating plasmid in M . xanthus ( X . Duan and H . B . Kaplan , personal communication ) , was introduced into the strain SA2273 by electroporation . Transformants were selected on the basis of their resistance to kanamycin and individually transferred to plates with 0 . 5% CTT supplemented with 1 . 5% agar and 50 μg kanamycin/ml and incubated at 32°C to test for gliding motility defects . After 24 hrs , mutant strains were scored for motility defects . A total of 15 , 000 transformants were isolated and screened for gliding motility defects . Among these , 36 were deficient in gliding motility . The transposon insertion sites were identified by arbitrary PCR with subsequent sequencing as described [56 , 57] . Sequences were examined against the M . xanthus genome using BLASTn [58] to identify insertion sites . The entire collection of mutants will be described elsewhere . Cells from exponentially growing cultures were concentrated to density of 5 × 109 cells/ml in TPM buffer ( 10 mM Tris-HC1 pH 7 . 5 , 1 mM KH2PO4 , 8 mM MgSO4 ) . 5 μl of the cell suspensions were spotted on 0 . 5% CTT plates containing 0 . 5% agar or 1 . 5% agar [42] and incubated at 32°C for 24 h . Colony edges were visualized with a Leica MZ75 stereomicroscope equipped with a Leica DFC280 camera or a Leica DM6000B microscope equipped with a Cascade II camera . Total RNA was isolated from exponentially growing M . xanthus DK1622 cells in 1 . 0% CTT using a hot phenol extraction method [59] . Subsequently , RNA was treated with DNase I ( Ambion ) and purified with the RNeasy kit ( Qiagen ) . cDNA was synthesized from 1 . 0 μg total RNA using the High capacity cDNA Archive kit ( Applied Biosystems ) and random hexamer primers . Total RNA and genomic DNA was used as negative and positive controls , respectively . SYBR Green PCR Master Mix ( Applied Biosystems ) was added to cDNA from the reverse transcription of 30 ng RNA together with 100 nM each of the two primers . The RT-PCR reaction was performed on a Mastercycler personal ( Eppendorf ) . Cells were treated as described for motility assay . Donor and recipient cell suspensions were spotted separately as well as in a 1:1 mixture on 0 . 5% CTT plates containing 1 . 5% agar and incubated at 32°C for 48 h . Colony edges were imaged as described for motility assays after 48 hrs of incubation . To visualize AglZ-YFP and AglQ-mCherry , cells from the edges of the colonies were harvested and resuspended in 30 μl CTT , spotted on 1 . 5% agar pads supplemented with TPM , covered with a cover slip and imaged as described for fluorescence microscopy . Cells were fractionated as described [60] . Briefly , cells were grown exponentially to a density of 5 × 108 cells/ml . Cells were harvested by centrifugation at 8000×g at RT for 10 min and resuspended in 50 mM Tris-HCl pH 7 . 6 containing “complete Protease inhibitor cocktail” ( Roche ) . Cells were disrupted by sonication and samples were centrifuged at 3000×g at 4°C for 10 min to remove cell debris . Subsequently , the supernatant was centrifuged at 45 . 000×g at 4°C for 30 min to separate soluble and insoluble ( membrane-associated ) components . The resulting supernatant is enriched in soluble cytoplasmic and periplasmic proteins . The pellet containing a crude envelope fraction was washed with 50 mM Tris-HCl , resuspended in 50 mM Tris-HCl pH 7 . 2 , 2% Triton X-100 , 10 mM MgCl2 and incubated overnight with gentle shaking at 4°C and then subjected to centrifugation at 45 . 000×g for 30 min at 4°C . The resulting supernatant is enriched in membrane proteins . OMVs were isolated as described [32] . Briefly , culture supernatants were passed through a 0 . 2μm vacuum filter ( Millipore ) . The resulting filtrate was centrifuged at 150000×g for 2 h at 4°C to recover membrane vesicles . The supernatant was carefully removed and the vesicle pellet was resuspended in 50 mM Tris-HCl pH 8 . 0 and centrifuged at 150000×g for 2 h at 4°C to concentrate vesicles . 1 ml of exponentially growing M . xanthus cells in CTT were directly incubated with Proteinase K ( Sigma ) with gentle shaking at 32°C for 10 min at the indicated concentrations . 100 μl of solution 1 ( 1 tablet “complete mini-protease inhibitor cocktail” ( Roche ) dissolved in 1 ml CTT ) was added to stop the reaction . The cell suspension was centrifuged at 16 . 000×g at 4°C for 5 min . The cell pellet was washed with 1 ml of solution 2 ( 1 tablet “complete mini-protease inhibitor cocktail” dissolved in 10ml CTT ) and resuspended in solution 1 to a calculated density of 1×1010 cells/ml . Samples were incubated at 100°C for 15 min and mixed with SDS loading buffer containing protease inhibitors ( 1 tablet “Complete mini-protease inhibitor cocktail” dissolved in 1ml SDS lysis buffer ) to a density of 5×109 cells/ml . Cells from exponentially growing cultures were concentrated and resuspended in SDS lysis buffer to a density of 2 . 5 × 109 cells/ml . Proteins from the same number of cells were loaded per lane . Immunoblots were performed using standard procedures [61] with polyclonal rabbit α-CglC , α-GltB , α-GltA , α-GltC , α-PilB [44] , α-PilC [43] , α-PilQ [43] , α-Oar , α-MalE ( New England Biolabs ) , α-mCherry ( Roche ) and secondary anti-rabbit immunoglobulin G peroxidase conjugate ( Sigma ) . For YFP-tagged protein detection , monoclonal anti-GFP mouse antibodies ( Roche ) and peroxidase-conjugated rabbit anti-mouse immunoglobulin G secondary antibodies ( DakoCytomation ) were used . Immunoblots were developed using Luminata Western HRP Substrate ( Merck Millipore ) . The α-CglC , α-GltB , α-GltA , α-GltC antibodies were generated against purified His6-tagged proteins ( see below ) . α-Oar antibodies were generated against the Oar peptides AB1 ( 237GTLEGTRKGIREEGT ) and AB2 ( 1009SVDGDVNKNFKNPLS ) . Oar has a length of 1037 amino acid residues without the signal peptide . To purify GltC25-673-His6 , MalE-CglC20-172 , MalE-GltB20-275 , MalE-GltA22-256 and GST-GltB20-275 the plasmids pBJA9 , pBJA26 , pBJA27 , pBJA28 , pBJA29 were introduced into E . coli Rosetta 2 [F-ompT hsdSB ( rB-mB- ) gal dcm pRARE2] ( Novagen ) . Cultures were grown in LB medium at 37°C to OD600 = 0 . 7 . Protein expression was induced by addition of IPTG to 0 . 1mM final concentration . Proteins were expressed overnight at 18°C . Cells were harvested by centrifugation at 9 . 000×g for 10 min at RT and resuspended in lysis buffer containing “EDTA-free complete protease inhibitor cocktail” ( Roche ) . For MalE- and GST-tagged proteins , CB1 lysis buffer ( 20 mM Tris-HCl pH7 . 4 , 200 mM NaCl , 1 mM DTT , 1 mM EDTA ) was used while for His6-tagged proteins the lysis buffer was ( 50 mM NaH2PO4 , 300 mM NaCl pH 7 . 5 , 1 mM DTT ) . Harvested cells were disrupted by sonication and centrifuged at 20 . 500×g for 30 min at 4°C to remove debris . MalE-tagged proteins were purified on amylose matrix column ( New England Biolabs ) and eluted with CB1 buffer supplemented with 10 mM maltose . GltC25-673-His6 was purified on Ni2+-NTA-agarose column as recommended by the manufacturer ( Qiagen ) and eluted with elution buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 250 mM imidazole , pH 8 . 0 ) . GST-GltB20-275 was purified on GST-bind column ( Novagen ) and eluted with CB1 buffer supplemented with 10 mM reduced glutathione . His6-CglC20-172 , GltB20-275-His6 and His6-GltA22-256 were purified from inclusion bodies . The plasmids pBJA1 , pBJA10 , pBJA3 were introduced into E . coli Rosetta 2 . Cultures were grown as described . Cells were resuspended in lysis buffer ( 50 mM NaH2PO4 , 300 mM NaCl pH 7 . 5 , 1 mM DTT ) containing “EDTA-free complete Protease inhibitor cocktail” ( Roche ) , sonicated and harvested as described . The cell pellet was resuspended in buffer B ( 100 mM NaH2PO4 , 10 mM TrisHCl , 8 M urea , pH 8 . 0 ) and incubated overnight at 4°C . The mixture was centrifuged at 20 . 500×g for 30 min at 4°C to remove cell debris . Proteins were purified on Ni2+-NTA-agarose column as described by the manufacturer ( Qiagen ) . The column was washed with buffer C ( buffer B; pH 6 . 3 ) and proteins were eluted with buffer D ( buffer B , pH 5 . 9 ) and buffer E ( buffer B , pH 4 . 5 ) . The purified proteins were dialyzed against buffer B containing 10% glycerol , frozen in liquid nitrogen , stored at -80°C and used for polyclonal rabbit antibody generation . Purified proteins were dialyzed against CB1 buffer . Protein concentration was measured by using a Bradford assay ( BioRad ) . In pull-down experiments , 300 μg of each protein were mixed in a total volume of 1 ml of buffer CB1 and incubated at 4°C for 1 h . Subsequently , these mixtures were incubated with an amylose matrix ( New England Biolabs ) for 2 h at 4°C , followed by a washing step with 10-times the matrix volume of buffer CB1 . Proteins were eluted with buffer CB1 supplemented with 10 mM maltose . DIC and fluorescence microscopy was performed as described [62] . Briefly , cells from exponentially growing cultures were spotted on 1 . 5% agar pads supplemented with TPM or A50 ( 10 mM MOPS pH 7 . 2 , 1 mM CaCl2 , 1 mM MgCl2 , 50 mM NaCl , 1 . 5% agar ) and covered with a cover slip . Cells were incubated at RT for 15 min to attach to the agar surface . Microscopy was performed using a Leica DMI6000B microscope with an adaptive focus control , a motorized stage , a temperature-controlled stage and a Hamamatsu Flash 4 . 0 camera . Images were recorded with Leica MM AF software package and processed with Metamorph ( Molecular Devices ) . We performed sequence analysis of all of the gliding machinery components included in Fig 1 . Type I and type II signal peptides were identified using the SignalP [63] and LipoP [64] webservers , respectively . Trans-membrane α-helices were identified using the DAS webserver [65] . OM β-barrel domains were identified using HHomp [66] . The HMMER3 software package [67] was used in conjunction with the Pfam26 domain library [68] for domain architecture analysis with default gathering thresholds . In the event of domain overlaps , the highest scoring domain model was chosen for the final domain architecture . | Motility facilitates a wide variety of processes such as virulence , biofilm formation and development in bacteria . Bacteria have evolved at least three mechanisms for motility on surfaces: swarming motility , twitching motility and gliding motility . Mechanistically , gliding motility is poorly understood . Here , we focused on four proteins in Myxococcus xanthus that are essential for gliding . We show that CglC is an outer membrane ( OM ) lipoprotein , GltB and GltA are integral OM β-barrel proteins , and GltC is a soluble periplasmic protein . GltB , GltA and GltC are components of the gliding motility complex , and CglC likely stimulates the integration of GltB and GltA into the OM . Moreover , CglC , in a cell-cell contact-dependent manner , can be transferred from a cglC+ donor to a ΔcglC mutant leading to stimulation of gliding motility in the recipient . We show that upon physical transfer of CglC , CglC stimulates the assembly of the gliding motility complex in the recipient . The data presented here adds to the growing list of cell-cell contact-dependent activities in bacteria by demonstrating that gliding motility can be stimulated in a contact-dependent manner by transfer of a protein that stimulates assembly of the gliding motility complexes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Contact- and Protein Transfer-Dependent Stimulation of Assembly of the Gliding Motility Machinery in Myxococcus xanthus |
Questionnaires of reported blood in urine ( BIU ) distributed through the existing school system provide a rapid and reliable method to classify schools according to the prevalence of Schistosoma haematobium , thereby helping in the targeting of schistosomiasis control . However , not all schools return questionnaires and it is unclear whether treatment is warranted in such schools . This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S . haematobium at every school along the Kenyan coast . Data from a questionnaire survey conducted by the Kenya Ministry of Education in Coast Province in 2009 were combined with available parasitological and environmental data in a Bayesian bivariate spatial model . This modeled the relationship between BIU data and environmental covariates , as well as the relationship between BIU and S . haematobium infection prevalence , to predict S . haematobium infection prevalence at all schools in the study region . Validation procedures were implemented to assess the predictive accuracy of endemicity classification . The prevalence of BIU was negatively correlated with distance to nearest river and there was considerable residual spatial correlation at small ( ∼15 km ) spatial scales . There was a predictable relationship between the prevalence of reported BIU and S . haematobium infection . The final model exhibited excellent sensitivity ( 0 . 94 ) but moderate specificity ( 0 . 69 ) in identifying low ( <10% ) prevalence schools , and had poor performance in differentiating between moderate and high prevalence schools ( sensitivity 0 . 5 , specificity 1 ) . Schistosomiasis is highly focal and there is a need to target treatment on a school-by-school basis . The use of bivariate spatial modelling can supplement questionnaire data to identify schools requiring mass treatment , but is unable to distinguish between moderate and high prevalence schools .
The pharmaceutical industry and the global health community have recently committed to providing up to 250 million tablets of praziquantel each year for treatment of schistosomiasis [1] . It is essential that these treatments are targeted to schools and communities at greatest need , based on up-to-date epidemiological information . Whilst challenges still exist in the rapid assessment of Schistosoma mansoni prevalence [2] , [3] , [4] , the use of morbidity questionnaires on reported blood in urine ( BIU ) to identify schools/communities with a high prevalence of S . haematobium is a well-established and reliable approach , based on extensive validation [3] , [4] , [5] . Such questionnaires can readily be distributed through the existing school system at sub-national and national levels [6] , [7] , [8] . However , a challenge often encountered when relying upon questionnaires distributed via the education system is incomplete return of questionnaires by schools from local to central levels [4] . Consequently , there is little basis upon which to base treatment decisions for those schools with missing questionnaire data . One possible solution to this problem is to predict the prevalence of S . haematobium on the basis of either questionnaire data from neighbouring schools or environmental factors known to influence infection risk [9] , or a combination of both factors . The spatial modeling of schistosomiasis is predicated on the sensitivity of snail intermediate hosts to variations in temperature and rainfall [9] , [10] , [11] and such environmental factors , along with proxy variables such as vegetation index , have successfully been used to explain and predict S . haematobium risk in a number of settings [9] , [12] , [13] , [14] . For example , Clements et al . [7] used Bayesian geostatistics to model available area-level data on the percentage of school children reporting blood in urine from Tanzania with information on urban-rural status , elevation and vegetation index , to predict infection prevalence in those administrative unit ( wards ) with insufficient data . This study did not , however , include any validation of the reliability of the blood in urine data , using parasitological data . In mapping Loa loa in west Africa , Crainiceanu et al . [15] developed a bivariate spatial model to calibrate the RAPLOA methodology and to map the spatial variation in the predictive probability that prevalence of L . loa infection exceeds a predetermined policy intervention threshold . Here we adapt this modelling approach to predict the prevalence of S . haematobium at schools without infection data throughout coastal Kenya , using both BIU data available for schools which returned questionnaire results to the Kenya national deworming programme and parasitological data from a validation study [8] . Modeling BIU and parasitological data together makes it possible to predict S . haematobium prevalence at schools for which this information is missing , which would not be possible if modeling questionnaire data alone .
Two distinct datasets relating to urinary schistosomiasis in schools across six districts in Coast Province , Kenya were available for analysis ( Figure 1 ) : i ) parasitological data for 33 schools on the prevalence of S . haematobium , based on urine filtration; and ii ) data on the proportion of school children reporting BIU for 613 schools , based on a survey distributed by the Ministry of Education to all schools . Details of these datasets and their collection are outlined elsewhere [8] . Briefly , parasitological surveys were conducted between September 2008 and March 2009 . In a random selection of schools , selected with probability proportional to the population of the district , 10 boys and 10 girls were selected from each of classes 2–6 to supply a urine sample , which was filtrated through polycarbonate membrane filter ( 10 mls ) and examined for presence of S . haematobium eggs . Between May 2009 and November 2009 , the Ministry of Education distributed BIU questionnaires , which were administered by teachers in schools and collated by head teachers for forwarding to the district education office which then sent collated district-level results to Nairobi for analysis . Of the 33 schools with parasitology data , 27 also returned questionnaire data . In total , therefore , there were 587 schools with only questionnaire data , 27 schools with joint questionnaire and parasitology data , 6 schools with only parasitology data , and 138 without any data ( Figure 1 , Figure 2 ) , making a total of 758 schools . School-level parasitological and questionnaire data were related to a variety of high-resolution environmental data . Maximum land surface temperature , altitude and precipitation at 30-arcsec ( ∼1 km ) resolution were taken from the WorldClim website [16] . Distance to nearest river was estimated in ArcMap 10 from an electronic map obtained from HydroSHEDS project [17] . Enhanced vegetation index ( EVI; a measure of vegetation density ) for 2001–2005 were obtained from the Moderate Resolution Imaging Spectroradiometer ( MODIS ) [18] and global land-cover type was estimated using electronic maps generated by the GlobCover at 300 m resolution for the year 2005 [19] . Scatter plots revealed a non-linear relationship between infection and distance to nearest river and based on observed data three categories of distance were generated , <2 , 2–5 and ≥5 km . A likelihood ratio test revealed that distance to water categorized in this way provided a better model fit than when included as a linear ( p = 0 . 038 ) or quadratic term ( p<0 . 001 ) . The aim of the analysis was to predict the prevalence of S . haematobium infection in schools for which there were no parasitology data , using all available questionnaire and parasitology data . To do this , we used Bayesian model-based geostatistics ( MBG ) to model the proportion of children reporting blood in urine on the basis of environmental covariates , whilst simultaneously modeling the relationship between the questionnaire data and parasitological data from schools where both sets of information were available ( Figure 2 ) . A bivariate binomial model for the number of children reporting BIU and infected with S . haematobium was fitted as follows: ( 1 ) ( 2 ) where and are the numbers positive according to questionnaire and parasitology respectively , and the number of individuals sampled using questionnaire and parasitology and and the proportion positive according to questionnaire and parasitology at location . The proportion of children reporting BIU was modelled using a hierarchical logistic regression model ( Equation 1 ) where α is the intercept , is a vector of N predictor variables measured at each location multiplied by their coefficients and a geostatistical random effect modeled using an isotropic , stationary exponential decay function: where is the straight-line distance between pairs of points a and b , and is the rate of decline of spatial correlation . To model the relationship between reported BIU and prevalence of S . haematobium infection , we assumed that following an empirical logit transformation , the relationship was linear ( Equation 2 , Figure 3 ) . This assumption was supported by a likelihood ratio test comparing a linear to quadratic relationship between BIU and S . haematobium infection prevalence which showed no difference between models ( p = 0 . 454 ) . The more parsimonious linear relationship was , therefore , assumed . Non-informative priors were used for and the coefficients ( normal prior with mean 0 and precision 1×106 ) , the prior distribution of was uniform with upper and lower bounds set at 0 . 05 and 100 and the precision of and were given non-informative gamma distributions . Bayesian multivariate logistic regression models , with a non-spatial , unstructured school level random effect , were subsequently generated in a stepwise fashion in WinBUGs version 1 . 4 . 1 ( MRC Biostatistics Unit , Cambridge and Imperial College London , UK ) . Covariates that remained significant at the 95% level were included in an identical Bayesian model , replacing the non-spatial random effect for a geostatistical random effect . Covariates that remained significant at the 95% level were then included in the final model . Model parameter estimates were used to predict the prevalence of S . haematobium at schools for which parasitological data were missing . At schools for which only questionnaire data were available , predictions were made using only step 2 of the model , which models the relationship of BIU with infection prevalence . At schools for which only covariate values were known , predictions were made using both steps 1 and 2; step 1 to predict BIU prevalence based on covariate values and step 2 to predict infection prevalence from predicted BIU ( Figure 2 ) . Within a Bayesian framework , these predictions are in the form of a posterior distribution , which is formed of the distribution of possible prevalence values a site may take . This feature of Bayesian statistics makes it possible to estimate the probability that the prevalence of S . haematobium is greater than any specified threshold . Following a burn-in of 9 , 000 iterations , the values for the intercept and coefficients were stored for 1 , 000 iterations and model convergence was assessed using diagnostic tests for convergence and by visually inspecting the time series plots . Convergence was successfully achieved after 10 , 000 , and the model was run for a further 10 , 000 iterations with thinning every ten iterations , during which predictions were made . To evaluate the performance of the final model , each of the three datasets ( Figure 1 ) were first randomly split into 10 subsets . To generate a single set of training data , 9 of the 10 subsets from each of the three datasets were selected . The excluded subsets from each of the three datasets were combined to form the corresponding validation dataset . This process was repeated ten times so that every data point was included once in a validation set . As there were only 6 schools for which only parasitological data were available , these could not be split equally over the 10 validation sets , therefore only a random selection of 6 of the 10 validation datasets contained schools with only parasitology data . For each validation dataset both BIU and infection prevalence was predicted at each location using models built with the corresponding training dataset . To assess discriminatory performance of the predictive model the following validation statistics were calculated: sensitivity; specificity; positive predictive value ( PPV ) ; negative predictive value ( NPV ) and; area under the curve ( AUC ) of the receiver operator characteristics ( ROC; a plot of sensitivity vs . 1-specificity ) . AUC values of <0 . 7 indicate poor discriminatory performance , 0 . 7–0 . 8 acceptable , 0 . 8–0 . 9 excellent and >0 . 9 outstanding discriminatory performance [20] . For the questionnaire data , the predicted probability of prevalence of blood in urine being greater than 10% and 30% were compared to observed prevalence by questionnaire in the 614 for which questionnaire data were available . For the parasitological data , the predicted probability of prevalence of infection being greater than 10% and 50% were compared to observed prevalence by microscopy in the 33 schools for which parasitology data were available . These thresholds correspond to those used to guide frequency of interventions by questionnaire and microscopy [21] . Where infection prevalence is <10% no action is required , where infection prevalence is ≥10% and <50% treatment should occur biennially and where infection prevalence is ≥50% or reported blood in urine prevalence is ≥30% annual treatment is warranted . Mean error and mean absolute error were used to assess bias and accuracy of predictions respectively .
Questionnaire data were available from 312 , 575 individuals ( mean 509 . 1 per school ) and parasitological data were available from 3 , 486 individuals ( mean 105 . 6 per school ) . The overall prevalence of reported blood in urine and infection was 12 . 6% ( range 0–81 . 7% ) and 25 . 6% ( 0–81% ) respectively ( Figure 4 ) . Table 1 presents the results from the Bayesian models including a non-spatial and spatial random-effect . The non-spatial model suggested that three covariates should be retained: EVI , GlobCover and distance to nearest river . Once a spatial random effect was introduced to the model , however , only distance to nearest river remained statistically significant ( Table 1 ) . Whilst the non-spatial model produced a lower DIC value than the spatial model ( 4581 . 6 vs 4605 . 6 ) , inspection of the non-spatial random effect at each school revealed significant residual spatial autocorrelation ( Moran's I , p<0 . 001 ) . As residual spatial autocorrelation can overestimate associations between outcomes and covariates due to non-independence [22] , a model accounting for spatial autocorrelation between schools was therefore chosen over a non-spatial model . DIC values suggested that a spatial model including distance to water provided a better model fit than one without ( 4605 . 6 vs 4610 . 6 ) and this was chosen as the final model . This final model suggested that the odds of being infected when being >5 km from the nearest river were nearly half that when <2 km from the nearest river ( OR 0 . 58 , BCI 0 . 30–0 . 99 ) . The rate of decline in spatial correlation was 22 . 19 ( 95% BCI 16 . 9–28 . 41 ) which corresponds to a range ( i . e . the distance at which spatial correlation falls to <0 . 05 ) of 15 km ( 95% BCI 11 . 7–19 . 7 km ) . The calibration component of the model , which aimed to investigate the relationship between reported blood in urine and parasitology , showed a predictable positive non-linear association between the two indicators ( Figure 5 ) . The fact that reported blood in urine consistently underestimated infection prevalence is in line with previous studies and supports the finding that a reported blood in urine prevalence of 30% corresponds to an infection prevalence of 50% [4] , the WHO threshold required for annual praziquantel treatment . Interestingly , a reported blood in urine prevalence of 10% appeared to correspond to an infection prevalence of 10% , the WHO threshold for biennial mass treatment [21] . The model was validated in terms of its ability to predict the prevalence of BIU at the 614 schools with questionnaire data and prevalence of S . haematobium infection at the 33 schools with parasitological data ( Figure 6 ) . In terms of predicting the prevalence of BIU , the model had reasonable sensitivity and specificity in distinguishing schools according to the 10% treatment threshold , but had very poor sensitivity at the 30% threshold ( Table 2 ) . Mean error and mean absolute error indicated very little overall bias and relatively accurate predictions ( ±6% ) . When predicting the prevalence of S . haematobium , the model had excellent sensitivity and moderate specificity at the 10% threshold , but again poor sensitivity at the higher ( 50% ) threshold . The mean error suggested a tendency to underestimate prevalence and mean absolute error suggested predictions were on average out by ±10% . Figure 7 shows the mean of the predicted posterior of S . haematobium infection prevalence at all schools in the study region . Highest prevalence of infection was predicted in the southern inland areas of the coast , and lowest prevalence in the central coastal areas .
Due to the highly focal nature of infection , targeted use of praziquantel is central to the cost-effective control of S . haematobium . Whilst questionnaires have been used with success in a variety of settings , imperfect return rates can hinder the targeting of treatment . This study uses bivariate spatial modelling to jointly analyze questionnaire and parasitology data in order to predict the prevalence of S . haematobium infection for schools with missing questionnaire data . Whilst the model was unable to reliably discriminate between high and medium risk schools , it was very good at identifying schools that required treatment based on a 10% prevalence threshold . The success of the model lies in the clear relationship between the prevalence of blood in urine and prevalence of S . haematobium , assessed by urine filtration . A new insight provided by the analysis is that a prevalence of BIU of ≥10% is equivalent to a parasitological prevalence of ≥10% . The spatial predictions were also possible due to the observed negative relationship between prevalence of BIU and prevalence of S . haematobium and distance to river , a finding consistent with previous studies [12] , [23] . The distribution of schistosomiasis is restricted to areas populated by intermediate snail hosts , which are themselves constrained by the availability of freshwater habitats [10] . It is this feature of their biology which results in such a highly focal spatial distribution . The fact that we found no association with other covariates such as land surface temperature , altitude and precipitation , is in contrast to previous studies of S . haematobium [7] , [9] , [23] , but is most likely due to the relatively small scale of the study area which results in a relatively homogeneous landscape . The final model displayed low sensitivity when identifying high risk schools ( i . e . those with a reported blood in urine prevalence of ≥30% or infection prevalence of ≥50% ) . This is most likely due to the relatively low numbers of such schools which , in a statistical sense , become outliers . In the absence of suitable covariates , geostatistical models , which spatially interpolate the school level random effect , will only accurately predict those high prevalence schools if they are in very close proximity to other high prevalence schools . Due to the highly spatially heterogeneous nature of infection , this is quite often not the case and high prevalence schools are either beyond the scale of spatial autocorrelation , in this case up to just 15 km , or are near to schools with lower prevalence , resulting in an underestimation of prevalence . High levels of performance , combined with their low cost , make questionnaires a highly cost-effective approach as a community level diagnostic tool [3] , [24] , [25] . Efforts should be made to maximise the return rates of questionnaire , but instances where this is not possible , the current modelling approach enables prediction of prevalence in schools without questionnaire data , on the basis of the calibration of the questionnaire survey and ecological correlates of infection . In addition to this use and the prediction of the prevalence of Loa loa [15] , bivariate modelling has the potential to improve mapping and decision making for a number of diseases for which rapid diagnostic tests are used alongside other ‘gold standard’ techniques . For example , diagnosis of infection with Wuchereria bancrofti , the parasite causing lymphatic filariasis , is often made using an Immunochromatographic Test ( ICT ) cards which detect circulating filarial antigen [26] . Such tests are cheap , easy to use and display adequate levels of performance making them the field diagnostic of choice [27] , [28] . Modelling ICT data together with gold standard infection data from night time bleeds and/or PCR [29] , both of which are time consuming , expensive and technically challenging , would allow more accurate predictions of infection prevalence . Such calibration of less than perfect ICTs is likely to become increasingly important as elimination programmes decide where and when to withdraw MDA [28] . Likewise , malaria prevalence estimates are often generated from rapid diagnostic tests ( RDTs ) , microscopy and PCR data which could be modelled together using this approach . A major limitation of the present analysis is that parasitological infection was based on urine filtration and this approach may have missed some infections , especially those of light intensity . This is particularly true in chronically infected adults within whom the passage of eggs over time causes the development of lesions and fibrous tissue that trap eggs [30] . Newer , more sensitive molecular and immunological methods for the detection of infection are being developed [31] , [32] and statistical methods , such as latent class modeling [33] , offer a number of advantages and investigation of their use in future analyses is encouraged . A second limitation relates to the use of the proxy measure distance to river as a risk factor for schistosome infection . In reality , transmission is restricted to areas around specific water bodies inhabited by Bulinas spp . snails , and while spatial data relating to the locations of such transmission sites would be ideal , such information is typically available through small-scale research studies and is not available across large spatial scales . Notwithstanding the potential limitations of using distance to river as a risk factor , the results of our modeling suggests that it can increase the statistical robust of the final model . Furthermore , the predictive accuracy of this model is greatly improved by inclusion of a spatially varying random effect which allows the underlying risk surface based on distance to river to be modified at any point according to observed prevalence of BIU in neighbouring schools . Predictions at schools that lie near to transmission sites should , therefore , be adjusted in accordance with higher prevalence of BIU observed at neighbouring schools . A third limitation relates to the availability of covariate data which varied in spatial resolution between variables ( between 300 m for GlobCover data and ∼1 km for WorldClim data ) . Whilst it is possible to aggregate the higher resolution data to match the lowest resolution data to form a unitized dataset , given the loss of information and the desire to predict over fine scales , this was not undertaken . Similarly , there was a temporal difference between collection of covariate and disease data , with parasitological and questionnaire data collected some years after the covariates . Unfortunately the relevant covariate data were not available for the same period as the epidemiological surveys , but we feel that it is unlikely that these variables would have changed substantially over the short time period of the study , especially as no large-scale intervention had been previously implemented in the study area In conclusion , here we demonstrate how Bayesian bivariate spatial modelling can model the relationship between the prevalence of report blood in urine and the prevalence of S . haematobium infection and , in conjunction with environmental data , predict the small-scale distribution of infection . While the approach was shown to be less reliable in distinguishing between moderate and high prevalence schools , it reliably identified schools requiring mass treatment . In doing so , the approach helps overcome incomplete returns of questionnaires distributed through the existing school system and helps support the rational targeting of schistosomiasis control . | The highly focal nature of schistosomiasis means that treatment is most cost-effective when delivered on a school-by-school basis . Questionnaires of reported blood in urine ( BIU ) distributed through the existing school system are a rapid , valid method to classify schools according to WHO treatment thresholds . Their usefulness , however , can be hampered by incomplete return rates that impede treatment decisions in schools with missing questionnaire data . Using data from coastal Kenya , this study describes the use of Bayesian spatial modeling that combines questionnaire data with available parasitological data to make predictions of S . haematobium infection prevalence at all schools . Results showed that reported BIU was highly focal and was negatively associated with the distance to the nearest river . The final model was able to discriminate between schools that require treatment or not , but was less reliable at distinguishing between medium and high prevalence schools . Similar Bayesian spatial models may prove useful for modeling and predicting other diseases where multiple diagnostic techniques are employed . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"disease",
"mapping",
"public",
"health",
"and",
"epidemiology",
"epidemiology",
"spatial",
"epidemiology"
] | 2013 | The Use of Bivariate Spatial Modeling of Questionnaire and Parasitology Data to Predict the Distribution of Schistosoma haematobium in Coastal Kenya |
The discrepancy between structural and functional connectivity in neural systems forms the challenge in understanding general brain functioning . To pinpoint a mapping between structure and function , we investigated the effects of ( in ) homogeneity in coupling structure and delays on synchronization behavior in networks of oscillatory neural masses by deriving the phase dynamics of these generic networks . For homogeneous delays , the structural coupling matrix is largely preserved in the coupling between phases , resulting in clustered stationary phase distributions . Accordingly , we found only a small number of synchronized groups in the network . Distributed delays , by contrast , introduce inhomogeneity in the phase coupling so that clustered stationary phase distributions no longer exist . The effect of distributed delays mimicked that of structural inhomogeneity . Hence , we argue that phase ( de- ) synchronization patterns caused by inhomogeneous coupling cannot be distinguished from those caused by distributed delays , at least not by the naked eye . The here-derived analytical expression for the effective coupling between phases as a function of structural coupling constitutes a direct relationship between structural and functional connectivity . Structural connectivity constrains synchronizability that may be modified by the delay distribution . This explains why structural and functional connectivity bear much resemblance albeit not a one-to-one correspondence . We illustrate this in the context of resting-state activity , using the anatomical connectivity structure reported by Hagmann and others .
Much of the current focus in the empirical study of large-scale neuronal networks has been on their intrinsic activity and the degree to which the coherent patterns of this intrinsic activity reflect anatomy . The use of fMRI and diffusion spectrum imaging has allowed for a comprehensive evaluation of the structure-function map of resting-state networks ( RSNs ) . In fMRI the spatial patterns of spontaneous changes in blood oxygenation level-dependent signals seem to reflect the generating neural architecture of RSNs . Despite the very slow changes of these signals , Biswal and co-workers [1] defined RSNs as networks of brain areas that exhibit temporally coherent activity in the absence of identifiable externally imposed or measurable events . More recently , RSNs penetrated the field of encephalography [2] , [3] . For M/EEG , locally synchronized neural activity is considered to yield macroscopic oscillations that provide a basis for defining functional brain networks [4] . In most studies , structural connectivity is considered a good predictor of functional connectivity [5] , [6]: Structural connectivity agrees with the anatomical connections between network nodes and functional connectivity covers the statistical relationship of nodal activity . The predictive value of structure for function found support in recent modeling work using full brain systems with realistic anatomy , which demonstrated the structural dependency of functional network configurations [7] . There , functional connectivity has been estimated between all nodes over several hundred seconds of simulated time yielding the pattern of functional connectivity over this time window that largely reproduced the structural connectivity . At smaller time windows , however , shorter-living patterns of functional connectivity emerged that had not been predicted by anatomy . To understand this discrepancy we investigated effects of time delays vis-à-vis effects of structural inhomogeneity on synchronization patterns of neuronal networks . Delays are inherent in neuronal networks due to finite conduction velocities [8] and synaptic transmission [9] . Ignoring delays may be a valid starting point for mathematical analysis but when doing so one runs the risk of loosing biological plausibility . However , incorporating delays in oscillatory networks does come with immense challenges . Already for low-dimensional oscillatory systems ( or for high-dimensional ones with strong symmetry ) the presence of delays is known to change the dynamical repertoire significantly [10] , [11] . Yeung and Strogatz showed for very large networks how time delays can alter synchronization properties , even if the structure is isotropic and homogeneous [12]; see also [13] , [14] . Numerical assessments revealed similar results for biologically motivated and hence more inhomogeneous connectivities . Delays seem to be crucial in establishing the spatio-temporally organized fluctuations typically observed in resting state brain recordings [15]–[17] . In the present study we sought to tackle this issue and separated the effect of time delays from that of inhomogeneous connectivity by studying networks consisting of distinct neural masses . Neural mass models offer a low-dimensional description of the dynamics of a large neuronal population and exist in a variety of forms [18] . We chose for Freeman's seminal model [19]–[21] , since it covers the dynamics of mean membrane potential changes that relate closely to encephalographic signals . A network of such entities may constitute RSNs if we regard the neural masses to be representative of individual brain areas . Throughout the paper we describe functional connectivity by means of phase synchronization whose dynamics can be estimated in voltage-based and firing-rate models using a combination of rotating wave and slowly varying amplitude approximations , or in brief averaging , see [22] , [23] . In the Methods section this combination of approximations is briefly summarized for Freeman neural mass models in the oscillatory regime . Central outcome measure is thus the phase dynamics of the individual nodes in the network or , to be more precise , the density of the nodes' phases as a function of time , often also referred as time-dependent population distributions . We note that we applied this approach before to instantaneously coupled Wilson-Cowan firing rate models [24] ( see also [25] ) but , as said , we here chose for the Freeman model for an easier comparison with M/EEG studies . For coupled Freeman models we could analytically determine the corresponding stationary distributions even in the presence of delays and inhomogeneous coupling between neural masses . We could not only prove the existence of these solutions , but we were also able to determine the loss of stability of the desynchronized state as soon as the overall coupling strength exceeded a critical value . More complicated scenarios including biological plausible anatomical adjacencies were treated numerically to illustrate the non-trivial relationship between structural and functional connectivity .
In the homogeneous case we employed the coupling scheme sketched in Fig 1A . Excitatory and inhibitory populations were fully connected ( apart from self-coupling ) with coupling values and discriminating within-pair and between-pair coupling . For the numerical assessment we always fixed the within-pair coupling to . In more detail , we chose the overall homogeneous coupling matrix as with sub-population connectivities In the absence of delays , i . e . for ( ) and sufficiently strong coupling we found robust distributions with phase clusters , one containing all the excitatory populations and one all the inhibitory ones: An example of this solution is illustrated in Fig 3A; the remaining panels in that figure refer to cases of non-vanishing delay that will be summarized below . We note that due to symmetry the homogeneous case with can be readily transformed , proving its resemblance with the Kuramoto network [31] . The somewhat lengthy analytic derivations are given in the Methods section . Next to the homogeneously synchronized state we found a solution with phase clusters given by Again we refer to the Methods for the analytical treatment with respect to the existence of this stationary solution; see Figs 3A and 4A for the corresponding numerical assessments . The specific form of the dynamics ( 3 ) and ( 4 ) already suggested that is just a special case of . We therefore expected the existence of the solutions above not to be affected by introducing homogeneous , finite delays , even if this appeared somewhat counterintuitive . In fact , numerics confirmed this expectation as displayed in Figs 3B , 3C , 4B and 4C . The introduction of distributed delays instead of a single changed results profoundly . To exemplify this , we distributed delays by drawing them at random from a uniform distribution over a certain interval . Recall that according to the transformation from ( 1 ) to ( 3 ) , a distribution of delays generally implies an equivalent distribution of phase shifts . If differed for all populations and , the stationary solution of the continuity equation ( 4 ) required the presence of many distinct phase clusters . We could prove the existence of that set and , although the generic solution appeared similar to the homogeneous delay case , it did contain centroid values instead of the small number or shown above . We depict examples of phase distributions for several parameter settings in Fig 5 . Interestingly , the heterogeneity in , or equivalently in , agreed with weakening the between-population coupling in that both cause a profound widening of the phase distributions; compare Fig 5A with 5C . That is , for a network with homogeneous structural connectivity , it is not the presence of delays per se that hinders synchronization but rather the distribution of delays ( or the lack of coupling strength ) . According to ( 3 ) both distributed phase shifts ( or delays ) and heterogeneous coupling may in principle result in inhomogeneity of phase coupling . In other words , distributed delays and structural heterogeneity may yield inhomogeneity in functional connectivity . We therefore expected a heterogeneous coupling matrix with homogeneous delays to be accompanied by desynchronization equivalent to the case of homogeneous coupling and distributed delays . To verify this , we used the inhomogeneous coupling sketched in Fig 1B , which can be given more formally as with sub-population connectivities where abbreviates denoting that the off- ( sub ) diagonal entries were randomly drawn from a uniform distribution centered around . The numerical simulations depicted in Fig 6 confirmed our hypothesis . For and we observed a widening of the phase distribution similar to that shown in Figs 5B and 5C where coupling was established by but with delay distributions and respectively . Increasing coupling strength reduced the width of the phase distribution comparable to the switch from Fig 5C to 5A . Functional connectivity thus seems to result from an interplay between structural connectivity and delay structure . Therefore both should be taken into account when studying functional connectivity in neuronal networks . The coupling matrices considered so far were admittedly quite academic . However , these seminal examples did provide important insights that | as we will show here | generalize to more complicated and biologically plausible cases . We performed simulations using the coupling scheme displayed in Fig 7 . The matrix had the same structure as but now the blocks were given by , , and . The acronym SC stands for ‘structural connectivity’ that here refers to a neuroanatomical connection matrix as can be derived using DTI/DSI imaging [32] , [33] and is the identity matrix with along the diagonal . To be precise , we used a binary form of the Hagmann connection matrix; see [24] for specifics of pre-processing . In line with earlier studies we quantified functional connectivity in terms of phase uniformity or phase locking value of the pair-wise relative phases , i . e . . Using this synchronization measure , simulation results can be best summarized in the form of functional connectivity matrices constructed from values for all available pairs . The effects of delay structure on these functional connectivity matrices are depicted in Figs 8D-F with the underlying structural connectivity given in Fig 8A . The functional connectivity matrix appeared rather sensitive for parameter values , as increasing coupling strength from , which was the value used in Fig 8D-F , up to resulted in a fully synchronized network as can be seen in Fig 8C . The sudden synchronization is reminiscent of the phase transition towards the fully synchronized state at the critical coupling strength in the Kuramoto network . In a nutshell , from ( 4 ) we could deduce the mechanism responsible for the general finding that structural and functional connectivity are positively correlated [5] , [6]; see also Fig 8B . Our results clearly show that delay distribution affects both the spatial distribution of functional connectivity ( Figs 8B; 8D-F ) and the overall level of synchronization in the network ( Fig 8C ) . The increase of overall synchronization is caused by a decreased phase shift by which the phase dynamics ( 3 ) converges towards the Kuramoto model , i . e . the delay induces a change in stability of the ( partially ) synchronized state .
We investigated the effect of time delays in the coupling between neural mass dynamics , where we consider an oscillatory regime , established by creating pairs of excitatory/inhibitory neural masses . Although we employed a specific neural mass model , we do consider our results generic because the mappings and are largely independent of the generating nodal dynamics , presuming that the time scales in the system are sufficiently separated; cf . Methods . By using this oscillatory dynamics to describe activity in certain brain areas , our approach links directly to the ongoing dispute about changes of functional connectivity in resting state networks ( RSNs ) . There is growing evidence from experimental research that spontaneous brain activity during rest is highly structured into characteristic RSN patterns [1] , [3] , [34] , [35] . These activity patterns seem not to be the result of structural connectivity alone [5] , [36] , but to reflect a non-trivial interplay between the neuroanatomical structure and dynamics [37] . The distribution of time delays involved in this dynamics may have an important role in shaping patterns of activity per se and neuronal synchronization in particular [15]–[17] , [38] . Key to our analysis was the reduction of a neural mass network to a system of phase oscillators summarized in ( 3 ) . Several previous studies struggled with computational complexity when trying to unravel effects of delays vis-à-vis coupling on network dynamics [15]–[17] , [38] . By contrast , our analytic reduction ‘readily’ allowed for disentangling the contributions of both structural connectivity and delays to the phase dynamics ( 3 ) . Delays entered the phase dynamics as phase shifts , given a proper time scale separation of oscillatory and phase dynamics . Furthermore , we found that heterogeneity in delays yields effects equivalent to those of heterogeneity in structural connectivitiy . That is , connectivity and delay effects cannot be easily distinguished when solely looking at functional connectivity patterns . The decrease of as a function of delay , as depicted in Fig 2 , agreed with the analytical findings of [13] as well as with our small-delay approximations outlined in the Methods . However , when further comparing the current results with the literature , one has to realize that some fundamental differences exist between general phase oscillator networks and our dynamics ( 3 ) . One of those differences is the finite dimensionality of the system ( 3 ) . We assumed every excitatory/inhibitory neural mass pair to represent a single brain area , by which the dimension of the system under study may be fairly low . On the contrary , most analytical work on phase oscillator networks considered the limit [14] , [39]–[41] rendering one-to-one comparisons all but trivial . This can already be appreciated by the rather dramatic finite-size effects in Kuramoto networks [42]–[45] . Moreover , our structural connectivity is rather atypical due to its strong -asymmetry . Usually , the connectivity structure in similar phase oscillator networks comprises either fully homogeneous coupling or the entries are distributed according to some unimodal distribution [42] , [46] . An exception is [47] , who investigated repulsive coupling , which is similar to the excitatory/inhibitory connections in . That study reported the presence of two anti-phase clusters reminiscent of the separate /-groups observed here . The time scale separation in ( 3 ) and the resulting simplification of delays as phase shifts also hinders direct comparison with studies on delays in the Kuramoto network . This may indeed explain our seemingly contradicting results . For example , we did not observe the emergence of multi-stability mediated by specific ( ) -combinations as reported in [12] , [48] . In those studies , regions in the ( ) phase plane were found in which synchronization was entirely absent . This is clearly not the case in our study . We did find synchronized solutions irrespective of delays . This is not trivial , because for the phase dynamics equation ( 3 ) may be reduced to a cosine-variant of the traditional Kuramoto model , which is known for its inability to display synchronized behavior [49] . By exploiting the pairing of the -groups and the -asymmetry in we could map our averaged neural mass network to a conventional , fully homogeneous Kuramoto network via a mere transformation of variables . Hence our system can display synchronized behavior even for vanishing values . This is consistent with [50] who did not find any qualitative effects of a phase shift on the stability of the Kuramoto network . Apart from choosing random values as entries of the -matrix , inhomogeneous coupling might also stem from creating distinctively different sub-populations in the network . It can then be studied by modifying within- versus between-network interactions . Particularly interesting in this respect is the occurrence of clustering in the network . When delays are not incorporated one needs either structural inhomogeneity [51] or higher-order Fourier harmonics , in combination with an appropriate phase shift [30] , to achieve clustering . The phenomenon of clustering is important in the light of the study of RSNs , i . e . the strong spatiotemporal organization observed in brain activity during resting state conditions [1] , [3] , [34] , [35] . We briefly consider a simple , low-dimensional example of three isolated excitatory nodes . We define a cluster as a number of ( excitatory ) masses attaining the same centroid phase value . By denoting the excitatory and inhibitory centroid values as and , respectively and assuming , from the phase derivation ( 3 ) and its corresponding continuity equation ( 4 ) one finds constraining equations for . In fact , these forms already hint at the interference between coupling and delays and its effect on synchronization structure . First , all terms on the right-hand side must have equal magnitude requiring specific combinations of and . However , both and are constrained by biology: by the neuroanatomical coupling as part of ; and by the spatial structure of the brain , as delays are proportional to distance between masses and due to finite conduction velocity . Second , because the left-hand side does not vanish , must have some lower bound . If , then cannot compensate this and the equality cannot be satisfied . Because determines , there must be some minimal coupling strength between nodes for synchronization to emerge . This explains the positive correlation between structural and functional connectivity , see , e . g . , [6] , [17] , [38] . It also shows the intricate interplay between structure and delays in establishing synchronization structure . Interestingly , may be regarded as the effective coupling matrix that is typically encountered in dynamic causal modeling approaches [52] . The fact that is directly determined by also explains the finding that models using the structural connectivity as a prior do show more evidence than models using other priors [53] . That is , models that have structural connectivity as a starting point , perform better in terms of data explanation . The sparsity of induced by may yield coexisting synchronized and desynchronized groups within the network , which are often labelled chimera states in the study of phase oscillator systems . It has been found that they crucially depend on the combination of coupling strengths and phase shifts [45] , [54] , [55] ( or delays [56] ) , confirming that there has to be a specific matching of coupling and delays for synchronization to occur . Against the background of the aforementioned -dependence of functional connectivity and the functional-structural connectivity correlation for a biological plausible network , we numerically investigated this by performing simulations of ( 1 ) with structural connectivity given by the anatomical connectivity matrix reported by Hagmann and co-workers [32] . Functional connectivity was quantified as pair-wise phase uniformity , i . e . the phase locking value . Our numerical assessment is summarized in Fig 8 . It clearly revealed off-diagonal patches with synchronization between nodes that are not coupled ( contradicting what has been sketched above ) . The topology of the Hagmann et al . network shares similarities with the Watts and Strogatz' small-world network [57] , i . e . both have a relatively large clustering coefficient with a small average path length . This kind of topology is often believed to be generic in biological neural networks like our brain [5] and enhances synchronizability compared to random networks [57]–[59] . The presence of sparsely connected clusters establishes synchronization between nodes that are only indirectly coupled via their clusters . This causes ‘blurring’ of the structural connectivity matrix: The functional connectivity matrix is less sparse than the structural one [60] . Although this ‘blurring’ is similar to the effects attributed to volume conduction [61] , in this case it is solely due to network topology . Next to such clustering phenomena , we can make even more general predictions about the effect of delays in this network . Structural and functional connectivity are most prominently correlated for homogeneous delays , since yields an interaction term in ( 3 ) that is merely a scaled version of . Hence , the resulting spatial synchronization distribution largely resembles and thus , presuming that the overall coupling strength is not excessively large . This effect can be seen in Fig 8B , where we depicted the Pearson correlation coefficient between the lower triangular parts of and the functional connectivity matrix . Increasing the width of the delay distribution results in a decrease in structure-function correspondence . The positive correlations are consistent with the finding that the pattern of resting state activity is spanned by the eigenmodes of the underlying connectivity matrix in [62] . This is not as trivial as it may seem because the node dynamics in this study were noise-driven fluctuations around a stable fixed point and therefore entirely different from the self-sustained oscillations considered in the current study . Widening the delay distribution also had another effect: It increased its average value and consequently the mean phase shift tended to vanish . Therefore , the interaction term became more similar to an ‘ordinary’ sine-term , which is known for its capacity to enhance synchronizability [63] . We illustrate this effect in Fig 8C , where overall synchrony is shown as a function of coupling strength for different average delay-values . A similar phenomenon has been reported for a system of coupled Hindmarsh-Rose neurons , where a stable synchronized region appears to exist despite the presence of a ( constant ) delay [64] . Throughout this study we assumed the amplitudes to be constant . The relation between the envelope dynamics of M/EEG and fMRI-BOLD signals [3] suggests that considering the temporal change of the amplitude may be very important for unravelling the spatio-temporal structure of resting state brain activity . Given that we focused on phase synchronization together with the slow time scale on which the BOLD dynamics evolve ( Hz , [1] ) , we believe that the assumption of constant amplitude is justified here . Investigating this assumption in depth , however , is beyond the scope of the current study . We summarize that the dynamics of a system of coupled Freeman neural masses ( 1 ) can be captured by the averaged phase dynamics ( 3 ) , in which the role of the structural connectivity and delay distribution become explicit . By this , one can identify the relative contributions of structure and delay to phase synchronization , i . e . to the functional connectivity of the neural network . Heterogeneity in structural coupling and distributed delays have equivalent effects on the observed phase distributions . Overall , this supports the notion that structure and delay are both crucial determinants of network behavior and should therefore be taken into account in unison whenever modeling realistic neural networks [37] . Our examples on clustering detailed how the intricate interplay between coupling and delays determines the form and spatial distribution of clustering in these networks . Pinpointing the explicit contributions of and in the phase dynamics ( 3 ) enabled us to understand their roles in establishing synchronization structure and why functional and structural connectivity are so closely correlated . This implies that the observed temporal changes in synchronization structure in resting state and task conditions can be modulated through either or amplitudes .
To average the dynamics ( 9 ) we defined the averaging operator as . We first substituted ( 8 ) into ( 9 ) and used The convolution integral on the right-hand side of the second equation of ( 8 ) required more attention . Recall the definition of in ( 2 ) and the definition of the convolution operator , with which one can write ( 10 ) When multiplied by , this yielded the two averaged trigonometric expressions and After substituting this in ( 9 ) we obtained the phase dynamics ( 11 ) where we defined the constants and as By this procedure we omitted all fast oscillating terms as they averaged out ( cf . rotating wave approximation ) . As mentioned in the Results section , we focused on the case in which our convolution kernel did not contain any memory . That is , we considered the limit . In this limit only the first terms in the numerators of and remained non-zero and we could cast ( 11 ) in the form ( 3 ) using Note that in this form , the delays only appeared in the phase shift . Last but not least we simplified expression ( 3 ) by exploiting the homogeneity of to explicitly formulate the matrix multiplication . In particular for equal delays , i . e . for , this led to a greatly simplified form of ( 4 ) that we summarized in the Results section and will be discussed in more detailed below . The neural masses do not oscillate around the origin but around the fixed points , which have a direct influence on the coupling terms through the term . Delay values do not influence the positions of the fixed points because by definition . Hence , holds , presuming the fixed points exist . Therefore we were free to choose , such that under the limit the coupling term in ( 6 ) reduced to After inserting the form of we explicitly found which implied and . That is , in the case of the homogeneous coupling the fixed points of the excitatory masses are equal and the same holds for the inhibitory masses . The coupling also depended on the amplitudes of the neural masses — see also [24] . Accounting for the high degree of homogeneity in the system , we assumed the amplitudes to be equal for equal types of neural masses , i . e . , and , . Furthermore we randomized the parameters , by introducing as a mean-centered random variable . Whenever appropriate we chose sufficiently small to restrict discussion to the mean values and . Since the phase oscillator system ( 3 ) can be cast in Kuramoto form , fully synchronized solutions may be stable despite the presence of equal delays . But how about solutions other than the fully synchronized ones ? In what follows we discuss existence and form of partially synchronized solutions of ( 3 ) for general delays . We concentrated on homogeneous coupling and varied the distribution of . In the homogeneous case we found the dynamics of the -th node's phase distribution to be ( 12 ) where the subscript when and when . Note that equation ( 12 ) may differ for every . Homogeneity of enable us to express explicitly and to define the following constants ( 13 ) Sufficient for the existence of a stationary solution is the case in which the drift coefficient in the dynamics of the probability density vanishes , here the bracketed term on the right-hand side of ( 12 ) . From the dynamics ( 12 ) it readily follows that the phase distribution obeys the form ( 5 ) , i . e . , containing different centroid phase values . Both distributed delays and heterogeneous coupling called for numerical assessments , particularly when it came to the stability of solutions . We performed numerical simulations of the coupled neural masses ( 1 ) using a conventional Euler-forward integration scheme with time step over a time span of 10 s . We verified the appropriateness of this simple implementation against a more elaborate predictor/corrector integrator [67] , which revealed little to no difference but demanded far more numerical resources . The simulated network consisted of 500 nodes ( 250 pairs ) with and being randomly drawn from uniform distributions ( and ) to mimic distributed natural frequencies per -pair of nodes . Although randomly drawn , these sets were fixed across simulations trials . Initial conditions where chosen randomly and did vary between trials; , and similarly for . The external input was set to , , , . Coupling between masses was achieved by using the sigmoidal activation function that was given as The fraction in this equation may be interpreted as the cumulative distribution function of the normal distribution of the firing thresholds across the population , whereas the constant is just a scaling factor [68] . In the simulations we used the following values for the activation function parameters: , and . In order to compare the numerics with our analytical results , we determined the phase values from the simulated potentials by means of the Hilbert phase . To this end , we first determined oscillation frequency as the lowest frequency with a coinciding peak in the power spectra for all nodes . Voltage traces were band-pass filtered using a 1-st order bi-directional Butterworth filter in the frequency band . For each sample in the interval , phase values were then calculated as the angle of the analytical signal . By restricting analysis to that interval we avoided transient behavior as well as possible filter artifacts . The so-determined contained the frequency component , which we first subtracted to obtain . Then , we opted to compute phase distributions over the time interval and over successive trials . The ( circular ) mean phase , however , differed from trial to trial because of the randomly chosen initial conditions ( see above ) . Hence for every trial we shifted phases by the mean phase of the excitatory population prior to concatenating trials . By this , the phase distributions of the excitatory phases always became centered around zero and the inhibitory phases were considered relative values . The mean phase per trial was given as where denotes the quadrant-corrected inverse tangents . The distributions displayed in the figures are the phase distributions obtained from 100 simulated trials with different initial conditions . As said , parameter values were identical across trials . For the simulations involving connectivity , we used a binary form of the 66 areas parcelated Hagmann et al . matrix [24] , [32] as block; see Fig 7 and 8A for the coupling scheme and , respectively . Functional connectivity was quantified as phase coherence given by . We performed one hundred simulation runs of 10 s for each distribution with different initial conditions and and averaged the matrices over these runs . This was done to avoid high values due to common oscillation frequency alone . For each run , data in the interval was used to determine . The overall coupling strengths were set to and for the matrices displayed in Fig 8 . Structure-function correspondence was quantified as the Pearson correlation coefficient between the lower triangular parts of both matrices to avoid spurious correlation values due to common terms along the diagonal . To estimate the oscillatory regime of the system of coupled Freeman neural masses ( 1 ) we considered the linearized dynamics ( 8 ) for which in general reads Assuming all delays to be ( very ) small we expanded in the sense of Taylor and approximated up to the linear order in : For the sake of legibility we here considered a single isolated -pair with and . We also assumed equal delays , i . e . . Then , we found the resulting linear dynamics as where we abbreviated and The matrix came with eigenvalues where we abbreviated . These eigenvalues had the following real and imaginary parts A necessary condition for the existence of a stable limit cycle , and hence for the system ( 1 ) to display oscillatory behavior , is that the fixed point is unstable . This means that for at least one of the conjugate pairs , must hold , which was indeed the case irrespective of . The corresponding then provided a rough estimate for the frequency as a function of , as shown in Fig 2 ( solid line ) . In the particular case of we found revealing that for the imaginary parts did not vanish , i . e . the -unit always displayed oscillations around the fixed point because the sigmoid's derivative is positive definite: Further , for the real-part to be positive the coupling constant had to be sufficiently large and the intrinsic damping and/or sufficiently small but finite , because of where we note that . For the separation of time scales underlying all the major approximations in the current study we considered the case in which two distinct time scales are present in the system of coupled neural masses: the oscillation described by the ( mean ) frequency and its corresponding period — from here-on referred to as fast time scale — as well as a slower time scale , on which the dynamics of evolve . In what follows we will verify the expression for in ( 9 ) and show how the separation of time scales enabled us to determine the role of in the convolution . As is conventional in multiple-scaling approaches , we set the time as ‘fast’ time and the ‘slow’ time as with . For the sake of legibility we here adopted the dot-notation for temporal derivatives and further abbreviated partial derivatives as . We denoted the deviation of the voltage from its fixed-point as where we assumed that evolved on the slow time , i . e . . Note that an equivalent approach can be adopted for the amplitude dynamics , i . e . , which is referred to as the slowly varying amplitude approximation . As we were primarily interested in the dynamics , we regarded amplitude as constant on both time scales; see Discussion section . By this we could readily apply the chain rule and obtained where the last equality follows from ( 8 ) . For the derivative we found Next we considered the expression ( 9 ) for , where we emphasize that could here be identified as , since evolves only on the slow time scale . To anticipate: ( 9 ) discarded all terms evolving on the fast time scale , i . e . all terms in favor of terms and higher . To show this , recall that the right-hand side of ( 9 ) read In words , only expressions evolving on the slow time scale were retained , i . e . only and -order terms . When focusing on the slow time sale and discarding the even slower time scale , we could conclude that , up to a constant , is given by ( 9 ) ; note that we here applied the so-called two-timing method; see , e . g . , [23] , [69] , [70] . As said , we used the constancy of ( on the fast time scale ) to evaluate the convolution term . We exploited the description in two time scales to justify the transformation of the delay into phase shifts . We explicitly evaluated the integral to show that ( 10 ) is its result . For the sake of readability we dropped the explicit time dependence of whenever possible . First , by integrating by parts twice we obtained Then , when discarding terms , we found that the integral with which we started appeared again on the right-hand side . This allowed us to write which resulted in With this form we could finally express the convolution terms as For time scales are sufficiently separated to ignore all terms by which we arrived at ( 10 ) . The derivation of ( 10 ) required the intuitive assumption , which might be motivated by a ( relatively ) small-delay approximation . Consider the Taylor expansion of around , which reads If and is of the order , i . e . of the same order of magnitude as the oscillatory period , then one may conclude that the approximation is valid . Note that this consistent with [71] . In particular , when is of order , i . e . of the same order of magnitude as the slow time scale , the approximation fails . | Separating the time scale of oscillations from that of the phase dynamics allowed for reducing a network of coupled neural mass models to a system of phase oscillators . We studied the dynamics of networks of phases and their synchronization characteristics as being seminal for functional neural networks . We put particular focus on effects of time delays in the coupling on the network dynamics and contrasted that to effects due to altered structural connectivity . Does neuroanatomical structure prescribe all the macroscopic activity patterns that we observe through electrophysiological brain recordings ? We found that heterogeneity in structural coupling and distributed delays have equivalent effects on the shape of phase distributions , i . e . , on functional connectivity . The contribution of changes in structural connectivity to network synchronization can therefore not readily be distinguished from that of distributed delays . Interestingly , the emergence of phase clusters in networks requires a subtle interplay between coupling and delays , which may form a window into disentangling structural effects from those induced by delay distributions . Therefore , when investigating neural network behavior , both structural connectivity and delay distribution should be addressed . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"computer",
"and",
"information",
"sciences",
"systems",
"science",
"mathematics",
"neuroanatomy",
"anatomy",
"neural",
"networks",
"computational",
"neuroscience",
"nervous",
"system",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology",
"nonlinear",
"dynamics",
"neuroscience"
] | 2014 | Structure-Function Discrepancy: Inhomogeneity and Delays in Synchronized Neural Networks |
Palpalis group tsetse flies are the major vectors of human African trypanosomiasis , and visually-attractive targets and traps are important tools for their control . Considerable efforts are underway to optimise these visual baits , and one factor that has been investigated is coloration . Analyses of the link between visual bait coloration and tsetse fly catches have used methods which poorly replicate sensory processing in the fly visual system , but doing so would allow the visual information driving tsetse attraction to these baits to be more fully understood , and the reflectance spectra of candidate visual baits to be more completely analysed . Following methods well established for other species , I reanalyse the numbers of tsetse flies caught at visual baits based upon the calculated photoreceptor excitations elicited by those baits . I do this for large sets of previously published data for Glossina fuscipes fuscipes ( Lindh et al . ( 2012 ) . PLoS Negl Trop Dis 6: e1661 ) , G . palpalis palpalis ( Green ( 1988 ) . Bull Ent Res 78: 591 ) , and G . pallidipes ( Green and Flint ( 1986 ) . Bull Ent Res 76: 409 ) . Tsetse attraction to visual baits in these studies can be explained by a colour opponent mechanism to which the UV-blue photoreceptor R7y contributes positively , and both the green-yellow photoreceptor R8y , and the low-wavelength UV photoreceptor R7p , contribute negatively . A tool for calculating fly photoreceptor excitations is made available with this paper , and this will facilitate a complete and biologically authentic description of visual bait reflectance spectra that can be employed in the search for more efficacious visual baits , or the analysis of future studies of tsetse fly attraction .
Tsetse flies ( Glossina spp . ) are the vectors of trypanosomes that cause nagana in cattle , and sleeping sickness ( human African trypanosomiasis , HAT ) in humans [1] . There are no vaccines against HAT , no prophylactic drugs are recommended , and diagnosis and treatment of the disease is difficult [2] , [3] . Control of tsetse flies is , therefore , of great importance for public health in sub-Saharan Africa [2] , [3] . HAT is chiefly transmitted by riverine tsetse flies ( = Palpalis species group ) [3] , and insecticide-treated screens and targets ( two-dimensional cloth panels ) , and traps ( three-dimensional structures ) are an important part of control , eradication , and monitoring operations for these species [4] , [5] , [6] . The most effective visual bait material for catching tsetse flies is widely accepted to be phthalogen blue cotton ( e . g . [5] , [7] ) , but this material is now reportedly difficult to obtain , and modern synthetic fabrics are expected to be more durable and cost-effective for field use [5] . For all of these reasons , there has been considerable interest in understanding the attractive properties of visual baits so that they may be further optimised in terms of cost and efficacy [3] , [4] , [5] , [8] , [9] . Tsetse flies are caught at visual baits as a result of two behavioural processes: their initial attraction to approach the bait from a distance , and their tendency to land upon it ( or enter it , in the case of a trap ) once within range [10] . Several studies have attempted to relate the effectiveness of variously coloured visual baits at attracting tsetse flies or eliciting landing responses , to their reflectance at particular wavelengths of light , both for Palpalis group and Morsitans group ( savannah ) tsetse flies [5] , [7] , [11] . Sometimes these studies have considered total bait reflectance within several mutually exclusive wavelength bands [5] , [7] , [11] , or more recently , point reflectance at the sensitivity peaks known for fly photoreceptors [5] . These analyses have indicated positive contributions of blue wavelengths , and negative contributions of green/yellow/red and UV wavelengths , to a visual bait's effectiveness at attracting tsetse [5] , [7] , [11] . In many investigations , the proportion of attracted flies that contacted the visual bait was positively influenced by its reflectance of UV wavelengths , or low overall luminance [7] , [10] , [12] , [13] ( but see also [5] ) . However , since only a relatively small proportion of the flies attracted to a bait actually land [5] , [8] , [10] , [14] , flanking nets of fine , insecticide-treated mesh are advocated to improve the efficiency of screens and targets by capturing non-landing , circling flies through accidental collisions with the net [8] , [15] . As a result of these investigations , it has been suggested that the search for new visual bait materials must be guided by full spectral analysis of them , rather than just visual inspection of candidate fabrics and qualitative description of their colour [5] . However , whilst the approaches used so far for such analyses have been motivated by the mechanisms of fly vision , they represent this process relatively crudely . Flies possess five classes of photoreceptor over the majority of a compound eye , and these have complicated sensitivity functions that both overlap with one another , and are not well-described by the single sensitivity peak of each receptor type [16] , [17] , [18] ( see Fig . 1 ) . The responses of these receptors do not just depend on the reflectance of a visual bait but also the predominant background that the bait is viewed against , because photoreceptors adapt to constant stimulation ( e . g . [19] ) . Finally , the light reflected by a visual bait depends not only on its reflectance spectrum , but also the spectrum of light that it is illuminated with ( e . g . [20] ) . Although the validity of findings relating visual bait effectiveness to reflectivity is certainly not in question , an improved , biologically-motivated method of analysing and quantifying the appearance of baits from the fly's eye view is clearly required for their further optimisation . Methods to model photoreceptor responses taking into account the above described aspects of their response properties are now well established and have been widely employed to understand visually-guided behaviour in a variety of species ( e . g . [21] , [22] , [23] , [24] , [25] , [26] ) . Here , I apply these techniques to understand attraction to visual baits in three species of tsetse fly ( two riverine and one savannah species ) for which large datasets of tsetse catches at coloured visual baits were available in published studies [5] , [7] , [11] . Rather than attempting to analyse the positions of these visual baits within a fly colour space determined by the responses of all photoreceptor types ( c . f . [22] , [23] , [27] ) , I employ regression methods to determine the subset of photoreceptors , and linear interactions between them , that best explain the behaviour of tsetse flies . A broadly similar approach has successfully identified opponent colour coding mechanisms in hymenoptera [28] , and explained innate colour preferences in butterflies and flies seeking oviposition or feeding sites [24] , [29] . This approach was chosen because the latter studies of innate colour preferences have revealed that the behaviours are often driven by only a subset of the photoreceptor types possessed by the subject organism [24] , [29] , [30] . On the basis of my analysis , I present a simple colour opponent model that explains attraction in all three tsetse fly species based upon photoreceptor excitations , and I make this available as electronic supporting material . This model provides a means to analyse visual bait reflectance spectra completely , and in a biologically-authentic manner , and can be employed for the analysis of candidate visual baits or of future studies of tsetse fly attraction .
Photoreceptor spectral sensitivity functions have been thoroughly characterised in Musca spp . , and are similar across flies from other genera ( e . g . [17] ) . Although photoreceptor spectral sensitivity functions have been recorded for Glossina spp . , these were affected by a diet-induced lack of screening pigment that may or may not affect wild populations [18] . Since the overall organisation of tsetse photoreceptors was , nevertheless , similar to that in Musca [16] , [17] , [18] , data from the latter were used in this study . Each ommatidium in the dipteran compound eye contains eight photoreceptors , or retinula cells , named R1-R8 . R1-6 are similar across all ommatidia in the eye , have a double-peaked spectral sensitivity function that peaks in UV and blue wavelengths , and make output synapses in the first neuropile of the optic lobe , the lamina [17] . Photoreceptors R7 and R8 are located centrally within each ommatidium and bypass the lamina to make output synapses in the medulla of the optic lobe . R7 and R8 occur in two forms across the majority of the eye ( excluding specialised areas for perception of polarised light in both sexes , and for tracking of females in male flies ) [17] . In 70% of these ommatidia the ‘y’ ( yellow ) form occurs in which R8y is most sensitive to green-yellow wavelengths but has an accessory , sensitising pigment sensitive to UV [16] , [17] , and R7y is most sensitive to UV wavelengths ( ∼355nm ) with a pronounced shoulder of sensitivity extending into the blue region of the spectrum [16] , [17] . In the remaining 30% of ommatidia , the ‘p’ ( pale ) form of these photoreceptors occurs , where R8p is most sensitive to blue wavelengths , and R7p to lower UV wavelengths ( ∼330nm ) [16] , [17] . Despite some anatomical and physiological differences between the R1-6 and R7/8 receptors , several mechanisms ensure that their responses are comparable [31] . For example , due to their structure and position within the ommatidium , photoreceptors R7 and R8 intercept less photons than R1-6 , but they compensate for this with a greater voltage gain per photon so that overall , voltage gain per unit contrast is equalised across photoreceptor types [31] . As such , I employed the same method to model the responses of each photoreceptor type . Spectral sensitivity functions for each of the five receptor classes were extracted from published studies [16] , [17] using DataThief software [32] , and are plotted in Fig . 1 . To avoid extrapolation beyond published data , spectral sensitivity functions were considered between 310 and 600nm , but the sensitivity of a fly's eye to wavelengths outside of this range is expected to be negligible . In addition to their spectral sensitivity functions , the relative sensitivities of each receptor class are also determined by their adaptation to stimulation from the background . Following established methods that are accessibly described by Chittka and Kevan ( 2005 ) [26] , the range sensitivity factor ( R ) was calculated for each receptor class in order to adjust their sensitivities such that background stimulation would elicit a half maximal response in each receptor class: Where IB ( λ ) is the spectral reflectance function of the background; S ( λ ) is the spectral sensitivity function of a particular receptor ( see Fig . 1 ) ; D ( λ ) is the illuminant; and dλ signifies a wavelength step for each of these functions . Following previous work , I used a standard illuminant function ( D65 , [33] ) expressed as normalised quanta ( values provided in [26] ) . Values for this function were available at 5 nm increments , and linear interpolation was used to achieve 2 nm wavelength resolution . I used published values for typical leaf reflectance as a background spectrum ( values as provided in [26] , linearly interpolated for a 2 nm wavelength resolution ) , which was a reasonable simplifying assumption given the typical riverine forest and thicket habitat of Palpalis group tsetse ( e . g . figure 2 of [34]; figure S6 of [6] ) . Based on these data , the effective quantum catch ( P ) of reflected light from a given visual bait , adjusted by R to reflect adaptation to the background , was calculated for each of a fly's five photoreceptor classes by: Where IS ( λ ) is the spectral reflectance function for the stimulus under investigation . Quantum catches were non-linearised to represent the transduction process in each photoreceptor , providing excitation ( E ) based upon: This equation has been used to describe intensity-response functions of photoreceptors across a variety of taxa . The exponent , n , determines the slope of the function and varies with the state of light adaption , approaching 1 . 0 in fully light-adapted photoreceptors ( as demonstrated for R1-6 of flies , and also for locust photoreceptors [35] ) . As such , n = 1 . 0 was used in this study , and the above equation reduced to a simplified form without exponent ( c . f . [26] , [28] ) . Using these methods , the relative excitation in each of the five photoreceptor classes could be calculated , accounting for the spectra of illumination and bait reflectance , the complete spectral sensitivity function of each photoreceptor , and adaptation of that photoreceptor to stimulation from the predominant background . A spreadsheet that performs these calculations is provided ( Table S1 ) , as is the complete dataset of photoreceptor excitations calculated during this study ( S1 Dataset ) . I applied the above analysis techniques to the datasets collected in the three most comprehensive studies of tsetse fly catches at coloured baits under field conditions conducted to date [5] , [7] , [11] . My focus was the initial attraction of tsetse flies to those baits ( rather than their landing responses ) , since recent work optimising visual baits for Palpalis group tsetse flies recommends small targets with flanking nets , which are effective against both landing and circling flies [8] . My intention was not to meta-analyse colour attraction in tsetse , which has been investigated in multiple species , locations , and using a plethora of visual bait designs , all of which mean that the studies are not necessarily comparable . The three selected studies were identified through searches of Web of Science ( Thomson Reuters ) , and of the cited references from studies identified in those searches , and from key review papers [10] , [36] . The criteria for their selection were that ( i ) they each investigated a large sample ( >25 in these cases ) of visual baits that varied only in colour , and ( ii ) they provided full reflectance spectra from 300 to 700 nm for each visual bait . Thus , although numerous other studies have compared tsetse fly catches at visual baits that vary in colour , they were not analysed due to the lack of complete reflectance spectra ( e . g . [37] ) , or the relatively small selection of colours investigated [12] , [38] , [39] , [40] . Data on catches of G . f . fuscipes ( Palpalis group ) at 37 differently coloured small ( 0 . 25 m2 ) cloth targets were obtained from Lindh et al . ( 2012 ) [5] . This study was conducted on the Chamaunga islands of Lake Victoria , Kenya . Data were mean combined tsetse fly catches over both a surface electrocuting net ( to sample flies landing on the cloth target ) , and an equal-sized flanking electrocuting net enclosing a fine , black mesh panel ( to sample circling flies ) . Data were gained from 15 experiments in which combinations of five differently coloured targets were tested . A phthalogen blue standard target was included in each experiment , and mean tsetse fly catches for each target were normalised to that for the standard target [5] . These data were read directly from tables in the source publication . Lindh et al . ( 2012 ) provide reflectance spectra for each of their targets at 10 nm resolution , which I linearly interpolated to achieve 2 nm resolution for the purposes of analysis . Data on catches of G . p . palpalis ( Palpalis group ) at large ( 1 . 0 m2 ) cloth screens in one experiment , and biconical traps in another , were obtained from Green ( 1988 ) [7] . This study was conducted in the Bouaflé area of Ivory Coast . Although cloth screen experiments examined a variety of electrocuting net configurations , I analysed only the data from experiments in which a surface net and one flanking net ( 1 . 0 m×0 . 5 m ) were used ( for which most data were available , and both landing and circling flies were sampled; data in tables IIIb , IV , and V , from [7] ) . Data were thus mean combined surface and flanking net tsetse fly catches for 27 screens , gained from 10 experiments in which four different screens were compared . As above , mean tsetse fly catches were normalised to that of a standard phthalogen blue screen in each experiment , although the reflectance spectrum for this stimulus was not exactly equivalent to that for the standard target of Lindh et al . ( 2012 ) . Biconical trap experiment data were mean tsetse fly catches for 26 biconical traps with differently coloured lower cones , normalised to those of a biconical trap with standard phthalogen blue lower cone [7] . All biconical traps had standard black interior screens and an upper cone of white mosquito netting . Catch data were read directly from data tables , and screen/trap reflectance spectra were extracted from figures using DataThief software ( measurements provided in supporting dataset S1 ) . I also analysed data on the catches of G . pallidipes ( Morsitans group ) in F2 traps from Green and Flint ( 1986 ) [11] . This study was conducted in the Zambezi valley , Zimbabwe . Data were mean tsetse fly catches for 30 F2 traps with differently coloured outer cloth covers but standard black interiors , gained from five experiments in which five or six differently coloured traps were tested . Trap catches were normalised by the catch of a white standard trap in each experiment . Reflectance spectra and normalised trap catch data were both extracted from figures using DataThief software ( measurements provided in supporting dataset S1 ) . Since the above described target and screen experiments used surface and flanking electrocuting nets to sample both landing and circling flies , they provide a good indication of attraction to the visual baits that should be relatively little affected by any variation in landing responses across baits . The biconical and F2 trap experiments , meanwhile , sampled only those flies that entered the trap and were caught . These were expected to provide a less accurate indication of attraction since outer trap surfaces that have strong positive or negative influences on landing responses may have affected trap catches . For example , dark outer trap surfaces are believed to stimulate flies to land on them , rather than entering the trap and being sampled [10] , [11] . The original field studies asserted a log-log linear relationship between visual bait reflectance and normalised tsetse fly catches ( e . g . [5] , [11] ) , and on the basis of the same data I observed that calculated photoreceptor excitations related approximately linearly to log transformed normalised tsetse fly catches . Thus , for each study , tsetse fly catches were expressed as a percentage of the standard bait's catch , and log ( n+1 ) transformed for analysis . All available data are presented graphically , but statistical analyses were conducted on a sub-set of data in which each bait was represented once , against its mean normalised tsetse fly catch if presented multiple times within a given study ( the latter collated dataset is provided; supporting dataset S1 ) . Photoreceptor excitations were generally not normally distributed , so correlations between the excitation of different photoreceptors were assessed using Spearman's rank correlation . Linear regression was used to relate log transformed normalised tsetse fly catches to various combinations of calculated photoreceptor excitations , or indices derived from them . Due to highly multicollinear photoreceptor excitations , exploratory multiple regressions were carried out using the partial least squares ( PLS ) regression procedure . This procedure maps photoreceptor excitations and catch scores to a series of latent factors that explain their variability , and the number of latent factors was chosen so as to minimise predicted error sum of squares ( PRESS ) , and maximise predicted r2 . In one case it was felt that the model specified in this way was over-fitted due to a marginal improvement in these statistics versus a model with one latent factor less , and the simpler model was , therefore , selected on parsimony grounds ( this case is identified in the relevant results table ) . Directed , follow-up linear regressions were then employed to test the models implicated by PLS regression statistically , through sequential addition of predictors with F tests of r2 change . PLS regression analyses were conducted using Minitab 14 . 20 ( Minitab Inc . , State College PA , USA ) ; all other statistical analyses were conducted using SPSS version 19 . 0 ( IBM Corp . , Armonk NY , USA ) .
Photoreceptor excitations were calculated from the reflectance spectra of 101 visual baits that were used in previous studies of tsetse fly capture in the field . These photoreceptor excitations are plotted against the log-transformed , normalised tsetse fly catch of each bait from the original studies ( Figs . 2 and 3 ) . Tsetse fly catch was significantly negatively related to calculated excitation in photoreceptor R8y in six out of the eight experimental datasets comprising different combinations of species , sex , and visual bait ( linear regressions , p<0 . 05; Figs . 2 and 3 ) . Of the other photoreceptor types , tsetse fly catch was significantly related to calculated excitations in a minority of the datasets ( three each for R7p and R8p; two each for R7y and R1-6; Figs . 2 and 3 ) . The responses of individual photoreceptor types provide achromatic ( luminance ) information , and receptors R1-6 are thought to be especially important in this role [31] , [41] . Comparisons of the responses of different photoreceptor types , meanwhile , provide chromatic ( spectral ) information [41] , [42] , and previous studies have implicated this kind of information as an important determinant of tsetse fly attraction [5] , [7] , [11] . Relative photoreceptor excitations were visualised for each visual bait by expressing the excitation of each photoreceptor as a proportion of the mean excitation across all five photoreceptors ( Figs . 4 and 5 ) . Linear regression identified consistent , significant , positive relationships between the relative R7y photoreceptor response and log-transformed , normalised tsetse catch , and consistent , significant , negative relationships between the relative R8y receptor response and log-transformed , normalised tsetse catch ( Figs . 4 and 5; note that these relationships were not significant at p<0 . 05 for male G . f . fuscipes but have been plotted for comparison; other non-significant relationships have not been plotted ) . Since the ‘y’ form of the R7 and R8 receptor is the most abundant in the fly retina [17] , and the relative excitations of these two cell types had consistent , opposite-sign relationships with tsetse fly catch , it was plausible that an opponent interaction between these two photoreceptor types could drive the attraction of tsetse flies to visual baits . An R7y/R8y opponent interaction is visualised in Fig . 6 as the difference in the calculated excitation of these two photoreceptors . Linear regression analysis identified a significant positive relationship between this opponency index and normalised tsetse fly catch for all datasets except that for male G . f . fuscipes ( Fig . 6; note that linear regression analyses of these datasets using R7y and R8y excitations as separate predictors of tsetse fly catches are presented in the next section ) . Although this analysis implicates an R7y/R8y opponent interaction as an important mechanistic element underlying tsetse fly attraction to visual baits , this finding may not be particularly useful for the optimisation of visual baits for field use . This is because the tsetse fly catch of the preferred phthalogen blue target was not well predicted by this index – it was , for example , easily and consistently the most effective target in the study of Lindh et al . ( 2012 ) [5] , but its calculated + R7y - R8y opponency index was not particularly unusual among the tested targets ( red data points , Fig . 6 ) . Colour categorisation based on the signs of R7y/R8y and R7p/R8p opponent interactions has been proposed to explain colour discrimination learning in blowflies [43] , so I next investigated whether this model of fly colour vision could better explain tsetse fly attraction to visual baits . However , blowfly-inspired colour categorisation had significant explanatory value for tsetse catches in only half of the datasets analysed , and was also unable to explain the unique effectiveness of phthalogen blue baits at catching tsetse flies ( Figure S1; Table S2 ) . Across the complete set of 101 visual baits from all three field studies , calculated excitations in the five classes of photoreceptor were significantly positively correlated with one another ( table 1 ) . Spearman's rank correlation coefficients were particularly high ( >0 . 85 ) between photoreceptors R7y , R8p , and R1-6 , indicating some redundancy in the stimulus information encoded by these photoreceptors . Correlations between the responses of these cells and those of R7p and R8y were weaker ( with the exception of the correlation between R1-6 and R8y excitations ) , suggesting three more distinct sub-sets of visual information available from the photoreceptor array . Due to the significant correlations between photoreceptor responses ( table 1 ) , I explored the possibility of a more complex interaction between photoreceptor types using multivariate partial least squares regression to generate models that predicted both the male and female tsetse catches within each of the four combinations of tsetse species and visual bait type ( table 2 ) . In these analyses , photoreceptors R7y and R8p were always positive contributors to the prediction of tsetse catches , whilst R8y and R7p were always negative contributors . R1-6 excitation was a negative predictor in three studies but a positive contributor in a fourth . Ranking the importance of these photoreceptor types by their standardised regression coefficients , and taking the median rank for each across the four studies , suggested that R7y and R8y were the most important predictors of tsetse catch ( in line with the previously described analyses ) , followed by R7p and R8p , with R1-6 the least important predictors . The importance of R7p over R8p was supported by a number of additional lines of evidence: the R7p response was not strongly correlated with that of any other photoreceptor ( table 1 ) ; standardised regression coefficients ranked R7p as a more important predictor than R8p in the target and screen datasets , in which tsetse catches provided a measure of attraction that could not have been confounded by varying landing or entering responses ( see methods; table 2 ) ; and previous analyses indicated that UV wavelengths contribute negatively to tsetse fly attraction [5] , [7] , [11] . I next ran a series of linear regression models that introduced photoreceptor excitations sequentially in the order suggested by the above analysis ( table 3 ) . As expected , models containing R7y and R8y excitations provided a significant fit to the data in all but one case ( male G . f . fuscipes ) , with adjusted r2 exceeding that for models containing only one photoreceptor ( see Figs . 2 and 3 ) , in six out of eight datasets . Addition of the R7p excitation parameter to these R7y/R8y models increased r2 and adjusted r2 for all datasets ( table 3 ) . A significant increase in r2 was achieved in six out of eight datasets , and of the remaining two , a significant fit to the data was obtained where previously there was none for male G . f . fuscipes ( table 3 ) . Subsequent addition of R8p and then R1-6 excitations to that model improved r2 , but did so significantly in only one case for each of these additions ( both trap datasets ) . Adjusted r2 was reduced by the addition of the R8p excitation parameter for two datasets , and by the addition of the R1-6 excitation parameter for four datasets ( table 3 ) . In four- and five-parameter regression models , variance inflation factors ( VIFs ) indicated high multicollinearity between R7y , R8p , and R1-6 ( and in the five-parameter model , R8y ) excitations , as was expected from their strong correlation ( table 1 ) . As a result , the signs and significance levels for the correlation coefficients were inconsistent between datasets . The observed collinearity supports the idea that R7y , R8p , and possibly R1-6 play essentially redundant roles in tsetse fly attraction , meaning that although it is possible that they contribute to the behaviour , their responses are not important in explaining the coarse detail of it for the purposes of visual bait optimisation . As implicated by statistical analyses reported so far , of several three-parameter regression models , that containing R7y excitation provided the best fit to tsetse fly catch data . However , R8p or R1-6 excitations could be substituted for those of R7y , and could still provide a significant explanation for the attraction data ( although with reduced r2 in all but one case; Table S3 ) . If candidate visual baits are to be assessed prior to field testing , it may be useful to represent the opponent interaction between photoreceptors implicated in the above analysis as a simply calculable index that ignores predictor weightings specific to individual studies , tsetse species , or visual baits . Such an index calculated by subtracting the unweighted excitations of R8y and R7p , from the unweighted excitation of R7y , is plotted in Fig . 7 . Linear regression identified significant positive relationships between this simple opponency index and normalised tsetse fly catch for all eight sets of experimental data ( Fig . 7; see also Table S4 for analysis of other simple indices calculated by linear combination of unweighted photoreceptor excitations ) . Although this simple index was a good predictor of tsetse catches at targets and screens , it was not the best-fitting index for trap catches which were , for example , better predicted by the + R7y - R8y opponency index considered above . However , in contrast to that index , phthalogen blue stimuli were characterised by more extreme values of the + R7y - R8y - R7p index , reflecting their observed effectiveness at catching tsetse flies in field experiments ( red data points , Fig . 7 ) .
I reanalysed data gathered in previous studies of tsetse fly catches at coloured visual baits using a model of fly photoreceptor excitations . This approach provides a biologically-authentic method of analysing the complete reflectance spectrum of a visual bait , as perceived by a fly . My reanalysis indicates that tsetse fly attraction to these visual baits can be explained by an opponent interaction to which a photoreceptor sensitive to blue wavelengths ( R7y ) makes a positive contribution , whilst photoreceptors sensitive to green-yellow ( R8y ) and UV wavelengths ( R7p ) make negative contributions . This finding is broadly consistent with the wavelengths of reflected light implicated in determining tsetse fly catches by the original studies [5] , [7] , [11] , but provides more precision in mechanistic detail . The described approach will facilitate the appraisal of candidate visual baits prior to field testing , and can be used to analyse future studies of tsetse fly attraction . At a coarse level , my findings are in agreement with those from the original investigations [5] , [7] , [11] . Monochromatic , luminance information from single photoreceptors did not provide a strong explanation for tsetse fly catches , which were better explained by the excitation of multiple photoreceptor types . Previous studies of G . pallidipes , G . p . palpalis , and G . f . fuscipes , found that tsetse fly catches at blue baits exceeded those at grey baits with a range of intensities [5] , [7] , [11] , and regression models based upon catches of these species all implicated multiple reflectance bands in determining attraction [5] , [7] , [11] . In all three field studies , blue , UV , and green-yellow reflectance bands were implicated in determining attraction [5] , [7] , [11] , corresponding to the peak sensitivities of three different photoreceptor classes and the findings of my reanalysis . Such comparison of the excitation of different classes of photoreceptor is plausible based upon neuroanatomical and behavioural studies of Drosophila . Photoreceptors R7 and R8 project to the medulla of the optic lobe , and neurons are found there that make appropriate contacts to facilitate comparison of outputs between and within ommatidia [44] . Furthermore , behavioural experiments reveal that Drosophila can discriminate colours based upon comparisons of excitation between different photoreceptor classes ( including R1-6 ) [42] . Despite the overall agreement of my analysis with those of the original studies , there are differences in the detail . Glossina pallidipes catches were previously explained based upon visual bait reflectance in four colour bands: UV ( 300–410 nm ) , blue-green ( 410–520 nm ) , green-yellow-orange ( 520–615 nm ) , and red ( 615–700 nm ) [11] . The eyes of higher flies have been thoroughly investigated ( e . g . [17] ) , but no physiological study has yet identified a red-sensitive photoreceptor . Sensory information on reflectance in the red band is , therefore , unlikely to be available to the fly and was not represented in my analysis . A similar approach applied to G . p . palpalis conforms more closely to the findings of my reanalysis , with three wavelength bands explaining catches: UV ( 300–380 nm for biconical traps , 350–390 nm for screens ) , UV-blue ( 380–480 nm or 390–470 nm , respectively ) , and blue-green-yellow-red ( 480–620 nm , or 470–600 nm ) [7] . The most visual-system-motivated study so far , on G . f . fuscipes , found 360 nm ( UV ) and 520 nm ( green ) to be repellent wavelengths , and 460 nm ( blue ) to be attractive [5] . The implication of that study is that the UV-blue receptor R7y ( which has peak sensitivity at higher UV wavelengths ) should have a repellent role , but my reanalysis ( accounting for its shoulder of blue sensitivity ) , found that it was most likely to encode attractive stimulus information . The lower wavelength UV receptor R7p was implicated in a repellent role in my analysis , but point reflectance at its approximate peak sensitivity was not a significant predictor of attraction in the original study [5] . These discrepancies in fine detail indicate the importance of a more complete analysis that accounts for the entire sensitivity curve of a photoreceptor , rather than its peak sensitivity alone . As in the original studies , my findings are based on the assumption that tsetse fly catches at coloured visual baits are indicators of attraction . However , two behavioural processes lead tsetse flies to become caught at visual baits: their attraction to approach the visual bait from a distance , and their decision to land upon it once close [10] . Target and screen experiments employed both surface and flanking nets to sample landing and circling flies , and are , therefore , presumed to be good indicators of attraction to approach the visual baits that are relatively unaffected by varying landing responses . These data were well-explained by the excitations of photoreceptors R7y , R8y , and R7p , and the simple opponency index based on these will be a useful tool for further optimisation of the small target visual baits with flanking nets that are currently advocated for controlling riverine tsetse flies [5] , [8] , [9] . Biconical and F2 trap experiments , meanwhile , only sampled tsetse flies that entered a trap , and their catches may have been affected by trap outer surface colours that stimulated or inhibited fly landing responses there in preference to trap entry [10] , [11] . Addition of the R7p excitation parameter to a regression model containing R7y and R8y excitations did improve its fit to trap catch data , in line with the findings for targets and screens . However , PLS regressions suggested that R7p excitations were a relatively less important negative influence on tsetse catches at traps than they were at targets and screens ( compare ranked standardised coefficients for this parameter in table 2 ) , and the simple , unweighted + R7y – R8y – R7p opponency index fitted trap data less well than the index based on R7y and R8y excitations alone . Since high UV reflectance is reported to promote landing responses [10] , [12] , [13] , it was expected to induce flies to land on the outer surface of a trap rather than entering it , enhancing the apparent negative effects of UV on attraction by further reducing the number of flies trapped . However , this seems not to have been the case . Since flies that land on a trap are subsequently more likely to enter it [14] , it is plausible that enhanced landing responses as a result of UV reflectance ultimately led to enhanced trap catches , which then somewhat masked the negative effect of UV wavelengths on initial attraction in the trap datasets . Clearly the effects of visual bait colour on landing responses are an important topic for future investigation , and the methods described in this study can be applied to that problem . However , it will also be important to further investigate the behaviour of tsetse flies in the vicinity of traps ( c . f . [14] ) , if these devices are to be optimised . The methods applied in this study sought to identify the photoreceptor types that best explain tsetse fly attraction to visual baits based upon linear combination of their responses . However , the layout of opponent interactions between photoreceptors within the fly nervous system may differ from this simple scheme . R7y and R8y excitations appeared to be important predictors of tsetse attraction , and an opponent interaction between these receptors explained fly behaviour to a significant degree . The only studies of photoreceptor sensitivity in tsetse flies found an enhanced blue sensitivity in R7y and R8y due to the lack of a screening pigment that was attributed to dietary deficiency [18] . It is unknown whether wild tsetse experience this same elevated sensitivity to blue light , but such a pattern would still be consistent with these cells' involvement in attraction to visual baits , and might even enhance the attractiveness of blue surfaces . A negative input of R7p excitation to attraction was necessary to explain the particular attractiveness of phthalogen blue baits , and improved the fit to the tsetse catch data , but whether input from R7p interacts directly with that from R7y and R8y , is evaluated independently , or is processed in a separate opponent interaction with R8p ( c . f . [43] ) , cannot be determined from the reported analyses . However , the latter scheme has intuitive appeal given the pairing of y- and p-type photoreceptors in separate ommatidia ( although an R8p receptor has not yet been successfully recorded from in tsetse flies [18] ) . R8p , and especially R1-6 , excitations did not appear to be as important as those of the other receptors in explaining the coarse detail of attraction . The highly-correlated nature of R7y , R8p , and R1-6 responses , and the fact that three-parameter regression models substituting R8p or R1-6 excitations for R7y excitations could still explain tsetse fly attraction reasonably well , help explain this apparent lack of importance in statistical analyses , and are consistent with recent findings that there is considerable redundancy among the opponent pathways in fly vision [42] , [45] . Why tsetse flies are attracted towards blue stimuli remains an unanswered question . It has been suggested that , because shadow is illuminated only by skylight , which is bluer than direct illumination , flies might use blue as a chromatic cue to help them locate shaded resting places [46] , [47] . Alternatively , since tsetse caught at visual baits are relatively starved [38] and , therefore , presumed to be host-seeking , It has been suggested that blue surfaces might contrast strongly with the surrounding vegetation , providing a strong signal of ‘not vegetation’ for the searching fly [36] . Natural spectra can be grouped within three broad categories: ( i ) living leaves , which have a green reflectance peak at 555 nm due to chlorophyll; ( ii ) most inorganic and organic surfaces , where reflectance increases gradually with wavelength; and ( iii ) diverse signalling colours like those of flowers and fruit which evolved so as to stand out from their background , but conform to no generalised template [48] . The innate attraction of many plant-feeding or plant-ovipositing insects towards green leaves can be explained by an opponent interaction in which a green-sensitive photoreceptor contributes positively , and a blue- ( and/or UV- ) sensitive photoreceptor contributes negatively [24] , [49] . Turning this opponent interaction on its head ( as demonstrated in this study for tsetse flies ) would indeed provide a means to detect reflectance spectra that are not leaves . This may , perhaps , help explain why a variety of flower-feeding insects also display an innate blue preference , although in these cases the preference is altered by subsequent learning of flower colours [50] , [51] , [52] . It will be fascinating to probe these sensory ecological questions in future studies , and an understanding of attraction based upon photoreceptor excitations will provide a basis with which to do so . The opponent model presented in this study refines previous analyses of tsetse fly catches at coloured visual baits , and will facilitate the kind of detailed spectral analysis of candidate baits called for by Lindh et al . ( 2012 ) [5] . To promote such analyses , a tool for the calculation of photoreceptor excitations is available as ESM , and this study has demonstrated that an easily calculable opponency index between the photoreceptor excitations with greatest explanatory power has good predictive value for tsetse catches , especially at small targets and screens . Thus , baits might be evaluated in this way from their reflectance spectra alone , prior to confirmatory field testing . Future studies may fine tune the model developed in this paper using more specific quantifications of background reflectance and illumination , and this approach is likely to explain the variations in the relative attractiveness of colours between habitats that have been noted in other tsetse species [38] . Improvements to visual baits in other respects such as shape [53] , odour cues [54] , and placement [6] , will all contribute to the further optimisation of these vitally important public health devices for sub-Saharan Africa . | Tsetse flies transmit sleeping sickness ( human African trypanosomiasis ) , and visually attractive targets and traps are important tools for the control of the flies and prevention of disease . Previous studies have tried to determine the best colour for visual baits by relating their light reflectance properties to their attractiveness to tsetse . However , these methods represent only part of the visual information captured by the fly's eye , which is encoded by five different types of photoreceptor with varying sensitivities to different wavelengths of light . I use established methods to calculate the excitation of each fly photoreceptor type by the visual baits used to catch tsetse flies in three previous field studies . This method more completely describes the visual information captured by the fly's eye . Tsetse fly attraction can then be largely explained by a comparison of the excitations of three different photoreceptor types within the fly's nervous system . This knowledge and approach will allow for the more complete quantification of visual bait reflectance spectra , so that more efficient bait materials can be identified and employed to control tsetse flies . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"vector-borne",
"diseases",
"geographical",
"locations",
"neuroscience",
"animal",
"behavior",
"vision",
"insect",
"vectors",
"zoology",
"africa",
"infectious",
"diseases",
"epidemiology",
"neuroethology",
"disease",
"vectors",
"behavioral",
"ecology",
"people",
"and",
"places",
"psychology",
"ecology",
"entomology",
"color",
"vision",
"vector",
"biology",
"arthropod",
"vectors",
"biology",
"and",
"life",
"sciences",
"sensory",
"perception"
] | 2014 | A Colour Opponent Model That Explains Tsetse Fly Attraction to Visual Baits and Can Be Used to Investigate More Efficacious Bait Materials |
Spontaneous canine head and neck squamous cell carcinoma ( HNSCC ) represents an excellent model of human HNSCC but is greatly understudied . To better understand and utilize this valuable resource , we performed a pilot study that represents its first genome-wide characterization by investigating 12 canine HNSCC cases , of which 9 are oral , via high density array comparative genomic hybridization and RNA-seq . The analyses reveal that these canine cancers recapitulate many molecular features of human HNSCC . These include analogous genomic copy number abnormality landscapes and sequence mutation patterns , recurrent alteration of known HNSCC genes and pathways ( e . g . , cell cycle , PI3K/AKT signaling ) , and comparably extensive heterogeneity . Amplification or overexpression of protein kinase genes , matrix metalloproteinase genes , and epithelial–mesenchymal transition genes TWIST1 and SNAI1 are also prominent in these canine tumors . This pilot study , along with a rapidly growing body of literature on canine cancer , reemphasizes the potential value of spontaneous canine cancers in HNSCC basic and translational research .
During the past several decades , great advances have been made in our understanding of the biology of head and neck squamous cell carcinoma ( HNSCC ) [1–8] . For example , owing to the rapid development in next-generation sequencing and other high throughput technologies , the cancer genome atlas ( TCGA ) [1] and others [2–4] have characterized hundreds of human HNSCC cases and discovered significantly altered genes and pathways ( cell cycle , PI3K signaling , etc . ) . However , translating these research findings into clinical success has been frustratingly slow , and drug development remains a lengthy and expensive process [9] , with costs currently estimated at over US$1 billion to bring a new drug to market [9] . With biomarker-based clinical trials not as advanced and half as many clinical trials currently available as lung or breast cancer [5] , the issue is even more serious for HNSCC . One significant challenge is the lack of effective predictive models [9] . Current widely-used HNSCC models [10–12] include: 1 ) cell line and xenograft models; 2 ) genetically engineered mouse models; and 3 ) carcinogen-induced models . While these models have made spectacular contributions in our molecular understanding of HNSCC and are clearly indispensable , they have issues and generally do not fully represent the great complexity and heterogeneity of human HNSCC . First , cell lines and subcutaneous xenograft models lack the specific interactions between tumor cells and their native microenvironment that significantly influences carcinogenesis . Orthotopic xenograft models allow tumor development in places that are closer to the natural anatomic site [13] , but the tumors still do not arise from the native head and neck epithelium , a significant issue considering the importance of cells of origin in cancer [14] . Second , genetically engineered mouse models often manipulate only one or a few major driver genes ( e . g . , TP53 , CCND1 , KRAS , AKT , etc . ) [12] , and normally fail to recapitulate the full alteration spectrum of human HNNCC that typically involves hundreds of genes per tumor [1–4] . Third , carcinogen-induced models such as the 7 , 12-dimethylbenz[a]anthracene ( DMBA ) –induced tumors in Syrian hamster cheek pouch do not grossly or histologically resemble human oral carcinomas [12] . Chronic administration of 4-nitroquinoline 1-oxide ( 4NQO ) , which can cause DNA adduct formation and Hras mutation , induces oral tumors in rodents [12] . However , these models do not represent the invasive pathology of human HNSCC [12] . In summary , the current widely-used HNSCC models usually fail to accurately recapitulate the full spectra of the biology , histopathology , complexity , and heterogeneity of HNSCC in humans . This , combined with the body size difference between the human and the rodents , make these models often incapable of accurately predicting the drug efficacy and toxicity in human patients [9] . Thus , HNSCC models that can bridge these models and human clinical trials are critically missing and urgently needed at present . Spontaneously occurring HNSCCs in pet dogs may bridge this gap as they overcome many of the issues of the current HNSCC models discussed above [15–30] . First , unlike genetically-engineered or xenograft rodent models , these cancers are naturally occurring and heterogeneous , capturing the essence of human cancers . Second , as companion animals , dogs share the human environment and are exposed to many of the same carcinogens in a similar fashion , unlike carcinogen-induced cancers . Third , dogs better resemble humans in biology , e . g . , with similar telomere and telomerase activities [31] and frequent occurrence of spontaneous epithelial cancers [15] , than mice [32] . This further allows a closer resemblance in biology of HNSCC . Notably , the availability of a genome assembly nearly as accurate as the mouse or rat genome [33 , 34] , in contrast to that available for another companion animal , the cat , make many analyses possible with canine cancers but not with feline cancers . HNSCCs are relatively frequent in the dog and like their human counterparts , the oral cavity is a common site , and oral SCC is the 2nd most common oral malignant tumor in dogs [15 , 35–37] . The prevalence rate for nontonsillar oral SCC is estimated at 6 . 4–7 . 3 per 100 , 000 [15] , compared to the average incidence rate of human head and neck cancers at 8 . 8 and 5 . 1 per 100 , 000 men and women respectively [38] . For canine tonsillar SCC , the prevalence rate varies dramatically , ranging from 91–120 per 100 , 000 in large cities such as London and Philadelphia to nearly none in rural areas [15] . Critically , numerous anatomic and clinical similarities of HNSCC are noted between the human and the dog [15 , 35] . For example , as in humans , canine HNSCCs are invasive with metastasis occurring late in disease and tumor cells spreading to the regional lymph nodes and occasionally to the lung , and local disease recurrence is common in many patients . An interesting exception to these resemblances is that canine tonsillar SCCs are more aggressive and often metastatic ( 73% ) compared to those in other locations , which however is observed in humans . Although far from being as extensively characterized as in human HNSCCs , papillomavirus DNA is also detected in canine oral SCCs [39] . Lastly , similar treatment schemes are practiced in the two species , which include surgery , radiation therapy , and chemotherapy with agents such as piroxicam and carboplatin [40 , 41] . Because of the features described above , along with a large population of pet dogs ( ~70 million estimated in the US alone ) , canine HNSCC could potentially be a useful and practical model that significantly speeds up bench to bedside translation . However , a major obstacle for this realization is that canine HNSCC is markedly understudied . Our current literature search indicates that not a single canine HNSCC genome or transcriptome has ever been investigated by sequencing , microarray , or other strategies . This drastically differs from other canine cancers such as lymphoma [42 , 43] , leukemia , and osteosarcoma [26] , of which hundreds of cancer genomes have been characterized via array comparative genomic hybridization ( aCGH ) or molecular cytogenetic analyses . Thus , unlike the better-studied canine cancers , there are simply insufficient data to evaluate the extent of molecular similarity between canine HNSCC and its human counterpart , a key factor in determining the usefulness of canine HNSCC in basic and translational research . To address this obstacle , we set out to conduct a pilot study that represents the first genome-wide characterization of spontaneous canine HNSCC .
We characterized 12 spontaneous canine HNSCC cases by genome-wide analyses including high density aCGH and RNA-seq . These cancers come from different dog breeds , including four Labrador retrievers , three mixed breeds , and others listed in S1A Table . Among the 12 HNSCC cases , nine are oral ( oSCCs ) and all nontonsillar , with one from the buccal mucosa , three from the tongue , and five from the gingiva . The remaining three cases are from the nasal planum , the nostril , and the eye ( the ocular adnexa ) . The tumors are all invasive ( Fig 1A and 1B ) , and most are well-differentiated although some are more disorganized than others ( S1A Table ) . Furthermore , two cases appear to be papillomavirus-positive , based on the analysis described in later sections . Analogous to human HNSCCs [1 , 2 , 6] , aCGH analysis reveals variations in the extent of genomic copy number abnormality ( CNA ) among these canine tumors . Specifically , while seven canine HNSCCs harbor extensive CNAs , the remaining five tumors have hardly any CNAs in their genomes ( Fig 2A and S1B Table ) . In some cases , the variation in the CNA prevalence is clearly related to the cancer progression stage ( see tumors 419 and 419_2 in Fig 2A ) . In other cases however , additional factors may also have contributed . For example , tumor 1152 lacks CNAs but is at a tumor-progression stage similar to those with extensive CNAs ( Fig 2A ) . Interestingly , among the nine oSCCs , those with a buccal mucosa or tongue location harbor significantly more CNAs than those located in the gingiva ( Fig 2A and S1A and S1B Table ) . As in human HNSCCs [1 , 2 , 6] , both focal and broad CNAs were detected in the canine tumors ( Figs 2A and S1 ) . For example , tumor 240 harbors a focal amplification of ~10Mb located on chromosome 16 , increasing the copy number of nearly all 63 genes inside by 2-fold and more ( Fig 2B and S1C Table ) . Of these genes , 40 are also overexpressed ( see later sections ) , among which “negative regulation of apoptosis” and “endopeptidase activity” are the most enriched functional groups . Indeed , anti-apoptosis associated genes IKBKB , POLB , SFRP1 and FNTA , as well as endopeptidase genes ADAM9 and PLAT encoded in the region are both amplified and overexpressed ( S1C and S1D Table ) ( these genes are also recurrently amplified in human cancers including HNSCC according to cBioPortal [44] at www . cbioportal . org ) . Notably , 42 of the 63 genes are also significantly amplified in human HNSCC based on the TCGA study [1] ( which will be examined further in a later section ) . These observations support that this focal amplification may have contributed to the pathogenesis of tumor 240 . Meanwhile , broad events such as recurrent gain of canine chromosome 13 , observed in seven tumors out of 12 total ( Fig 2A ) , were seen as well ( although other canine cancer types also harbor chromosome 13 gain [21 , 43] ) . The first 40Mb of canine chromosome 13 is syntenic to the last 48Mb of human chromosome 8q , which encodes genes including MYC and is one of the most recurrently amplified sites in human HNSCC [1 , 2] and other cancer types [45 , 46] . Consistent with published studies [21 , 43] , these observations support common cancer drivers between the human and the dog . In most canine HNSCCs , more amplifications than deletions were found , causing more genomic sequences and genes to be amplified than deleted ( Fig 2A and S1B Table ) . Amplifications are , on average , also larger than deletions ( S2 Fig and S1B Table ) . Importantly , a better correlation between the copy number status and the expression level ( see later sections ) was observed for amplified genes than deleted genes ( Fig 2C ) , supporting that amplified regions harbor more cancer drivers than deleted regions . This is also consistent with human findings [2] . Many known human HNSCC genes [1 , 2 , 47–49] are also amplified/deleted in the canine tumors . Examples include recurrent amplification of known oncogene MYC; protein kinase genes MET , PTK2 ( also known as FAK1 ) and FGFR1; apoptosis-related FADD; histone lysine methyltransferase gene WHSC1L1; NDRG1 ( N-myc downstream regulated 1 ) which is frequently altered in human cancers including HNSCC but whose roles remain controversial [50]; cell cycle genes CCNE1 , CDK6 and E2F3; and others listed in S1E Table . Notably , the most enriched functions among the amplified genes are protein kinase activity , with 15 serine/threonine kinases and 13 tyrosine protein kinases , and protease activity , with 10 serine proteases ( S1E and S1G Table ) . This is consistent with TCGA’s human HNSCC study [1] . Examples of deleted genes in the canine tumors ( Fig 2A ) include cell cycle gene CDKN2A , one of the best known gene deletions in human HNSCC [1 , 2 , 6]; and protein phosphatase PTPRD and cadherin superfamily membrane FAT1 , tumor suppressors [51 , 52] that are also frequently deleted in human HNSCC [1] . Furthermore , the most significantly enriched functions among the deleted genes are closely related to epithelial cell polarity including adhesion ( 18 genes ) , small GTPase regulator activity ( 19 genes ) , and cell junctions ( 11 genes ) ( S1F and S1G Table ) . This agrees with human HNSCC findings [1] and is consistent with the concept that loss of cell polarity is a hallmark of epithelial cancers such as HNSCC [53] . These observations support the dog-human molecular homology . To better understand alterations at the transcriptomic level , we performed RNA-seq on seven of the canine oSCCs and three matching normal tissue samples ( S2A Table ) . The study further reveals canine tumor heterogeneity and supports dog-human molecular homologies . First , principle component analysis ( PCA ) separates the tumors from the normal samples ( Fig 3A ) . More importantly , tumors 1172 , 465 , and 404 are distant from the other tumors in the PCA ( Fig 3A ) , the significance of which will be discussed in later sections . Second , compared to the normal samples , the genes upregulated in the tumors are enriched in functions including: 1 ) cell adhesion/motility , extracellular matrix , and endopeptidase activity; 2 ) hypoxia and polysaccharide metabolic processes; 3 ) blood vessel morphogenesis and cell differentiation; and 4 ) immune response ( Fig 3B and S2C–S2G Table ) . As in human cancers , these functions facilitate canine tumor cell proliferation and invasion . We followed published strategies [2 , 46] to identify over/under-expressed genes . Consistent with CNA findings ( Fig 2A ) , more genes are overexpressed than underexpressed in all tumors except tumor 404 ( S2B Table ) . Importantly , genes recurrently overexpressed among the tumors are significantly enriched in functions associated with cell cycle ( e . g . , cytoskeleton , spindle , centrosome , kinetochore , etc . ) , protein kinase activity ( e . g . , PTK2 , TEC , CHEK2 , etc . ) , nucleolus , and mRNA and ncRNA processing . Recurrently underexpressed genes are , however , significantly enriched in functions related to cell junctions ( e . g . , 9 tight junction genes ) , mitochondria ( e . g . , respiratory chain and oxidative phosphorylation ) , serine protease inhibitors , and apoptosis . These functions promote cancer cell proliferation and invasion , consistent with human cancer findings [1] . Critically , we found the same genes and pathways altered in these canine cancers as reported in human HNSCCs [1 , 2] . For example , gene members of the receptor tyrosine kinase ( RTK ) / PI3K/AKT pathway such as EGFR , PIK3CA , BRAF , and AKT1 are recurrently overexpressed among the canine tumors ( Fig 3C ) . AKT1 is especially noteworthy because its expression level in each tumor is consistently higher than in each normal sample by 2-4-fold ( Fig 3C ) . The PI3K/AKT pathway is indeed activated in the tumor cells as revealed by immunostaining with phospho-AKT ( Fig 4A ) . Cell cycle is also altered , as evidenced by overexpression of multiple cyclin genes , CDK4 , CDK6 and E2F1; as well as underexpression of CDKN2A and CDKN2B ( Fig 3C ) . These pathway alterations could promote cancer cell proliferation . Another pathway affected is TGFβ signaling , as evidenced by the recurrent overexpression of TGFB1 , TGFB2 , TGFBR2 , TGFBR1 , SMAD3 and SMAD4 in the canine tumors ( Fig 3C ) . Other notable changes include at least 12 matrix metalloproteinase ( MMP ) genes , whose expression increased by hundreds to thousands fold in at least one tumor ( Fig 3C ) . Likewise , epithelial to mesenchymal transition ( EMT ) genes TWIST1 and SNAI1 are also recurrently overexpressed ( Fig 3C ) . These observations indicate EMT in the canine tumors , which is confirmed by immunofluorescent staining showing increased expression of the mesenchymal marker vimentin and decreased expression of the epithelial marker E-cadherin in the tumor cells , compared to the normal squamous cells ( Fig 4B ) . Activation of these genes and pathways would facilitate the invasion of tumor cells into adjacent tissues . Interestingly , tumor 1172 appears to be an exception to the above observations ( Figs 3C and 4B ) , which will be revisited in the DISCUSSION section . Finally , two canine oSCCs appear to be canine papillomavirus ( CPV ) -positive , based on the detection of papillomavirus sequences , specifically CPV7 E2/E4 sequences in tumor 1172 and sequences with high homology ( >90% ) to human papillomavirus HPV77 E2/E4 in tumor 465 , among their RNA-seq reads ( S2H Table ) . However , unlike TCGA’s HPV-positive HNSCCs that harbor thousands of viral RNA-seq reads per sample [1] , we only identified a few viral RNA-seq reads for the two canine tumors ( S2H Table ) . This indicates that the infection is likely latent , or the CPV genome ( s ) infected has/have not been sequenced ( we were able to download 170 HPV genomes but only 15 CPV genomes from the PaVE database at pave . niaid . nih . gov at the time of the analysis ) . We took advantage of RNA-seq data to examine sequence mutations in the canine samples . Briefly , to achieve more accurate mutation-finding , we utilized only coding regions with 30-300X RNA-seq read coverage , which distribute across the genome and amount to 4–6 Mb sequences in total per sample ( S2I Table ) . The analysis again reveals dog-human homologies . First , base transitions C↔T/G↔A dominate base transversions in all samples ( Fig 5A and S2I Table ) , indicating similar mutation mechanisms in both species . At CpG sites , the two CPV-positive canine tumors harbor significantly more transversions compared to the CPV-negative tumors ( Fig 5B and S2I Table ) , consistent with human findings [1] . However , we did not observe a predominance of mutations at TpC sites in CPV-positive canine tumors ( S2I Table ) , unlike the human study [1] . Second , the analysis uncovered a somatic mutation , E233K , in TP53 . Similarly , consistent with human studies [3 , 4] , genes FAT1 , FAT2 , UBR2 , TNC and others were found to be mutated in the canine tumors ( S2I Table ) . As an example , Fig 5C shows the mutations found in the gene RELN in canine tumor 404 , including one nonsense , five non-synonymous and one synonymous changes . Importantly , RELN is significantly mutated in human HNSCC [1 , 4] , non-small cell lung cancer of smokers [54] , and acute lymphoblastic leukemia [55] . RELN encodes an extracellular matrix glycoprotein which is known to control cell–cell interactions to regulate neuronal migration and positioning in the developing brain . Thus , alteration of RELN may contribute to tumor cell invasion and spread in both the human and the dog . The dog-human molecular homologies described above rationalize the use of the dog-human comparison strategy for driver-passenger discrimination as described [30] for HNSCC . Because of the small sample size of canine tumors , we tested this strategy only on human 8q , one of the most recurrently amplified regions in human HNSCC [1 , 2] and other cancer types . Due to interspecies genomic rearrangements , human 8q is broken into two dog chromosomal regions , which include chromosome 29 and the first 38Mb of chromosome 13 ( Fig 6A ) . Notably , the entire human 8q is significantly amplified among TCGA’s 449 human HNSCCs ( FDR < 10–6 ) , leading to the amplification of all of its 398 genes ( Figs 6A and S1 ) . In canine tumors , however , only the chromosome 13 region is significantly altered , resulting in the amplification of 125 genes out of 210 total at FDR < 0 . 2 . For chromosome 29 in contrast , merely 2 genes out of 188 total are amplified . These numbers significantly differ ( p < 2 . 2×10–16 ) ( Fig 6A ) . Thus , based on our strategy [30] , amplified genes ( 125 total ) on chromosome 13 are considered as driver candidate genes ( DCGs ) , while unchanged genes on chromosome 29 ( 186 total ) are deemed passenger candidate genes ( PCGs ) ( Fig 6A and S1H Table ) . We then performed several analyses to examine the differences between the DCGs and PCGs identified . First , a significantly better correlation between the copy number status and the mRNA expression level was observed for DCGs than PCGs , using data from TCGA [1] ( Fig 6C and S1H Table ) . This indicates that amplification of DCGs is more functionally relevant than amplification of PCGs . Second , significantly more DCGs are mutated than PCGs , based on published human HNSCC studies [3] ( p < 0 . 0081 ) ( S1H Table ) . Lastly , well known cancer driver genes such as MYC are among the DCGs . Conversely , we also tried the dog-human comparison strategy on the10Mb amplicon of canine tumor 240 shown in Fig 2B . In the human genome , the 10Mb region is broken into three pieces , including human chromosome 7: 154 . 5–159 . 1Mb; chromosome 8: 18 . 3–19 . 7Mb and 37 . 5–43 . 1Mb . Based on TCGA data [1] , while 42 out of the 44 genes of chromosome 8: 37 . 5–43 . 1Mb are significantly amplified , none of the genes of the remaining two human regions is amplified or deleted ( Fig 6B ) . Thus , based on our strategy [30] , the chromosome 8: 37 . 5–43 . 1Mb region is more likely to harbor cancer drivers , which indeed encodes known driver gene FGFR1 [1] .
As described throughout the Results section , our genomic and transcriptomic studies support strong dog-human molecular homologies for HNSCC at various levels . For large genomic changes , the two species share a similar CNA landscape , with some tumors harboring extensive CNAs while others being nearly CNA-free [1 , 2] and with large amplicons that likely harbor cancer drivers discovered . Furthermore , unlike canine mammary cancers [21] , no potential oncogenic chimeric fusion genes are found in these canine oSCCs , consistent with very few such genes being reported for human HNSCC [1] . At the individual gene level , numerous amplified/deleted , over/under-expressed , or mutated genes are shared between the two species ( e . g . , FADD amplification , CDKN2A deletion , FAT1 mutation , etc . ) . The altered genes are also enriched in the same functional groups , including protein kinase or protease activity for amplified/overexpressed genes , as well as cell adhesion and other epithelial polarity related functions for deleted/underexpressed genes . At the pathway level , both species show alterations in cell cycle , RTK/PI3K/AKT signaling , and TGFβ signaling and EMT . Despite the strong homology described above , a few differences are also observed . For example , TP53 mutation is more frequent in human HNSCC [1] than in these canine tumors , which is also true for NOTCH1 . This difference however could be due to our small sample size and/or using RNA-seq instead whole exome-sequencing for mutation discovery . Another discrepancy is that more canine HNSCCs appear to be CNA-free when compared to their human counterparts , which again needs to be validated with a large sample size study . Our study indicates that canine tumors 1172 and 465 are CPV-positive but the infection is likely latent ( or the CPV genome ( s ) infected has/have not been sequenced ) . Both tumors ( especially tumor 1172 ) are distinct from other canine oSCCs in gene expression . Importantly , consistent with the finding that HPVs infect the basal layer of squamous epithelium [56] , tumor 1172 exhibits features indicating basal stem cell origin . Unlike other canine oSCCs , tumor 1172 overexpresses the pluripotent marker SOX2 and at least 20 homeobox genes that are associated with embryonic morphogenesis , but does not express EMT markers in many of its tumor cells . These observations again reveal extensive heterogeneity of canine HNSCCs and a strong homology to their human counterparts . The dominance of C↔T changes over other base substitution types indicates that deamination of C to U/T is a major sequence mutation mechanism in dogs as in humans . This result is consistent with aging being a risk factor for HNSCC development in both species [57] . Furthermore , analogous to the human [1] , base transversions at CpG sites are more dominant in CVP-negative tumors than in CPV-positive tumors . However , unlike the human [1] , CPV-positive tumors do not show predominance of mutations at TpC sites , which needs further study to resolve . We hypothesize the carcinogenic mechanisms of the canine tumors based on our findings ( Fig 7 ) . Regarding cells of origin , unlike tumor 1172 discussed previously , we propose that other tumors arise from more differentiated cells of the squamous epithelium ( Fig 7 ) , because of overexpression of protease genes and EMT genes . At the genome level , we hypothesize that primary drivers include focal amplifications for tumor 240 . Tumor 404 is also noteworthy , as it lacks genomic CNAs but has the largest number of underexpressed genes and appears to have a different epigenomic landscape , as shown by drastically reduced staining by 5-methylcytosine antibody ( Fig 4C ) . While emphasizing that the significance and reasons of the observed DNA hypomethylation need further investigation , we hypothesize epigenomic aberrations as the drivers of tumor 404 ( Fig 7 ) . Finally , notable gene alterations and recurrently altered pathways are listed as cancer drivers . Fig 7 summarizes once again the complexity and heterogeneity of these canine cancers and their numerous homologies to human HNSCCs . The dog-human molecular homologies described above indicate that canine HNSCC share similar pathogenic pathways as their human counterparts , which , if validated with a large sample study , justifies the use of our established dog-human comparison strategy [30] for driver-passenger [58] discrimination for HNSCC . Indeed , our pilot human 8q study has shown that this approach is valid . The use of canine cancers in this regard is highly significant . For example , our analysis with the copy number data of TCGA’s 449 human HNSCC cases has found over 7000 amplified/deleted genes at FDR ≤ 0 . 05 , including those harbored by both focal amplifications/deletions and broad ( chromosomal arm level ) gains/losses . Among these genes , some are drivers and some are passengers . Based on our analyses , a sample size of 449 tumors is already saturating , and studying additional human tumors no longer helps in determining which amplified/deleted genes are drivers and which are passengers . However , investigating about 90 canine tumors will reduce the number of driver candidates by at least half , according to our estimation , significantly reducing the workload of downstream functional validation which is time-consuming and expensive . As discussed in the Induction section , a major obstacle in translational research is the lack of effective predictive models [9] . Current preclinical models , including cell culture and xenograft or genetically-induced rodent models , typically fail to represent the vast heterogeneity and complexity of human cancers and often do not predict clinical results . Thus , a cancer model that can bridge the gap between these preclinical models and human clinical trials is urgently needed . Spontaneous canine HNSCCs could serve as such a much-needed translational model , if the dog-human molecular homologies concluded here are validated with a large sample study . Being molecularly complex and heterogeneous and recapitulating many molecular features of human HNSCC , these canine HNSCCs are more accurate representatives of their human counterparts than current cell line or rodent models . With an HNSCC incidence rate comparable to that in the human and a large pet dog population ( >70 million in the US ) , coupled with less stringent FDA regulations governing pet clinical trials and increasingly improved resource and infrastructure in pet cancer research [18] , spontaneous canine cancers have the potential to significantly accelerate the translation of basic research findings into successful clinical applications . In summary , our study indicates that spontaneous canine HNSCCs better recapitulate the full spectra of the biology , histopathology , complexity , and heterogeneity of humans HNSCC than the current widely used models . However , we caution again that our sample size is small , and the conclusions drawn from this pilot study need to be validated with a larger sample size .
Fresh-frozen normal and tumor tissue samples and formalin-fixed paraffin-embedded ( FFPE ) tissue sections of spontaneous canine HNSCCs were obtained from the Animal Cancer Tissue Repository of Colorado State University . Samples were collected from client-owned dogs that developed the disease spontaneously , under the guidelines of the Institutional Animal Care and Use Committee and with owner informed consent . The breed , age , histopathologic descriptions , and other information are provided in S1A Table . These data , along with our own H&E staining of each sample received , were reviewed by Dr . Angela E . Ellis , a canine cancer pathologist . Tissue cryosectioning , H&E staining , and cryomicrodissection were performed as described [19] to enrich for tumor cells in tumor samples and squamous epithelial cells in normal samples . Genomic DNA and RNA were then extracted from the dissected tissues using the AllPrep DNA/RNA Mini Kit ( cat . no . 80204 ) from Qiagen . Only samples with a 260/280 ratio of ~1 . 8 ( DNA ) or ~2 . 0 ( RNA ) and showing neither degradation nor other contamination on the agarose gels were subjected to further analyses . Canine aCGH experiments were conducted at the Florida State University Microarray Facility , with 385K canine CGH array chips from Roche NimbleGen Systems , Inc . as previously described [19] . Briefly , each array chip contains about 385 , 000 probes of approximately 50 bp long oligos selected from unique sequences in the canFam2 genome , providing an average resolution of one probe every 5–6 kb across the canine genome . Tumor DNA purified from the dissected tumor tissue sample and paired normal DNA purified from matching normal tissue , skin , or blood of the same dog patient ( S1A Table ) ware hybridized to a chip , following the manufacturer’s instructions . This was performed for each canine case except for case 573 ( a male ) , of which a normal sample from the same dog was unavailable and hence normal DNA from another dog ( female ) of the same breed was used instead ( S1A Table ) . Because of tumor 573 , chromosome X was excluded from the CNA-finding to minimize artifacts . CNAs were identified as previously described [19] . Briefly , CNAs in each tumor were detected by analyzing the log2-ratios using a software program called SEG , which we developed to more effectively decipher high density oligo aCGH data for CNA finding . As described in our previous publication [19] , SEG first identified change-points at the chromosomal level via dynamic programming and then detected CNAs at the whole genome level using false discovery rate ( FDR ) -controlled procedure [59] . For this study , we set the desired FDR to 0 . 01 , the total probe number cutoff to 3 , and the log2-ratio mean cutoff to 0 . 4 for CNA discovery . Significantly amplified/deleted genes were identified by GISTIC [60] as previously described [19 , 21] . For the human , level 3 data of SNP array analyses of 449 human HNSCC cases were downloaded from TCGA site ( cancergenome . nih . gov/ ) , and GISTIC [60] were used to detect amplified/deleted genes , in combination with published findings [1] . Sequencing was conducted using the Illumina platform , following the protocols from the manufacturer . RNA-seq was performed at the HudsonAlpha Institute for Biotechnology or the BGI-America , yielding 48 to 66 million paired-end sequence reads of 50bp or 49bp per sample ( S2A Table ) . RNA-seq data analyses were performed as described [21] . Briefly , read pairs were aligned to the dog reference genome [33] canFam2 with TopHat v2 . 0 . 5 ( tophat . cbcb . umd . edu ) . The uniquely mapped pairs were used to quantify a gene’s expression level by calculating its FPKM ( fragments per kilobase of exon per million mapped fragments ) value , using Cufflinks ( cufflinks . cbcb . umd . edu ) with default parameters and the canine gene annotation downloaded from the University of Santa Cruz ( UCSC ) genome site . Base substitutions were identified with VarScan2 ( varscan . sourceforge . net ) in coding regions with RNA-seq read coverage ranging from 30X to 300X . Over/underexpressed genes in cancers were identified as described [2 , 46] , i . e . , genes whose expression levels are outside 95% confidence internals of the expression of samples that are diploid in that gene . Differentially expressed genes between two groups of samples were identified by DESeq [61] and t-tests . Gene functional annotation and enrichment analyses were achieved using the DAVID Functional Annotation tool at david . abcc . ncifcrf . gov . Viral RNA-seq reads were identified using a similar approach as VirusSeq ( odin . mdacc . tmc . edu/~xsu1/VirusSeq . html ) . After aligning RNA-seq reads to the reference genome ( canFam2 ) with TopHat v2 . 0 . 5 , the unmapped reads of each sample were realigned to the HPV and CPV genomes downloaded from the PaVE database ( pave . niaid . nih . gov/ ) with Bowtie2 v2 . 2 . 3 and BWA v0 . 7 . 10 . Immunofluorescent staining was performed with 5-μm FFPE tissue sections as described [21 , 62] . Primary antibodies used include those against E-cadherin ( R&D Systems; AF648 ) , vimentin ( Abcam; ab92547 ) , phospo-AKT ( Ser473 ) ( Cell Signaling; 4060 ) , acetyl-H4 ( Millipore; 06–866 ) , 5-methylcytosine clone 33D3 ( Millipore; MABE146 ) , and Ki67 ( Life Technologies; 08–0156 ) . Alexa Fluor488– , 647—or 594–conjugated secondary antibodies are from Jackson ImmunoResearch . Images were taken with a Zeiss LSM 710 confocal microscope . RNA-seq data have been submitted to the NCBI SRA database under accession number SRP046723 . aCGH data have been submitted to the GEO database under accession number GSE61231 . | Head and neck squamous cell carcinoma ( HNSCC ) represents the sixth leading cancer by incidence in humans; thus , developing effective therapeutic interventions is important . Although great advance has been made in our understanding of the biology of HNSCC over the past several decades , translating the research findings into clinical success has been frustratingly slow , and anticancer drug development remains a lengthy and expensive process . A significant challenge is that drug effects in current preclinical cancer models often do not predict clinical results , and there lacks translational models that can bridge the gap between preclinical research and human clinical trials . Here we report a pilot study that represents the first genome-wide characterization of spontaneously occurring HNSCCs in pet dogs . The study reveals a strong dog-human molecular homology at various levels , indicating the likelihood that spontaneous canine HNSCC molecularly represents its human counterpart . If conclusions of this pilot study are validated with a large sample size and more efforts are put into building better resource and infrastructure for canine cancer research , spontaneous canine HNSCCs could effectively serve as a much-needed translational model that bridges the gap between preclinical research and human trials . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Canine Spontaneous Head and Neck Squamous Cell Carcinomas Represent Their Human Counterparts at the Molecular Level |
We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes . A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence . We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection . The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus , the synonymous and non-synynomous mutation rates , and species divergence times . Furthermore , our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software . The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R , and Markov chain Monte Carlo methods in WinBUGS . Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test , and two versions of the Poisson random field based method MKprf . Overall , we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data .
Populations evolve over time and how they evolve is the product of different evolutionary forces . Population genetic theory gives us mathematical descriptions of how each of these forces is thought to affect the patterns of genetic variability within and between species . However , if the goal is not to start with an evolutionary model and see what happens , but rather to start with the data and understand what caused it one usually encounters an identifiability issue . For this reason , most population genetic data analyses looking for mutations under selection start by assuming a neutral population genetics model ( constant population size , panmictic population , no migration ) , and test for deviations from this model . Commonly used examples of such procedures include tests based on summary statistics of the site frequency spectrum ( distribution of mutation frequencies ) , such as Tajima's D [1] . However , since demographic factors ( eg population growth ) also effect the site frequency spectrum these tests are usually inconclusive . Tests based on linkage disequalibrium are also quite sensitive to demography as well as assumptions on recombination rates [2] . The HKA statistic [3] makes use of divergence data as well as within species variation by estimating the variance of divergence to polymorphism ratios among loci . However , migration will result in a high variance of coalescent times among the loci , making the HKA test also sensitive to demography [4] . See Nielsen 2005 [2] , for an excellent review of these procedures . One class of tests which is generally robust to demography are those tests commonly referred to as “McDonald-Kreitman-type tests” [4] . This class includes the McDonald-Kreitman ( MK ) test [5] as well as MKprf [6] . The theory behind the MK test is developed in the following section . Unlike many of the tests mentioned above , the method we present here assumes no particular population genetic model - in other words it is a non-parametric approach . Similar to the MK statistic , it is also generally robust to demography . Our method , which we call SnIPRE for Selection Inference using Poisson Random Effects , works by modeling the variation within and between species as a combination of four types of “effects” , one for each class of variation . These effects are functions of unknown population parameters of interest , including the selection coefficients . Previously , we have developed a suite of powerful approaches that can estimate the average strength of selection operating on a locus and/or the distribution of fitness effects under a specified population genetic setting for MK polymorhism and divergence data ( see [7]–[12] ) . A main advantage of the “MKprf” approach is that it is much more powerful than carrying out individual MK tests and then correcting for multiple tests . A perceived disadvantage to some investigators is that it requires specifying a population genetic model and then fitting the parameters of that model . Some investigators have also been concerned about the use of Bayesian priors on the distribution of effects and the impact these can have on inference [13] . There are two main advantages of SnIPRE over MK and MKprf , which we highlight here . The first is that it can reliably identify genes under weak and strong negative as well as positive selection without needing to specify a population genetic model a priori . Nonetheless , because it “borrows information” from the rest of the genome regarding the average and variance in polymorphism to divergence , it outperforms the one-at-time MK test . This gain in power is attributable to SnIPRE's use of a “James-Stein” class of estimator . The second advantage is that if one is willing to assume a particular population genetic model , it is possible to view the SnIPRE parameters as a re-parameterization of the population genetic model . With these additional assumptions , we can extend our inference beyond idenfication of genes that are not evolving according to the neutral theory , to quantification of strength and directionality of the selection forces . In this paper we will develop the model and the interpretation of its terms , and then describe how that model can be fit in both the empirical Bayes ( SnIPRE ) and fully Bayesian ( B SnIPRE ) settings . We also show how this model is robust to demographic history and recombination using standard coalescent simulations . Furthermore , we demonstrate how the Poisson Random Field estimates of average selection intensity , species-split time , mutation rate , and degree of selective constraint at the locus can be “extracted” directly from the SnIPRE estimates . We then compare the SnIPRE methods to the MK statistic and MKprf methods in detecting and estimating selection and other population parameters in simulations , and apply SnIPRE to data from a Drosophila comparison and human-chimp comparison . Because SnIPRE works by picking up on the same type of signature of selection as the MK statistic , we will start with a review of this method and the theory behind it . While most techniques to identify loci under selection require assumptions about demography ( particularly constant population size and no substructure ) , the MK statistic does not . Like the HKA statistic , it works by comparing divergence information between inferred neutral sites ( such as synonymous sites in a protein-coding gene ) and sites potentially under selection ( such as non-synonymous sites at the same gene ) . Strictly speaking , the test is a test of the neutral protein evolution hypothesis which states that the vast majority of evolutionary changes at the molecular level are caused by random drift of selectively neutral mutants ( not affecting fitness ) [14] . Although very tempting , the test itself does not allow for inference about the type of selection ( negative , positive , or balancing ) . For example , as noted in original paper , negative selection in recently expanding populations may appear as positive selection . Thus , without additional assumptions on population dynamics the direction cannot be inferred . There have been notable extensions to the MK test , including using non-coding sites whereby upstream regions of a gene are compared to neighboring introns or synonymous sites [15] . Another extension is the estimator , [16] which estimates the the proportion of amino-acid substititutes which are driven by adaptive selection . These extensions , and the additional set of assumptions they require , are not considered here . In its traditional form the MK table consists of counts for four categories of mutations which occur in the coding region of a gene: polymorphic synonymous , divergent synonymous , polymorphic non-synonymous , and divergent non-synonymous , see Table 1 . A mutation that occurs in every individual in the sample from one species is considered divergent , otherwise considered polymorphic . A mutation that occurs where it changes the amino acid produced is considered non-synonymous , otherwise considered synonymous . If the mutations are neutral , one would expect the ratio of polymorphic synonymous ( ) to divergent synonymous ( ) mutations to be the same as the ratio of polymorphic non-synonymous ( PN ) to divergent non-synonymous ( DN ) mutations , . If this is not true , then we are seeing either an excess of mutations , or shortage mutations . Intuitively , it makes sense to consider an excess of as evidence supporting positive selection as it appears that mutations that change the amino acid are being fixed in the population at a higher rate . Alternatively , a shortage of could be considered as evidence of negative selection as it would appear as though mutations that change the amino acid are being fixed at a lower rate . This interpretation of the data is fairly straightforward considering an additive model of selection with stationary population sizes . However , as mentioned above and as discussed in [17] , asessment of directionality from the MK statistics should be used with caution as it is sensitive to changing population dynamics . It should be noted , however , that in the case of strong negative ( i . e . purifying ) selection , the signature will be less clear in an MK table since mutations are not likely to segegrate in the population long enough to contribute to the polymorphism count . Thus , in the case of strong negative selection a reduction in the number of both polymorphic and divergent non-synonymous mutations is to be expected , and the MK test will have reduced power to detect this type of selection . McDonald and Kreitman [18] use Fisher's exact test of independence on MK tables to identify genes under selection . This test can be justified using coalescent theory where we have the additional assumptions of i ) no recombination within a gene ii ) all mutations are selectively neutral [19] . In this setting , the MK test constitutes a test of this second assumption . Under the coalescent theory model , mutations are Poisson distributed across a gene genealogy with expected value across a geneology of length , where is the mutation rate . Thus , conditioning on the total mutations ( sufficient statistic for tree length ) we have thatWe wish to test , the probability that a synonymous mutation appears fixed is the same as the probability that a non-synonymous mutation appears fixed in the sample . Under this null hypothesis , follows a hypergeometric distribution with parameters , ( , , ) . As long as the non-synonymous and synonymous sites are interspersed among each other , they will be similarly affected by demography and have the same distribution of coalescent times , thus the test is robust to demography . Motivated by the MK statistic , the SnIPRE framework uses the MK table polymorphism and divergence data for identifying genes under selection . Using generalized linear mixed models we incorporate genome wide effects into our analysis as fixed effects , and individual gene effects as random effects . This method allows us to pool information across genes which increases our power to detect those under selection . MKprf is another method that was developed by us which directly estimates the posterior distributions of genomic parameters , such as the species divergence time , based on the MK tables' synonymous cell entries . The posterior of the selection coefficients for each gene are then calculated conditional on these genomic parameters and the non-synonymous cell entries in the MK table , see [7] .
The data consists of MK table counts for each gene , as well as the total number of synonymous sites and non-synonymous sites surveyed . Incorporating the number of sites into our model allows us to extend our inference beyond non-synonymous and divergent interaction effects to include effects due to changes in the mutation rate , both in the synonymous and non-synonymous sites . Let be number of genes in the sample . Thus we have mutation counts , where if the mutation is non-synonymous , 0 otherwise , if the mutation is fixed in the sample among the two populations being compared , 0 otherwise , and according to gene identification number . The mutation counts are assumed to be Poisson distributed , , conditional on the covariates . The log of the expected mutation count is modeled using a generalized linear mixed effects model . The fixed effects include an intercept , an effect if the mutation is non-synonymous , an effect if the mutation is fixed , and an interaction effect if the mutation is both fixed and non-synonymous . Additionally the model includes four random effects: a gene effect , and the two-way and three-way interactions between the gene , non-synonymous , and divergence effects . An offset term is used to control for the number of sites sampled in the gene where a mutation of type could occur , for synonymous mutations , for non-synonymous mutations . ( 1 ) By using fixed and random effects in the model we are assuming that these gene-specific effects come from some distribution , and that distribution is estimated from the data . The use of mixed effects is particularly relevant in this setting where it capitalizes on the fact that genes share a phylogeny . Thus , even though the mutation rate , coalescent times , constraint and selection forces will vary across genes , the distribution of the influence of these forces across genes can be well estimated by viewing the data set as a whole . From this perspective we estimate the fixed effect terms ( genome-wide average estimates ) of our model , as well as the variability in the distribution in of the random ( gene-specific ) effect terms of the model . The random effects , or gene-specific parameters , are then estimated given this context . Below we describe how the terms in this model allow us to estimate for any given gene the average effect of mutation , divergence , constraint and selection levels over time . Of primary interest is identifying genes under selection , either positive or negative . Identification of these genes can be done quite easily in the SnIPRE framework with only the assumptions of the MK test: i . synonymous and non-synonymous sites sampled are interspersed; ii . synonymous sites are not under selection . The non-synonymous-divergent interaction effects , and , capture an average genome-wide selection effect and the gene-specific selection effects . The gene-specific selection effect for a particular gene , captures how the gene varies from the average selection effect , , of all genes included in the sample . The gene's selection effect relative to neutrality is reflected in the sum of these two interaction terms , . Thus , we refer to as the selection effect for the gene . For example , an estimated of greater than 0 , say 0 . 5 , means that the expected selection coefficient for a gene from that data set is positive . A gene-specific selection effect , , may be negative , say −0 . 3 , indicating that the estimated selection effect for that gene is lower than the average for genes in the data set . The estimated selection effect on that gene relative to neutrality ( zero being neutral ) is the sum of these two effects . In this example , the estimate would be positive , 0 . 5+ ( −0 . 3 ) = 0 . 2 . The other terms in the SnIPRE model are also quite interpretable . The interecept and the gene specific effect , and reflect the mutation rate . Here again the term captures how the mutation rate for the gene varies from the average mutation rate of the genes in the sample , . We refer to as the gene effect . Similarly , and reflect divergence time , and is referred to as the divergence effect . The proportion of non-synonmyous mutations that are non-lethal are reflected in and . We refer to as the constraint effect . These relationships are summarized in Table 2 . A precise relationship between these model parameters and the evolutionary parameters that influence them is defined the Poisson Random Field framework and discussed in the next section . Examples of the interpretation of these model parameters is provided in the application section . We fit this model in R [20] using the lme4 package [21] , and a Bayesian implementation is also fit using WinBUGS [22] , [23] . In the Bayesian setting ( B SnIPRE ) we construct credible intervals for these effects based on the MCMC samples ( other packages may be used instead to fit the model , e . g . the R package MCMCglmm [24] or JAGS [25] ) . In the empirical Bayes setting ( SnIPRE ) confidence intervals are constructed for the random effect estimates based on the standard errors . When fitting SnIPRE using the lme4 package we specified a general ( unstructured ) covariance . Using a structure other than a general covariance structure presupposes a functional form , e . g . a covariance matrix with the off-diagonal elements all zero would indicate that the gene specific effects are independent of each other . Incorrectly assuming a particular form would lead to spurious results , and the the property of best linear unbiased estimates would no longer hold for the model coefficients . While inference would be more powerful if the correct form of the covariance matrix was known , the unstructured covariance allows for conservative estimation directly from the data . In practice we have found that allowing the general covariance structure versus assuming the random effects are independent of each other greatly improves the fit of the model and improves the prediction of genes under selection . Modeling a general covariance structure makes sense intuitively . For example , for a particular gene the non-synonymous and selection effects are especially likely to be correlated as selection affects the amount of time a non-synonymous mutation exists as a polymorphism before becoming fixed or eliminated . The selection effect reflects the selection coefficient , and the non-synonymous effect reflects mutation constraint , . Because of this relationship , one may be interested in examining the joint distribution for these estimated effects for a particular gene . This is easily accomplished in the Bayesian setting using the MCMC chains . As an example , see Figure 1 . For the Bayesian model the fixed effects have Normal priors with mean , and precision . The priors for the random effects for each gene were multivariate normal with mean , and precision . The precision matrix is modeled as a hyperparameter in order to estimate the covariance structrue among the random effects . Using the conjugate prior , the Wishart disribution , we set , where is the identity matrix . Because the mutation counts are low these priors are considered non-informative . An alternative formulation of the Bayesian model using hierarchical centering maybe be preferable as it results in quicker convergence [26] . In the hierarchical centering formulation the fixed effects appear as hyper parameters about which the random effects are centered . The models are equivalent and as long as convergence criteria are met will yield the same inference . In standard coalescent theory we have lineages coalescing at time points exponentially distributed with rate equal to . The number of segregating sites follows a Poisson process with rate per unit of time . Conditioning on the length of genealogy , , which is a function of the coalescent times , the number of segregating sites is Poisson distributed with mean . Thus , we have the expected mutation count , , is a function of the sample coalescent times , as well as the mutation rate [19] . Additionally , the expected mutation count should be adjusted for constraint , , and selection . This is consistent with our model where the effects of mutation rate and divergence is estimated from the synonymous mutations , and constraint and selection are estimated from the non-synonymous . Our model also works well in the Poisson random field ( PRF ) framework which assumes i . mutations arise at exponentially distributed times , ii . each mutation occurs at a new site , and iii . each mutant follows and independent Wright-Fisher process ( no linkage ) [27] . SnIPRE can be viewed as a re-parameterization of the PRF framework . Thus it is convenient to use the relationships between the SnIPRE coefficients and the PRF model to obtain estimates of ( , where 1+s is the fitness of mutants , and is the effective population size ) , as well as , , and ( where is the nucleotide mutation rate ) . We can derive the relationship between the population genetic parameters and the SnIPRE coefficients by comparing the predicted MK table counts provided by SnIPRE , see Table 3 , which are written in terms of model coefficients , to the theoretical expected MK table counts given in Table 4 . These relationships are derived below; and represent the number of samples from the population of interest and the outgroup . The gene effect , is a function of the mutation rate . ( 2 ) The divergence effect , , is a function of the divergence time . ( 3 ) ( 4 ) The selection effect , is a function of the selection coefficient , and the time to the most recent common ancestor . ( 5 ) ( 6 ) The selection effect reflects the interaction of the non-synonymous and divergent effects on the expected mutation count . Under the PRF framework we assume a neutral demography . Thus , a positive ( negative ) selection effect corresponds to a positive ( negative ) selection coefficient . That positive ( negative ) selection leads to the higher ( lower ) rate of fixation for non-synonymous mutations makes sense intuitively . A positive selection effect indicates that mutations that are non-synonymous are being fixed at a higher rate than expected under the null hypothesis of no selection . A negative selection effect indicates that mutations that are non-synonymous are being fixed at a slower rate than expected . The non-synonymous effect , , may also be thought of as a constraint effect since it is a function of the proportion of non-synonymous mutations that are non-lethal , as well as the selection coefficient , . ( 7 ) ( 8 ) The constraint effect , , reflects the effect that mutations being non-synonymous ( versus synonymous ) has on the expected count . A negative ( positive ) constraint effect indicates that non-synonymous polymorphic mutations are either being fixed or eliminated at a higher ( lower ) rate than synonymous mutations . Thus , after estimating the selection coefficient to account for the rate at which non-synonymous mutations are fixed , we can estimate from the constraint effect the proportion of mutations that are lethal , and therefore quickly eliminated from the population . While the selection effect is useful for identifying selection on mildly deleterious mutations as well as advantageous mutations , the constraint effect can be used to identify cases of strong negative or purifying selection . It is interesting to note that these are the relationships used by Sawyer and Hartl ( 1992 ) to fit their single locus PRF models to MK data . What is different about our approach is that we do not require a PRF parameterization for inference; rather , it naturally falls out from consideration of the standard log-linear model analysis of multi-way contigency tables . Several of the simulations in the next section are done in the PRF framework using PRFREQ [12] . Also included are several simulations using SFS_CODE [28] that show our estimation of population genetic parameters to be fairly robust to the PRF assumption of no linkage between sites . Specifically , the false positive rate remains low for identification of genes under selection . The primary consequence of linkage is underestimation of the magnitude of selection . We plan to explore these results more in a later paper .
To assess false positive rate FPR for each of the methods , we simulated data using standard coalescent theory . In Table 5 , we report the false positive rate for a data set with 1 , 000 neutrally evolving genes simulated from a pair of populations of constant size that split generations ago , with mutation rate . The standard MK approach had an FPR = 0 . 02 . SnIPRE performed very well with an FPR0 . 001 for both the Bayesian and empirical Bayes approaches . MKprf had mixed performance , depending on assumptions regarding the variance of the distribution of fitness effects . For fixed variance , , the FPR = 0 . 14 which is relatively high . This is a mode of MKprf that has a very wide prior distribution that is not updated by information from other loci . When that information is incorporated we see that MKprf ( estimated ) also has a low FPR , 0 . 012 . Next we investigated the impact of demographic history as well as recombination on the FPR of the methods using the forward simulator SFS_CODE . In Table 6 , we report simulation results for 5 demographic settings for 1 , 000 gene data sets including three bottleneck scenarios , one population growth model , and constant population size . From these simulations we see that both the MK method and SnIPRE methods have very low false positive rates , with the SnIPRE methods performing slightly better . MKprf with estimated variance has similarly very low false positive rates , however MKprf with has consistently higher false positive rates . As stated above , all these methods should be robust to demography . This appears to be the case in our simulations as the false positive rates remain consistent for each method across demographies . The key point from all these simulations is that SnIPRE performs just as conservatively as the MK test and better than MKprf under a litany of neutral scenarios that might be cause for concern in analyses for inference of selection . A particularly interesting application of SnIPRE is to identify regions of the human ( or a new genome ) that show very low levels of variation based on both polymorphism and divergence data . These might be interpretable as regions of high selective constraint either at the amino acid or non-coding level ( for comparison with a flanking “neutral” standard ) and may represent biologically meaningful sequences , see [29] , [30] . To quantify the power of SnIPRE to identify constrained loci , we used the coalescent method to simulate three different scenarios with varying degree of selective constraint , or , among genes in 1 , 000 gene data sets . Here we consider the case where some proportion of sites are very strongly constrained ( any mutation at these locations is considered lethal ) , and not the case where the mutations are of weak negative effect and could rise in frequency and contribute to polymorphism ( considered in the simulations below ) . That is , these regions do not exhibit a deviation in polymorphism verus divergence; however , they will be outliers with regard to the genome-wide pattern of overall genetic variation . In Table 7 and Figure 2 we see the results from three coalescent simulations with three different distributions on mutational constraint , . A comparable estimate of constraint from the MKprf methods is a function of its estimated nonsynonymous and synonymous mutation rates , and :The SnIPRE methods performed quite well on data from distribution one with 98% and 99% correct , the MKprf methods yielded only 43% and 67% correct . Distribution 2 has a wider variety of constraint and presents more of a challenge for both SnIPRE ( 66% and 86% ) and MKprf ( 38% and 51% ) methods . Distribution three contained only mild to moderate constraint and was the most challenging of the three distributions . Here , the B SnIPRE method proved to be the most powerful of the four methods , with 45% correctly classified , and the MKprf methods yeilded approximately 21% correct , and SnIPRE approximately 17% correct . For all three distributions the SnIPRE methods correctly classified the selection effects as neutral . From these results we see that the SnIPRE model is able to detect strong constraint , and can distinguish these effects from those of selection . A comparison can also be made when selection is present , and there is no constraint ( ) . To do this we considered a data set with selection coefficients drawn from a normal distribution with a mean of zero , a standard deviation of two , and with no constraint . In Figure 3 A we see that SnIPRE's estimated constraint effects are quite accurate ( very close to one ) , while the MKprf methods have much more variable estimates . The SnIPRE method's estimates of constraint are somewhat correlated with the selection coefficient , however we see in Figure 3 B that the effect of this trend is minimal . We also applied these methods to Drosophila simulans data with a Drosophila melanogaster outgroup . This data was originally presented by Begun et al [31] . Our results are consistent with others' findings of abundant positive selection among Drosophila [32]–[33] [16] . B SnIPRE identifies an additional 613 genes ( nearly a 60% increase ) with significant evidence of positive selection that were not significant by the traditional MK test using an un-adjusted p-value cutoff of 0 . 05 . We also find evidence of a significant amount of mutational constraint , see Figure 8 . These results are consistent with the large effective population size of Drosophila and the strong efficacy of selection . It is important to note when interpreting these results that all the tests discussed here have an underlying assumption that the synonymous sites are under no selection . These synonymous sites act as a baseline , thus conclusions of positive or negative are actually measured relative to the level of selection acting on synonymous sites . For example , if there is selection against unfavored codons , this may artificially inflate the non-synonymous to synonymous ratio and be misinterpreted as positive selection at non-synonymous sites . If codon bias is believed to be widespread amongst the genome , a better indicator of selection levels may be to compare the gene specific effects to the genome average , rather than comparing the sum of these effects to zero . In contrast , when we applied SnIPRE to human data , we found few genes with evidence of strong positive selection and an overwhelming signal of negative selection , see Figure 9 . This is consistent with our previous interpretation of the results in [10] and [12] , where we argued weak negative selection is the predominant mode of selection operating across the majority of human evolutionary history . Again , this is consistent with the small long term of our species . An implication of this result is that many genes likely harbor mutations of small negative effect that can reach appreciable frequencies . The application to humans in particular illustrates nicely the improved power in the SnIPRE model to detect genes under strong negative selection ( constraint ) and recurrent negative selection on mildly deleterious mutations . Because of the relatively low mutation rate in humans , genes under varying degrees of negative selection usually have such low mutation counts in the MK table that the MK test is unable to achieve significance . For example , consider the spermatogenic Odf2 gene , which plays an important role in sperm morphology and infertility . The MK table counts are as follows: PS = 1 , DS = 9 , PN = 1 , and DN = 1 . The MK test is testing the equality 1/9 = 1/1 , but failed to reach significance ( p-value = 0 . 32 ) . SnIPRE , however , found significant evidence of negative selection , as well as mutational constraint . The SnIPRE estimated selection effect for this particular gene was ( significantly different from zero , and lower than the genomic average of ) , and the estimated reduction in non-synonymous mutations was also quite strong , ( compared to a genomic average of ) . From here we can conclude that there is significant evidence of selection , and additionally , there may be evidence of mutational constraint , or purifying selection , as we are observing significantly fewer non-synonymous mutations than expected . It is difficult to interpret the significance of the constraint , however , without first estimating the strength of negative selection . This is because the strength of selection also influences the expected number of non-synonymous mutations . If we are willing to accept the additional assumptions of the PRF framework , then using the relationship defined in ( 7 ) and ( 8 ) we estimate the average selection coefficient acting on this gene to be equal to , and the estimated proportion of mutations that are non-lethal in this gene to be ( a proportion which is found to be significantly different from 1 ) . Under the PRF framework the SnIPRE model also tells us that the gene effect for Odf2 , may be interpreted as mutation rate of mutations per generation , per site ( slightly higher than the estimated genomic average estimated from this data of ) ; and the estimated divergence effect , , leads to an estimated scaled coalescence time for this gene at ( slightly higher than the genomic average estimated here of ) . The BRC2 gene , associated with breast cancer and important for DNA repair , is another illustration of a case where examining the individual MK table we are unable to find significant evidence of selection . However the SnIPRE model indicates a significant amount of mutational constraint , indicating strong negative selection . The MK table for this gene has PS = 13 , DS = 16 , PN = 9 , and DN = 17 . While there is little evidence of negative selection ( , not significantly different than zero ) , the SnIPRE model indicates evidence for mutational constraint ( ) . From the MK table alone we would not see this as the total synonymous and non-synonymous mutations are similar . However , considered with the additional information that the number of non-synonymous sites sampled was nearly three times the number of synonymous sites sampled , the SnIPRE model in the PRF framework estimates the proportion of mutations that are non-lethal to be , significantly different than one . The average mutation rate for this gene is estimated to be and a more recent coalescent time of . Due to the overwhelming evidence of negative selection and constraint in humans , signatures of positive selection are difficult to detect even with the increase in power with the SnIPRE framework . B SnIPRE detects only 4 genes under positive selection not identified by the traditional MK statistic , which identifies 10 genes . For this reason it may be informative to consider the effect of selection on a gene relative to the genome-wide average . Because the selection effect represents the average effect of selection on that gene throughout time , it may represent an average of both positive and negative selection forces . Assuming a model where we can interpret the the sign of the selection effect as indictive of the direction of selection , genes with selection effects significantly higher than the genome-wide average will have had either more positive selection or less negative selection acting on them than the typical gene . For example , in this data set B SnIPRE identifies 628 genes with selection effects significantly higher than the genome-wide average of −0 . 60 . The SnIPRE framework models MK table data in a way consistent with population genetic theory and with minimal assumptions on the demographic model may reject the neutral theory . However , just as with the traditional MK test , conclusions about type of selection ( positive , negative , or balancing ) require further assumptions . The parameters of the SnIPRE model are easily interpreted and can be effectively used to estimate the affects of selection , constraint , divergence time , and mutation rate on genome-wide patterns of variation on a gene-by-gene basis . Effects may be readily evaluated in the absolute , or relative to the genome-wide estimates . The simulations provided here illustrate the significant increase in power over the traditional MK test that the SnIPRE model provides , while maintaining a low false positive rate . This makes sense since we are using genome-wide data to improve our estimate of the influence of mutation rate , species divergence time , constraint , and selection effects . The fixed effects reflect genome-wide averages of these effects; the random effects reflect the gene-by-gene variation in the influence of these forces and provide estimates of this variation with James-Stein-type shrinkage . Both the empirical Bayes and fully Bayesian implementation borrow strength across genes to improve estimates of the parameters of interest . The success of the method in simualtions , as well as the consistency of the Drosophila and human-chimp results with other findings corroborates the legitimacy of this methodology in this setting . When the assumptions of the PRF are met , our simulations indicate the method provides estimates of the selection coefficient as un-biased as the more parametric method MKprf , and with generally smaller confidence intervals . While in this paper we have focused on the interpretation of SnIPRE parameters in the PRF framework , we believe an extension of the model could be used in another framework which allows for arbitrary dominance . One such framework is described in Williamson et al [34] in which the dominance parameter is estimated based on additional information from the site frequency spectrum . However , as with any method that makes conclusions about strength and directionality , such as MKprf or , in order to asses the type of selection assumptions would need to be made about effective population size changes and their timing . In the future , we will explore the impact of varying recombination rate on the accuracy of parameter estimates and , in turn , the efficacy of natural selection in weeding out deleterious alleles while promoting favorable mutations to high frequency . | We present a new methodology , SnIPRE , for identifying genes under natural selection . SnIPRE is a “McDonald-Kreitman” type of analysis , in that it is based on MK table data and has an advantage over other types of statistics because it is robust to demography . Similar to the MKprf method , SnIPRE makes use of genome-wide information to increase power , but is non-parametric in the sense that it makes no assumptions ( and does not require estimation ) of parameters such as mutation rate and species divergence time in order to identify genes under selection . In simulations SnIPRE outperforms both the MK statistic and the two versions of MKprf considered . We then apply our method to Drosophila and human-chimp data . | [
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] | [
"mathematics",
"theoretical",
"biology",
"natural",
"selection",
"statistics",
"genetics",
"population",
"genetics",
"biology",
"biostatistics",
"statistical",
"methods",
"genetics",
"and",
"genomics"
] | 2012 | SnIPRE: Selection Inference Using a Poisson Random Effects Model |
Mobile group II introns , which are found in bacterial and organellar genomes , are site-specific retroelments hypothesized to be evolutionary ancestors of spliceosomal introns and retrotransposons in higher organisms . Most bacteria , however , contain no more than one or a few group II introns , making it unclear how introns could have proliferated to higher copy numbers in eukaryotic genomes . An exception is the thermophilic cyanobacterium Thermosynechococcus elongatus , which contains 28 closely related copies of a group II intron , constituting ∼1 . 3% of the genome . Here , by using a combination of bioinformatics and mobility assays at different temperatures , we identified mechanisms that contribute to the proliferation of T . elongatus group II introns . These mechanisms include divergence of DNA target specificity to avoid target site saturation; adaptation of some intron-encoded reverse transcriptases to splice and mobilize multiple degenerate introns that do not encode reverse transcriptases , leading to a common splicing apparatus; and preferential insertion within other mobile introns or insertion elements , which provide new unoccupied sites in expanding non-essential DNA regions . Additionally , unlike mesophilic group II introns , the thermophilic T . elongatus introns rely on elevated temperatures to help promote DNA strand separation , enabling access to a larger number of DNA target sites by base pairing of the intron RNA , with minimal constraint from the reverse transcriptase . Our results provide insight into group II intron proliferation mechanisms and show that higher temperatures , which are thought to have prevailed on Earth during the emergence of eukaryotes , favor intron proliferation by increasing the accessibility of DNA target sites . We also identify actively mobile thermophilic introns , which may be useful for structural studies , gene targeting in thermophiles , and as a source of thermostable reverse transcriptases .
Mobile group II introns are bacterial and organellar retrotransposons that are hypothesized to be ancestors or closely related to ancestors of spliceosomal introns and retrotransposons in higher organisms ( reviewed in [1] , [2] ) . They consist of a catalytically active intron RNA ( “ribozyme” ) and an intron-encoded protein ( IEP ) , which has reverse transcriptase ( RT ) activity . Group II intron RNAs typically show little sequence conservation but have conserved secondary and tertiary structures that consist of six interacting double-helical domains ( DI–DVI ) [3] , [4] . The folded RNA catalyzes its own splicing via two sequential transesterification reactions that are the same as those for spliceosomal introns in higher organisms and yield spliced exons and an excised intron lariat RNA [5] . For mobile group II introns , the IEP assists splicing by stabilizing the catalytically active RNA structure ( “maturase activity” ) and then remains bound to the excised intron lariat RNA in a RNP ( ribonucleoprotein particle ) [6]–[8] . The latter promotes intron mobility by a mechanism that involves reverse splicing of the intron RNA directly into a DNA strand , reverse transcription of the inserted intron RNA by the IEP , and integration of the resulting intron cDNA into the genome by host enzymes [9]–[13] . This mechanism is used by group II introns both to retrohome into specific DNA target sites at high frequency and to retrotranspose into ectopic sites that resemble the retrohoming site at low frequency , and ancestral mobile group II introns may have used the same mechanism to invade and proliferate within the nuclear genomes of early eukaryotes , before evolving into spliceosomal introns , snRNAs , and non-LTR-retrotransposons [1] , [2] . Group II introns are common in eubacteria and in the mitochondrial ( mt ) and chloroplast ( cp ) genomes of fungi and plants but are rare in archaea , with the few known examples of archaeal group II introns attributed to horizontal transfer from eubacteria [14] , [15] . This phylogenetic distribution is consistent with a scenario in which mobile group II introns evolved in eubacteria and were transferred to eukaryotes with bacterial endosymbionts that gave rise to eukaryotic organelles [16] , [17] . Bacteria contain eight lineages of mobile group II introns , which are distinguished by different IEP types ( bacterial A–F , mitochondrial-like ( ML ) and chloroplast-like ( CL ) ) and distinct RNA structural subgroups ( IIA ( ML ) , IIB ( CL and bacterial lineages A , B , and D–F ) , and IIC ( bacterial lineage C ) ) [18] . Notably , while all eight group II intron lineages are found in bacteria with extensive horizontal transfer between different species [14] , [19] , only the ML and CL lineages are also found in organelles , consistent with the possibility that they were associated with the bacterial endosymbionts that evolved into mitochondria and chloroplasts . Evolutionary scenarios for the evolution of group II introns into spliceosomal introns suggest that group II introns harbored by bacterial endosymbionts invaded the host's nuclear genome , where they proliferated and degenerated , with the group II intron RNA domains evolving into spliceosomal snRNAs that form the core of a common splicing apparatus for multiple dispersed introns [2] , [20] . Key aspects of this scenario are supported by experimental evidence , including structural and functional similarities between group II intron domains and snRNAs and numerous examples of group II introns that are fragmented into two or three segments that functionally reassociate to catalyze trans-splicing [1] , [2] , [21] , [22] . Most bacteria , however , contain no more than one or a few group II introns [23] , suggesting that group II intron mobility is tightly controlled and/or that mutations that lead to uncontrolled intron proliferation are lost rapidly by purifying selection . Thus , it is unclear how group II introns could have proliferated to higher copy numbers in nuclear genomes . Although group II intron proliferation is rare in bacteria , it is evident in smaller organellar genomes , the most striking example being Euglena cp DNA , which contains ∼150 group II introns [24] . Most of the Euglena introns are highly degenerate , lacking different domains , with some ( referred to as group III introns ) containing only DI-like and DVI-like structures and even lacking DV , which is catalytically essential . These degenerate introns presumably rely on protein factors and/or trans-acting RNAs to promote RNA splicing . Only two of the Euglena cp group II introns encode RTs , which may act in trans to promote splicing and mobility of the ORF-less introns [25] . Although the Euglena group II introns can potentially provide insight into mechanisms involved in intron proliferation and generation of a common splicing apparatus , they have been largely intractable to detailed analysis . Another instance of group II intron proliferation has been revealed by genomic sequencing of the thermophilic cyanobacterium Thermosynechococcus elongatus strain BP-1 , which contains 28 group II introns comprising ∼1 . 3% of the genome [26] . These T . elongatus group II introns are closely related to each other and appear to have proliferated from a single group II intron . Here , we used bioinformatic analysis and intron mobility assays at different temperatures to identify four mechanisms that contribute to the proliferation of the T . elongatus introns . Our results provide insight into how group II introns proliferate within genomes; show how higher temperatures , which are thought to have prevailed on Earth during the emergence of eukaryotes , can contribute to this process; and identify actively mobile thermophilic group II introns , which may be useful for structural studies and biotechnological applications .
Figure 1 lists the T . elongatus introns classified according to intron family ( F1–F6 ) by criteria described below , along with their insertion sites in the T . elongatus genome . All 28 introns are closely related to each other ( 85%–100% sequence identity in pairwise comparisons ) . Twenty-five of the T . elongatus introns are intact containing all six conserved RNA domains , but three are fragments that have undergone large deletions ( TeI3g , TeI3m , and TeI3n; Figure 1 ) . Eight of the intact introns ( TeI4a–h ) contain ORFs encoding IEPs ( see below ) , while the remaining 17 introns ( TeI3a–t ) lack ORFs but are otherwise closely related to the ORF-containing introns . The closest known relative of the T . elongatus introns is EcI5 , a previously characterized Escherichia coli CL/IIB1 intron ( ∼50% sequence identity in pairwise comparisons [27] , [28] ) . Except for the presence or absence of the intron ORF , the 25 intact T . elongatus introns have very similar predicted RNA secondary structures , which are characteristic of subclass IIB1 introns and closely resemble that of EcI5 [28] . Figure 2A–C show a secondary structure model for TeI4h , one of the introns studied in detail below , and key differences in representatives of other T . elongatus intron families . As for other group II introns , the predicted structure consists of the six conserved RNA domains ( DI–DVI ) with a series of conserved motifs ( denoted by Greek letters , EBS ( exon-binding site ) , and IBS ( intron-binding site ) ) . The latter are involved in a series of long-range interactions that help fold the intron RNA into the catalytically active tertiary structure . Notable regions are DI and DV , which together comprise the minimal catalytic core; DIV , which encodes the IEP in the loop of subdomain DIVb; and DVI , which contains the branch-point A-residue [2] . Many of the sequence differences between the T . elongatus introns correspond to reciprocal changes in stem regions , but some introns have deviations in conserved structures or motifs that are expected to impair ribozyme activity ( e . g . , TeI4f and TeI4g have mispairings in the upper stem of DV and the lower stem of DVI ) . Inactive group II introns that have lost mobility functions are common in bacteria and organelles and presumably reflect selective pressure against intron mobility , which is deleterious to the host [1] . TeI4a–h have long ORFs in DIV that encode predicted IEPs of 561 amino acid residues with RT , X/thumb , DNA binding ( D ) , and DNA endonuclease ( En ) domains homologous to those of other group II IEPs ( Figure 2D ) [1] , [2] , [29] . Seven of the ORFs show strong conservation of amino acid sequences residues known to be required for activity of other group II IEPs ( see alignments in Figure S1 ) , but one has an early frameshift as well as multiple premature stop codons and other sequence deviations ( TeI4d , not shown in the alignment ) . Like the intron RNAs , the IEPs are closely related to each other ( 84%–100% identity in pairwise comparisons , excluding TeI4d ) , and their closest known relative is the EcI5 IEP ( 53%–55% identity in pairwise comparisons; Figure S1 ) . Two introns ( TeI4f and 4h ) have continuous ORFs , while in five other introns ( TeI4a , b , c , d , and e ) the ORF is interrupted by insertion of an ORF-less intron ( TeI3a , b , c , d , and e , respectively ) between the first two ATGs , which are separated by only five codons ( Figure 2B , Figure S2 ) . This configuration in which one intron inserts into another is known as a “twintron” [24] . The T . elongatus twintrons could potentially encode active IEPs initiated either from the first AUG after splicing of the inner intron or from the second AUG without splicing of the inner intron . In the remaining intron , TeI4g , the ORF is interrupted by an insertion element ( TeI4g::ISEL2f ) [26] . Thus , excluding TeI4d and TeI4g , six of the T . elongatus introns ( TeI4a , b , c , e , f , and h ) could potentially encode active IEPs . All 17 ORF-less T . elongatus introns ( TeI3a–t ) have a ∼1 . 5 kb deletion in subdomain DIVb , which removes most of the ORF ( Figure 2B and C ) . Sequence alignments show that the break-points of this deletion are the same in all 17 introns and that remnants of the N- and C-termini of the ORF are clearly discernible on either side ( Figure S2 ) . All of the ORF-less introns also have a single extra U residue in DIVa1 , which disrupts the remnant reading frame , and a second smaller deletion in DIVa2 ( Figure 2B and C; Figure S2 ) . These findings most simply suggest that the 17 ORF-less introns arose from a single ancestral intron that underwent the deletion and then proliferated . Phylogenetic analysis based on alignments of intron sequences supports this hypothesis and indicates that the ORF deletion was an early event that occurred before the divergence of the T . elongatus introns into different families ( Figures 3 and S3 , and see below ) . Notably , in the ORF-less introns , the terminal loop of DIVa2 resulting from the smaller deletion in DIV has co-varied with the terminal loop of DIVa1 in a manner suggesting a base-pairing interaction between the loops ( Figure 2C ) . As DIVa is a critical binding region for the IEP in subgroup IIA introns [30] , these changes could be pertinent to IEP recognition . Group II introns recognize DNA target sequences by using both the IEP and base pairing of the intron RNA [31] , [32] . In group IIB introns , the base-pairing interactions involve intron RNA sequences denoted EBS1 , 2 , and 3 and complementary DNA target sequences denoted IBS1 and 2 in the 5′ exon and IBS3 in the 3′ exon [28] , [33] , [34] . We noticed that the T . elongatus introns could be divided into six families , F1–F6 , based upon their EBS1 , 2 and 3 sequences ( Figure 1 ) , and phylogenetic analysis indicated that each of these families corresponds to a distinct clade ( Figures 3 and S3 ) . Most of the introns are inserted at genomic sites with largely complementary IBS sequences ( one or no mismatches ) , suggesting insertion by retrohoming , but some are inserted at sites with more poorly matched IBS sequences , suggesting insertion by infrequent retrotranspositions . Because the EBS/IBS interactions in the precursor RNA are required for RNA splicing , only those introns inserted by retrohoming at sites with complementary IBS sequences are expected to splice efficiently . Most introns with poorly matched IBS sequences and some introns with well-matched IBS sequences are inserted at sites in intergenic regions , where their splicing ability is less likely to affect host gene expression . The eight ORF-containing introns are divided into two families ( F1 and F2 ) , while the 17 ORF-less introns are divided into four families ( F3–6; Figure 1 ) . The F1 introns ( TeI4a–e ) are inserted in intergenic regions , as are two of the F2 introns ( TeI4f and 4g ) . Notably , the 5′ exon of TeI4f and the 3′ exon of TeI4g correspond to 5′ and 3′ segments of an ABC transporter pseudo-gene , respectively , suggesting these introns were derived from an exon-shuffling homologous recombination event between two ancestral introns . The remaining F2 intron , TeI4h , is inserted in a gene encoding the single copy of tRNAIleCAU , which is putatively essential [26] . The site of insertion in the tRNA gene is one with good EBS/IBS pairings , suggesting that TeI4h inserted by retrohoming and can splice to produce a functional tRNA . F3 consists of seven identical ORF-less introns , five of which ( TeI3a–e ) are inserted at a conserved site between the two closely spaced start codons of the RT ORF of F1 introns , while TeI3i is inserted at the same site in the remnant ORF of TeI3h , and TeI3o is inserted in an intergenic region . F4 consists of three introns inserted at a conserved site in insertion element ISEL1 , a member of the IS200 insertion element family [26] . F5 consists of three introns inserted in intergenic regions and a fourth intron ( TeI3j ) inserted within a glycosyl transferase gene . Finally , F6 consists of two introns inserted in intergenic regions and one intron inserted near the 3′ end of gene t112453 . The finding that only a small number of T . elongatus introns are inserted within genes may reflect purifying selection against insertions that are deleterious to the host , but mechanisms for actively avoiding insertion within genes are also possible . Importantly , most of the ORF-less introns are inserted at sites with good EBS/IBS pairings , suggesting insertion via retrohoming , further evidence that the ORF-less introns are actively mobile ( Figure 1 ) . The larger number of ORF-less introns presumably reflects that they are more efficiently mobile than ORF-containing introns due to their smaller size and more compact structure . The latter makes them less susceptible to degradation by host nucleases , which appears to be a major means of controlling group II intron mobility in bacteria [35] . To directly assay mobility of the T . elongatus introns , we used an E . coli plasmid assay in which an intron with a phage T7 promoter inserted near its 3′ end is expressed from a donor plasmid and integrates into a target site cloned in a recipient plasmid upstream of a promoterless tetracycline-resistance ( tetR ) gene , thereby activating that gene ( Figure 4A; [36] , [37] ) . Because the intron is expressed from a donor plasmid , the IEP must splice the intron RNA to generate RNPs , which then promote integration of the intron into the DNA target site . Previous studies showed that the Lactococcus lactis Ll . LtrB intron is efficiently mobile in this E . coli assay , reflecting that it has a wide host range and is not dependent upon host-specific factors for RNA splicing or intron mobility [13] , [36] , [38] , and we anticipated this would also be the case for the T . elongatus introns . For the mobility assays with the T . elongatus introns , the CapR ( chloramphenicol-resistant ) intron-donor plasmid uses a T7lac promoter ( PT7lac ) to express a precursor RNA containing a ΔORF-derivative of the intron RNA ( I-ΔORF ) with short flanking exons and a phage T7 promoter ( PT7 ) inserted in place of the intron ORF in DIVb . The IEP , which splices and mobilizes the intron , is expressed from a position downstream of the 3′ exon ( E2 ) . This configuration using an intron RNA with the ORF deleted and the IEP expressed from downstream of E2 gives high mobility frequencies for other group II introns [28] , [36] and facilitates the mixing and matching of intron RNAs and IEPs in experiments below . The AmpR ( ampicillin-resistant ) recipient plasmid contains the intron target site ( i . e . , ligated-exon sequences flanking the intron-insertion site ) cloned upstream of a promoterless tetR gene . For mobility assays , the donor and recipient plasmids are co-transformed into E . coli HMS174 ( DE3 ) , which contains an IPTG ( isopropyl β-D-1-thiogalactopyranoside ) -inducible T7 RNA polymerase . After induction of donor plasmid expression with IPTG , cells are plated on Luria-Bertani ( LB ) medium containing tetracycline plus ampicillin or amplicillin alone , and mobility efficiencies are calculated as the ratio of ( TetR+AmpR ) /AmpR colonies . We focused first on TeI4h , which is inserted within the tRNAIleCAU gene because it contains a continuous ORF and is inserted within an essential gene at a site with good EBS/IBS pairings . These characteristics imply insertion via retrohoming and active splicing to produce a functional tRNA . Nevertheless , TeI4h has features that deviate from the canonical group II intron structure , including a 5′ T residue and a mispairing in the δ-δ′ interaction in the intron RNA ( Figure 2A ) and YAGD instead of the highly conserved YADD motif at the RT active site in the IEP ( Figure S1 ) . Table 1 summarizes the mobility efficiencies for donor plasmids expressing different derivatives of the TeI4h-ΔORF intron and IEP at different induction temperatures . At 37°C , the wild-type TeI4h-ΔORF intron and IEP combination had very low mobility efficiency ( 2 . 5×10−5% ) , but changing the IEP's YAGD sequence to YADD increased the mobility efficiency dramatically to 3 . 4% . This modified IEP , denoted IEP-4h* , was used in all subsequent constructs . Combining the TeI4h* IEP with a modified intron ( denoted TeI4h* ) , which has the change C326T in δ′ to restore the δ-δ′ pairing , increased the mobility efficiency further to 39% . Surprisingly , however , combining the TeI4h* IEP with a modified intron in which the 5′-nucleotide residue was changed from T to the highly conserved 5′ G residue found in other group II introns decreased the mobility efficiency to 0 . 5% , and combining all three changes gave a mobility efficiency of only 8 . 4% . Thus surprisingly , the unusual 5′ T-residue is favored in TeI4h . Sequencing of retrohoming products from this experiment and from target-site definition experiments described below showed that the inserted TeI4h* intron begins with the unusual 5′ T-residue in all cases ( >600 sequences ) , confirming use of a corresponding 5′ U-residue in the intron RNA for both RNA splicing and reverse splicing . The maximally efficient donor plasmid expressing the TeI4h*-ΔORF intron with the C236T mutation restoring δ-δ′ and the TeI4h* IEP , with the mutation changing YAGD to YADD at the RT active site , is designated pACD2-TeI4h*/4h* to denote the intron RNA/IEP combination . At higher temperatures , the mobility efficiency of TeI4h and its derivatives increased dramatically , with the mobility efficiency of the optimal TeI4h*/4h* construct as well as several of the suboptimal constructs reaching 100% at 48°C ( Table 1 and Figure 4B ) . By contrast , the mobility efficiency of the mesophilic Ll . LtrB-ΔORF intron expressed from an analogous donor plasmid decreased with increasing induction temperature , with the residual ∼20% mobility at 48°C likely reflecting integrations that occurred at 37°C prior to induction or after plating . The native TeI4h/4h construct , which expresses the wild-type ΔORF intron and IEP without modification of the δ-δ′ pairing or YAGD sequence , had low mobility efficiency even at 48°C ( 6 . 7×10−2% ) , indicating that one or both of these suboptimal features inhibits mobility regardless of temperature ( Table 1 ) . We note that the mobility of the TeI4h at 48°C presumably relies either on residual activity of T7 RNA polymerase at the higher temperature and/or on RNPs made at 37°C prior to the temperature shift . Together , the above findings show that the TeI4h-ΔORF intron and IEP are thermophilic , presumably reflecting adaptation for retromobility in the native host T . elongatus . The finding that the T . elongatus ORF-less introns likely evolved from a single intron by ORF deletion and then proliferated to new sites by retrohoming suggested that their splicing and mobility might be promoted by one or more of the IEPs encoded by other T . elongatus introns . To test this hypothesis , we carried out E . coli mobility assays at 48°C with donor plasmids expressing different combinations of T . elongatus introns and IEPs ( Table 2 ) . The intron RNAs tested included at least one representative of each intron family ( the ORF-less introns TeI3c , 3k , 3f , and 3l , all of which have good EBS/IBS pairings with their flanking exons , and TeI4h*-ΔORF , 4c-ΔORF , and 4f-ΔORF , with ORF deletions matching the deletion break points in the naturally ORF-less introns; see Materials and Methods ) . The IEPs tested were TeI4h* , 4a , 4b , 4c , 4e , 4f , and 4g , the latter with the IS element precisely deleted . The TeI4c ORF , which in T . elongatus is present in a twintron with TeI3c inserted between the first two ATGs , was expressed from either ATG . After this choice of ATGs was found to have little effect on mobility efficiency ( see below ) , the remaining twintron IEPs were expressed only from the second ATG . The recipient plasmids contained the natural target site ( i . e . , ligated-exon sequences ) for each intron ( Figure 1; see Materials and Methods ) . The results , summarized in Table 2 , show that the TeI4h* IEP has very high specificity for its cognate intron . Its mobility efficiency with the TeI4h* RNA was 100% , while its mobility efficiencies with all other intron RNAs were 104- to 106-fold lower ( TeI4c , 3c , 3k , 3f , and 3l; 6 . 4×10−2 to 1 . 4×10−4% ) . The other IEPs were less specific . Among these , the TeI4c IEP was the most active . Expressed from either the first or second ATG , it mobilized the TeI4c-ΔORF RNA with efficiencies of 2 . 6 and 2 . 3×10−2% , respectively , while it mobilized representatives of other intron families at comparable ( 4 . 4×10−4 to 3×10−2%; 4h , 3k , 3f ) or higher efficiencies ( 0 . 96%–7 . 7%; 3c and 3l ) . We note that low mobility efficiencies for some introns are a consequence of suboptimal natural target sites , as the TeI4c IEP could also promote mobility of either TeI4c-ΔORF or TeI3c at 60%–80% efficiency with other target sites ( Table S1 and unpublished data ) . The TeI4a , b , c , and e IEPs showed different degrees of activity and specificity for different introns , but each had the ability to splice and mobilize multiple introns to some degree . Among the naturally ORF-less introns , TeI3c is most active with the widest variety of IEPs; TeI3l has relatively high activity ( 0 . 5%–2 . 4% ) with a subset of these IEPs; and TeI3f and TeI3k have low activity with most of the IEPs . The TeI4f IEP and restored TeI4g IEP with the IS element deleted gave low mobility efficiencies ( 3 . 7×10−2 to 2 . 1×10−4% ) with all introns tested , and the TeI4f RNA showed little or no mobility with any IEP tested , likely reflecting mutations that inhibit ribozyme activity , which is required for RNA splicing and reverse splicing ( see above; Figure 2B ) . Together , the findings in this section indicate that some but not all of the T . elongatus IEPs have decreased specificity for their cognate intron , enabling them to mobilize multiple ORF-less introns as or more efficiently than the intron that encodes them . Another mechanism that could potentially contribute to proliferation of the T . elongatus introns is relaxed DNA target specificity . For previously characterized group II introns , the IEP recognizes sequences in the distal 5′-exon and 3′-exon regions of the DNA target and promotes DNA melting , enabling the intron RNA's EBS sequences to base pair to the target site's IBS sequences [31] . The EcI5 IEP , which is closely related to the T . elongatus IEPs , recognizes five different nucleotide residues flanking the IBS sequences , C–18 , C–17 , A–15 , and A–14 in the distal 5′-exon region and T+5 in the 3′ exon [28] . A more relaxed DNA target specificity of the T . elongatus IEPs would enable intron RNAs with the same EBS sequences to insert at a greater number of sites . To identify nucleotide residues in the DNA target site that are recognized by the T . elongatus IEPs , we carried out selection experiments using the donor plasmids TeI4h*/4h* , TeI4c/4c , and TeI3c/4c with recipient plasmids in which the distal 5′-exon and 3′-exon regions potentially recognized by the IEP were randomized [28] . After selection for TetR+AmpR colonies in which the intron had inserted into the recipient plasmid , the 5′- and 3′-integration junctions in active target sites were amplified by colony PCR and sequenced . Figure 5 summarizes nucleotide frequencies at the randomized positions in active target sites in WebLogo format [39] . In each case , the nucleotide frequencies are based on sequencing of ∼100 active target sites and were corrected for nucleotide frequency biases in the libraries by sequencing a similar number of unselected recipient plasmids from the initial pools ( Figure 5 ) . Because we were uncertain about the boundaries of IBS2 , the randomized regions in the recipient plasmids extended 1–2 nucleotide residues into IBS2 ( shown in black in Figure 5 ) to confirm selection for complementary nucleotide residues in EBS2 . Figure 5A and B show selections at different temperatures for the donor plasmid TeI4h*/4h* in which the TeI4h* IEP promotes mobility of the TeI4h*-ΔORF intron . At 37°C , we see selection for two nucleotide residues in the distal 5′-exon region , A–15 and C–16 , along with G–14 and G–13 , which are part of IBS2 recognized by base pairing of C-residues at the corresponding positions in EBS2 of the intron RNA . Although the TeI4h IEP contains an En domain , there was no selection for any 3′-exon nucleotide , which is required for En cleavage by other group II IEPs [31] . At 48°C , we see similarly strong selection for the two EBS2 residues but somewhat weaker selection for A–15 and strongly decreased selection for C–16 compared to that at 37°C . Mobility assays confirmed that a mutation at position −16 ( C–16G ) inhibits mobility to a much greater extent at 37°C than at 48°C in agreement with the selection data ( Figure S4 ) . These findings likely reflect that the recognition of C–16 by the IEP is more stringently required for DNA melting at 37°C than at higher temperatures , which by themselves promote DNA melting . The selected distal 5′-exon sequence A–15 , C–16 matches that at the TeI4h insertion site in the T . elongatus genome ( Figure 1 ) . Figure 5C shows a similar selection at 48°C for the donor plasmid TeI4c/4c , in which the TeI4c IEP mobilizes its cognate TeI4c-ΔORF intron RNA . Here , we see selection for A or T at positions −13 to −16 , which diminishes with increasing distance from the last nucleotide of IBS2 ( T–12 ) , along with weak selection for A or T at positions +2 and +3 . This pattern most likely reflects selection for less stable AT base pairs that would facilitate melting of this region rather than any specific base recognition , potentially an example of a mobile intron that recognizes its DNA target site entirely by base pairing . The insertion site for TeI4c in the T . elongatus genome also shows A/T residues extending from −13 to −17 upstream of IBS2 and at +2 and +3 downstream of IBS3 ( Figure 1 ) . Finally , Figure 5D shows a selection at 48°C for the donor plasmid TeI3c/4c , in which the same TeI4c IEP mobilizes the ORF-less intron TeI3c . Surprisingly , we now see strong selection for A–14 and A–15 upstream of IBS2 , with weaker selection for A or T at position −16 , suggesting that the TeI4c IEP makes a greater contribution to DNA target site recognition when paired with the non-cognate TeI3c RNA than with its own cognate TeI4c-ΔORF RNA . The selected sequence again matches the genomic insertion site for TeI3c ( Figure 1 ) . The apparently altered DNA target specificity of the TeI4c IEP when paired with the TeI3c RNA may reflect that the non-cognate intron RNA induces a protein conformation that interacts differently with the DNA target site or that the different intron RNA/DNA base-pairing interactions lead to differences in DNA target site recognition by the IEP . Additionally , we cannot exclude that a small number of nucleotide residues upstream of IBS2 are recognized in some unknown way by the intron RNA rather than the IEP . In summary , the selection experiments show that all three T . elongatus introns tested recognize DNA target sites almost entirely by base pairing of the intron RNA , with the IEP making a much smaller contribution than for previously analyzed mesophilic group II introns , particularly at elevated temperatures that help promote DNA strand separation .
Here , we analyzed group II introns that have proliferated within the genome of the thermophilic cyanobacterium T . elongatus . Our results suggest that the 28 group II introns found in the T . elongatus genome arose from a single intron . An early event was deletion of the intron ORF , which appears to have occurred once prior to the divergence of intron families , giving rise to a smaller ORF-less intron that could be mobilized by the IEP of the intron from which it was derived . From that point , the ORF-containing and ORF-less introns diverged and proliferated in parallel by inserting into different sites , some of which were compatible with further mobility . We identify four mechanisms that contributed to the proliferation of T . elongatus introns . First , we find that the T . elongatus introns have diverged into six families with different EBS sequences that target the introns to different sites . Although it had been thought that group II intron dispersal to new sites occurs primarily via retrotransposition , our results suggest instead that most of the T . elongatus introns have inserted into new sites via retrohoming after EBS sequence divergence . Sequence comparisons show that T . elongatus intron EBS sequences are relatively malleable compared to RNA regions required for ribozyme activity ( Figure 2A ) . Mutations in the EBS sequences make it more difficult for an intron to splice and retrohome from its current site but increase its chances of retrohoming to a site not previously occupied by a related group II intron . Thus , this process enables waves of retrohoming into different sets of target sites , circumventing the problem of DNA target site saturation . As a proliferation mechanism , retrohoming has the selective advantage of ensuring that the intron inserts at sites with good EBS/IBS pairings from which it can subsequently splice efficiently , making intron insertion less deleterious to the host . By contrast , retrotransposition into essential genes with poorly matched exon sequences would be detrimental . Consequently , mutations leading to increased retrotransposition frequency may be lost by purifying selection , preventing this process from playing a greater role in group II intron proliferation . A second proliferation mechanism is that some but not all T . elongatus IEPs have evolved to have relaxed intron specificity , enabling them to act as “driver” IEPs to mobilize multiple ORF-less introns with the same or greater efficiency than the intron in which they are encoded . Deletion of the intron ORF favors proliferation because the smaller , more compact ORF-less introns are less susceptible to nuclease degradation , which appears to be a major mechanism limiting the mobility of bacterial group II introns [35] . To mobilize an ORF-less intron by retrohoming or retrotransposition , a driver IEP must be able to splice the intron to generate RNPs that promote mobility . Thus , the dispersal of degenerate ORF-less introns by a driver IEP automatically leads to the evolution of a common splicing apparatus . Other bacteria have also been found to contain ORF-less introns that are spliced by the IEP of a closely related intron , but because the introns are very closely related , it has not been clear to what extent this ability involved relaxation of IEP specificity [40] . A distinctive feature of the T . elongatus introns is that we identify one IEP , TeI4h , which retains high specificity for its cognate intron , presumably reflecting the ancestral situation , and other closely related IEPs , such as TeI4c , which have diverged to mobilize other introns as or more efficiently than their cognate intron . As the specific TeI4h and relaxed TeI4c IEPs have 87% sequence identity with only a few divergent regions ( Figure S1 ) , further comparisons may facilitate the identification of IEP features that dictate intron specificity . A third mechanism favoring intron proliferation in T . elongatus is the evolution of some introns to insert at a conserved site in another mobile element , either an insertion sequence or another mobile group II intron . The latter configuration , known as a twintron , could lead to intron proliferation either by retromobility of the composite intron or by separate splicing and mobility of the outer and inner introns . Supporting the latter mechanism , we find that the TeI4c IEP can independently splice and mobilize the outer TeI4c intron and the inner ORF-less TeI3c intron of the twintron ( Table 2 ) . Further , in all the T . elongatus twintrons , the inner F3 introns are identical ( i . e . , no sequence differences ) , while the outer F1 introns have diverged ( Figure S5 ) , as expected for recent independent insertions of an F3 intron into previously dispersed F1 introns . A previous example of group II intron proliferation via twintron formation was found in the archaebacteria Methanosarcina acetivorans , although in this case the twintron structure differs in involving nested arrays formed by repeated insertion of one group II intron into another [41] . A consequence of twintrons is the progressive expansion of non-essential genomic regions providing new target sites for intron insertion . Finally , we find that proliferation of the T . elongatus IEPs is favored by higher temperatures that promote DNA strand separation , facilitating access to DNA target sites and enabling their recognition almost entirely by base pairing of the intron RNA . For other mobile group II introns , IEP recognition of the distal 5′-exon region upstream of IBS2 is required for reverse splicing into double-stranded but not single-stranded DNA , implying a major role in DNA strand separation , while IEP recognition of the 3′ exon is not required for DNA strand separation but is critical for second-strand cleavage by the En domain [31] , [42] . The IEP encoded by EcI5 , a closely related mesophilic intron , stringently recognizes four bases in the distal 5′-exon region upstream of EBS2 and one base in the 3′ exon downstream of EBS3 [28] . By contrast , the T . elongatus intron IEPs analyzed here recognize at most 1–2 bases in the distal 5′-exon region and none in the 3′ exon , with the stringency of IEP recognition decreasing at higher temperatures . We note that these relaxed DNA target site requirements with minimal IEP recognition found in mobility assays in E . coli at 48°C agree closely with the sequences of intron-insertion sites in the T . elongatus genome , implying that in both organisms the DNA target site is recognized in an unwound or more readily unwound state at higher temperature . As a result , the T . elongatus introns have access to a larger number of potential target sites compatible with intron RNA base pairing than do group II introns whose IEPs have higher target specificity . The lack of recognition of 3′-exon residues by the T . elongatus IEPs could reflect that distal 5′-exon and EBS/IBS interactions are sufficient for site-specific second-strand cleavage , that En cleavage is not stringently site specific , or that the introns dispense with En cleavage and use a nascent strand at a DNA replication fork to prime reverse transcription [1] , [2] . As the other three proliferative mechanisms identified here are also available to mesophilic introns , retromobility at high temperature may be a key factor enabling the T . elongatus introns to proliferate to higher copy number than other group II introns . In order to proliferate to high copy number , the T . elongatus introns must overcome selective pressure against retromobility . Such selective pressure is evidenced by the accumulation of mutations that impair retromobility of some T . elongatus introns ( e . g . , the YAGD and δ-δ′ pairing mutations in TeI4h , multiple mutations that inactivate TeI4d and TeI4f , and insertion of an IS element into the ORF of TeI4g ) . Selection for mutations that decrease mobility is common for bacterial and organellar group II introns and presumably reflects that actively mobile introns are deleterious to the host because they can insert into essential genes or make harmful double-strand chromosome breaks [1] , [2] , [37] . The proliferative mechanisms evolved by T . elongatus introns enable them to partially overcome purifying selection , striking what is for them a more favorable balance toward intron accumulation . Importantly , we find that TeI4h and other T . elongatus introns are not only actively mobile but also thermophilic ( Figure 4B and Table 1 ) , suggesting that both the intron RNA and IEP have structural adaptations for thermostability . The T . elongatus intron ribozymes have a higher GC content ( 55 . 3%–56 . 2% for TeI4h , 4c , and 3c ) than does EcI5 ( 51 . 2% ) , possibly contributing to their greater stability at higher temperature . The actively mobile thermostable introns can potentially be used for structural analysis , as well as for practical applications . The latter include use of thermostable group II intron RTs for RT-PCR and next-generation RNA sequencing and use of the thermophilic introns as gene targeting vectors for thermophiles , patterned after “targetrons” developed from mesophilic group II introns that can be reprogrammed to insert at desired sites by modifying the base pairing sequences in the intron RNA [32] , [37] . Finally , we speculate that the mechanisms elucidated here could have contributed to the initial proliferation of mobile group II introns in the nuclear genomes of ancestral eukaryotes . Estimates of paleotemperatures based on isotopic measurements and phylogenetic reconstruction of ancient enzymes suggest that eukaryotes evolved at a time of higher than present-day temperatures ( 50–65°C ) [43] . By promoting DNA melting , elevated temperatures would facilitate access of group II introns to DNA target sites and increase the number of target sites that could be recognized by base pairing of the intron RNA , with little or no additional constraints for IEP recognition . Indeed , it is possible that ancestral group II introns evolved to insert by reverse splicing into RNA or single-stranded DNA at higher temperature and became dependent upon the IEP for DNA strand separation only after temperatures cooled . Another important factor in the initial intron invasion of eukaryotic genomes may have been the evolution of driver IEPs that could splice and mobilize multiple group II introns , enabling most introns to lose their own ORFs . In addition to providing a common splicing apparatus , the evolution of IEPs to function efficiently in trans rather than cis as for other mobile group II introns [44] would have been essential to support group II intron proliferation after evolution of the nuclear membrane , which separates transcription from translation . Ultimately , the evolution of the spliceosome , a common RNA-based catalytic machinery consisting of snRNAs derived from group II intron RNA domains , would have enabled more extensive intron degeneration , leaving only minimal recognition motifs for the splice sites and branch-point nucleotide . Coupled with the existence of a common splicing apparatus , such degeneration would have accelerated intron proliferation by increasing the chances that functional introns could arise de novo as a result of mutations or recombination events that introduce the minimal recognition sequences [45] , [46] .
T . elongatus introns and flanking exons were cloned via PCR of T . elongatus BP-1 DNA provided by Dr . T . Kaneko , Kazusa DNA Research Institute , Japan . PCRs were done using Taq DNA polymerase ( New England Biolabs , Ipswich , MA ) . For ORF containing introns , 5′ and 3′ segments of the intron were amplified separately by PCRs using an exon primer that appends a PstI , BamHI , or EcoRI site together with an intron primer that overlaps a unique site ( HindIII or EcoRI ) . The PCR products were then cloned between compatible sites in the polylinker of pUC18 or 19 . Intron-donor plasmids for mobility assays were constructed in two steps to insert the coding sequences for the intron RNA followed by the IEP . In the first step , the T . elongatus introns were swapped for the Ll . LtrB intron/IEP cassette in donor plasmid pACD2X [47] . For ORF-containing T . elongatus introns , this was done via two PCRs that separately amplify 5′ and 3′ segments of the intron , while introducing a 1 . 5-kb deletion in the ORF coding sequence matching that in the ORF-less T . elongatus introns . These PCRs used end primers that append short flanking exons ( ∼15 nts ) and unique cloning sites ( SpeI ( 5′ ) and PstI and/or XhoI ( 3′ ) ) and internal primers that replace the intron ORF in DIVb with a T7 promoter sequence and an MluI site . The two PCR products were then cloned between XbaI and XhoI sites of pADC2X . Twintrons were resolved by precisely deleting the internal intron using PCR primers spanning the insertion site and replacing the original fragment with the deleted one . The ORF-less T . elongatus introns were cloned directly into donor plasmid pACD2X by PCR of T . elongatus BP-1 DNA with primers that append SpeI and PstI sites . The T7 promoter and an MluI site were inserted into DIVb of the ORF-less introns by another round of PCR using the same external primer together with an internal primer that adds the T7 promoter and an MluI site and then swapping the PCR product back into pACD2X . Cloned introns were confirmed to have the sequence published for T . elongatus genomic DNA [26] , except for TeI4c for which all clones differed from the published sequence by a single base ( G1901A ) . In the second step of donor plasmid construction , the RT ORFs were amplified in two pieces from the pUC18 or 19 clones ( see above ) by PCR using a 5′ primer that appends a PstI site , phage T7 φ10 gene Shine-Dalgarno sequence , and ATG codon , a 3′ primer that appends an XhoI site , and internal primers that overlap a unique restriction site . The ORF was then cloned via a three-way ligation into the donor plasmid using the PstI and XhoI sites downstream of the previously inserted T . elongatus introns . Donor plasmids with point mutations in TeI4h ( T1G , C236T ) and the TeI4h IEP ( YAGD to YADD ) were derived by PCR amplifying the wild-type intron or IEP of previously constructed plasmids with primers that introduce the modification and then swapping the PCR product containing the mutation for the wild-type sequence to generate donor plasmids containing different combinations of mutations . The TeI4f intron's EBS1 , EBS2 , and EBS3 sequences were modified by quick-change site-directed mutagenesis ( Stratagene , La Jolla , CA ) to GTTCTG , TTCAA , and A , respectively , in order to improve complementarity to IBS1 , 2 , and 3 in the 5′ and 3′ exons . Recipient plasmids for the T . elongatus introns contain ligated-exon sequences ( TeI4h , −46/+22; TeI4f , −50/+15; TeI4c , −40/+20; TeI3f , 3k , 3l , 3c , 4a , 4b , 4d , 4e , −30/+15 ) cloned between the PstI site ( or AatII site for the TeI4f target ) and the EcoRI site of pBRR-tet [36] . The intron target sites were made either by PCR of cloned exon sequences with primers that append PstI and EcoRI sites or by inserting two complementary oligonucleotides with flanking PstI and EcoRI sites . Recipient plasmid libraries for TeI3c and 4c were constructed by starting with synthetic DNA oligonucleotides that contain randomized target site positions −30 to −12 or −13 and +2 to +20 with a 5′ PstI site and a 3′ EcoRI site followed by the 3′ sequence tag 5′ GAATTCGACAACCCAACAG . The opposite strand was then synthesized with Klenow DNA polymerase ( New England Biolabs ) using a primer complementary to the tag , and the resulting double-stranded DNA target site was cloned between the PstI and EcoRI sites of pBRR-tet . The recipient plasmid library for TeI4h was constructed similarly by using Klenow DNA polymerase to fill in annealed , overlapping oligonucleotides that contain the randomized 5′- or 3′-exon sequences and append PstI and EcoRI sites . All constructs were confirmed by sequencing the PCR amplified or modified region . Mobility assays were done in E . coli HMS174 ( DE3 ) ( Novagen , Madison , WI ) grown in LB medium , with antibiotics added as required at the following concentrations: ampicillin , 100 µg/ml; chloramphenicol , 25 µg/ml; tetracycline , 25 µg/ml . Cells , which had been co-transformed with the CapR donor and AmpR recipient plasmids , were inoculated into 5 ml of LB medium containing chloramphenicol and ampicillin and grown with shaking ( 200 rpm ) overnight at 37°C . A small portion ( 50 µl ) of the overnight culture was inoculated into 5 ml of fresh LB medium containing the same antibiotics and grown for 1 h as above . The cells were then induced by adding 1 ml of fresh LB medium containing the same antibiotics and 3 mM IPTG ( 500 µM final ) and incubating for 1 h at temperatures specified for individual experiments . In mobility assays with TeI4h and its mutant derivatives at 37°C , changing the IPTG concentration from 100 to 1 , 000 µM or induction time from 30 to 90 min gave at most a 2-fold increase in mobility efficiency . For determination of temperature dependence , the initial log-phase cultures ( 5 ml ) grown at 37°C were mixed with an equal volume of fresh LB medium containing antibiotics and 1 mM IPTG ( 500 µM final ) that had been pre-warmed to achieve the desired temperature . The cultures were then induced for 1 h at that temperature , placed on ice , diluted with ice-cold LB , and plated at different dilutions onto LB agar containing ampicillin or ampicillin plus tetracycline . After incubating the plates overnight at 37°C , the mobility efficiency was calculated as the ratio of ( TetR+AmpR ) /AmpR colonies . To verify correct insertion at the target site , TetR+AmpR colonies were picked into duplicate 96-well plates and used for colony PCR to separately amplify the 5′- and 3′-integration junctions using the primers Rsense ( 5′-ACAAATAGGGGTTCCGCGCAC ) plus Te680rc ( 5′-GTTGGTGACCGCACCAGT ) for the 5′ junction and Te420f ( 5′-AACGCGGTAAGCCCGTA ) plus Rev2pBRR ( 5′-AATGGACGATATCCCGCA ) for the 3′ junction . The PCR products were purified with Sera-Mag magnetic beads ( Seradyne , Indianapolis , IN ) and sequenced using the primers TargetSeq ( 5′-ATGCGAGAGTAGGGAACTGC ) for the 5′ junction and Te500f ( 5′-AAACCGTAAGGAATGCTGATG ) or Te420f ( see above ) for the 3′ junction . E . coli HMS174 ( DE3 ) that had been co-transformed with the CapR donor plasmid and an AmpR recipient plasmid in which regions of the DNA target site had been randomized was induced with 500 µM IPTG for 1 h at the specified temperature and then plated on LB agar containing tetracycline and ampicillin , as described above for mobility assays . TetR+AmpR colonies were picked into 96-well plates for colony PCR and sequenced for target-site determination , as described above . | Group II introns are bacterial mobile elements thought to be ancestors of introns and retroelements in higher organisms . They comprise a catalytically active intron RNA and an intron-encoded reverse transcriptase , which promotes splicing of the intron from precursor RNA and integration of the excised intron into new genomic sites . While most bacteria have small numbers of group II introns , in the thermophilic cyanobacterium Thermosynechococcus elongatus , a single intron has proliferated and constitutes 1 . 3% of the genome . Here , we investigated how the T . elongatus introns proliferated to such high copy numbers . We found divergence of DNA target specificity , evolution of reverse transcriptases that splice and mobilize multiple degenerate introns , and preferential insertion into other mobile introns or insertion elements , which provide new integration sites in non-essential regions of the genome . Further , unlike mesophilic group II introns , the thermophilic T . elongatus introns rely on higher temperatures to help promote DNA strand separation , facilitating access to DNA target sites . We speculate how these mechanisms , including elevated temperature , might have contributed to intron proliferation in early eukaryotes . We also identify actively mobile thermophilic introns , which may be useful for structural studies and biotechnological applications . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"biology/rna-protein",
"interactions",
"biochemistry/rna",
"structure",
"molecular",
"biology/molecular",
"evolution",
"molecular",
"biology/rna",
"splicing",
"molecular",
"biology/bioinformatics"
] | 2010 | Mechanisms Used for Genomic Proliferation by Thermophilic Group II Introns |
The acid-sensing ion channel 1 ( ASIC1 ) is a key receptor for extracellular protons . Although numerous structural and functional studies have been performed on this channel , the structural dynamics underlying the gating mechanism remains unknown . We used normal mode analysis , mutagenesis , and electrophysiological methods to explore the relationship between the inherent dynamics of ASIC1 and its gating mechanism . Here we show that a series of collective motions among the domains and subdomains of ASIC1 correlate with its acid-sensing function . The normal mode analysis result reveals that the intrinsic rotation of the extracellular domain and the collective motions between the thumb and finger induced by proton binding drive the receptor to experience a deformation from the extracellular domain to the transmembrane domain , triggering the channel pore to undergo “twist-to-open” motions . The movements in the transmembrane domain indicate that the likely position of the channel gate is around Leu440 . These motion modes are compatible with a wide body of our complementary mutations and electrophysiological data . This study provides the dynamic fundamentals of ASIC1 gating .
Extracellular acidosis has profound effects on neuronal function , and acid-sensing ion channels ( ASICs ) are the key receptors for extracellular protons [1] , [2] . ASICs are members of the degenerin/epithelial channel family , which transport Na+ through the cell membrane [1] , [3] , and serve as a paradigm for all proton-gated channels . Six ASIC isoforms , 1a , 1b , 2a , 2b , 3 , and 4 , have been identified , among which 1a , 2a , and 2b are expressed in the central nervous system ( CNS ) [2] , [4] . In the CNS , ASICs are tightly connected to synaptic plasticity as well as learning and memory in the brain [5] , [6] . In addition , it has been demonstrated that activation or sensitization of Ca2+-permeable ASIC1a channels are responsible for acidosis-mediated ischaemic brain injury [7] , [8] and neuroinflammatory damage [2] , [9] . ASICs are therefore becoming increasingly important drug targets [2] , [10] . While studies have led to the characterization of ASICs and have furthered the role that they play in neurological diseases , one of the remaining challenges is to fully elucidate their gating mechanisms , which are critical for understanding their biological functions and for developing effective therapeutics [2] . These studies are challenged by the complicated process of ASIC gating: it is proton concentration-dependent , can be blocked by amiloride , and its sodium selectivity and variations of desensitization differ from subtype to subtype [2] . In addition , investigations of the ASIC1 gating mechanism have advanced slowly because of the lack of detailed structural information at atomic resolution . The recent low-pH crystal structure of the chicken ASIC1 ( cASIC1 ) at 1 . 9 Å resolution has revealed the overall organization of the ASIC1 , which provides a framework for probing the mechanism underlying the gating of ASICs [11] . The crystal structure of cASIC1 revealed that receptors in the superfamily are homo- or heterotrimers [11] . Structurally , the ASIC1 has three subunits with a stoichiometry α3 , forming a chalice-like architecture . Each subunit is composed of two domains , a large extracellular ( EC ) domain , and a transmembrane ( TM ) domain . The EC domain resembles a clenched hand , which can be further divided into finger , thumb , palm , knuckle , and β-turn subdomains . The TM domain comprises two transmembrane helices , TM1 and TM2 , in a “forearm” arrangement ( Figure 1 ) . This structure has provided insight into the architecture of ASICs and raises intriguing questions about its gating mechanism . For example , what is the function of the large EC domain ? Where is the gate located ? How is proton concentration sensed by the channel and how does this process trigger opening and closing of the channel ? In particular , the intrinsic dynamics of the receptor underlying the gating mechanism is still unclear . Computational simulation has been a promising tool to address the dynamic behavior of biological molecules . Recently , Shaikh and Tajkhorshid carried out molecular dynamics ( MD ) simulations on cASIC1 , which provided useful information for the potential binding sites of cations and protons in ASIC1 [12] . However , the current MD methods are limited to address the local movements of proteins . As a complementary approach , normal mode analysis ( NMA ) [13]–[15] is efficient for predicting the collective dynamics and inherent flexibilities in biological macromolecules . This method has been widely applied in studying the structure ( dynamics ) -function relationship for several important ion channels , such as the prokaryotic large conductance mechanosensitive channel ( MscL ) [16] , the potassium ion ( K+ ) channe1 [17]–[19] , and the nicotinic acetylcholine receptor ( nAChR ) [20]–[23] . In the present study , the dynamic behavior of ASIC1 has been studied on the basis of the crystal structure of cASIC1 using NMA along with complementary mutagenesis and electrophysiological experiments . The NMA revealed complementary twisting motions throughout the receptor , with which the ion channel may undergo an open motion . Further analyses on the motion modes detect a series of collective movements among the subdomains of EC domain , which control and induce the motions of channel pore . Furthermore , the twisting motion modes of the TM domain indicate the probable position of the channel gate . Electrophysiological assays on the human ASIC1a ( hASIC1a ) mutants of a series of key residues associated with the motions support the computational results . This study provides new information on the intrinsic dynamic behavior of ASIC1 motions associated with the channel opening , enabling us to construct a new model for the gating mechanism of the channel . This model , supported by a number of key experimental observations from others as well as our own , for the first time to our knowledge , provides a clear picture of the correlation between the structural dynamics of ASIC1 and its gating mechanism .
NMA is a computational approach that can efficiently predict the collective dynamics and inherent flexibilities in biological macromolecules . Accordingly , we used NMA to detect the intrinsic motion modes of ASIC1 . First , we examined the low-frequency modes of ASIC1 produced by NMA because they may reflect the global motions of the ASIC1 channel and are often potentially related to biological functions [24] , [25] . The 100 lowest frequency modes resulted from NMA were used to describe the overall motions of the entire channel , since these normal modes are sufficient to capture all the collective motions of the ASIC1 , as revealed in other proteins [25] , [26] . The detailed motions between the essential subdomains ( e . g . , thumb and finger ) are discussed in the following sections and the motions of some important modes are presented in Figures S1 and S2 and Videos S1 , S2 , S3 , S4 , S5 . In brief , these modes revealed conformational changes that may involve the whole receptor including a rocking motion of the EC domains around the wrist region which connects the EC and TM domains ( Figure 2A ) and coinstantaneous rotations of the EC and TM domains around subunit C ( Figure 2B ) . NMA revealed that the most relevant modes of ASIC1 to its gating mechanism might be modes 1 and 3 , which undergo similar motions . In addition to the rock and rotation of the EC domain ( Figure 2 ) , the six TM helices underwent a concerted global rotation in both clockwise and anticlockwise directions , as indicated by the rotating angles along the harmonic period ( Figure 3A ) . However , the whole TM domain did not rotate in a simple manner; instead , it may adopt a twisting rotation during the conformational change . As illustrated by the motion of the TM domain in mode 1 ( Figure 3B ) , the direction of the motion as well as the displacement of the movement for different regions along the pore axis ( e . g . , top and bottom ) were different . As shown in Figure 3C , with the exception of TM1 ( A ) , the other five helices may undergo twisting motions , changing the direction of their motion around the hinges: L60 for TM1 ( B ) , L58 for TM1 ( C ) , Q437 and L440 for TM2 ( A ) and TM2 ( B ) , and L440 for TM2 ( C ) . As a result , the twisting motions of the five helices synthesize a twisting rotation for the whole TM domain as represented in Figure 3B . This result implies that the region near the hinge for the twisting rotation of TM domain is important for controlling the gating of the ASIC1 . This hinge is located between G436 and L440 of the TM2 helices ( Figure 3D ) . Because of the asymmetric nature of the ASIC1 structure , the innermost residues of three TM2 helices around the hinge form two rings ( designated ring-I and ring-II hereinafter ) . Ring-I is composed of G436 ( A ) , Q437 ( A ) , Q437 ( B ) , and L440 ( C ) , and ring-II consists of L440 ( A ) , L440 ( B ) , and A444 ( C ) ( Figure 3D ) . Similar to the nAChR [23] , the position of the gate of ASIC1 is possibly located near the hinge of the pore . Accordingly , we monitored the radius profiles along the pore axis for each motion mode . Indeed , the channel pore has a bottleneck around the hinge ( ∼125 Å ) ( Figure 3E ) . Rather , the radius profiles for modes 1 and 3 display a visible opening of the channel pore ( Figure 3E ) , suggesting that the twisting motion of TM tends to increase the width of the entire pore . This result indicates that the structure of the closed , desensitized state of ASIC1 intrinsically tends to undergo a twisting motion to open the gate . The hinge sharply divides the pore into two portions: a top segment ( 110–125 Å ) and a bottom segment ( 125–150 Å ) . The displacements of the channel diameters for the bottom segment are larger than those of the top , suggesting that the top segment may play a larger role in ion selectivity . Moreover , the motion of mode 3 indicates that the diameter of the bottleneck may maximally increase ∼2 Å ( Figure 3E ) . This displacement in pore diameter is significant for the function of the channel , because it has been suggested that even relatively minor displacements in the gate area can trigger the functional transition of the pore from closed to open [27] . After predicting the positions of the ASIC1 channel gate using NMA , we investigated the molecular basis of the ASIC1 gating further using mutagenesis and electrophysiological experiments . Using human ASIC1a ( hASIC1a ) , we constructed eight mutants around the putative gate position of ring-I and ring-II ( Figure 3D ) : G436A , G436P , Q437A , Q437E , Q437N , Q437R , L440A , and A444G . The profiles of the inward currents elicited by acidic solution in the wild-type ( WT ) ASIC1 and its mutants are shown in Figure S3 . Three mutants ( G436A , G436P , and Q437R ) resulted in nonfunctional channels as no current was detected in response to pH 5 . 0 in these mutants , suggesting that G436 and Q437 are key determinants for ASIC1 function . In addition , several mutations ( Q437N , L440A , and A444G ) altered the reversal potentials of acid-induced currents ( Figure S4; Table S1 ) . Consistent with the altered reversal potentials , the relative Na+ , Li+ , and K+ permeability of these mutated channels were also significantly shifted ( Figures 3F and S4 ) . Interestingly , whereas the reversal potentials of the Q437A , Q437E , and Q437N mutants were relatively unchanged ( i . e . , reflecting unaltered Na+/K+ selectivity ) with respect to the WT channel under standard intra- and extracellular ion compositions ( Table S1 ) , the selectivity of Na+ over Li+ ( pNa/pLi-value ) of Q437N mutant decreased significantly ( Figure 3F ) . Furthermore , replacement of Q437 with a positively charged residue Arg ( Q437R ) caused the loss of function of the mutated channel presumably because of the repulsion between monocations and the Arg side chain in the pore region ( Figure S3 ) . These results suggest that Q437 is critical for ion passage . Besides G436 and Q437 , we found that two additional residues L440 and A444 affected the ASIC1 gating markedly because the reversal potential and the selectivity of Na+ over K+ ( pNa/pK-value ) for both L440A and A444G mutants decreased dramatically ( Figure 3F ) . We attribute this phenomenon to the enlargement of ring-I or ring-II due to L440A and A444G replacement , and the mutated channels pass more K+ than Na+ with respect to WT channel . These results indicate that L440 and A444 play important roles in ion selectivity and gating of ASIC1 . It should be emphasized that the identified key residues contributing to the possible gate of ASIC1 are mostly conserved among the superfamily of ASICs as indicated by the sequence alignment ( Figure 3G ) . On the basis of electrophysiological studies , previous studies suggested that G587 and S589 in the α-subunit are key residues to define the selectivity filter of epithelial sodium channel ( ENaC ) [28]–[31] . In ASIC1 these two residues correspond to G443 and S445 , respectively . Mutations of G443C , G443V , G443P , S445C , S445T , and S445V all resulted in a nonfunctional ASIC1 ( Figure S3 ) . Interestingly , as mentioned above , mutating A444 , a residue located between G443 and S445 , to Gly , resulted in an intact ASIC1 with significantly altered ion selectivity ( Figure 3F ) . However , the corresponding residue of A444 in ENaC ( S588 of α-subunit ) does not affect the ion selectivity of ENaC [29] . Thus the region for ion selectivity in ASIC1 may be the same as that in ENaC , but the residue contribution is different between these two ion channels ( Figure 3G ) . In addition , we found that L440 also played a critical role in the ion selectivity of ASIC1 ( Figure 3F ) . Nevertheless , the corresponding residue of L440 in ENaC ( L584 of α-subunit ) does not contribute to the ENaC ion selectivity [31] . We attribute this difference to the distinct “gating” mechanisms of ASIC1 and ENaC . In fact , one striking difference between these two channels is that ENaC is constitutively active ( i . e . , without a functional “gate” ) [3] , whereas ASIC1 opens following channel gating by agonist ( proton ) binding . Taking together , we conclude that the region around ring-I and ring-II ( Figure 3D ) may undergo a substantial conformational change that is coupled to channel gating and constitutes an important regulatory region of ASIC1 function . After detecting the motions of the pore and the probable gate position of the channel , we asked how the channel pore undergoes the twisting-to-open motion . For this purpose , we examined the relationship between conformational changes of other parts of the EC and the TM domain . Structurally , the β1 and β12 strands are connected to TM1 and TM2 , respectively; the β9 and β10 strands are linked to the thumb subdomain; β1 , β12 , β9 , and β10 form a metacarpal plane ( M-plane ) ( Figure 1 ) . There is a loop between β9 and α5 , and the β turn of the loop interacts with the TM domain via directly interactions of Y72 with W288 and P287 ( Figure 4B ) . Accordingly , the motions of M-plane and the loop should be essential to the conformational changes of the TM domain . To test this idea , we closely examined the motions of the M-plane and the loop between β9 and α5 . Indeed , the motions of the M-plane in most of the modes are associated with the motions of the channel pore . For example , the shearing vibration of the M-plane in mode 3 induces TM1 and TM2 helices to undergo twisting movements , and the bending of the M-plane in mode 11 triggers a swinging motion to the TM domain ( see Videos S1 and S2 ) . Intriguingly , the motions of the TM domain are coupled with the motions of the β turn ( Figure 4A ) . The synchronous motions of the β turn along with the TM helices are possibly due to the strong hydrophobic interactions of Y72 with W288 and P287 ( Figure 4B ) , indicating the importance of these residues in the gating of the channel . In addition , on the basis of the NMA modes , we also derived the cross-correlation map , which displays the correlations between the movements of different residues ( Figure S1 ) . The cross-correlation map also shows that the movement of Y72 correlates strongly with that of W288 and P287 with correlation coefficients of 0 . 92 and 0 . 82 , respectively ( Figure 4C ) . Key interactions between the β turn and the TM domain important for the gating mechanism were further characterized by electrophysiological experiments . To this end , we designed mutations to respectively disrupt the π–π stacking interaction between Y72 and W288 , the C-H···π hydrogen bonding [32] between P287 and Y72 , and the disulfide bond between C291 and C366 ( Figure 4B ) . We found that the W288A mutation abolished the opening of the channel ( Figure 4D ) , and the Y72A mutation greatly decreased the pH sensitivity of the channel . These results suggest the importance of the hydrophobic ( mainly π–π stacking ) interaction between W288 and Y72 for controlling the channel gating ( Figure 4B ) . To further test this hypothesis , we investigated the pH sensitivity of the Y72F mutant , which may keep the π–π stacking intact . As expected , the electrophysiological characteristic of this mutant was unchanged ( Figure 4D ) . The C-H···π hydrogen bond between P287 and Y72 represents another important interaction that may be responsible for the collective motion of the β turn with the TM domain . In addition , because P287 is located at the lip of the β turn , it may play a structural role in stabilizing the β-turn conformation and likely affect channel gating . As predicted , the P287G mutation abolished the channel opening capacity ( Figure 4E ) . On the other hand , P285G and P286G mutants were unaffected , suggesting that these two residues are not important for the channel gating . Finally , we studied the disulfide bond ( S–S bond ) interactions between C291 and C366 ( Figure 4B ) . In addition to stabilizing the conformation of the β turn , this disulfide bond is a linkage between the β turn and the β10 strand , which is a bridge that conducts the motions of the finger and knuckle to the β turn as will be discussed in next section . Hence , we hypothesized that this disulfide bond may also play a role in the channel gating . To test this idea , electrophysiological characterizations were performed on C291A and C366A mutants . Both C291A and C366A mutations resulted in termination of the channel opening activity ( Figure 4D ) . Of note , the importance of the interaction between Y72 and W288 to the channel gating was also addressed by Li et al . [33] . The result was reported online during the reviewing process of this manuscript . The crystal structure of cASIC1 suggests that one end of the β turn links to the thumb domain and the other end connects to the knuckle domain through the β9 strand; the S–S bond between C291 and C366 also plays an important role to the interaction between the β turn and the M-plane . In addition , the TM1 and TM2 helices connect with the finger and knuckle domains through the β1 and β12 strands , respectively ( Figure 1 ) . This structural arrangement indicates that the EC domain may communicate with the TM domain and β turn through a series of collective motions , implying that the collective motions of different regions in the EC domain are possibly relevant to the channel gating . Accordingly , we analyzed the motion modes of the EC domain . Interestingly , all motion modes revealed that the thumb always moves correlatively with the finger ( Figure 5A ) . This result suggests that channel gating may be facilitated by the attractive forces between the thumb and finger domains . Essential hydrogen bonding and hydrophobic interactions between the thumb and finger are shown in Figure 5B and 5C , respectively . To test this hypothesis , we first disrupted several pairs of hydrogen bonds ( H-bonds ) and electrostatic interactions between the thumb and finger via site-directed mutagenesis , including D238···D350 , E239···D346 , and R191···D350 pairs , which are identified by Jasti et al . as tentative proton binding sites ( Figure 5B ) [11] . Abolishment of the R191···D350 and E239···D346 H-bonds by substituting R191 with Ala , E239 with Gln or Lys , and D346 with Asn , and the D238···D350 H-bond by substituting D238 with Ala or Asn had profound effects on the pH50 ( pH of half-maximal activation ) of the acid-induced currents ( Figure 5D ) ; all of these mutations reduced the pH50 values of the ASIC1 ( Figure 5D ) . Similar effects had been observed with the D346N mutation by Jasti et al . [11] . Binding free energy ( ΔGbinding ) calculations indicate that all these mutations decrease the binding affinity between the thumb and finger ( see Discussion , Figure 5E , and Table S2 ) . To firmly establish the importance of the attractive interaction between the thumb and finger to the opening of the gate , we designed another two mutants , D238K and D238S , which might enhance the interaction between the thumb and finger . Binding free energy calculations are consistent with this notion , as the ΔGbinding values between the two subdomains for these two mutations are respectively reduced by ∼10 . 0 and 3 kcal/mol relative to WT ( Figure 5E; Table S2 ) . Consistently , these mutations also led to higher pH50 values than that of the WT channel ( Figure 5D ) . Hydrophobic interactions are another dominant component to the collective motion between the thumb and finger . NMA results indicate that the pairs of hydrophobic interaction between these two subdomains move together with a high correlation as their cross-correlation coefficients ( Cij ) are larger than 0 . 9 ( Figure 5C ) . This suggests that mutations that decrease the hydrophobic interaction between these two subdomains would cause the channel to respond to a lower pH . We thus mutated H328 and P338 to Ala to weaken the hydrophobic interaction between the thumb and finger domains . The electrophysiological results are consistent with the computational predictions , i . e . , both mutations decreased the pH50 values ( Figure 5D ) . Remarkably , the calculated binding free energies between the thumb and finger for the WT channel and all its mutants correlate well with the pH50 values with a high correlation coefficient , R2 = 0 . 61 ( Figure 5E ) . These results , together with our NMA analysis , strongly support that the collective motions between the thumb and finger are of significance to the channel gating . Our studies revealed collective motions that occur amongst the subdomains of the EC domain , so we sought to map the deformation pathway related to channel gating because of its importance in understanding the overall function of ASIC1 . In addition to the collective motions between the β turn and TM domain and between the thumb and finger domains mentioned above , bending and swing vibrations between the finger and knuckle were also revealed by NMA ( Figure S2 ) . These motions lead to bending and twisting motions of the M-plane ( β1 , β12 , β9 , and β10 ) , which further evoke different motions of the TM domain ( Videos S3 , S4 , S5 ) . Accordingly , the NMA modes clearly show the deformation pathway for domain motions: collective motions of thumb with finger ( class I motions ) couple with the vibrations between finger and knuckle ( class II motions ) , which further associate with the bending and twisting motions of the M-plane ( class III motions ) ( Figure 6A ) . Both class I and class III motions are able to trigger the TM domain to undergo rotation and twisting motions and the β turn to engage in swinging motions; the latter movement enhances the motions of the TM domain through noncovalent interactions ( Figure 4B ) . This deformation pathway demonstrates the inherent structural flexibility of ASIC1 for implementing their functions and also implies the functional importance of the EC domain of the receptor . To further understand the dynamic behavior of each subunit in isolation versus its homotrimer conformation and their relation to gating , NMA was also performed on the monomer using the structure of the subunit taken from the X-ray crystal structure of cASIC1 [11] . Again , the first 100 lowest frequency normal modes were obtained . Most of the motions of the monomer are , in general , similar to those of each subunit within the trimer , suggesting that the intrinsic properties of each subunit determine the receptor's motions , which further control the gating of the whole channel . Figure 6B shows the differences in flexibility between the monomer treated as an isolated subunit ( red trace ) versus part of the trimer ( black trace ) , as shown in the profile of the root-mean-square-fluctuations ( RMSFs ) from the 100 lowest frequency normal modes . Here , we only use subunit A to discuss the differences , because similar results were obtained for subunits B and C . As shown in Figure 6B , the flexibility of each domain is restricted in the trimer compared to the monomer . In both the monomer and as part of the trimer , the tips of the thumb and finger are two of the most flexible portions . This result is consistent with the harmonious motions of these two domains , demonstrating again the importance of their motions in gating . In fact , Jasti et al . have hypothesized that the cleft between the thumb and finger contains an acidic pocket for sensing acidic conditions ( i . e . , proton levels ) [11] . Moreover , our electrophysiological experiments on the mutants of the residues along the acidic pocket also suggest that the motions of these two domains are linked to the physiological function of this protein ( Figure 5 ) . Another region that displays pronounced flexibility in the individual monomer is the knuckle domain . Although the mobility of the knuckle is restricted in the trimer , its tip still shows high fluctuation ( Figure 6B ) , enabling vibration motions between the knuckle and finger . Other regions showing high fluctuation are the TM1 and TM2 helices , but their flexibility is also restrained in the trimer , especially the TM2 helix ( Figure 6B ) . Remarkably , the RMSF profiles for both TM1 and TM2 helices in the trimerized channel form inverted parabola-like curves , indicating that the two ends are more mobile than the middle , and the flexibility of the hinge position is seriously restricted ( Figure 6B ) . This result is consistent with the twisting motion modes and site-directed mutagenesis results ( Figures 3 and S3; Table S1 ) . When existing as part of the trimer , most of the β strand's flexibility is also reduced , but they still undergo local motions induced by the motions of thumb , finger and knuckle . The collective evidence obtained by the NMA results and the flexibility map suggest that the deformation pathway involves the following domain motions: thumb , finger , and knuckle are activists of the receptor , their dynamic behaviors concomitantly propagate to the palm , leading β1 , β9 , β10 , and β12 to undergo bending and twisting motions and the β turn to undergo a swinging vibration . These motions are further transmitted to the TM domain , triggering a twisting motion that opens the channel pore .
The function of the ASIC large EC domain in the gating mechanism remains to be elucidated . Results of this study indicate that motions of all subdomains and regions of the EC domain may collectively stimulate the motions and conformational changes of the TM domain , affecting the shape of the channel pore ( Figure 6A ) . The dynamic pathway seems to be associated with the function of EC domain , which raises an intriguing question: what is the driving force that triggers these motions ? A large body of evidence has indicated that the channel opens in response to hydrogen ions , allowing sodium ions to pass into the cell [1] , [2] . The recently determined X-ray crystal structure of cASIC1 [11] and MD simulation [12] suggest that H+ ion may bind to an acidic pocket between thumb and finger subdomains . This process provides the energy to trigger movement of the EC domains; however , how the H+ binding would drive such events is still unclear . Here , we provide a possible explanation based on our NMA and mutagenesis experiments . On the basis of the holistic motion modes of the EC domain and the consistent motion of thumb and fingers to the channel gating described above , we hypothesize that the initial driving force for the EC domain movements is the attraction between thumb and finger ( Figure 6A ) . H+ binding to the acidic pocket enhances the interaction between these two domains , which heightens their intrinsic motion during gating process . This hypothesis has been verified by a series of mutations and electrophysiological determinations: mutations that either abolish the H-bonds or weaken the hydrophobic interaction between these two subdomains shift the dose–response curve to a lower pH region and decrease the pH50 values; and the mutations of D238K and D238S that might enhance hydrogen bonding between the thumb and finger shift the dose–response curve to the higher pH region and increase the pH50 ( Figure 5D and 5E ) . Theoretical calculations are consistent with these results: removing the H-bonding interactions and decreasing the hydrophobic interactions between thumb and finger domains decrease the binding free energy between these two domains whereas the D238K and D238S mutants , which strengthen the hydrogen bonding interaction , increase the binding affinity between the two subdomains . Moreover , the binding free energies correlate well with the pH50 values ( Figure 5E ) . This computational result demonstrates again that the attractive interaction between thumb and finger might be a driving force to channel gating . The current study shows that ASIC1 exhibits an intimate connection between the intrinsic structural dynamics and the gating process . On the basis of the NMA results and related mutagenesis and electrophysiological experiments , we propose a dynamic mechanism for the proton-activated gating of ASIC1 . The first step of the mechanism is the binding of H+ to the acidic pocket [11] . In contrast to an earlier hypothesis that was raised , on the basis of inspection of the crystal structure of cASIC1 [34] , we believe that H+ binding does not displace the thumb during gating , but instead enhances the binding affinity between thumb and finger through strengthening the H-bonds formed between acidic residues . This conclusion is based on the collective evidence gathered from the mutagenesis and electrophysiological measurements as well as binding free energy calculations on the WT channel and mutants ( Figure 5 ) . Thus , H+ binding induces thumb and finger domains to move close to each other , thereby initiating and magnifying a series motions along the intrinsic deformation pathway of the receptor ( Figure 6A ) . These motions trigger conformational changes of the TM domain , which provoke the TM domain to undergo a twisting motion to open the gate . This mechanism is clearly of general evolutionary significance of ASIC . The hand-like structure of the monomer and the chalice-like architecture of the entire receptor provide an elegant solution for controlling the gating mechanism of ASIC .
The atomic coordinates for the crystal structure of cASIC1 ( Protein Data Bank [http://www . rcsb . org/pdb] entry 2QTS ) [11] was used as the starting structure in a series of computational simulations and calculations . NMA was conducted using the web server developed by Delarue et al . ( http://lorentz . immstr . pasteur . fr/nomad-ref . php ) [35] , [36] . During the NMA simulations , the single-parameter Hookean potential , a simplified all-atom potential [35] , was used ( Equation 1 ) , ( 1 ) where dij is the distance between two atoms i and j , is the distance between the atoms in the 3-D structure , c is the spring constant of the Hookean potential ( assumed to be the same for all interacting pairs ) , and Rc is an arbitrary cut-off . In this study , Rc was set to be 10 Å . RMSF of each atom from the nontrivial modes and frequencies was calculated using the method of [37] , ( 2 ) where mi is the mass for atom i; ωk is the vibration frequency of mode k; aik is the ith components of the kth eigenvector . Cross-correlations ( Cij ) of atomic motion were computed with the modes and frequencies derived from the NMA using Equations 3 and 4 [37] , ( 3 ) ( 4 ) where mi and mj are the masses for atoms i and j; ωk is the vibration frequency of mode k; aik and ajk are the ith and jth components of the kth eigenvector . All constructs were expressed in CHO cells . Transient transfection of CHO cells was carried out using Lipofectamine 2000 ( Invitrogen ) . Electrophysiological measurements were performed 24–48 h after transfection . The cDNA encoding hASIC1a was a generous gift from Jun Xia ( The Hong Kong University of Science and Technology , Hong Kong , China ) . The incubation solution contained the following components ( in mM ) : 124 NaCl , 24 NaHCO3 , 5 KCl , 1 . 2 KH2PO4 , 2 . 4 CaCl2 , 1 . 3 MgSO4 , and 10 glucose , aerated with 95% O2/5% CO2 ( to a final pH of 7 . 4 ) . The standard external solution contained ( in mM ) : 150 NaCl , 5 KCl , 1 MgCl2 , 2 CaCl2 , and 10 glucose , buffered to various pH values with either 10 mM HEPES ( pH 6 . 0–7 . 4 ) , or 10 mM MES ( pH<6 . 0 ) . The standard patch pipette solution for whole-cell patch recording was ( in mM ) : 120 KCl , 30 NaCl , 1 MgCl2 , 0 . 5 CaCl2 , 5 EGTA , 2 Mg-ATP , 10 HEPES . The internal solution was adjusted to pH 7 . 2 with Tris-base . Unless otherwise noted , the electrophysiological recordings were carried out under standard conditions . For measurement of the relative permeability of Li+ , K+ , and Na+ , the internal solution contained ( in mM ) : 150 NaCl , 10 EGTA , and 10 HEPES , and the external solution contained ( in mM ) : 150 test monovalent cation ( X ) , 10 HEPES ( replaced with MES when pH is 5 . 0 ) , 10 glucose , and 2 CaCl2 . The relative permeability of Li+ and K+ over Na+ was measured by comparing the reversal potentials when the external solution contained LiCl , KCl , or NaCl with internal NaCl in each case . The osmolarities of all these solutions were maintained at 300–325 mOsm ( Advanced Instruments ) . Solutions with different pH values were applied using a rapid application technique termed the “Y-tube” method throughout the experiments [38] . This system allows a complete exchange of external solution surrounding a neuron within 20 ms . The human ASIC1a cDNA was subcloned into the pEGFPC3 vector ( Promega Corporation ) . Each mutant was generated with the QuikChange mutagenesis kit ( Stratagene ) in accordance with the manufacturer's protocol . The electrophysiological recordings were performed in the conventional whole-cell patch recording configuration under voltage clamp conditions . Patch pipettes were pulled from glass capillaries with an outer diameter of 1 . 5 mm on a two-stage puller ( PP-830 , Narishige Co . , Ltd . ) . The resistance between the recording electrode filled with pipette solution and the reference electrode was 4–6 MΩ . Membrane currents or potentials were measured using a patch clamp amplifier ( Axon 700A , Axon Instruments ) and were sampled and analyzed using a Digidata 1320A interface and a computer with the Clampex and Clampfit software ( version 9 . 0 . 1 , Axon Instruments ) . In most experiments , 70%–90% series resistance was compensated . Unless otherwise noted , the membrane potential was held at −60 mV throughout the experiment under voltage clamp conditions . All the experiments were carried out at room temperature ( 22–25°C ) . Results were expressed as the mean±SEM . Statistical comparisons were made with the Student's t-test . The permeability ratios of pNa/pLi and pNa/pK were determined by the modified Goldmann-Holdgkin-Katz equation: pX/pNa = exp ( ΔVrevF/RT ) due to the equimolar cations in the external and internal solution , where X represents the test cation , ΔVrev is the change in reversal potential when Na+ was replaced by the tested cation , F is the Faraday constant , R is the gas constant , and T is the absolute temperature . | The acid-sensing ion channels ( ASICs ) are key receptors for extracellular protons and are becoming increasingly important drug targets . However , their gating mechanism is still not fully understood . The crystallographic structure of the ASIC1 protein provides a clue , but the dynamics of the channel remains to be elucidated . Using computational biology , site-directed mutagenesis , and electrophysiological recordings , we investigated the dynamics of ASIC1 gating . Through “normal mode analysis , ” we detected a series of collective motions between the beta turn and transmembrane domain , and between the thumb and finger domains , suggesting a deformation pathway related to channel gating . The intrinsic rotation of the extracellular domain and the collective motions between the thumb and finger domains that are induced by proton binding serve to deform the channel from the extracellular to the transmembrane domain , triggering a “twist-to-open” motion of the channel pore . The relationship between the dynamics and the gating mechanism was experimentally confirmed by a series of complementary mutations in ASIC1 and electrophysiological measurements . Our study also indicated that the likely position of the channel gate is around Leu440 within the ASIC1 protein . We propose a clear model correlating the structural dynamics of ASIC1 and its gating mechanism . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"computational",
"biology/molecular",
"dynamics",
"biophysics",
"neuroscience"
] | 2009 | Inherent Dynamics of the Acid-Sensing Ion Channel 1 Correlates with the Gating Mechanism |
The yellow fever ( YF ) 17D vaccine is one of the most effective human vaccines ever created . The YF vaccine has been produced since 1937 in embryonated chicken eggs inoculated with the YF 17D virus . Yet , little information is available about the infection mechanism of YF 17DD virus in this biological model . To better understand this mechanism , we infected embryos of Gallus gallus domesticus and analyzed their histopathology after 72 hours of YF infection . Some embryos showed few apoptotic bodies in infected tissues , suggesting mild focal infection processes . Confocal and super-resolution microscopic analysis allowed us to identify as targets of viral infection: skeletal muscle cells , cardiomyocytes , nervous system cells , renal tubular epithelium , lung parenchyma , and fibroblasts associated with connective tissue in the perichondrium and dermis . The virus replication was heaviest in muscle tissues . In all of these specimens , RT-PCR methods confirmed the presence of replicative intermediate and genomic YF RNA . This clearer characterization of cell targets in chicken embryos paves the way for future development of a new YF vaccine based on a new cell culture system .
Yellow fever ( YF ) is a viral disease associated with a flavivirus infection that affects individuals in the tropical regions of South America and Sub-Saharan Africa . The course of the disease may be mild , subclinical , or abortive ( with flu-like symptoms ) , or severe . The severe form is pansystemic: it affects the liver , kidneys , and myocardium , and includes hemorrhage and shock . Over 50% of patients with severe YF die [1 , 2] . Studies have described the pathology and pathogenesis of YF in fatal human cases , and in experimental infections of non-human primates , golden hamsters , and mice [3–7] . However , all these models can only provide information on YF pathology of fatal cases . There are no available models for the study of moderate , mild , and subclinical forms of YF [6] . Natural infection happens when an infected mosquito bites a person and inoculates the virus into the dermis of the host . The inoculated virus at first infects dendritic cells in the skin , which are also susceptible to virus infection in vitro and likely play an important role in infection by other flavivirus such as Dengue [3 , 8–10] . Then , lymphatic vessels drain these cells to lymph nodes , where the virus is replicated and released into the bloodstream , causing the first viremia [3 , 8 , 11–13] . Once in hematogenous route , the virus can affect the liver , kidneys , heart , spleen , and other organs , infecting mainly hepatocytes , Kupffer cells , cardiomyocytes , and epithelial cells of the renal tubule [4 , 11] . Morphologically , infection of these cells may generate acidophilic corpuscles ( in the liver called Councilman corpuscles or Rocha-Lima lesions ) , microsteatosis , and apoptotic bodies [14] . The local inflammatory response , when compared to organ injuries , is not significant . Minimal or moderate infiltrates are observed in the portal space , with lymphocytes and monocytes predominating [11] . There is no antiviral treatment for this disease , and the only way to control it is to preventively vaccinate populations living in at-risk areas [1 , 2] . The YF 17D vaccine effectively protects over 98% of immunized individuals for at least 20–35 years and probably for life , following vaccination [15] . Despite the wide availability of this vaccine , YF continues to cause morbidity and mortality in tropical regions of Africa and South America [16 , 17] . In these regions , both persons residing in endemic or epizootic areas and unvaccinated travelers are at risk of infection . Complicating matters , the vaccine is also contraindicated or demands precaution for a number of patients , including: those with allergies to eggs or other vaccine components ( which are difficult to identify due to trade secret laws ) , women who are pregnant or breastfeeding , children less than six months old , individuals older than 60 , transplant recipients , patients with AIDS , patients presenting primary immunodeficiency , and immunosuppressed patients with cancer or thymic diseases [18] . Although the YF vaccine is generally very safe and effective , viscerotropic and neurotropic vaccine-associated diseases have occurred , especially in patients with immunodeficiency or the elderly [19] . These vaccine-associated diseases can kill up to 65% of affected patients due to the lack of available treatment . The physiopathology of these unexpected reactions to the vaccine remains unclear [8] . The YF vaccine was developed from the sample Asibi , isolated from a patient named Asibi , a mild case of YF , who survived to this infection in Ghana in 1927 [20] . The 17D strain became attenuated for humans after serial passages in chicken and mouse tissue cultures . Two main sub-strains were independently derived from 17D , called 17DD and 17D-204 [21] . The 17DD strain was first used in Brazil in 1937 , and approximately 500 million doses have been administered worldwide since the seed lot system was introduced in 1945 [22] . The vaccine is still produced today by inoculating 17DD YF virus in ninth day embryonated chicken eggs free of specific pathogens ( SPF ) , which are processed 72 hours later according to the standards set by the World Health Organization [22] . Although chicken embryos have been used since 1937 to produce the YF virus , the histopathology of this infection is scarcely studied and the molecular mechanisms that regulate the viral infection in this biological system are still not well understood . For instance , it is not known which cells biosynthesize 17DD viral particles in infected chicken embryos . This knowledge would be of great importance , since these virus-producing cells would be the initial candidates for the future development of a YF vaccine based on a cell culture system . In this study , our aim was to establish which tissues and cells are responsible for YF viral production , and to characterize the 17DD YF virus infection in Gallus gallus domesticus embryos in terms of the histopathological changes mediated by the viral infection in conditions similar to those used in the production of YF vaccine . Our data contribute to the literature of the histopathology of the chicken biological system , and help advance the knowledge of the histopathological peculiarities involved in the pathogenesis of YF . Identifying these competent cells could help researchers develop a vaccine with lower non-viral protein content , based on a cell culture system . A vaccine with lower chick protein content has the potential to reduce allergic and other adverse reactions , and therefore to help at least a subset of the population for whom the traditional vaccine is counter-indicated .
Specific pathogens-free ( SPF ) fertilized White Leghorn chicken eggs ( Gallus gallus domesticus; Linnaeus , 1758 ) were obtained from the YF vaccine production unit ( FIOCRUZ ) . Eggs were infected in the yolk sac with 17DD EPlow virus seed lot ( 1–5 x 103 PFU per inoculum ) in the ninth day of development [21 , 23 , 24] . Eggs were kept in an IP70 brooder ( Premium Ecologica , Brazil ) with controlled temperature at 37 . 5°C , and 55% relative air humidity . As negative controls , embryos kept under the same conditions were inoculated with water for injection . For all analyses , embryos were collected at 72 hours post infection ( 12 days of development ) . The mean of the yield of virus from infected eggs , titrated by plaque formation assays ( PFU ) , was 6 , 74 log10 PFU/ml ( ranging from 6 , 24 to 6 , 97 log10 PFU/ml ) . This work was conducted with fertilized White Leghorn chicken eggs ( Gallus gallus domesticus ) with nine to twelve days of development , obtained from Instituto de Tecnologia em Imunobiológicos ( Fiocruz , Rio de Janeiro , Brazil ) . All experiments are in accordance to the yellow fever vaccine production protocol , which has been applied since 1937 , when the vaccine production started at the mentioned unit , under ethical approval of Fiocruz . Chicken embryos , yolk sacs , and chorioallantoic membranes were collected and dissected . Membranes were cleaved in regions defined by quadrants . For each embryo , the head , whole wings , and whole legs were separated from the trunk , and subsequently cleaved . The trunks were transversely sectioned into subsequent samples of about 3 mm . All fragments were fixed in Carson`s formalin-Millonig for 48 hours at room temperature [25] , and processed according to standard histological techniques for paraffin embedding . Sections ( 5 μm thick ) were stained with hematoxylin-eosin [26] . The slides were analyzed in an Axiovert Z1 microscope ( Carl Zeiss , Germany ) , and the images were acquired with an mRC5 Axiocam digital camera ( Carl Zeiss , Germany ) . Twenty-four hours after they were obtained , sections of all paraffin blocks from infected and control animals were de-waxed , dehydrated , and washed in PBS . Antigenic retrieval was carried out in 0 . 01 M citrate buffer pH 6 . 0 in Pascal chamber ( Dako , USA ) , according to the manufacturer’s recommendations . The sections were incubated with a blocking solution ( 2% skimmed milk , 2 . 5% bovine serum albumin , and 8% fetal bovine serum in the same buffer ) in a humid chamber for 30 minutes at room temperature , and kept overnight with an anti-YF virus antibody at 4°C . Two polyclonal mouse antibodies directed against YF virus were used ( Yellow Fever virus hyperimmune serum–Evandro Chagas Institute , and Yellow Fever 17D hyperimmune ascitic fluid cod . V525701562 –NIH ) . There was no difference between the anti-YF antibodies: both recognized the same set of cells , and did not react in negative controls . AlexaFluor 488-conjugated goat anti-mouse secondary antibody in 1:750 dilution ( cat . A11001 , Life Technologies , USA ) was applied at 37°C for 1 hour followed by counterstaining with 1:5 , 000 DAPI ( cat . 03571 , Molecular Probes , USA ) . Double staining used an anti-desmin antibody in 1:100 dilution ( cat . RB-9014 , Thermo Scientific , USA ) applied at 37°C for 1 hour followed by an AlexaFluor 546-conjugated goat anti-Rabbit secondary antibody in 1:750 dilution ( cat . A11010 , Life Technologies , USA ) . Negative controls were performed by duplicating each sample and omitting treatment with the primary antibodies , so that any reactions resulting from the secondary antibodies or reagents employed in the analyses could be adequately traced . Sections were mounted in ProlongGold ( cat . P36934 , Life Technologies , USA ) and analyzed in an LSM 710 or LSM 880 Airyscan confocal microscope or an ELYRA SR-SIM microscope ( Carl Zeiss , Germany ) . RNA samples were extracted from formalin-fixed , paraffin-embedded tissue from the same blocks used in immunofluorescence analysis , which were either positive or negative to 17DD virus . Two 10μm thick sections were put in a microtube and submitted to PureLink FFPE Kit ( cat . 45–7015 , Life Technologies , USA ) , according to the manufacturer's recommendation for RNA extraction . RNA samples eluted after the procedure were amplified by Reverse Transcription-PCR carried out with Thermoscript RT-PCR kit ( cat . 11146016 , Life Technologies , USA ) , with universal Flavivirus primers described by Tanaka [27] ( YF1–5`GGTCTCCTCTAACCTCTAG 3`and YF3–5`GAGTGGATGACCACGGAAGACATGC 3` ) . After that , a second amplification was carried out with internal primers designed by our group ( YF2–5`CGAGTTTTGCCACTGCTAAGCT 3`and YF4–5`TAGACCCCGTCTTTCTACCACC 3` ) . Two different reactions were performed using specific primers in RT-PCR using forward and reversed YF-1 and YF-3 primers to detect the genomic RNA and the replicative intermediate . After amplification , the nested-PCR product was sequenced in DNA Analyzer ABI 3730 ( Applied Biosystems , USA ) , and aligned to the YF Virus 17DD genomic sequence ( GenBank U17066 . 1 ) using ClustalW2 [28] .
Different embryonic tissues were stained with hematoxylin and eosin to investigate the histopathological alterations caused by the YF 17DD at 72 hours post-infection . In most cases , few differences in controls and infected embryos were observed . However , some embryos had mild focal reactions to infection , expressed by apoptotic bodies in infected tissues , including muscular tissue ( Fig 1A and 1B ) , renal tubular epithelium ( Fig 1C ) , parenchyma of the gizzard ( Fig 1D and 1E ) , and fibroblastoid cells in perichondrium ( Fig 1F ) . In addition , both infected and control embryos showed extensive areas of hematopoiesis in the yolk sac , which presented scattered blastoid cells in perivascular sheaths . The bone marrow of some embryos ( infected and controls ) was already formed and functional . In this embryonic stage , gastric and respiratory epithelia were developing and showed heterogeneous cellular morphology , and sometimes apoptotic cells . Because our histopathological data revealed mild changes that could be associated with YF viral infection , we decided to analyze several tissues of YF-infected embryos using more sensitive techniques: specifically , confocal and super-resolution immunofluorescence microscopy . Using anti-YF antibodies , we clearly identified viral proteins in skeletal muscle tissue ( Fig 2 ) , cardiomyocytes ( Fig 3 ) , neurons and glial cells in the brain ( Fig 4A and 4C ) , spinal cord neurons ( Fig 4B ) , renal tubular epithelium ( Fig 5 ) , lung parenchyma ( Fig 6A and 6B ) , and fibroblasts associated with connective tissue in the perichondrium ( Fig 6C ) and dermis ( Fig 6D ) . We also found either isolated and intense positive cells ( or small cell clusters ) contrasting with extensive negative cell areas in all studied tissues . These YF-infected cells had a characteristic staining pattern with perinuclear and hypertrophied endoplasmic reticulum , and vesicles dispersed throughout the cytoplasm ( Figs 2B , 2D and 4C ) . When analyzed by super-resolution microscopy with 0 . 16 μm optical slice , the endoplasm reticulum and vesicles carrying viral proteins were more evident ( Figs 7 and 8 ) . The viral antigen was found in skeletal muscle cells throughout the body of the embryos . The infection affected the entire length of some muscle bundles ( Fig 2A ) , where sometimes viral antigen detection followed the striations of the cytoskeleton ( Figs 2B and 8 ) . In some regions , it was possible to identify cells with pyknosis and karyorrhexis figures close to infected cells ( Fig 2B ) . At least at 72 hours post infection , the striate muscle tissue seemed to be an important site of production of the YF 17DD virus in embryos of Gallus gallus domesticus . The infection affected muscle bundles of the head , trunks , legs , and wings of the chicken embryos . However , the distribution of infected cells was similar across these regions . An anti-desmin antibody was then used to improve the identification of the muscle fibers , and showed a strong positive result in these cells ( Fig 2C and 2D ) . Virus proteins forming positive clusters were found in focal areas of the heart ( Fig 3 ) . Double staining with the desmin antibody revealed that only cardiomyocytes were infected ( Fig 3B ) . In the brain ( Fig 4A and 4C ) , the number and localization of positive cells varied among embryos , with the cerebellum showing the highest number of positive cells . In some embryos , isolated neurons in the spinal cord were positive for virus production ( Fig 4B ) , and we identified positive cells with a fibroblastoid pattern in the meninges ( Fig 4D ) . Viral antigens were identified in some epithelial cells of the kidney tubules , with an intense cytoplasmic pattern ( Fig 5 ) . The infection occurred in one or more cells per tubule section . Often , the presence of fluorescent vesicles suggests virus excretion to the lumen of the tubules ( Fig 5B and 5C ) . On the other hand , Bowman's capsule was always negative to virus protein labeling . In some embryos , virus proteins were observed in mesenchymal cells of the lung parenchyma surrounding the bronchi and parabronchi ( Fig 6A ) . These cells were strongly positive for desmin ( Fig 6B ) . The lung epithelium of all animals was free of viral antigens . Besides the positive organs and cells , we also detected viral protein labeling in fibroblastoid cells along the bodies of some animals ( in isolated areas of the subepithelial connective tissue ) ( Fig 6D ) . A few animals also had positive fibroblastoid cells in the muscular gizzard region ( Fig 6E ) . Using immunofluorescence , we were able to detect the virus in cells of the muscular layer of yolk stalk of one animal ( Fig 6F ) . All cells in the vitelline and in the chorioallantoic membranes were negative . Remarkably , the livers of chicken embryos were always free of viral antigens , suggesting that at least in this embryonic stage and time of infection , the liver of Gallus gallus domesticus may be impervious to YF infection . We confirmed the pattern of YF17DD virus distribution in different tissues using RNA extraction from formalin-fixed , paraffin-embedded tissues ( FFPE ) followed by viral RNA amplification by Nested-PCR . This technique showed itself to be sensitive enough to amplify RNA viral fragments , and it was possible to detect genomic viral RNA in all immunofluorescence positive blocks . We were able to detect amplicons of 156 bp ( the expected size products corresponding to the YF genome position from 10556 to 10711 ) in specimens from the legs , wings , head , and trunks of all YF 17DD-infected embryos . Fragments from the chorioallantoic and vitelline membranes were negative in both molecular biology and immunofluorescence assays ( Fig 9 ) . Notably , amplicons in positive specimens were obtained from viral RNA using either the specific primer to the viral genome , or the replicative intermediate , indicating active replication of viral RNA ( Fig 9 ) . We then sequenced the amplicons of these samples and compared them to the viral genome of the strain 17DD , finding 100% identity . Corroborating the specificity of this analysis , control animals and blocks without viral antigen ( which were negative in the immunofluorescence microscopic studies ) were all negative in the genomic viral RNA detection .
The YF vaccine is one of the most successful human vaccines ever made . Yet , until recently little was known about its mechanisms of immunity [29] . Similarly , although the 17DD vaccine has been produced in embryonated chicken eggs since 1937 , the properties of viral proliferation in this model were poorly elucidated . The present results show that in Gallus gallus embryos , the yellow fever 17DD virus is replicated in skeletal muscle cells ( Fig 2 ) , cardiomyocytes ( Fig 3 ) , renal tubular epithelium ( Fig 5 ) , lung parenchyma ( Fig 6A and 6B ) , fibroblastoid cells of the connective tissues ( Fig 6C and 6D ) , and in glial cells and neurons ( Fig 4A and 4C ) . Our data also show that the skeletal muscle tissue has an important role in production of yellow fever 17DD viral particles , whereas no infection ( including acidophilic corpuscles or steatosis ) was detected in the liver . To clarify these findings and to test if the liver might become infected at a different stage , further studies should be conducted at different times of infection . To emulate conditions used in YF vaccine manufacturing , we chose to work with 12-day-old chicken embryos inoculated with the YF vaccine virus and analyzed after 72 hours of infection [21 , 24] . While this increases the validity of the present study , in this stage , the events related to the normal embryo formation and the high rate of cell proliferation complicate the identification of histopathological changes putatively associated with the viral infection events . For example , the bone marrow formation starts and there is a high granulocytic and erythrocytic production in the yolk sac [30] . Because the immune system is still immature , the virus spreads and proliferates more easily [31–33] . The lung epithelium and gastrointestinal tract are under development , presenting cells in mitosis and apoptosis , as well as cells with cytoplasmic budding . To establish that these findings were not directly related to the infection it was necessary to study a sufficient number of embryos , and to use the sensitive immunostaining approach . As a result , we detected the sites of viral proliferation in a broad range of cell types from different tissues . Examining the positive cells with hematoxylin and eosin-stained serial sections allowed us to observe that some apoptotic cells were related to the viral infection ( Fig 1 ) . Molecular tools were performed to corroborate the specificity of our results . Using RT-PCR we were able to detect viral genomic RNA in all immunofluorescence positive blocks ( Fig 9 ) . In contrast , the blocks where the infection was not detected by immunofluorescence were also negative to viral genomic RNA . Every PCR product was sequenced and compared with 17DD yellow fever virus reference genome . In samples where viral RNA was amplified , the presence of the replicative intermediate was evidenced , suggesting that viral replication is occurring where the virus protein was detected . An intracellular pattern of viral antigen location could be clearly seen in infected cells ( Figs 2B , 2D and 4C ) . We observed cells with intensive perinuclear labeling , consistent with the endoplasmic reticulum location , and a pattern suggesting cytoplasmic vesicular exocytosis . In muscular cells , the viral proteins detected follow the pattern of striations of the cytoskeleton consistent with the sarcoplasmic reticulum localization ( Figs 2B , 2D , 7 and 8 ) , showing the commitment of this organelle in virus production . Although the steps of cell interaction , assembly , and exit of the YF virus in the host cell are not fully understood , these mechanisms have been documented in other flaviviruses . The intracellular localization pattern we observed is consistent with that described for other flaviviruses , where the assembly of the viral particles happens in the surface of the endoplasmic reticulum , and their elimination is carried out by exocytosis [2 , 3 , 8 , 22 , 34 , 35] . Studies of wild yellow fever virus pathology in man , as well as other primates , and hamsters prove that the liver , kidneys and heart are the organs most affected by infection [3–7 , 36–38] . Fatal cases of vaccine-adverse diseases usually have the same histopathological findings as the wild YF infection [19 , 39 , 40] . Our results suggest that chicken livers are not affected by the infection . In man and other primates , Kupffer cells are the first infected cells in this organ , and for some authors , they constitute a barrier which protects hepatocytes from the yellow fever virus infection [3 , 8 , 11] . Although liver maturation is an early phenomena in chicken development [30] , its embryos seem to have no Kupffer cells until the 13th development day [41] , so the absence of these cells could be a bottle neck to virus infection in this organ . Our results don’t lead us to reach a definitive explanation for this absence of liver infection , but it is possible that Kupffer cells are actually a dissemination component of the virus in the liver and that the absence of these cells in chicken embryos , in this stage of development , may be one of the reasons why in this model the liver is not infected . Extrapolation of these data to other animal models could break the paradigm of Kupffer cell function during YFV infection , making these cells a spreading , not a protective , component . On the other hand , liver infection could be a subsequent event in YFV infection , so it is necessary to investigate later times of infection , to confirm this data . The kidneys were positive in the tubular epithelium without apparent involvement of the glomerulus ( Fig 5 ) . The infection of the kidney tubular epithelium cells by yellow fever virus has been demonstrated in man and Rhesus monkeys , who , unlike our chicken embryos , also experienced renal failure [8 , 42] . Although some cells undergoing apoptosis were found in the same areas where viral antigens were detected ( Fig 1C ) , apparently the Gallus gallus kidney damage is mild relative to severe cases in man . It was interesting to find the virus in the lumen of the renal tubular epithelium ( Fig 5B and 5C ) , because others have found the vaccinal 17DD virus in the urine of vaccinated patients [43] . We also detected the virus and apoptotic cells in the myocardium of infected animals ( Fig 3 ) . The presence of virus in the heart has been identified in humans , where viral antigens are found in the myocardium of infected patients , with a necroapoptotic and steatosis profiles similar to what is observed in the kidneys and liver [8 , 42] . In the present model , we did not find any steatosis , and although apoptotic figures were seen in the affected areas , there was no associated necrosis . In this study , infected skeletal muscle cells were found throughout the bodies of the embryos . Because of its large area and the observed intensity of infection , this tissue could be the major site of viral replication . Since no other work on the pathology of YF infection mentions skeletal muscle infection , this finding is unprecedented . Yet , our results corroborate Fox and Laemmert’s [44] identification of higher viral titers in muscle and nerve tissues after 72h of infection . Although no other data on YF skeletal muscle infection are available , clinically it is known that one of the main symptoms of yellow fever is muscle pain , in addition to the significant increase of aminotransferases in critically ill patients , which can be partially explained by the cytopathic effect in cardiac and skeletal muscle [3 , 4] . Although it is necessary to examine this phenomenon in humans , infection of these cells ( and the consequent rhabdomyolysis observed ) could justify these symptoms . Conversely , other arboviruses associated with symptoms of muscular pain similar to those seen in YF , such as Chikungunya [45] , Mayaro [46] , and Ross River [47] viruses , can infect muscle cells in humans and in experimental models . In Gallus gallus , desmin expression is not restricted to skeletal muscle cells , but also occurs in mesenchymal-like cells . The anti-desmin antibody helps to detect skeletal muscle cells in different regions of the animal , and allows the disclosure of mesenchymal infected cells in the lung parenchyma ( Fig 6A and 6B ) . Different tissues also showed rare infected fibroblastoid cells ( Fig 6 ) . The infection of fibroblasts in culture is already known [48] , but apparently the susceptibility to infection of these cells in vivo seems less expressive than in vitro , at least at 72 hours post infection . Although the yolk sac is the site of virus inoculation , this extraembryonic membrane showed no evidence of infection . Only fibroblastoid cells in the muscular layer of the yolk stalk were positive ( Fig 6F ) . Likewise , molecular detection techniques also showed the chorioallantoic membrane was negative . Our results do not corroborate Fox and Laemmert’s detection of virus titer in extracts of these membranes [44] . The 17DD strain maintains some neurovirulence , observed when a virus sample is intracerebrally inoculated in mice and non-human primates . In addition , rare cases of vaccine-associated neurotropic disease ( YEL-AND ) have been documented in children younger than 9 months , mainly due to the immaturity of the blood–brain barrier . Patients with immunodeficiency also show documented YEL-AND cases [1 , 8 , 29] . In this study , we observed animals with infection in the brain ( Fig 4A and 4C ) , cerebellum , and spinal cord cells ( Fig 4B ) , probably also due to immaturity of the blood–brain barrier , which is formed in chicken embryos after 15 days of development [49] . The lack of inflammation in the affected tissues could be due to the immature immune system of these animals [31–33] , but it may also be related to characteristics of YFV . In affected tissues , infected cells were either isolated or present in small clusters , suggesting that the infection is mild in most of these tissues . Overall , there was great variability in response and susceptibility to infection among our sample , possibly because Gallus gallus is not an isogenic animal . Yet , despite the YF 17DD infection , most of the animals , when left under favorable conditions , are born without any sequelae of the disease [44] . In conclusion , our data suggest that YF 17DD infection of Gallus gallus embryos is mild but systemic , and affects various tissues and cells with different embryonic origins; however , not all cells are susceptible to virus infection . The skeletal muscle tissue seems to be the main site of production of the YF 17DD virus due to its large body area and the intensity of its cell labeling . The elucidation of cell and tissue competence could help develop new possibilities for producing the YF 17D virus ( e . g . , in cell cultures ) . This approach could , in turn , minimize problems related to high levels of chicken proteins in the vaccine [48 , 50] . Our data are particularly relevant to this problem because recent studies have suggested the possibility of using virus 17DD as another vaccine production platform , in which 17DD has proved to be an effective viral vector to recombinant proteins of other flaviviruses ( including Japanese encephalitis , West Nile , and dengue viruses ) , and of other unrelated organisms , such as Plasmodium yoelli and Trypanosoma cruzi [51–53] . Nevertheless , further studies are needed to better elucidate YFV infection , and to establish how it occurs in humans . Of particular interest , kinetic studies should seek to clarify , for example , when and how viral particles reach the organs and tissues identified in the present work . | Since 1937 , the vaccine against yellow fever has been produced in chicken embryos without any critical modification . Despite this highly available and effective vaccine , yellow fever remains an important cause of morbidity and mortality in tropical regions of Africa and South America , mainly by maintaining the sylvatic cycle ( in which mosquitoes transmit the virus from non-human primates to people who visit or work in the jungle ) . To our knowledge , the present study offers the first clear elucidation of cells and tissues associated with the biosynthesis of the17DD yellow fever virus in chicken embryos . We detected that the virus causes only mild lesions in the embryos , but affects different cells and tissues , including muscles , cells in the heart muscles and in the nervous system , certain tissues in the kidneys and lungs , and collagen-producing cells that exist in connective tissues in the cartilage and skin . Identifying these cells may help scientists develop a cell-culture-based vaccine with lower chick protein content . This understanding is important because recent data indicate that the YF virus 17DD may serve as a platform to produce other new recombinant vaccines . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Yellow Fever 17DD Vaccine Virus Infection Causes Detectable Changes in Chicken Embryos |
Disruption of proteostasis , or protein homeostasis , is often associated with aberrant accumulation of misfolded proteins or protein aggregates . Autophagy offers protection to cells by removing toxic protein aggregates and injured organelles in response to proteotoxic stress . However , the exact mechanism whereby autophagy recognizes and degrades misfolded or aggregated proteins has yet to be elucidated . Mounting evidence demonstrates the selectivity of autophagy , which is mediated through autophagy receptor proteins ( e . g . p62/SQSTM1 ) linking autophagy cargos and autophagosomes . Here we report that proteotoxic stress imposed by the proteasome inhibition or expression of polyglutamine expanded huntingtin ( polyQ-Htt ) induces p62 phosphorylation at its ubiquitin-association ( UBA ) domain that regulates its binding to ubiquitinated proteins . We find that autophagy-related kinase ULK1 phosphorylates p62 at a novel phosphorylation site S409 in UBA domain . Interestingly , phosphorylation of p62 by ULK1 does not occur upon nutrient starvation , in spite of its role in canonical autophagy signaling . ULK1 also phosphorylates S405 , while S409 phosphorylation critically regulates S405 phosphorylation . We find that S409 phosphorylation destabilizes the UBA dimer interface , and increases binding affinity of p62 to ubiquitin . Furthermore , lack of S409 phosphorylation causes accumulation of p62 , aberrant localization of autophagy proteins and inhibition of the clearance of ubiquitinated proteins or polyQ-Htt . Therefore , our data provide mechanistic insights into the regulation of selective autophagy by ULK1 and p62 upon proteotoxic stress . Our study suggests a potential novel drug target in developing autophagy-based therapeutics for the treatment of proteinopathies including Huntington’s disease .
Protein homeostasis , or proteostasis , is controlled by cellular pathways responsible for protein synthesis , folding , trafficking and degradation . Understanding the cellular functions that maintain proteostasis is central to the elucidation of the disease mechanisms associated with protein misfolding and aggregation . Autophagy is a cell catabolic pathway that , through the formation of autophagosomes , sequesters and delivers cytosolic cargos to lysosomes for degradation . Autophagy occurs constitutively in almost every cell type and plays an important role in the prevention of ubiquitinated protein overflow/aggregation [1] . Autophagy is up-regulated in response to cellular stresses such as nutrient starvation , hypoxia , growth factor withdrawal , endoplasmic reticulum ( ER ) stress , and pathogen infection [2] . Autophagy activity can be increased to compensate for the deficiency of the ubiquitin proteasome system ( UPS ) and alleviate subsequent proteotoxic stress [3] . However , the regulatory mechanisms remain largely elusive . The ULK1/Atg1 complex , consisting of ULK1 kinase , ATG13 , FIP200 and ATG101 , is required for the initiation of autophagy [4] . ULK1 is a mammalian homolog of the C . elegans uncoordinated 51 serine/threonine protein kinase [5] and its activity is regulated by mTOR and AMPK in response to nutrient availability [6] . ULK1 controls autophagy activity by phosphorylating multiple substrates , such as FIP200 , ATG13 , Beclin 1 , AMPK , Ambra1 , ATG9 and FUNDC1 [7–14] . ULK1 kinase activity is upregulated in response to hypoxia and is required for hypoxia-induced autophagy activation [15] . Emerging evidence indicates that autophagy has selectivity in substrate degradation , a process called selective autophagy , which is mediated by a specific group of autophagy receptors [16] . p62/SQSTM1 ( Sequestosome-1 ) is a prototypical autophagy receptor that recognizes ubiquitinated cargos and tethers them to the autophagy machinery by direct binding to microtubule-associated protein light chain 3 ( LC3 , a mammalian Atg8 homolog ) [17] , enabling the degradation of selective substrates [18] . Additional autophagy receptors have been identified ( e . g . NBR1 and ALFY ) later and they are expected to have similar functions or collaborate with p62 during selective autophagy [19–21] . It is believed that p62 plays a fundamental role in the clearance of protein aggregates [17 , 22] , damaged mitochondria [23] , midbody rings [24] , peroxisomes [25] and invading microbes [26] through selective autophagy . However , the molecular and structural characteristics underlying the precise function of p62 in this pathway are poorly understood . Previous reports suggested that phosphorylation of p62 at S405 by casein kinase 2 ( CK2 ) or TBK-1 may regulate the clearance of expanded polyglutamine ( polyQ ) proteins or mycobacteria , respectively [27 , 28]; Interestingly , a recent report implicates ULK1 in this phosphorylation of p62 under oligomycin-induced metabolic stress [29] , but whether phosphorylation of p62 occurs in response to disease proteins and the molecular basis of p62 phosphorylation in regulating cargo recognition has yet to be shown . Here we report that proteotoxic stress triggers phosphorylation of p62 at multiple sites in its ubiquitin association ( UBA ) domain . We find that accumulation of protein aggregates , such as polyubiquitinated proteins ( due to proteasome inhibition ) or polyQ-expanded proteins , induce the interaction of p62 with ULK1 and ULK1-dependent p62 phosphorylation in its UBA domain . Phosphorylation of a novel site S409 regulates the dimer interface of p62’s UBA domain and enhances the affinity of p62 to ubiquitin , while lack of the phosphorylation impairs the recruitment of autophagy proteins and degradation of ubiquitinated proteins or polyQ-expansion proteins . Our study thus reveals that selective autophagy can be triggered by ULK1-dependent p62 phosphorylation , and that this event regulates ubiquitinated protein or aggregate-prone disease protein clearance .
A previous report suggested a link of p62 and ULK1 , prompting us to test p62 as a potential ULK1 substrate [30] . We thus performed an in vitro phosphorylation assay with purified MBP-tagged p62 proteins and immune-isolated Myc-tagged ULK1 wild type ( WT ) or kinase inactive ( KI ) mutant [31] in the presence of 32P-ATP . ULK1 WT , but not KI , was able to phosphorylate p62 in vitro , while the labeling was abolished with dephosphorylating alkaline phosphatase ( AP ) ( Fig . 1A ) . To map the putative phosphorylation site/s , we repeated the same assay using various purified truncation mutants of p62 ( S1A , B , C Fig . ) [32] . While M1 mutant ( PB1 domain deletion ) retained the evident 32P-labeling in the presence of ULK1 WT , M4 ( UBA domain deletion ) or M7 ( both PB1 and UBA domain deletion ) showed reduced 32P-labeling . The result suggests that the UBA domain contains primary ULK1 phosphorylation sites . To precisely map the residues phosphorylated by ULK1 , we performed mass spectrometry analysis with immuno-isolated FLAG-p62 from HEK293T cells transfected with ULK1 WT . Our analysis identified serine 409 in p62 UBA domain as a potential phosphorylation site ( S1D Fig . ) To validate S409 as the target site , we substituted serine 409 with alanine to generate a phosphorylation-null mutant ( S409A ) , purified MBP-p62-S409A proteins and performed an in vitro phosphorylation assay . In agreement with the mass spectrometry results , ULK1-dependent phosphorylation of the S409A mutant was markedly reduced compared to p62 WT ( Fig . 1B ) , indicating that S409 is a ULK1 substrate . Residual 32P-labeling in S409A may suggest that ULK1 phosphorylates additional sites in p62 . To investigate S409 phosphorylation event in cells , we raised a phosphorylation-specific antibody against phosphorylated S409 of p62 ( p-S409 ) and validated its specificity . Incubation of purified p62 with ULK1 WT ( but not KI mutant ) results in the detection of a strong signal with anti-p-S409 antibody ( Fig . 1C ) , and incubation with phosphatase abolished this band . To further validate the specificity of anti-p-S409 antibody , we immunoprecipitated ( IPed ) FLAG-p62 or-S409A in the presence of overexpressed Myc-ULK1 WT or KI from transfected HEK 293T cells , followed by anti-p-S409 antibody detection of the phosphorylation . We observed a strong signal only in the sample co-expressing FLAG-p62 WT and Myc-ULK1 WT ( Fig . 1D ) . Collectively , our data indicate that S409 of p62 is a novel ULK1 kinase substrate in vitro . Based on the previous report on the occurrence of p62 phosphorylation at S405 upon MG132 treatment , a proteasome inhibitor causing accumulation of polyubiquitinated proteins and p62 [27] , we first tested whether accumulation of polyubiquitinated proteins would lead to ULK1-mediated phosphorylation of p62 at serine 409 . p62 S409 phosphorylation was induced by the exposure of HEK293T cells to MG132 ( Fig . 2A ) . This effect was also observed by overexpressing WT ULK1 , but not a KI form of this kinase in the absence of MG132 . Although MG132 treatment led to higher levels of total p62 protein , p-S409 upon MG132 treatment was severely impaired in ULK1 KO MEFs ( Fig . 2B ) . The incomplete loss of p-S409 in the absence of ULK1 suggests that other kinases ( e . g . ULK2 , a homolog of ULK1 ) could alternatively phosphorylate S409 . To test the redundant role of ULK1 and ULK2 in p62 phosphorylation , we studied ULK1/2 double knockout ( DKO ) MEFs treated with MG132 ( S2A Fig . ) . Deletion of both ULK1 and ULK2 completely abolished p-S409 signal upon MG132 treatment , suggesting that ULK1 and ULK2 are the major kinases that phosphorylate p62 at S409 under this condition . These results altogether indicate that p62 is phosphorylated at S409 in response to proteotoxic stress , at least in part , by ULK1 , although we cannot rule out that CK2 or TBK1 might also phosphorylate S409 , similar to S405 [27 , 28] . Using p62 KO MEFs stably transfected with FLAG-p62 WT or control empty vector , we performed IP with anti-FLAG or-ULK1 antibodies after MG132 treatment . We detected a clear p-S409 signal along with enhanced ULK1-p62 interaction in MG132-treated cells , compared to normal medium condition ( Fig . 2C ) . Since ULK1 regulates autophagy initiation by phosphorylating several autophagy related proteins in response to nutrient starvation [6 , 9] and p62 also plays a role in the early stage autophagosome formation [30] , we investigated if nutrient starvation induces ULK1-p62 interaction and/or increases p-S409 levels . In contrast to proteasome inhibition , neither glucose withdrawal nor amino acid starvation induces p62 phosphorylation at S409 or interaction between p62 and ULK1 ( Fig . 2C ) , suggesting a specific effect of the proteasome inhibition ( associated with accumulation of protein aggregates ) in S409 phosphorylation . To further understand the physiologic significance of ULK1-mediated p62 phosphorylation , we next examined whether expression of aggregate-prone disease proteins would induce p62 S409 phosphorylation . We employed an inducible HeLa/polyQ-mCFP cell line , which expresses an mCFP-tagged polypeptide encoding the first 17 amino acids of huntingtin with a polyQ expansion under control of a doxycycline-responsive promoter ( Tet-off system ) [33] . The expanded trinucleotide ( CAG ) tract in exon 1 of the huntingtin gene is the major cause of Huntington’s disease ( HD ) [34] and particularly , CAG repeats beyond 35 in number are known to increase disease risk . Since protein aggregation correlates with the length of polyQ tract in these cases , we tested three different lengths of polyQ tract: 25Q , 65Q and 103Q . Notably , while induction of a non-toxic form of 25Q failed to induce p-S409 , expression of the toxic species 65Q- and 103Q-mCFP triggered robust p62 phosphorylation at S409 , whose abundance correlated with the length of polyQ ( Figs . 3A , 3B ) . The phosphorylation coincided with ULK1 activation upon 103Q expression , evidenced by the reduction of a known inhibitory modification ( S757 phosphorylation ) in this kinase ( Fig . 3A ) [6] . The induction of p62 S409 phosphorylation by 65Q- and 103Q-mCFP was accompanied by S405 phosphorylation ( equivalent to human S403 ) , a modification previously reported [27 , 28 , 35] . Importantly , both S409 and S405 phosphorylation were dependent on the expression of polyQ tracts , since addition of doxycycline that represses the expression of polyQ expansion proteins resulted in loss of the phosphorylated form ( Fig . 3C ) . These results indicate that both S405 and S409 phosphorylation are specific responses to the accumulation of polyQ aggregates . To confirm the physiologic relevance of these observations , we examined the brain lysates of z_Q175 HD mice , which carry a knock-in allele of approximately 175 CAG repeats [36] . Immunoblot analysis detected p62 phosphorylation at both S405 and S409 in the striatum and cortex of 10 and 15 months old animals , but not younger ( 1 or 5 months old ) animals . These observations confirm that the phosphorylation of p62 UBA domain at S405 and S409 also occurs in vivo in response to the accumulation of polyQ-expansion protein ( Htt ) , in an age-dependent manner ( Fig . 3D ) . IF staining with the anti-p-S409 antibody primarily labeled aggregate structures by surrounding the 103Q-CFP inclusions . In contrast , the absence of aggregates in 25Q-CFP cells coincided with the lack of the p-S409 signal and redistribution of p62 in small puncta ( Fig . 3E ) . This observation was in agreement with the immunoblot analysis and suggested the involvement of p-S409 in the recognition of expanded polyQ inclusions . Atg7 conditional KO mouse is another validated disease model of accumulation of ubiquitinated proteins [1 , 37] . We examined brain lysates of two different Atg7 conditional KO mice ( Atg7f/f;Syn-Cre and Atg7f/f;Nes-Cre ) and found marked accumulation of total p62 as well as p-S409 but not in control brains . It is likely due to an arrest of degradation of p62 with p-S409 when autophagy is inactivated ( S2B Fig . ) . p62 and p62-p-S409 was absent in Atg7 and p62 double-KO mouse brains ( S2C Fig . ) , confirming the specificity of anti p-S409 antibody and the occurrence of p62 p-S409 in tissue , perhaps due to ULK1—p62 signaling as a response to the ubiquitinated protein accumulation . A similar result was observed with anti-p-S405 antibody in Atg7f/f;Syn-Cre or Atg7f/f;Nes-Cre versus control brains ( S2B , C Figs . ) . Taken together , the results support the idea that ULK1-mediated S409 phosphorylation in p62 is a specific response to the expression of aggregate-prone disease proteins . Since we observed that p62 phosphorylation , along with the interaction of p62 and ULK1 , is enhanced upon the accumulation of ubiquitinated proteins ( due to MG132 treatment ) , but not upon nutrient starvation ( Fig . 2C ) , we hypothesized that the enhanced interaction leads to ULK1-mediated phosphorylation of p62 at serine 409 . To validate the interaction at endogenous level , we performed IP using anti-ULK1 antibody from mouse embryonic fibroblasts ( MEF ) cells . Under normal condition , p62 was not detected in the immunoprecipitants with an anti-p62 antibody . However , treatment with MG132 induced the interaction of the endogenous ULK1 and p62 , despite the reduction of endogenous ULK1 levels ( Fig . 4A ) . In the absence of ULK1 , anti-ULK1 antibody did not pull down p62 in ULK1 knock-out ( KO ) MEFs [38] , supporting the specificity of the interaction . We next validated the interaction by using a p62 KO MEF cells stably expressing FLAG-tagged p62 ( FLAG-p62 ) . Consistently , IP experiment with anti-ULK1 antibody demonstrates the interaction of FLAG-p62 and ULK1 only under MG132 treatment , despite the reduction of endogenous ULK1 levels ( Fig . 4B ) . Furthermore , our immunofluorescent ( IF ) staining showed that stably-expressed FLAG-p62 and endogenous ULK1 co-localize in a large number of puncta or protein aggregates upon MG132 treatment ( Fig . 4C ) , in agreement with enhanced interaction between ULK1 and p62 in that condition . In contrast , ULK1 and p62 partially co-localize under normal condition in small dots , consistent with the previous observation [30] . We next investigated if the accumulation of aggregate-prone disease proteins induces interaction between p62 and ULK1 . Interestingly , induction of the toxic species 65Q- and 103Q-mCFP triggered ULK1—p62 interaction; in contrast , non-toxic form of 25Q-mCFP showed no effect in enhancing ULK1—p62 interaction ( Fig . 4D ) . Taken our data together , our study suggests that accumulation of protein aggregates induce the interaction between ULK1 and p62 and consequent phosphorylation . To further examine the ULK1 and p62 interaction , we next mapped the sequence of p62 and ULK1 important for their interaction . First , we asked if ubiquitin binding activity of p62 is required for their interaction . We transfected FLAG-p62 WT or mutant p62 F408V mutant ( impaired in ubiquitin binding ) [39] and performed IP . In contrast to p62 WT , p62 F408V mutant showed a reduced interaction with ULK1 after MG132 treatment ( S3A , B Figs . ) , indicating that the C-terminal UBA sequence motif important for ubiquitin binding is critical for ULK1 interaction . Previously , it was shown that ULK1 KI has an impaired autophosphorylation and conformational changes leading to the exposure of C-terminal domain , which shows reduced interaction with Atg13 , a well-known ULK1 substrate [31] . To see if a similar mechanism is involved in ULK1 and p62 interaction , we performed IP between Myc-ULK1 WT or KI with FLAG-p62 . Our data showed that ULK1 KI mutant binds poorly to p62 compared to ULK1 WT , suggesting that ULK1 interacts with p62 , and their interaction depends on an intact kinase activity of ULK1 ( S3C Fig . ) . In searching for a binding domain in ULK1 , we transfected mCherry-p62 wild type ( WT ) plasmid together with various deletion mutants of FLAG-tagged ULK1 plasmids and then examined the interaction by IP with anti-FLAG antibody after MG132 treatment . Overexpressed ULK1 WT and p62 interact with each other; however , deletion of N-terminal kinase domain of ULK1 ( Δkinase ) caused a reduction in binding to mCherry-p62 protein , compared to WT or other mutants ( S3D Fig . ) , suggesting an important role for the kinase domain of ULK1 in binding p62 . S409 resides in the p62 UBA domain near S405 , whose phosphorylation has been reported to enhance the p62–Ubiquitin ( Ub ) interaction [27] . We therefore asked whether p-S409 has any impact on the affinity of p62 for ubiquitin . To this end we used p62 KO MEF cell lines stably expressing FLAG-p62 WT , S409A or a phosphorylation-mimicking mutant S409E . To minimize the interference of p62 self-ubiquitination in this experiment , we incubated cell lysates from p62 KO MEFs treated with MG132 ( providing enriched polyubiquitinated proteins ) and MEF lysates stably expressing FLAG-p62 variants ( providing bait ) , followed by IP with anti-FLAG antibody . S409E pulled down significantly higher levels of polyubiquitinated ( poly-Ub ) proteins compared to p62 WT; in contrast , S409A pulled down the similar levels of poly-Ub proteins as WT ( Figs . 5A , B ) . Furthermore , we examined the binding of p62 WT , S409A or S409E to K48- versus K63- linked poly-Ub chains in vitro . Purified MBP-p62 variants were incubated with either K48 or K63 poly-Ub chains , followed by MBP pull down . The results showed no difference between MBP-p62 variants in binding K48-linked poly-Ub in our assay , whereas S409E binds higher amounts of K63-linked poly-Ub compared to WT and S409A ( S4A , S4B Figs . ) . We next investigated the binding affinity of the p62 UBA to mono-Ub by performing Isothermal Titration Calorimetry ( ITC ) assays . The analysis indicated that the Kd for purified p62 UBA WT was 51 . 4 μM , confirming a weak interaction between p62 UBA domain and mono-Ub as previously reported ( Fig . 5C , left ) [40] . The Kd for S409E mutant , however , was 27 . 5 μM , suggesting an increased Ub binding by S409E ( Fig . 5C , right ) . Previous structural analysis indicated that the p62 UBA domain dimerizes and exists in a dimer-monomer equilibrium in solution , while there is a shift of dimer to monomer of UBA domain upon binding to Ub [41 , 42] . To study the structural basis of how p-S409 influences Ub binding , we performed NMR spectrum analysis . The 1H-15N correlation spectra of the 15N-labeled p62 UBA WT and S409E were collected in the absence of ubiquitin ( Fig . 6A ) . The overall dispersion patterns of cross peaks for S409E mutant and WT were similar . However , a few chemical shifts were significant . For example , the residues D410 , G412 , W414 and L418 were evidently altered in position using the published WT spectra as reference [41] . The “shift” residues are located in the vicinity of S409 . Thus the S409E mutation altered these residues’ local environment and led to chemical shift in the HSQC spectra . Noticeably , residues W414 and L418 locate at the UBA dimer interface and are both important residues for dimer formation in the WT structure . The result suggests that the chemical perturbation by S409E leads to local destabilization of the UBA-UBA dimer interface . To test this idea , we examined overall thermal stability of the dimer conformation . We measured melting temperature ( Tm ) of p62 UBA WT and S409E by Differential Scanning Calorimetry ( DSC ) . The UBA S409E mutant showed a much lower Tm ( 61 . 5°C ) than WT ( 68 . 5°C ) ( Fig . 6B ) , supporting the hypothesis that the S409E UBA forms a less-stable dimeric structure than the p62 WT UBA . Furthermore , we carried out NMR titration experiments by adding 6-fold molar excess of unlabeled mono-Ub to the p62 UBA WT and S409E . The addition of mono-Ub induced a large set of chemical shifts in the spectra of S409E , consistent with the idea that S409E UBA undergoes the dimer-monomer transition upon Ub binding as reported in the previous studies for WT p62 UBA domain [41] . The analysis , however , revealed that the disperse pattern of S409E UBA in the presence of Ub is similar to that of WT , with only a few noticeable chemical shifts involving residues such as S409E and W414 ( Fig . 6C ) . These data suggest that S409E mutant follows the similar pattern as the WT in the overall folding of UBA structures in the absence or presence of Ub . Collectively , our data suggest that the S409E mutant destabilizes the UBA dimeric structure without impacting overall folding of the UBA domain bound to ubiquitin . Previous reports showed that S405 of p62 ( equivalent to human S403 ) can be phosphorylated by different kinases ( i . e . CK2 , TBK-1 and ULK1 ) [27 , 28 , 35] . Consistent with a recent report [35] , we also observed ULK1-dependent S405 phosphorylation in response to ULK1 WT overexpression , but not ULK1 KI mutant ( Fig . 6D ) . Interestingly , mutation of S409A precluded p62 phosphorylation at S405 , suggesting that S405 phosphorylation depends on S409 phosphorylation . Furthermore , we observed that S405A mutation did not affect ULK1-dependent phosphorylation of p62 at S409 . Thus it is likely that p-S409 precedes p-S405 or that p-S409 is required for the stability of p-S405 . We next investigated how phosphorylation of S409 in p62 affects the autophagic degradation of poly-Ub proteins . We first examined p62 and ubiquitin co-localization upon MG132 and under normal culture condition in MEFs stably expressing FLAG-p62 variants . IF staining pattern of p62 WT , S409A and S409E appeared similar under normal culture conditions ( S5 Fig . , left ) . However , upon MG132 treatment , cells expressing the S409A mutant formed large protein aggregates that are labeled with Ub antibody , in contrast to the distribution of p62 in small and dispersed puncta observed in WT or S409E MEFs . These results suggested a likely block in the degradation of p62 and ubiquitinated proteins when p62 cannot be phosphorylated at S409 ( S5 Fig . , right ) . To test the above idea , we treated MEFs stably expressing FLAG-p62 variants with MG132 ( pre ) and then with serum starvation ( S . S . ) to induce autophagic degradation of accumulated poly-Ub proteins ( post ) [43] . Serum starvation efficiently cleared the accumulated poly-Ub proteins that follow MG132 treatment in both p62 WT and S409E MEFs . Interestingly , chloroquine ( CQ , a blocker of lysosomal degradation ) treatment results in significant accumulation of ubiquitinated proteins in p62 S409E , suggesting that S409 phosphorylation of p62 mediates the degradation of ubiquitinated proteins mainly through autophagy ( Figs . 7A , B ) . In contrast , accumulation of poly-Ub proteins remained mostly unaltered with serum starvation conditions in S409A MEFs , and further CQ treatment had little effect in the amount of poly-Ub proteins compared to WT or S409E MEFs , suggesting a block in the autophagic degradation of poly-Ub proteins in S409A MEFs ( Figs . 7A , B ) . We next evaluated a number of autophagy markers in the context of p62-associated aggregates by using imaging analysis . IF staining results showed that p62 WT puncta co-localized with autophagy-related proteins , such as WIPI2 , LC3 , Rab7 and LAMP2 in small and dispersed dots , whereas p62 S409A formed large inclusions , which randomly sequester cellular proteins including some autophagy-related proteins , as a fraction of the autophagy proteins were seen outside of the inclusions ( Fig . 7C ) . These results strongly suggest that p62 phosphorylation at S409 is critical for linking aggregated poly-Ub proteins to autophagy compartments for their efficient degradation . Finally , to test the role of p-S409 in degradation of disease protein aggregates , we overexpressed the mCherry-p62 variants in HeLa/65Q-mCFP . Induction of 65Q-mCFP expression in cells transfected with p62 WT or S409E resulted in a decrease in the number of cells positive for 65Q-mCFP aggregates when autophagy was enhanced with rapamycin treatment , as compared to normal culture conditions . In contrast , in S409A overexpressing cells , rapamycin did not affect the number of cells producing 65Q-mCFP aggregates ( Figs . 8A , B ) . While the cell number containing 65Q-mCFP aggregates were significantly lower in p62 WT- or S409E- transfected cells than in S409A-transfected cells with rapamycin treatment , the efficiency for rapamycin-stimulated clearance of 65Q-mCFP aggregates was the highest in S409E cells compared to WT or S409A cells , based on the ratio of cell numbers with 65Q-mCFP aggregates in the absence or presence of rapamycin ( Fig . 8B ) . We then performed immunoblot analysis of 65Q-mCFP levels in those same transfected HeLa cells to confirm the effects of mCherry-p62 variants . We found that the majority of 65Q-mCFP remained in a detergent-insoluble fraction and that incubation with rapamycin had little effect on 65Q-mCFP levels in the soluble fraction in all cases . However , rapamycin treatment resulted in a reduction in the levels of insoluble 65Q-mCFP protein in p62 WT- or S409E- expressing cells , but not S409A-expressing cells ( Figs . 8C , D ) . Indeed , 65Q-mCFP levels were significantly higher in S409A cells than WT or S409E cells after rapamycin treatment . Accordingly , the efficiency of autophagy-stimulated clearance ( ratio of 65Q-mCFP levels before versus after rapamycin treatment ) of S409E was higher than that of WT or S409A mutant ( Fig . 8C ) . These results suggested that phosphorylation of p62 at S409 potentiates autophagic degradation of 65Q-mCFP .
Our study reveals that proteotoxic conditions trigger a response of selective autophagy involving phosphorylation of autophagy receptor p62/SQSTM1 by ULK1 . ULK1-mediated phosphorylation of S409 as well as S405 in its UBA domain occurs in response to ubiquitinated protein accumulation ( upon proteasome inhibition ) and aggregate-prone polyQ-expanded Htt protein that is causal to Huntington’s disease ( see our working model in Fig . 9 ) . In contrast , p62 S409 phosphorylation does not occur when cells are starved of amino acid or glucose , conditions upon which ULK1 is typically activated to induce macroautophagy , a bulk degradation pathway with little selectivity of substrates [6 , 9] . Thus our study suggests that the ULK1-p62 cascade plays an important role in the regulation of selective autophagy . The increased binding of modified p62 with ubiquitinated cargo supports the idea that the signaling cascade responds to pathological protein aggregates by increasing their recognition and degradation . The lack of co-localization between the non-phosphorylated p62 ( S409A ) mutant and autophagy markers suggests that p62 S409A fails to recognize autophagic cargos and recruit the autophagy machinery . Our data indicates that ULK1-mediated phosphorylation of p62 is likely activated by mechanisms distinct from canonical nutrient pathways ( Fig . 2C ) . Instead , it may involve sensing of proteotoxic stresses such as accumulation of misfolded poly-ubiquitinated proteins ( Fig . 2 ) or disease-related protein aggregates ( Fig . 3 ) . The exact molecular mechanism by which upstream signaling activates the ULK1-p62 cascade remains to be characterized . Our data suggest that accumulation of protein aggregates triggers the interaction of ULK1 with p62 ( Fig . 4 ) and subsequent ULK1-dependent phosphorylation of p62 at two serine sites in p62’s UBA domain . Consistently , ULK1-p62 interaction involves ULK1’s kinase domain and the ability of p62’s UBA domain to bind Ub ( S3 Fig . ) . Our results also suggest that S409 phosphorylation , similar to that of S405 , is important for linking autophagic cargos to the autophagy machinery , degradation of ubiquitinated proteins and polyQ-expanded proteins ( Figs . 6 , 7 ) . To our surprise , a recent report showed that deletion of p62 ameliorated the pathology in R6/2 HD mouse model [44] , in contrast to the previous evidence that p62 depletion exacerbated disease progression in spinal and bulbar muscular atrophy ( SBMA ) , another polyQ-associated disease [45] . Given that p62 is a multifaceted protein that is involved in multiple cellular pathways [46] and that it shuttles between the nucleus and cytoplasm [47] , the toxicity of p62 as described in the report could be related to nuclear shuttling of mutant huntingtin or/and the de-regulation of Keap1-Nrf2 pathway in R6/2 HD mice . Revelation of the underlying mechanisms that determine the outcome of p62 deletion in different disease models will aid in understanding the pathophysiology of each disease and developing specific therapeutic targets . Our study also provides novel structural and functional insights into the role of the novel S409 and previously identified S405 p62 phosphorylation in regulating ubiquitinated protein binding [27 , 28 , 35] . Previous studies showed that the UBA of p62 has only weak affinity to free Ub [40] or unanchored tetra-Ub [48] , casting a doubt on the significance of p62’s UBA domain in ubiquitinated cargo binding . Here our study demonstrates that the increased affinity of p62 UBA to ubiquitinated proteins is achieved following ULK1-dependent phosphorylation of p62 at S409 and S405 . The p62 UBA domain exists in dimer–monomer equilibrium in vitro , with the dimer conformation incompatible for interaction with Ub [41 , 42] . Modulation of the dimer—monomer transition of UBA domain likely plays a role in regulating p62 function in the recognition and degradation of ubiquitinated proteins , although no experimental evidence had been reported previously . Our NMR analysis also shows that , although the overall structural folding of the p62 UBA S409E differs little from that of WT p62 ( either before or after Ub binding ) , the S409E mutation alters the local relationship between select critical residues , including W414 and L418 at the p62 UBA-UBA dimer interface ( Figs . 6A , C ) [41] . Furthermore the overall thermal stability of the UBA domain is significantly reduced in the S409E mutant , suggesting a reduced interaction between the two S409E monomers ( Fig . 6B ) . Thus we conclude that phosphorylation at S409 causes a destabilized dimer interface and facilitates dimer–monomer transition in favor of binding to ubiquitinated proteins . This event could also be connected to the requirement of S409 phosphorylation for the subsequent S405 phosphorylation , while p-S405 is dispensable for p-S409 . In fact , the S405 and S409 are separated by 3 amino acids , ( MGF ) , a conserved motif on the L1 loop of the UBA domain important for Ub binding [41] . While S409 is located at the L1 loop of the dimer interface , S405 resides at the end of a α-helix required for Ub binding . We thus propose a model in which S409 is phosphorylated first by ULK1 to promote a dimer to monomer transition , followed by the exposure of S405 to ULK1 ( or CK2 or TBK-1 ) for further phosphorylation ( Fig . 6D ) . In this model , and based on our observations , p62 phosphorylation at S405 and S409 might have distinct roles in modulating p62-Ub binding: while located at the Ub binding site , p-S405 results in enhanced affinity between Ub and p62 perhaps via charged residue interactions; in contrast , p-S409 destabilizes the UBA dimer interface and allows S405 phosphorylation to occur or maintains the steady state of p-S405 , potentiating Ub and p62 interaction . Although the crosstalk between autophagy and ubiquitin proteasome system ( UPS ) has been described previously and available evidence suggests that autophagy can be activated as a salvage pathway under UPS impairment [3 , 49 , 50] , the exact molecular mechanism is not well understood . Undigested proteasomal substrates resulting from a block of the UPS become sequestered in Ub-positive protein inclusions also known as aggresomes [51] or p62 bodies [52] , where the autophagy receptor p62 tethers ubiquitinated cargos to the autophagy machinery via LC3 for degradation . Thus our study provides insights into the mechanism for the cross-talk between the UPS and the autophagy pathway whereby enhanced autophagy , through ULK1-p62 coordinated action , compensates for the inhibition of UPS degradation to clear ubiquitinated proteins . It remains to be shown whether other types of ubiquitinated cargos , such as injured mitochondria , peroxisomes and invading microbes can also be recognized and degraded through the similar ULK1-p62 mechanism . In summary , our study reveals a molecular and structural mechanism underlying the autophagy receptor p62-mediated degradation of ubiquitinated or aggregated disease proteins through selective autophagy . Our results thus provide a rationale for the development of therapeutics against human diseases associated with protein aggregates ( proteinopathies ) , based on ULK1 and p62 interaction and signaling .
All animal studies were performed in compliance with IACUC ( Institutional Animal Care and Use Committee ) at Icahn School of Medicine at Mount Sinai . MG132 ( calbiochem ) , chloroquine ( CQ; Sigma-Aldrich ) , polybrene ( Sigma-Aldrich ) , lipofectamine 2000 ( Invitrogen ) , puromycin ( InvivoGen ) , EDTA-free protease inhibitor cocktail and phosphatase inhibitor cocktail ( Roche Diagnostics ) , mouse monoclonal M2 FLAG affinity gel beads ( Sigma-Aldrich ) , ammonium bicarbonate ( NH4HCO3; Sigma-Aldrich ) , iodoacetamide ( IAM; Sigma-Aldrich ) , formic acid ( Sigma-Aldrich ) , Trifluoroacetic acid ( TFA; Pierce ) , tris ( 2-carboxyethyl ) phosphine ( TCEP; Pierce ) , bovine trypsin ( Roche Applied Science ) , acetonitrile ( ACN;Thermo Fisher Scientific ) , POROS 20 R2 beads ( Applied Biosystems ) , C18 ZipTips ( Merck Millipore ) , isopropyl-β-D-thiogalactopyranoside ( IPTG; Sigma-Aldrich ) , Dynabeads protein G ( Invitrogen ) , protein G Sepharose ( GE Healthcare Life Sciences ) , NuPAGE Bis-Tris and Tris-Acetate gels running system ( Invitrogen ) , QuikChange Lightning Site-Directed Mutagenesis Kit ( Agilent Technologies ) , Hybond-P PVDF membrane ( GE Healthcare Life Sciences ) , BCA Protein Assay Reagent Kit ( Pierce ) , Radioactive [γ-32P]ATP ( PerkinElmer ) , Calf intestinal alkaline phosphatase ( New England Biolabs ) , Factor Xa ( New England Biolabs ) , K48- and K63-linked poly ubiquitin chains ( Boston Biochemical ) , DAPI-containing fluorescence mounting medium ( Invitrogen ) were purchased from indicated suppliers . ULK1 ( Sigma-Aldrich , #A7481 ) , p62 ( Progen Biotechnik , #GP62-C ) , p-p62 Ser403 ( Millipore , #MABC186 ) , β-Actin ( Cell Signaling Technology , #3700 ) , FLAG-M2 ( Sigma-Aldrich , #F1804 ) , myc ( Cell Signaling Technology , 9B11 , #2276 ) , Ubiquitin ( Dako , #Z0458; Abcam , #ab7780; Biomol , clone FK2 , #PW8810 ) , LC3B ( Cell signaling , #2775 ) , WIPI2 ( abcam , #ab101985 ) , Rab7 ( Cell signaling , #9367 ) , LAMP2 ( DSHB , #H4B4 ) , GAPDH ( Chemicon , #MAB374 ) , polyQ ( Merck Millipore , #MAB1574 ) , GFP ( Life technologies , #A11122 ) , DsRed ( Clontech lab , #632496 ) were purchased from the indicated suppliers . Anti–phosphorylated p62 polyclonal antibody was raised in rabbits using the peptide SMGF ( pS ) DEGGWLTRC as an antigen by Abgent and Cocalico Biologicals . FLAG p62-wild type , -F408V , -ΔPB1 , and MBP p62-wild type , -M1 , -M4 , -M7 constructs were provided by Dr . Masaaki Komatsu ( Niigata University ) . Myc-ULK1 wild type and kinase inactivated ( K46I ) mutant were provided by Dr . Sharon Tooze ( London Research Institute ) . FLAG ULK1-wild type , -Δkinase , -ΔC , -ΔS/ΔC constructs were provided by Dr . Mondira Kundu ( St . Jude Children’s Research Hospital ) . LPC retroviral vector and helper vector were provided by Dr . Wei-Xing Zong ( Stony Brook University ) . FLAG-p62 was cloned into HindIII and XhoI sites of LPC retroviral vector . mCherry p62-wild type was provided by Dr . Thomas Weber ( Icahn School of Medicine at Mount Sinai ) . The full length of mouse ubiquitin was inserted into the modified pET32 vector between the restriction sites BamHI and EcoRI as a Trx-His6-tagged protein . The UBA domain of mouse p62 ( residues 391–438 ) was cloned as a GST-His6-tagged protein in a modified pET49 vector containing the human rhinovirus ( HRV ) 3C protease cleavage site . Based on each p62 wild type constructs , site-directed point mutation was performed to substitute serine 409 to either alanine ( S409A ) or glutamic acid ( S409E ) and serine 405 to alanine ( S405A ) . HEK 293T cells and MEFs were maintained in Dulbecco’s modified Eagle’s medium ( DMEM , Invitrogen ) supplemented with 10% fetal bovine serum ( FBS , Invitrogen ) and 50 μg/ml penicillin and streptomycin ( Invitrogen ) . Immortalized wild type and p62 knockout Mouse Embryonic Fibroblasts ( MEFs ) are provided by Dr . Masaaki Komatsu ( Niigata University ) [53] and Dr . Mondira Kundu ( St . Jude Children’s Research Hospital ) provided wild type , ULK1 knockout , and ULK1/2 double knockout MEFs [38] . HeLa/polyQ-mCFP cells were maintained as previously described [33] , which is generous gifts from Dr . Ai Yamamoto ( Columbia University ) . Transient transfection was performed using Lipofectamine 2000 as per the manufacturer’s instruction . Retroviral infection was done as described previously [54] . Briefly , HEK 293T cells were transfected with LPC-retroviral constructs and helper viral construct . After 24 hr , the supernatant was collected and filtered through 0 . 45 μm pore size nylon filter and supplemented with 10 μg/ml of Polybrene . p62 KO MEFs were infected with this supernatant and then selected with 3 μg/ml of Puromycin . Single colonies were picked and cultured . For total cellular lysates , cells were lysed on ice in TNTE buffer ( 1% Triton X-100 , 20 mM Tris-HCl pH 7 . 5 , 120 mM NaCl , 1 mM EDTA ) containing 1% SDS , complete protease inhibitor cocktail , and phosphatase inhibitor cocktail . Homogenization using a 1 ml syringe with a 26-gauge needle was followed and supernatants were collected after centrifugation at 13 , 000 g for 15 min at 4°C . Supernatants were subjected to BCA assay and then were resolved by SDS-PAGE . For immunoprecipitation , cells were lysed in RIPA buffer ( 50 mM Tris-HCl pH 7 . 4 , 1% NP-40 , 0 . 25% Na-deoxycholate , 150 mM NaCl , 1 mM EDTA ) containing complete protease inhibitor cocktail , and phosphatase inhibitor cocktail for 30 min at 4°C . After centrifugation at 13 , 000 g for 15 min at 4°C , collected supernatants were incubated with antibodies overnight at 4°C . Lysates were further incubated with Dynabeads protein G or protein G Sepharose for 1 . 5 hr at 4°C and then washed with RIPA buffer 5 times and subjected to immunoblot assay . Cells were fixed in 4% paraformaldehyde in PBS for 30 min and permeabilized in PBS containing 0 . 1% saponin for 10 min at room temperature . Cells were further blocked in PBS containing 5% goat serum and 0 . 2% Triton X-100 for 1 hr and then incubated with primary antibodies in PBS containing 1% goat serum and 0 . 2% Triton X-100 overnight at 4°C . For phospho-p62 ( S409 ) antibody , PBS containing 0 . 2% Triton X-100 and 3% BSA or 1% BSA for blocking and antibody incubation , respectively , was used . After washing with PBS three times , cells were incubated with Alexa-conjugated secondary antibody for 1 hr at room temperature . Secondary antibodies used are goat anti-rabbit Alexa Fluor 555 and 488 , goat anti-rat Alexa Fluor 488 , goat anti-mouse Alexa Fluor 488 , goat anti-guinea pig Alexa Fluor 555 and 647 . Washing with PBS was followed and then mounted with mounting medium ( ProLong Gold antifade mountant with DAPI , Invitrogen ) . Cells were examined under Carl Zeiss upright or invert confocal microscopes ( LSM780 system ) . Images were taken with 63X oil immersion objective lens at room temperature and image acquisition was performed by Zen2012 software . Digitized images were analyzed and processed by using Image J software . Line profiling was performed with Image J software . All animal studies were performed in compliance with IACUC ( Institutional Animal Care and Use Committee ) at Icahn School of Medicine at Mount Sinai . Floxed Atg7 mice ( Atg7f/f ) were characterized previously and were crossed with Synapsin-Cre mice to generate Atg7f/f:Synapsin-Cre mice [1] . Whole brains of Atg7f/f: p62+/ − , Atg7f/f: nestin-Cre: p62+/ − , Atg7f/f: nestin-Cre: p62−/− , Atg7f/+: nestin-Cre: p62−/ − were provided by Dr . Masaaki Komatsu . Brain lysates of mice brains were prepared as described previously [22] . z-Q175 KI line was derived from the CAG 140 KI mouse model and carries around 175 CAG repeats [36] . Heterozygous z_Q175 and its age-matched wild type littermate control were obtained from the CHDI colony at the Jackson Laboratories . Cortical and Striatal lysates were prepared with RIPA buffer supplemented with 1% SDS , complete protease inhibitor cocktail , and phosphatase inhibitor cocktail , followed by homogenizing tissues with blue pestles and heating at 60°C for 1 hr . Supernatants obtained after centrifugation at 15 , 000 g for 30 min at 4°C were subjected to immunoblot assay . Cells were lysed on ice 1% Triton X-100 in PBS supplemented with complete protease and phosphatase inhibitor cocktails for 30 min . After centrifugation at 15 , 000 g for 30 min at 4°C , 1% Triton X-100-soluble fractions were collected . The pellets were washed four times with 1% Triton X-100 in PBS and further solubilized with addition of 1% SDS for 1 hr at 60°C . Subsequently , Triton X-100-insoluble fractions were collected by centrifugation at 15 , 000 g for 30 min at 4°C and protein samples were submitted to immunoblot assay . Expression of MBP-p62 WT and mutants were induced in E . Coli BL21 ( DE3 ) cells by growing at 25°C for 16 hr with 0 . 05 mM of isopropyl-β-D-thiogalactopyranoside ( IPTG ) . Bacterial cells were lysed with TNE buffer ( 10 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , and 1% NP-40 ) and centrifuged for 20 min at 9 , 000 g at 4°C . Supernatants were incubated with amylose resin at 4°C for overnight and MBP-p62 bound resins were washed three times with TNE buffer . Subsequently , MBP-p62 proteins were eluted by 10 mM of maltose in 20 mM Tris-HCl pH 7 . 5 , 150 mM NaCl . GST-His6 Ubiquitin full length and UBA domain constructs were expressed in E . coli BL21 ( DE3 ) cells at 30°C after induction by IPTG and purified by affinity chromatography ( HisTrap HP , GE Healthcare ) . The Trx- or GST-His6 tags were removed by 3C cleavage and the untagged proteins were further purified by size-exclusion chromatography ( Superdex 75 , GE healthcare ) . In vitro p62 phosphorylation assay by ULK1 was performed as described before [6] . In brief , MBP tag of purified MBP-p62 proteins was cleaved with Factor Xa and then incubated with Myc-ULK1 immunoprecipitants from transfected HEK 293T cells in Kinase buffer ( 20 mM HEPES at pH 7 . 4 , 1 mM EGTA , 0 . 4 mM EDTA , 5 mM MgCl2 and 0 . 05 mM DTT ( dithiothreitol ) containing 100 μM of cold ATP and 5 μCi [γ-32P]-ATP per reaction at 37°C for 30 min . The reaction was terminated by adding SDS sample buffer and subjected to SDS–PAGE and autoradiography . For cold reaction , 500 μM of cold ATP was used instead and subjected to immunoblot assay with customized pS409-p62 antibody . For dephosphorylation assay , the membrane incubated with p-p62 S409 antibody was further incubated with alkaline phosphatase for 1 hr at 37°C . SDS sample buffer was added to stop reaction and then SDS-PAGE and autoradiography or immunoblot assay with pS409-p62 antibody were performed . For cell-based poly-Ub binding assay , p62 KO MEFs stably transfected with FLAG-p62 WT , S409A , S409E , or empty vector were lysed with RIPA buffer . To generate poly-Ub proteins , normal p62 KO MEFs were treated with MG132 and then lysed with RIPA buffer . Same amount of protein lysates from each pool were mixed and then incubated with M2 FLAG affinity gel beads for overnight at 4°C . Beads were extensively washed with RIPA buffer five times and then subjected to immunoblot assay . For the in vitro linkage specific Ub binding assay , 15 μg of purified MBP-p62 wild type and mutant proteins were incubated with amylose resin in reaction buffer ( 50mM HEPES , pH 7 . 5 , 10% Glycerol , 150mM NaCl , 1% Triton X-100 , 1mM EDTA , 1mM EGTA ) at 4°C for 1 hr . Incubation with 0 . 8 μg of K48− or 0 . 4 μg of K63−linked poly Ub chains was followed at 4°C for 2 hr . Reactants were extensively washed five times with reaction buffer and then subjected to immunoblot assay . Isothermal Titration Calorimetry was performed using an iTC200 microcalorimeter ( MicroCal Inc . ) . Samples were dialyzed into 50 mM Tris , pH 8 . 0 , and 150 mM NaCl . For UBA-ubiquitin interactions the injection syringe was loaded with 40 μl of ubiquitin sample and the cell was loaded with 220 μl of the respective UBA sample including wild type and S409E . Typically titrations consisted of 20 injections of 2 μl , with 200-s equilibration between injections . The data were analyzed using Origin 7 . 0 . All the 1H-15N HSQC spectra of WT and S409E p62 UBA domain were collected at a concentration of 100 μM protein in 20mM sodium phosphate buffer , pH6 . 8 , 5mM potassium chloride , 1mM EDTA and 10% D2O . For the ubiquitin titration , 6-equimolar ubiquitin was mixed with the 15N labeled UBA samples before the data collection . The spectra were acquired with a Bruker Avance 700 MHz spectrometer at 20°C and data were processed by the software provided by the manufacturer ( Bruker Corporation ) . Differential Scanning Calorimetry measurements were carried out using a MicroCal VP-DSC calorimeter ( MicroCal Inc . ) with 0 . 5ml cells under a constant pressure of 2 . 5 atm . For the thermal stability data collection , all the protein samples were exchanged to buffer containing 20 mM HEPES , pH7 . 4 , 115 mM NaCl , 1 . 2 mM CaCl2 , 1 . 2 mM MgCl2 and 2 . 4 mM K2HPO4 by dialysis . Five rounds of buffer to buffer scans from 10–90°C by a ratio of 60°C /hr were performed to acquire a high quality baseline and a consistent thermal history prior to the protein data collection . The protein samples at a concentration of 200 μM were degassed and warmed to 25°C before being loaded to the sample cell . All the protein samples were injected in a temperature window between 15–25°C when the cell cooling down to 10°C after the previous scan . Data were analyzed by the software provided by the manufacturer ( MicroCal Inc . ) , including baseline subtraction , normalization and model fitting . For each experiment , at least three independent scans were performed . Mass spectrometric analysis to identify ULK1-mediated p62 phosphorylation was performed as described previously with slight modification [55] . HEK 293T cells transfected with FLAG-p62 along with either ULK1 wild type or kinase mutant were immunoprecipitated with anti-FLAG antibody and resolved by SDS-PAGE . The gel was stained with Coomassie blue and then p62 band was excised , followed by de-staining with 45% acetonitrile in 100 mM ammonium bicarbonate . Subsequently , gel slices were reduced with 10 mM tris ( 2-carboxyethyl ) phosphine hydrochloride ( TCEP ) and alkylated with 50 mM iodoacetamide . The proteins in each gel piece were then subjected to trypsin digestion and the reactions were stopped by 5% formic acid in 0 . 2% TFA . The extracted peptides by using POROS 20 R2 beads were concentrated and desalted using C18 zip-tips and eluted with 0 . 1% TFA in 40% acetonitrile followed by 0 . 1% TFA in 80% acetonitrile . The eluents were dried down using a Speed Vac-concentrator and reconstituted with 0 . 1% formic acid in 2:98 ACN: H2O for liquid chromatography tandem mass spectrometry ( LC-MS/MS ) analysis . A NanoAcquity UPLC system ( Waters ) interfaced to an LTQ-Orbitrap mass spectrometer ( Thermo Scientific ) equipped with a nanospray ionization source was employed for LC/MS/MS analyses . Reversed-phase LC was performed on Waters BEH130 C18 column ( 100 μm x 100 mm , 1 . 7 μm particle size ) . Samples were trapped and washed in Waters Symmetry C18 trap column ( 180 μm x 100 mm , 5 μm particle size ) prior to separation in the capillary column . Gradient elution was carried out with 0 . 1% formic acid in water as solvent A and in ACN as solvent B , with solvent B raised from 1 to 50% in 30 minutes , then 50 to 85% in the next 10 min . A flow rate of 0 . 5 μL/minute was used . MS/MS spectra were searched against IPI mouse database ( ver . 3 . 87 ) using Sequest ( ver . 27 , Rev . 11 ) , Mascot ( Ver . 2 . 4 . 0 , Matrix Science , UK ) and X ! Tandem ( The GPM , thegpm . org; version CYCLONE ( 2010 . 12 . 01 . 1 ) ) algorithms ( 1 , 2 ) . Scaffold ( version 4 . 2 . 1 , Proteome Software Inc . , ) was used to validate MS/MS based peptide and protein identifications . Searches were performed with full tryptic specificity ( 2 missed cleavages ) ; carbamidomethylated cysteine residues as static modification; deamidated asparagine and glutamine ( +0 . 9840 Da ) , oxidized methionine , histidine and tryptophan ( +15 . 9949 Da ) , and phosphorylated serine , threonine and tyrosine ( +79 . 9663 Da ) as differential modifications . Scaffold PTM version 2 . 1 . 2 . 1 ( Proteome Software Inc . , Portland , Oregon , USA ) was used to annotate Post Translational Modification ( PTM ) sites contained in MS/MS spectra . All experiments were performed at least 3 times unless it is indicated . Data are presented as mean ± SEM from at least three independent experiments . Statistical analyses and graphing were performed with GraphPad Prism v5 . 0 ( GraphPad Software ) . One sample t-test and unpaired Student’s t-test were used . A p value less than 0 . 05 was considered as statistically significant . | Accumulation of misfolded proteins deposited in the form of inclusion bodies is a common pathological hallmark for many human genetic diseases , particularly for the neurodegenerative disorders . The aggregation of the disease related proteins suggests a failure of the cellular machineries that maintain the protein homeostasis or proteostasis . The cellular clearance pathways , e . g . autophagy-lysosomal pathway , may not be of high efficiency in the face of rapid formation of misfolded protein aggregates . Thus , understanding of intrinsic mechanism whereby autophagy offers protection to cells by removing toxic protein aggregates is important . Here we report that a signaling transduction event that chemically modifies autophagy receptor protein p62/SQSTM1 regulates the receptor’s binding affinity to small molecule called ubiquitin ( essential for marking the protein for degradation ) , as well as the selective degradation of targeted proteins . Furthermore , we find that expression of Huntington’s disease ( HD ) associated protein aggregates ( containing polyglutamine or polyQ expansion ) triggers the same modification of p62 , which is dependent on the length of the polyQ expansion , suggesting a protective response of the cell by activating autophagy toward degradation of toxic aggregates . The modification of p62 also occurs in HD model brains in an age-dependent manner . Our study sheds light on the regulation of selective autophagy and provides a rationale for targeting p62 modification to treat aggregate diseases including HD . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Proteotoxic Stress Induces Phosphorylation of p62/SQSTM1 by ULK1 to Regulate Selective Autophagic Clearance of Protein Aggregates |
KSHV is the etiological agent of Kaposi's sarcoma ( KS ) , primary effusion lymphoma ( PEL ) , and a subset of multicentricCastleman's disease ( MCD ) . The fact that KSHV-encoded miRNAs are readily detectable in all KSHV-associated tumors suggests a potential role in viral pathogenesis and tumorigenesis . MiRNA-mediated regulation of gene expression is a complex network with each miRNA having many potential targets , and to date only few KSHV miRNA targets have been experimentally determined . A detailed understanding of KSHV miRNA functions requires high-through putribonomics to globally analyze putative miRNA targets in a cell type-specific manner . We performed Ago HITS-CLIP to identify viral and cellular miRNAs and their cognate targets in two latently KSHV-infected PEL cell lines . Ago HITS-CLIP recovered 1170 and 950 cellular KSHVmiRNA targets from BCBL-1 and BC-3 , respectively . Importantly , enriched clusters contained KSHV miRNA seed matches in the 3′UTRs of numerous well characterized targets , among them THBS1 , BACH1 , and C/EBPβ . KSHV miRNA targets were strongly enriched for genes involved in multiple pathways central for KSHV biology , such as apoptosis , cell cycle regulation , lymphocyte proliferation , and immune evasion , thus further supporting a role in KSHV pathogenesis and potentially tumorigenesis . A limited number of viral transcripts were also enriched by HITS-CLIP including vIL-6 expressed only in a subset of PEL cells during latency . Interestingly , Ago HITS-CLIP revealed extremely high levels of Ago-associated KSHV miRNAs especially in BC-3 cells where more than 70% of all miRNAs are of viral origin . This suggests that in addition to seed match-specific targeting of cellular genes , KSHV miRNAs may also function by hijacking RISCs , thereby contributing to a global de-repression of cellular gene expression due to the loss of regulation by human miRNAs . In summary , we provide an extensive list of cellular and viral miRNA targets representing an important resource to decipher KSHV miRNA function .
Kaposi's sarcoma-associated herpesvirus ( KSHV ) or Human Herpesvirus type 8 ( HHV-8 ) is associated with Kaposi's sarcoma ( KS ) and two lymphoproliferative disorders: primary effusion lymphomas ( PEL ) and a subset of multicentricCastleman's disease ( MCD ) [1]–[3] . In KS tumors and PEL viral gene expression is highly restricted to the latency-associated region which encodes four proteins and the viral microRNAs ( miRNA ) . MiRNAs are 21 to 23 nucleotide ( nt ) long , non-coding RNAs that preferentially bind to 3′UTRs of mRNAs to prevent translation and/or induce degradation ( for review see [4] ) . The first viral miRNAs were identified in 2004 in Epstein-Barr virus ( EBV ) -infected Burkitt's lymphoma cells [5] and subsequently more than 140 miRNAs have been identified in all herpes viruses studied thus far with the exception of Varicella Zoster virus ( for review see [6] , [7] ) . The 12 KSHV miRNA genes [8]–[11] can each give rise to two different mature products [12] , miR and miR* . MiR-K12-10 is moreover edited [13] bringing the total number of mature miRNAs to 25 . KSHV miRNAs are expressed during the latent phase of infection and expression has been detected in tissues and biopsies of classical and AIDS-associated KS as well as in PEL and MCD [14]–[16] . Since aberrant expression of miRNAs is associated with many human diseases including cancer [17] , it was hypothesized early on that KSHV-encoded miRNAs may contribute to pathogenesis and/or tumorigenesis by de-regulating host cellular gene expression . Until recently , only a small number of target genes have been identified mainly by combining bioinformatics predictions with gene expression profiling and 3′UTR luciferase reporter assays in cells that either ectopically express viral miRNAs or in tumor cell lines in which viral miRNAs are inhibited by antagomir approaches [18]–[21] . Although limited in number , the initially reported targets immediately suggested that KSHVmiRNAs contribute to the regulation of pathogenesis-relevant processes such as angiogenesis , apoptosis , cell cycle control , endothelial cell differentiation , and immune surveillance ( for review see [6] , [7] ) . Moreover , one KSHV miRNA , miR-K12-11 , shares the same seed sequence as human miR-155 , one of the first “oncomirs” discovered [22] , [23] . MiR-K12-11 was shown to mimic miR-155 function to induce a splenic B cell expansion in a NOD/SCID mouse model [24] . Investigating the combinatorial nature by which viral miRNAs expressed within a background of tissue-specific host miRNAs interact with their cognate transcriptomes requires genome-wide ribonomics-based approaches . Recently , high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation ( HITS-CLIP ) and Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation ( PAR-CLIP ) techniques have been developed that are based on the enrichment of Ago-miRNA-mRNA complexes from cells after UV cross-linking [25] , [26] . While HITS-CLIP uses 254 nm UV light to cross-link RNA protein complexes , in PAR-CLIP cells are first treated with nucleoside analogs such as 4-thiouridine ( 4-SU ) that are incorporated into nascent mRNAs , which are then cross-linked at 365 nm . After cross-linking , RNase treatment , and immunoprecipitation , small RNAs representing both miRNAs and their bound targets are extracted and converted into small RNA libraries that are analyzed by high-throughput sequencing . Very recently , Gottwein et al . reported a list of more than 2000 putative KSHV miRNA targets that were identified by PAR-CLIP in BC-1 and BC-3 cells [27] . Here we report on a detailed HITS-CLIP analysis of two commonly studied PEL cell lines , BCBL-1 and BC-3 , which are both KSHV-positive but represent different B cell developmental stages [28] , [29] . We identified 1170 and 950 genes , respectively , that were enriched for clusters of sequence tags containing KSHV miRNA seed sequence matches within 3′ UTRs and exons . Comparative analysis between both PEL cell lines revealed dramatic differences in Ago-associated miRNA repertoires and in the number and nature of miRNA targets , further supporting the idea that miRNA regulation can be highly cell type-and developmental stage-specific . In addition , comparison of our HITS-CLIP data with the PAR-CLIP data set reported by Gottwein et al . revealed about 42% overlap , which suggests that neither method enriches miRNA targets in a saturating manner . In summary , we have identified KSHV miRNA targets highly enriched for the gene ontology terms apoptosis , glycolysis , and lymphocyte activation , which will provide an important resource to further delineate the role of KSHV-encoded miRNAs for viral biology and pathogenesis .
To identify genes that are targeted by KSHV and human miRNAs in latently KSHV-infected cells Ago HITS-CLIP was performed in the KSHV-positive and EBV-negative PEL cell lines BCBL-1 and BC-3 . BCBL-1 cells are post germinal center B cells characterized by rearranged immunoglobulin loci [30] . BC-3 cells are pre-B cells , which have not undergone antigen-dependent B cell maturation [31] . As a result , both cell lines harbor significantly different transcriptomes [28] , [29] , [32] . HITS-CLIP was performed according to Chi et al . with minor changes of the immunoprecipitation ( IP ) and library construction protocols ( for details see Materials and Methods and Text S1 ) . IP of cross-linked and RNase-treated Ago-miRNA-mRNA-complexes from 1–2×108 cells yielded two complexes migrating approximately at 110 kDa and 130 kDa ( Figure 1A , B ) . While the smaller complex contained only short 20–25 nt long RNAs ( presumably miRNAs ) , the 130 kDa complex contained two different RNA species: short RNAs ( miRNAs ) and 50–70 nt long RNAs ( presumably target mRNAs ) ( Figure 1C , D ) . Both short and long RNA species derived from the 130 kDa complex were extracted and processed separately for library construction and deep sequencing ( in the following referred to as miRNA libraries and mRNA libraries , respectively ) . To account for biological variance as observed in published HITS-CLIP data sets [25] , [33]–[35] we performed three biological replicates for each cell line ( BR1-3 ) . As additional quality control one BCBL-1mRNA library ( BR1 ) was sequenced in two technical replicates ( TR1 , 2 ) . High throughput sequencing of six mRNA and five miRNA ( 2 from BCBL-1 , 3 from BC-3 ) libraries was performed as 40 nts single strand runs and yielded more than 250 million sequence tags ( 16–31 million per run ) . To validate known and identify potential new host and viral miRNAs , sequence tags from miRNA libraries were aligned to miRBase ( http:/mirbase . org/ , release 17 ) using BLAST , and in addition analyzed using the miRDeep software package [36] . Nearly 90% of the miRNA library reads originated from human and KSHV miRNAs and comparison across BRs revealed a very high correlation of R2>0 . 92 ( Figure 1E and Figure S1A , and S1B ) . The comparison between BCBL-1 and BC-3 was lower ( R2 = 0 . 65; Figure S1C ) , indicating significant differences in Ago-associated miRNA profiles betweenBCBL-1 and BC-3 as described in detail below . Sequencing reads from all mRNA libraries were uploaded to the CLIPZ database , an open source software package specifically developed for the analysis of HITS-CLIP and PAR-CLIP data [37] , and annotated to the human genome ( hg19 ) . The correlation for technical replicates was R2 = 0 . 88 ( Figure S1D ) . Observed correlations across biological replicates ( R2 = 0 . 53–0 . 72; Figure S1E , F ) were comparable to previously reported HITS-CLIP data sets [25] , [34] . mRNA libraries were analyzed for clusters of overlapping reads using the CLIPZ sequence cluster tool . Of all the clusters aligning to mRNAs about two thirds were located in exons and one third in introns . Read distribution within exons largely reflected the current understanding of miRNA targeting , as the majority aligned to 3′UTRs and CDS ( Figure 1F ) , and about 4% to 5′UTRs ( 7–8% after adjusting for possible UTR length bias; see Text S1 ) . Read distribution within exons is also in agreement with recently published Ago HITS-CLIP and PAR-CLIP data sets [25]–[27] , [34] , [38] , [39] . With respect to intron/exon distribution no significant differences were observed between mRNA libraries from BCBL-1 and BC-3 cells ( Figure 1F and Figure S2 ) . A miRNA was counted as present if it was sequenced with at least one read in each BR and the average count over all BRs was at least 10 . In BCBL-1 , all 25 KSHV miRNAs were recovered , inBC-3 cells all except for miR-K12-9 and -9* , which are highly polymorphic and not expressed [12] , [14] . However , we note that 7 KSHV miRNAs in both cell lines were detectable at very low read numbers ( below 200 reads/million total reads; Figure 2A ) . We also detected 370 and 306 human miRNAs in BCBL-1 and BC-3 , respectively . As observed previously by Chi et al . [25] the 30 most abundantly expressed miRNAs represent 94% of all miRNA reads ( the top 20 contribute 90% ) , suggesting that only a small number of miRNAs act as major players in miRNA-mediated regulation of gene expression . A comparison of the miRNA libraries showed remarkable differences in the miRNA composition between the two PEL cell lines . While in BCBL-1 82% of the miRNA reads originate from human miRNAs , in BC-373% are KSHV-derived ( Figure 1G ) . A more detailed analysis revealed that in BCBL-19 KSHV miRNAs rank within the top 30 , with the most frequent one , miR-K12-4-3p , at position4 . The three human lymphocyte-specific miRNAs hsa-miR 30a , 30d , and 142-3p occupy more than 50% of all RISCs in BCBL-1 cells ( Figure 2B ) . In contrast , inBC-3 the top 5 miRNAs ( miR-K12-3 , -1 , -4-3p , -10a , and 10b ) , as well as 15 of the top 30 miRNAs originate from KSHV ( Figure 2C ) , contributing 74 . 5% of the top 30 and 71% of all miRNAs associated with Ago . At the same time , the read counts of miR-30a , -30d and miR-142-3p are dramatically decreased from 50% of all miRNA reads in BCBL-1 to 12% in BC-3 . Also , individual viral miRNAs were associated with Ago at highly different frequencies in BCBL-1 and BC-3 cells . For example miR-K12-3 , the most prevalent miRNA in BC-3 , was 10-fold less abundant in BCBL-1 ( Figure 2A ) . These results indicate that especially in BC-3 cells the KSHV miRNAs out-compete human miRNAs by displacing them from RISC complexes . In addition , we note that the pattern of human miRNA abundance differs between PEL cell lines , with some of the most abundant miRNAs in one cell line being found at much lower levels in the other . These abundantly expressed human miRNAs in BCBL-1 included miR-146 , a major regulator of the inflammatory response [40] , which was detected in BC-3 at almost 30-fold lower read numbers ( data not shown ) . Vice versa , miR-155 , whose aberrant expression is associated with multiple malignancies [41] , [42] is not expressed in BCBL-1 but within the top 30 in BC-3 . The differential Ago-association of KSHV and host miRNAs between these PEL cell lines suggests that marked differences may also exist in their respective miRNA targetomes . For identification of putative miRNA targets , mRNA-derived clusters of overlapping reads were built on the human genome ( hg19 ) within each BR followed by a search for overlapping clusters across BRs ( superclusters ) . Super clusters were called at two different stringency criteria: clusters present in two of three BRs ( stringency 2of3 ) or in all three BRs ( 3of3 ) . Super clusters matching these criteria were then scanned for the presence of 7-mer seed matches ( nt 2 to 8 ) of KSHV and the top 30 human miRNAs . Seed sequences for KSHV miRNAs that were recovered at very low frequencies ( Figure 2A ) were initially included in the search but not considered for the final target lists ( for BCBL-1: miR-K12-6p , 11* , 2* , 5* , 10a* , and 1*; for BC-3 additionally miR-K12-9 , 9*; for exclusion criteria see Text S1 ) . Seed match-containing clusters were further filtered for alignment to annotated transcripts and sufficient coverage ( for details see Text S1 ) . Clusters that passed all filtering steps showed a tight width distribution of 41–150 nts ( ∼64% in BCBL-1 and 84% in BC-3 ) , with more than 90% of all clusters being between 41 and 300 nts wide ( Figure 3A and Table S1 ) . The 41–300 nts wide , seed match-containing clusters , their associated genes , and targeting miRNAs identified at stringencies 2of3 and 3of3 were compiled into putative miRNA target lists for each cell line . We observed that clusters wider than 300 nts often consisted of overlapping peaks of different sizes , which didn't allow the identification of biologically meaningful seed pairing sites without individual visual inspection of each cluster . These clusters were therefore not included in the main target lists , but are listed as potential additional targets in separate tables . Prior to further analysis , we also asked whether target enrichment was correlated to transcript abundance , or biased by 3′UTR length and/or sequence composition . As expected , Ago HITS-CLIP recovered highly abundant transcripts with higher frequency ( Table S2 , Figure S3A and Text S1 ) . With respect to 3′UTR length we found a weak bias towards longer 3′UTRs , however , short 3′UTRs ( <300 bases ) were enriched 5-fold higher than expected if enrichment would only be due to 3′UTR length instead of target specificity ( Figure S3B ) . Finally , targets were enriched for genes with low GC content ( Figure S3C ) , which may reflect less secondary structure and therefore better RISC accessibility . We note , however , that the overall variation in GC content across transcripts is moderate , with most transcripts being in the range of 35–55% GC . BCBL-1 data ( 2of3 ) yielded 1516 clusters ( 41–300 nts wide ) corresponding to 1170 transcripts , which contained one or more of the 18 included KSHV miRNA seed matches ( Figure 3B ) . Using the highest stringency by calling clusters across all three BRs ( 3of3 ) yielded 648 clusters representing 552 transcripts . Stringency 2of3 inBC-3 yielded 1135 clusters ( 950 transcripts ) targeted by 16 KSHV miRNAs , which was reduced to 470 clusters and 413 transcripts at the highest stringency ( 3of3 ) . Comparing putative KSHV miRNA targets of both cell lines revealed that 50% or 468 of the transcripts targeted in BC-3 cells ( 2of3 ) were also targeted in BCBL-1 ( Figure 3B ) . Complete target lists can be found in Tables S3 and S4 . Remarkably , despite the much larger number of KSHV miRNAs in BC-3 cells , the overall number of KSHV miRNA targets in the two cell lines is not very different and even lower in BC-3 . Only the percentage of transcripts targeted exclusively by KSHV miRNAs is higher in BC-3 than in BCBL-1 ( 47 vs . 33% of all KSHV miRNA target transcripts , respectively; Figure 3C ) . Conversely , in congruence with the much higher levels of Ago-associated cellular miRNAs in BCBL-1 , the overall number of transcripts containing human miRNA seed matches ( with or without additional KSHV miRNA seed matches ) as well as the number of transcripts exclusively targeted by host miRNAs was much higher in BCBL-1 than in BC-3 ( Figure 3C ) . In addition , the overall number of seed match-containing clusters and targets in BC-3 cells is smaller , which may be a result of the reduced miRNA complexity . These data show that the miRNA targetome in BC-3 cells is dominated by viral miRNAs . A small proportion of the mRNA library reads , ranging from 0 . 15% to 1 . 05% ( average 0 . 43% ) , originated from KSHV transcripts . Similar to miRNA expression levels and target numbers , these reads differed between both cell lines ( Figure 4A ) . Overall , in BCBL-1more viral transcripts were enriched than in BC-3 , which could be a result of the larger number of cellular miRNAs associated with Ago in BCBL-1 , as described above . A prominent peak was present in both cell lines in the 3′UTR of K2 , the viral interleukin6 ( vIL-6; Figure 4A , B ) , which is expressed in a subset of tumor cells at low levels during latency [43] . In addition , strongpeaks originated from the K12/Kaposin and ORF71/vFLIP 3′UTRs , and across the vFLIP/vCyclin ( ORF72 ) transcripts ( more prominent in BC-3 than in BCBL-1 ) , as well as minor peaks at the miRNA cluster region and K5 . Moreover , BCBL-1 showed additional peaks within K4 , T1 . 1/PAN , RTA/ORF50 , ORF58 , and ORF59 ( Figure 4A ) . Within ORF50 , some peaks were located within the open reading frame as well as downstream; we detected small clusters of reads over one of the potential miR-K12-5 binding sites [44] and over the miR-K12-9* target site [45] in the putative 3′UTR of RTA . Reads originating from the miRNA cluster likely represent the 1 to 2% miRNA reads recovered from the mRNA target libraries as well as pre-miRNA sequences [39] . We further validated the prominent peak within the 3′UTR of vIL-6 , which contained a miR-K12-10a seed match by luciferase reporter assay as described below . While overall the enrichment of viral 7mer2-8 seed match-containing clusters was low across the viral genome , some ORFs and/or putative 3′UTRs contained clusters with host miRNA seed matches . Tracks showing enriched read clusters on the KSHV genome for all viral and the top 30 human miRNAs are provided in the supporting information ( Dataset S1 , S2 , S3 , S4 ) . As a first validation of the target data set , we analyzed the read distribution over seed matches of31 experimentally confirmed KSHV miRNA targets reported by multiple groups [18] , [19] , [21]–[24] , [44] , [46]–[52] . From these , 16 were enriched by Ago HITS-CLIP and all but two showed enriched read clusters harboring the experimentally confirmed seed match ( Table S5A ) . Some transcripts contained additional clusters with seed matches for other viral miRNAs . Figure S4 shows the read distribution of eight previously characterized targets visualized as wiggle plots in the UCSC genome browser . The target interactions of miR-K12-11 , an ortholog of the oncomir miR-155 [22] , [23] , with BTB and CNC homology 1 , basic leucine zipper transcription factor 1 ( BACH1 ) , Src-like-adaptor ( SLA ) , FBJ murine osteosarcoma viral oncogene homolog ( FOS ) , and CCAAT/enhancer binding protein beta ( C/EBPβ ) have been confirmed by 3′UTR mutagenesis [22]–[24] . The 3′UTR of BACH1 contains four , FOS and SLA each contain two , and C/EBPβ one seed match for miR-K12-11 . Three of the BACH1 sites previously demonstrated to be important for miR-K12-11 targeting were indeed enriched by HITS-CLIP; for the other three transcripts all miR-K12-11 seed match sites were occupied by clusters , although sometimes only in one biological replicate . Further comparison of recovered miR-K12-11 targets with a list of 151 putative miR-155 targets reported by multiple groups [22] , [23] , [53]–[61] revealed 30 commonly targeted transcripts ( Table S5B ) . Dolken et al . reported on114putative KSHV miRNA targets that were enriched using immunoprecipitation in the absence of cross-linking ( RIP-CHIP ) [46] . Of these , 33 overlap with our data set including NHP2 non-histone chromosome protein 2-like 1 ( NHP2L1 ) and leucine rich repeat containing 8 family , member D ( LRRC8D ) , which both recovered high frequency clusters for the validated miR-K12-3 target sites ( Figure S4 and Tables S5A , S6A ) . Also , Thrombospondin1 ( THBS1 ) was previously shown to be targeted by multiple KSHV miRNAs [19] . Correspondingly , the HITS-CLIP data revealed seed match-containing clusters for miR-K12-1 , -3 , -3* , -6-3p , and -11 . We note that all of the previously reported target sites for THBS1 and LRRC8D consist of a 6-mer seed match and are therefore not included in the overall target lists ( Table S3 and S4 ) , but could be confirmed by manual investigation of the seed match sites ( Figure S4 ) . Figure 5 shows the read distribution for eight newly identified targets: Annexin A2 ( ANXA2 ) , CCAAT/enhancer binding protein alpha ( C/EBPα ) , major histocompatibility complex , class I , C ( HLA-C ) , protein tyrosine phosphatase , non-receptor type 11 ( PTPN11 ) , stress-induced-phosphoprotein 1 ( STIP1 ) , tumor protein p53 inducible nuclear protein 1 ( TP53INP1 ) , tumor protein D52 ( TPD52 ) , and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein , epsilon polypeptide ( YWHAE ) , and their corresponding KSHV miRNAs . Both targeting and miRNA-specificity for these transcripts were further validated by 3′UTR luciferase assays ( see below ) . This initial target validation demonstrates that our experimental HITS-CLIP conditions in combination with stringent filtering of clusters across biological replicates yielded a reliable working list of putative targets for KSHV miRNAs in BCBL-1 and BC-3 cells . Very recently , Gottwein et al . reported more than 2000 putative KSHV miRNA targets that were identified using PAR-CLIP in BC-1 and BC-3 cells [27] . We found that 830 or 42% of the putative targets identified by PAR-CLIP in BC-3 were also enriched by Ago HITS-CLIP in at least one biological replicate from BCBL-1 and/orBC-3 ( Table S6B ) . Potential new KSHV miRNA targets were first aligned to their corresponding miRNA using RNA hybrid ( Figure S5 ) . We then cloned eight 3′UTRs and four enriched seed match-containing CDS downstream of a luciferase reporter cassette and performed miRNA sensor assays in HEK293 cells . For each transcript , we tested the predominantly identified miRNA or miRNA combinations and for some additionally the miRNA cluster , which contains 10 of the 12 miRNA genes as previously reported [19] , [23] . As positive controls , we used the known miR-K12-11 targets BACH1 and C/EBPβ [23] , [24] . All eight 3′UTRs responded to miRNA expression with a dose-dependent decrease of luciferase expression by at least 20% ( Figure 6A ) . These included ANXA2 , C/EBPα , HLA-C , high mobility group AT-hook 1 ( HMGA1 ) , interferon regulatory factor 2 binding protein 2 ( IRF2BP2 ) , TP53INP1 , TPD52 , and YWHAE . We moreover introduced three point mutations in the miR-K12-11 seed match sites in the 3′UTRs of ANXA2 and YWHAE ( Figure S5 ) . This resulted in a de-repression of both luciferase reporter constructs , thus further confirming the functionality of these target sites ( Figure 6B ) . Finally , we showed by Western blot analysis a decrease of the TP53INP1 and YWHAE protein levels in the presence of miR-K12-11 ( Figure 7 ) . Of the four transcripts enriched for CDS seed matches , PTPN11 and STIP1 responded to miRNA expression while HLA-E , and complement component 1 , q subcomponent binding protein ( C1QBP ) did not . This is in congruence with the literature reporting that miRNA target sites located within exons are less often functionally active [62]–[65] . Overall , 10 out of 12 putative targets were functionally confirmed . In addition , we tested the 3′UTR of vIL-6 , which revealed a strong cluster that contained a 6-mer miR-K12-10a seed match . The vIL-6 reporter was inhibited in a dose-dependent manner up to40% in the presence of miR-K12-10 ( Figure 6a ) . This effect was abolished by the introduction of two different miR-K12-10a seed match mutations ( Figure 6B , S5 ) . These data functionally confirm the first KSHV latency-associated gene to be modulated by a viral miRNA . Other putative viral targets including RTA , vFLIP , vCyclin and Kaposin are currently under investigation . All HITS-CLIP-derived KSHV miRNA targets found at analysis stringency 3of3 were subjected to Gene Ontology ( GO ) analysis using DAVID [66] , [67] . GO analysis was performed against two different backgrounds: ( i ) the published BCBL-1 and BC-3 transcriptomes [32] and ( ii ) all human transcripts . Pathway enrichment was analyzed for five target subsets: all targets enriched in each cell line , cell line-specific targets , and overlapping targets between cell lines . A partial representation of enriched GO terms is shown in Table 1 , the detailed GO analysis in Table S7 . Genes involved in highly regulated processes often have long 3′UTRs and thus potentially contain more miRNA target sites . We therefore tested if GO terms were identified due to a bias for 3′UTR length rather than a functional enrichment . However , GO terms for three highly regulated processes ( apoptosis , cell cycle , and glycolysis ) showed only very moderate association with intermediate 3′UTR length and no association with long 3′UTRs ( Figure S3D ) . In both cell lines Ago HITS-CLIP significantly enriched for KSHV miRNA targets involved in different pathways regulating apoptosis . Among the more than 40 genes were the tumor necrosis factor receptor superfamily member 10b ( TNFRSF10B , miR-K12-1 , -3 ) and the TP53 apoptosis effector PERP ( miR-K12-3; p53 pathway ) , FEM1B ( miR-K12-4-3p; Fas/TNFR1 signaling ) , and Transforming Growth Factor beta Receptors ( TGFBR ) 1 ( miR-K12-2 ) and 3 ( miR-K12-4-3p ) ( TGFBR pathway ) . The latter two proteins together with growth factor receptor-bound protein 2 ( GRB2 , miR-K12-4-3p ) also signal in the pro-apoptotic P70S6K pathway . Moreover , we identified the tumor suppressor phosphatase and tensin homolog ( PTEN , miR-K12-4-3p , -7 ) , a negative regulator of the anti-apoptotic Akt/PKB . Finally , we recovered several known apoptosis targets: cyclin-dependent kinase inhibitor 1A and 1B ( CDKN1A ( miR-K12-11 ) , 1B ( miR-K12-K12 ) [22] , [27] , which are also involved in cell cycle control , Caspase 3 ( CASP3 ) , which was recently reported as miR-K12-1 , -3 , and -4-3p target [52] , and BCL2-associated transcription factor 1 ( BCLAF1 , miR-K12-2 ) , which appears to have dual roles in PEL cells . While it was originally characterized as pro-apoptotic factor [68] , [69] , Ziegelbauer et al . found that in KSHV-infected cells BCLAF1 impairs apoptosis and also regulates lytic viral replication by sensitizing latent cells to reactivation stimuli [18] . The most enriched GO term in both cell lines was glycolysis ( 11 genes , p<4×106 ) . Recently , it was shown that KSHV infection of endothelial cells induces the Warburg effect during latency [70] , which is observed in many human tumors and results in increased aerobic glycolysis and decreased oxidative phosphorylation [71] . Interestingly , initial experiments showed that latent KSHV infection of SLK cells leads to increased oxygen consumption ( data not shown ) . However , testing 293 cells engineered to express the KSHV miRNA cluster containing 10 of the 12 miRNAs [19] failed to show a similar effect . Additional studies in SLK and primary endothelial cells are currently ongoing . In BCBL-1 , Ago HITS-CLIP enriched for two inhibitors of the NFκB pathway , lectingalactoside-binding soluble 1 ( LGALS1 , miR-K12-10b ) [72] and Interleukin 10 ( IL-10 , miR-K12-12* ) [73] . This suggests that , in addition to positively regulating NFκB via the latency-associated vFLIP [74] , KSHV further reinforces this crucial pathway for PEL cell survival by miRNA regulation . IL-10was also part of the GO term lymphocyte activation , which was highly enriched in BCBL-1-specific targets . We recovered 13 genes of this pathway including growth and/or differentiation factors such as inosine 5′-monophosphate dehydrogenase 1 ( IMPDH1 , miR-K12-7 ) , early growth response 1 ( EGR1 , miR-K12-4-3p ) , CD48 ( miR-K12-7* ) , and bone marrow stromal cell antigen 2 ( BST2 , miR-K12-8* ) . In BC-3 cells , putative KSHV miRNA targets were enriched for factors that inhibit cell proliferation and G1 to S phase transition via multiple pathways . Among them were four inhibitors of Cyclin-dependent Kinase 2 , including the previously characterized targets CDKN1A ( p21; miR-K12-1 ) [22] , CDKN1B ( p27; miR-K12-11 ) and Protein Phosphatase 2A ( PP2A; miR-K12-1 ) [27] , as well as the WEE1 homolog ( WEE1; miR-K12-1 , -12 ) . PP2A in addition causes G1 arrest via the BTG protein pathway . Moreover , p21 and p27 together with WEE1 block the progression of the cell cycle via E2F activation . We note that p21 and p27 expression is also regulated by vCyclin [75] , [76] . The downregulation of these transcripts by KSHV miRNAs suggests a release of the cell cycle arrest and increased proliferation . It was previously reported that KSHV miR-K12-7 targets MHC class I polypeptide-related sequence B ( MICB ) [47] , which is also targeted by HCMV and EBV miRNAs [47] , [77] . HITS-CLIP did not enrich for MICB , which might be expressed at levels too low to detect in PEL cells . However , HITS-CLIP enriched for KSHV miRNA targets involved in antigen presentation in the context of cellular immunity , i . e . the Major Histocompatibility ( MHC ) class I alpha-chain genes ( HLA-C , -E , -F , and –G ) , and also genes involved in the process of loading and transporting MHC , i . e . calreticulin ( CALR , miR-K12-4-3p , -10b ) and adaptor-related protein complex 3 , delta 1 ( AP3D1 , miR-K12-3 ) . We note that KSHV additionally encodes two E3 ubiquitin ligases , ORF K3 ( MIR1 ) and OFR K5 ( MIR2 ) that potently downregulate MHC I on the surface of infected cells [78] , [79] , suggesting another concerted action of KSHV miRNAs and proteins . Several members of the ubiquitin conjugating ( TMEM189-UBE2V1 , miR-K12-1;UBE2V2 , miR-K12-11; UBE2L3 , miR-K12-1; and UBE2D3 , miR-K12-2 , -4-3p ) and ubiquitin ligase ( UBE3C , miR-K12-3 ) families were enriched in both cell lines , while negative regulators of kinases ( e . g . CDKN1A , CDKN1B , and PP2A ) were BC-3-specific ( Table 1 , Table S7 ) . Potential signaling pathways modulated by these kinases and the question whether ubiquitin-dependent protein turnover is modulated by KSHV miRNAs needs to be experimentally addressed .
The best characterized KSHV miRNA targets so far are mostly involved in regulating immune evasion ( MICB ) , pro-apoptotic pathways ( BCLAF1 ) , and cell cycle control ( BACH1 , FOS , THBS1 , CDKN1A , and C/EBPβ ) ; for review see [7] , [81] . The Ago HITS-CLIP-derived targetome shows strong enrichment for genes involved in these pathways , thus significantly expanding what to this point was solely based on single target gene studies . In addition , GO analysis suggests new host cell pathways to be targeted , such as glycolysis , lymphocyte activation and the ubiquitin/proteasome pathway , opening up additional interesting themes for functional studies . Finally , one clearly emerging concept from this HITS-CLIP data set is that multiple key pathways and processes such as the NFκB pathway , MHC class I-mediated immune surveillance , and cell cycle control can be co-regulated by both virally encoded proteins and miRNAs . MiRNA library analysis revealed strong differences in Ago-associated miRNAs in BCBL-1 and BC-3 cells , with KSHV miRNAs comprising 18% of all miRNA reads in BCBL-1 , and an astonishing 73% in BC-3 , and numbers of single KSHV miRNAs being up to 10-fold higher in BC-3 . Similar results for the overall KSHV versus human miRNA count in both cell lines were obtained by the recent PAR-CLIP study [27] ) . Interestingly , several studies have analyzed KSHV miRNA expression in additional PEL cell lines and found differences not only with respect to overall expression levels but moreover also differences in the relative abundance of specific viral miRNAs [15] , [82] . The fact that such expression differences likely affect targeting further supports the notion that miRNA targetomes are strictly context dependent . Surprisingly , despite the much higher levels of KSHV miRNAs in BC-3 cells compared to BCBL-1 , we identified similar KSHV miRNA target numbers in both cell lines , which were even 15–20% lower in BC-3 . Only the number of transcripts exclusively targeted by KSHV miRNAs was slightly higher in BC-3 ( Figure 3C ) . In contrast , we found that the number of genes targeted by human miRNAs ( either exclusively or with additional KSHV sites ) , was almost 2-foldhigher in BCBL-1 than in BC-3 . Thus , while the presence of more human miRNAs is correlated with more putative targets , the same appears to not be true for KSHV miRNAs . In this context it is interesting to note that we observed some differences between reported relative miRNA frequency observed by small RNA cloning [12] , [27] and the relative frequency by which they were associated with Ago in BCBL-1 and BC-3 cells . Specifically , KSHV passenger strand miRNAs ( miR-K12-3* , -5* , -8* , as well as -9* in BCBL-1 ) , but also guide strand miRNAs ( miR-K12-3 , -10a , and 10b ) are very modestly expressed , but have a relatively higher Ago-association rate ( Figure S7 ) . Moreover , for two of the three miRNAs with the highest incorporation-to-expression ratio and also an overall high incorporation level , miR-K12-3* and -8* , we identified only few targets . This raises the possibility of an additional function of some viral miRNAs besides seed sequence-specific target silencing: by being present in very high numbers in KSHV-infected PEL cells ( especially in BC-3 , but to a lesser extent also in BCBL-1as well as in BC-1 [27] ) , they might prevent human miRNAs from accessing RISCs , which would lead to a global de-repression of host genes . Indeed , we observed a strong impact on the target numbers of human miRNAs . Read counts of miR-142-3p and the miR-30 family , which are the most frequent Ago-associated miRNAs in BCBL-1 , were reduced 4–5-fold in BC-3 ( Figure 2B , C ) . Accordingly , we also identified about 4-fold less targets in BC-3 . Gene Ontology analysis showed that a significant fraction of the BCBL-1-specific miR-142-3p and miR-30 targets ( many of them targeted by both miRNAs/families ) are involved in protein transport and localization , chromatin organization , macromolecule catabolic processes , and protein degradation . Hence , these processes might be de-repressed in BC-3 . Recent very elegant studies interrogating the quantitative aspects of miRNA targeting documented how shifting the ratio between miRNA and target mRNA copy numbers profoundly affects silencing efficiency [83] , [84] . Hence , flooding host cells with viral miRNAs , a phenomenon first described by Dolken et al . in the context of denovo HCMV infection [85] , maybe an additional mechanism by which herpesviruses induce cells into an activated state . Together with the fact that miRNAs from different viruses have evolved to target common pathways ( i . e . apoptosis and cell cycle control ) by direct silencing , this suggests that specific gene targeting and global inhibition of host miRNA function both contribute to gene expression differences in KSHV-infected cells . In summary , our stringent and well-controlled approach provides a working list for functional follow-up studies to decipher viral ( and host ) miRNA function in KSHV-infected cells . In addition , the data strongly demonstrate that the KSHV miRNA targetome can significantly vary based on the miRNAs' overall abundance and RISC-incorporation , and by transcriptome differences between different PEL cell lines . As a consequence the putative PEL miRNA target catalogues presented by our HITS-CLIP data and the recently reported PAR-CLIP data [27] represent an important starting point for many mechanistic studies . However , a full understanding of the role that KSHV miRNAs play in viral biology will require the combination of viral genetics with ribonomics approaches performed in all cell types associated with KSHV pathogenesis as well as in primary tumor biopsies .
Ago HITS-CLIP procedure was performed in three biological replicates as described in Chi et al [25] with some minor modifications ( for details see Text S1 ) . Briefly , cells were harvested at a density of <0 . 8×106 cells/ml and cross-linked at 254 nm prior to cell lysis . Ago-miRNA-mRNA complexes were immunoprecipitated from RNase-treated cross-linked lysates using the anti-Ago 2A8 antibody [86] . Immunoprecipitated RISC complexes were washed twice with cold high stringency buffer ( 15 mMTris-HCl , pH 7 . 5 , 5 mM EDTA , 2 . 5 mM EGTA , 1% TX-100 , 1% Na-deoxycholate , 0 . 1% SDS , 120 mMNaCI , 25 mM KCI ) , twice with high salt buffer ( 15 mMTris-HCl , pH 7 . 5 , 5 mM EDTA , 2 . 5 mM EGTA , 1% TX-100 , 1% Na-deoxycholate , 0 . 1% SDS , 1 M NaCI ) [87] , and then as described in Chi et al [25] . 130 kDaAgo-miRNA-mRNA complexes were separated by SDS-PAGE and RNA extracted from these complexes , yielding two different RNA species: short , 20–25 nt RNAs and longer , 50–70 nt RNAs . Both RNA species were treated as separate miRNA and mRNA libraries , respectively . RNA was reverse transcribed and PCR amplified for deep sequencing . Libraries were sequenced in 40 bp runs on an IlluminaGAIIx sequencer . miRNA libraries were analyzed using an in-house algorithm ( see Text S1 ) and the software package miRDeep [36] . mRNA libraries were analyzed using the CLIPZ database [34] . Briefly , libraries were analyzed for overlapping reads ( clusters ) , and then for overlapping clusters between biological replicates . Only clusters that overlapped at least between two biological replicates were considered for miRNA target search . These clusters were then analyzed for the presence of KSHV and human miRNA seed matches ( nt 2–8 ) . Gene Ontology ( GO ) analysis was performed using the web-accessible database DAVID ( http://david . abcc . ncifcrf . gov; [66] , [67] ) on KSHV miRNA targets found in all three biological replicates . miRNA expression plasmids either contain a region of approximately 200 bp encompassing the pre-miRNA stem loop or the complete intronic miRNA cluster inserted into pcDNA3 . 1/V5/HisA [19] . Firefly luciferase reporter plasmids were created using the pGL3 promoter vector ( Promega ) . Sequences of 3′UTRs or CDS were obtained from RefSeq . 3′UTRs were PCR amplified from BCBL-1 genomic DNA , CDS from BCBL-1cDNA , and cloned into the pGL3 promoter vector by GeneArt Seamless Cloning ( Invitrogen ) downstream of the Luciferase gene between the XbaI and the FseIsites . HEK293 cells were transfected with 2 ng of pCMV-Renilla control vector ( Promega ) , 20 ng of the Firefly pGL3 reporter construct and 0 , 400 or 800 ng of the pcDNA3 . 1 miRNA expression vector . The different concentrations of pcDNA3 . 1 miRNA expression vector were complemented with empty pcDNA3 . 1 vector to reach a total of 800 ng in each transfection . Cells were harvested 72 hrs post transfection and luciferase activity was quantified using the Promega Dual Luciferase Reporter kit according to the manufacturer's protocol . Immunoblotting was carried out to detect down-regulation of miRNA targets at the protein level . Cell lysates were the same as used for luciferase reporter assays . 10–12 ug of total protein per lane were separated on 10% or 12% SDS gels and transferred to PVDF membranes using standard procedures . Membranes were probed with the following antibodies: rabbit anti-TP53INP1 ( eBioscience , 14-6049 ) , rabbit anti-YWHAE ( Thermo Scientific , PA5-17104 ) ) , goat anti-actin-HRP ( Santa Cruz , sc-1616 ) , and goat anti-rabbit-HRP ( Jackson Immunoresearch , 111-036-047 ) Raw data have been uploaded to the CLIPZ database ( www . clipz . unibas . ch ) under the group name ‘Renne CLIP’ and are freely available for analysis and comparison with other CLIP data sets in the CLIPZ database and for download . | Kaposi's sarcoma-associated herpesvirus is the etiological agent of KS and two lymphoproliferative diseases: multicentricCastleman's disease and primary effusion lymphomas ( PEL ) . KSHV tumors are the most prevalent AIDS malignancies and within Sub-Saharan Africa KS is the most common cancer in males , both in the presence and absence of HIV infection . KSHV encodes 12 miRNA genes whose function is largely unknown . Viral miRNAs are incorporated into RISCs , which regulate gene expression mostly by binding to 3′UTRs of mRNAs to inhibit their translation and/or induce degradation . The small subset of viral miRNA targets identified to date suggests that these small posttranscriptional regulators target important cellular pathways involved in pathogenesis and tumorgenesis . Using Ago HITS-CLIP , a technique which combines UV cross-linking , immunoprecipitation of Ago-miRNA-mRNA complexes , and high throughput sequencing , we performed a detailed analysis of the KSHV miRNA targetome in two commonly studied PEL cell lines , BCBL-1 and BC-3 and identified 1170 and 950 putative miRNA targets , respectively . This data set provides a valuable resource to decipher how KSHV miRNAs contribute to viral biology and pathogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"rna",
"nucleic",
"acids",
"viral",
"immune",
"evasion",
"virology",
"viral",
"persistence",
"and",
"latency",
"viruses",
"and",
"cancer",
"biology",
"rna",
"stability",
"microbiology"
] | 2012 | Ago HITS-CLIP Expands Understanding of Kaposi's Sarcoma-associated Herpesvirus miRNA Function in Primary Effusion Lymphomas |
The introduction of forensic autosomal DNA profiles was controversial , but the problems were successfully addressed , and DNA profiling has gone on to revolutionise forensic science . Y-chromosome profiles are valuable when there is a mixture of male-source and female-source DNA , and interest centres on the identity of the male source ( s ) of the DNA . The problem of evaluating evidential weight is even more challenging for Y profiles than for autosomal profiles . Numerous approaches have been proposed , but they fail to deal adequately with the fact that men with matching Y-profiles are related in extended patrilineal clans , many of which may not be represented in available databases . The higher mutation rates of modern profiling kits have led to increased discriminatory power but they have also exacerbated the problem of fairly conveying evidential value . Because the relevant population is difficult to define , yet the number of matching relatives is fixed as population size varies , it is typically infeasible to derive population-based match probabilities relevant to a specific crime . We propose a conceptually simple solution , based on a simulation model and software to approximate the distribution of the number of males with a matching Y profile . We show that this distribution is robust to different values for the variance in reproductive success and the population growth rate . We also use importance sampling reweighting to derive the distribution of the number of matching males conditional on a database frequency , finding that this conditioning typically has only a modest impact . We illustrate the use of our approach to quantify the value of Y profile evidence for a court in a way that is both scientifically valid and easily comprehensible by a judge or juror .
A forensic Y-chromosome profile typically consists of the allele at between 15 and 30 short tandem repeat ( STR ) loci . For autosomal STR profiles , there are two alleles per locus and because of the effects of recombination , the alleles at distinct loci are treated as independent , after any adjustments for sample size , coancestry and direct relatedness . Y profiles usually have only one allele per locus , the exceptions are due to genomic duplications [1] . The loci lie in the non-recombining part of the Y chromosome , which behaves like a single locus and so the Y profile can be regarded as a single allele , or haplotype . This represents a major contrast with autosomal profiles , the implications of which have not been fully addressed [2] . At the core of the weight of evidence for autosomal STR profiles is the match probability , which is the conditional probability that a particular individual X has a matching profile , given that the queried contributor Q has it [3] . Matching at a single , autosomal STR allele is relatively common: typically a few percent of individuals from the same population share a given allele . The probability of matching is increased when X is a relative of Q , but for typical population sizes most of the individuals sharing a given allele are not closely related to Q . Because of the absence of recombination , the situation for Y-STR profiles is different [4] . The profile mutation rate is approximately the sum of the mutation rates at each STR locus , which can be above 0 . 1 per generation for profiling kits in current use ( Fig 1 ) . This , together with the large space of possible profiles , implies that the probability of a matching Y profile between distantly-related males is negligible [5] . Our results confirm that in practice matches only occur between males connected by a lineage path of at most a few tens of meioses . Thus , Q can have many matching relatives , but the relatedness will typically be too distant to be recognised . Thus an attempt to exclude known patrilineal relatives of Q as alternative sources of the DNA will typically be of limited value . Similarly , while matching surnames can be a guide to matching Y profiles in many societies [6] , the correlation may not be strong enough for this to be useful in eliminating alternative sources . The number of matching patrilineal relatives of Q is insensitive to population size , and the latter is difficult to define in forensic contexts . Thus , unlike for autosomal profiles , it is difficult to develop an approach to weight-of-evidence based on population match probabilities . Moreover , in most approaches to weight-of-evidence for Y profiles , a count of the profile in a database plays a central role [7–11] , but because of ( i ) the large number of distinct Y profiles , ( ii ) the concentration of matching profiles in extended patrilineal clans that may be clustered geographically or socially [12] , and ( iii ) the fact that the databases are not scientific random samples [13] , database information is of limited value , and difficult to interpret in a valid and useful way [2 , 5] . For these reasons we believe that current approaches to evaluating Y profile evidence are unsatisfactory . Proposals have been made that use population genetic models to allow for coancestry [11 , 14] , but the problem remains of setting the coancestry parameter . The appropriate value depends on the fraction of patrilineal relatives of Q in the relevant population , which is not well-defined in most crime cases . Coalescent-based modelling of the match probability can make full use of the available information [15] , but is computationally demanding and requires the database to be a random sample from the relevant population . Instead of a population fraction or match probability , we focus on the number of males with Y profile matching that of Q , and we propose a simulation model , implemented in easy-to-use software , to approximate its distribution ( Fig 2 ) . Key parameters of the model include the locus mutation rates , the variance in reproductive success ( VRS ) and the population growth rate . We extend our simulation framework to provide a novel approach to using counts from a database assumed to be sampled randomly in the relevant population . While this assumption may be unrealistic , it serves to illustrate the limited value of database counts even in this optimistic setting . In contrast with other methods , our approach easily and correctly takes into account a zero database count for the profile of Q . Our software and method allow Y profile evidence to be quantified in a way that is valid and directly interpretable to courts .
We consider three Y-STR profiling kits: Yfiler ( 17 loci ) , PowerPlex Y23 ( 23 loci ) , and Yfiler Plus ( 27 loci ) . Mutation count data for these loci are given in S1 Table . All Yfiler loci are present in the other two kits , but PowerPlex Y23 has two loci not present in Yfiler Plus ( DYS549 and DYS643 ) and Yfiler Plus has six loci not present in PowerPlex Y23 ( DYS627 , DYS460 , DYS518 , DYS449 and two loci at DYF387S1 ) . All three kits include both copies of the duplicated locus DYS385 . In our simulation study , DYS385a and DYS385b are treated as two independent loci , whereas in practice the order of the duplicated locus is unknown and so e . g . allele pair 13/14 cannot be distinguished from 14/13 . We use the same mutation rate for DYS385a and DYS385b , estimated by halving the reported mutation count for locus DYS385 . Yfiler Plus includes another duplicated locus , DYF387S1 , for which similar comments apply . We adopt a Wright-Fisher model , both with a constant per-generation population size N and with a growth rate of 2% per generation . Because the profile mutation rate is high , |Ω| , the number of live males with Y profile matching that of Q , is usually at most several tens , and provided that N > 103 , it is insensitive to N . Although larger than necessary , we chose N = 105 for the constant population size , and we verified empirically that increasing this to N = 106 led to the same results . The growing population had initial size 7 , 365 growing over the simulation to 106 in the final generation . We generate Y profiles for the live individuals by assigning allele 0 at all loci for each founder , and running a neutral , symmetric , single-step mutation process at each locus , forward in time over G = 250 generations , so that with high probability all lines of descent from founder to current generation include multiple mutations . We estimate locus mutation rates using the data from S1 Table , allowing for uncertainty by assuming a Beta ( 1 . 5 , 200 ) prior distribution ( S1 Fig ) , so that the posterior distribution given the count data is Beta ( x + 1 . 5 , y + 200 ) , where x and y are the numbers of meioses in which mutations did and did not occur for that locus . The symmetric , single-step mutation model is a reasonable first approximation to the STR mutation process [16 , 17] and adequate here because most of the matches of interest are between individuals sharing the same haplotype with no intervening mutations , in which case only the mutation rate is important , not any other detail of the mutation process . However , some matches arise with intervening mutations that cancel , and so any deficiencies in this model can have a small impact on our results . The results presented below are based on 5 × 105 values of |Ω| for each profiling kit , value of VRS and growth rate: we repeated the genealogy simulation 5 times , and for each of these we simulated the mutation process 100 times , each time starting by resampling each mutation rate from its posterior distribution . For each population/mutation simulation we made 103 selections of Q , each time recording the number of his Y-profile matches and the number of meioses between each matching pair . Competition for mates and other factors can lead to a high variance in reproductive success among males in many non-human species and some human societies , notably those in which polygyny is practised [18] . We perform simulations to investigate the extent to which variability in reproductive success affects the number of Y profiles matching that of Q . We posit a symmetric Dirichlet distribution that specifies the probability for each man in a generation to be the father of an arbitrary male in the next generation . To avoid dependence on population size , we work with relative paternity probabilities that have mean one and variance denoted VRS . The standard Wright-Fisher model assigns all N men a relative probability of paternity equal to one , and VRS = 0 . We also consider values 5 and 1 for the parameter of the symmetric Dirichlet , which correspond to VRS = 0 . 2 and VRS = 1 , respectively . When VRS = 0 . 2 , a man’s relative paternity probability has 95% probability to lie between 0 . 32 and 2 . 05 , and in a constant-size population the corresponding standard deviation in offspring count is about 1 . 1 , which is close to an estimate of 1 . 07 for the modern US population [19] . VRS = 1 implies that each man’s relative paternity probability has an Exponential ( 1 ) distribution , with 95% equal-tailed interval from 0 . 025 to 3 . 7 . The corresponding standard deviation in offspring count is about 1 . 4 , which is very high for a modern developed society but higher values have been recorded in human societies [18] . The available Y-profile databases are not random samples from a well-defined population . Although sampling biases can also affect databases of autosomal profiles , the mixing effect of recombination reduces their impact , whereas these biases can be important for Y profiles . We are unable to mimic database selection processes , which are diverse , and the populations from which real databases are sampled are often only loosely defined . Therefore we cannot rigorously test the realism of our simulations against empirical data , but some comparison is informative . We compared results from our population and mutation simulations against data from 6 databases of PowerPlex Y23 profiles drawn from: Central Europe ( n = 5 , 361 ) , Eastern Europe ( n = 1 , 665 ) , Northern Europe ( n = 903 ) , Southeastern Europe ( n = 758 ) , Southern Europe ( n = 1 , 462 ) and Western Europe ( n = 2 , 590 ) [20] . For each real database , we randomly sampled databases with the same n from simulated populations with constant population sizes N = 105 and N = 106 . For each profiling kit and value of N , we fixed VRS = 0 . 2 and repeated the population simulation 10 times , for each population we simulated the mutation process 10 times , and for each of these we simulated 10 databases . Hence , each boxplot in Fig 3 is based on 1 , 000 datapoints . Given a population and mutation simulation , we treat the males in the final three generations as potentially of interest , for brevity we label them as “live” . We choose one at random among the live males to represent Q , the queried source of the Y profile and then identify the set Ω of live males carrying the same Y profile as Q . The males in Ω represent the set of potential sources of the crime scene DNA profile , including Q himself . Depending on the case circumstances , some males with Y profiles matching Q will be of an age or live in a location that makes them unlikely to be a source of the crime scene DNA . A forensic DNA expert is not usually entitled to make these judgments , which are a matter for the court and we do not attempt to model such information here . A low database count of the profile suggests a low population count . However because in a large population almost all Y profiles are very rare , a profile count from a database of modest size ( typically in practice up to a few thousand ) provides little information [5] . To quantify this , we modify the distribution of |Ω| by conditioning on a count of m copies of the profile of Q in a database of size n . Here , we assume that the database has been sampled randomly from the live males , which is typically unrealistic but can be useful for illustration . We could have obtained the required approximation in our simulation framework as follows: for every simulation of Ω , we sample a database of size n at random from the live males , and if the number of males from Ω included in the database equals m , we retain the simulation , otherwise we reject it . However , this rejection sampling approach can be inefficient , particularly when m > 1 . Instead we use importance sampling to reweight the |Ω| values to reflect conditioning on m . Writing π for the fraction of the live individuals that match the profile of Q , the required weights are the binomial probabilities of observing m copies of the profile in the database of size n: ( nm ) πm ( 1−π ) n−m , ( 1 ) normalised to have an average of one over the full simulation [21] . Intuitively , a large value of m is implausible if |Ω| is small , and hence π is close to zero , resulting in a low weight . To assess the efficiency of the importance sampling , the effective sample size ( ESS ) was calculated [21] for n = 100 and 1 , 000 and for m from 0 to 6 . The results from the simulations with constant population size ( S2 Fig ) , show that the ESS decreases rapidly as m increases , so that the approximation to the conditional distribution of |Ω| is typically poor for m > 3 .
Fig 3 shows a broad similarity between real and simulated databases , with properties of real databases often lying between the N = 105 and N = 106 simulated databases . However these values of N are much lower than the census male populations corresponding to the database labels , suggesting restricted sampling . Northern Europe is an outlier , which could reflect non-random sampling . Finland is greatly over-represented in the Northern Europe database relative to its population size , whereas for example Norway is not represented . Moreover , samples are obtained from just a few centres per country , without comprehensive coverage of the population . In the north of Finland lives one of the most important population isolates in Europe and its over-representation could substantially affect database frequencies . The two most common profiles were both restricted to Finnish samples . Fig 4 shows , for each of 18 combinations of profiling kit , VRS and constant/growing population size , the distribution of |Ω| , the number of live males with profile matching that of Q . Here “live” means in the final three generations of the population simulation . As expected , |Ω| tends to be larger when the haplotype mutation rate is lower . However , the 18 distributions are all highly dispersed and overlap substantially . If Q is a defendant who denies being a source of the DNA , larger values of |Ω| are more helpful to his case . Therefore , consistent with the recommendation [11] to use an upper 95% confidence interval for the population frequency , if a court wishes to consider scenarios that are helpful to the defendant while remaining realistic , it might regard the 95% quantile as a useful summary of the distribution of |Ω| ( Table 1 ) . Fig 5 shows for the same 18 parameter combinations as Fig 4 , the distribution of Δ , the number of meioses between Q and another male with matching Y profile . We see that matching males are predominantly separated from Q by a handful of meioses ( such as uncles and cousins ) . There exist some matches with Δ > 20 but almost all have Δ < 50 . While Δ > 20 is too remote for the relatedness to be recognised , for random pairs in a population of size 105 there is probability <0 . 001 of Δ < 50 , which implies that “random man” match probabilities can be misleading for Y profiles . From Fig 5 we can infer that Y profile matches between distantly-related males are so unlikely as to be negligible . In the limit as their common ancestor is increasingly further back in the past ( that is , as Δ increases ) , the match probability converges to the product of population allele fractions , which is negligibly small for a full Y profile from a modern kit . Our simulations exaggerate this possibility because all founders 250 generations in the past are assigned the same haplotype . Nevertheless for our most discriminating kit , Yfiler Plus , no matches were observed between any pair of descendants of distinct founders over a total of 4 . 5 million population simulations . Table 2 gives properties of the conditional distribution of |Ω| given three values of the database count m , for each of three database sizes n , drawn from a simulated constant-size population with VRS = 0 . 2 . As expected , |Ω| increases stochastically with m , but the distributions substantially overlap and the practical impact of the dependence on m for the decision process of a court may be only modest . For example , with the most discriminatory kit that we consider , Yfiler Plus , the upper 95% quantile of the unconditional distribution of |Ω| is 37 ( Table 2 , row 1 ) . If , however , we note that the profile of Q is unobserved in a database of size n = 103 , this apparently useful information only has the effect of reducing the 95% quantile from 37 to 36 , even under optimistic assumptions about the database . In reality , the males in Ω are likely to be clustered geographically and/or socially , and the database is usually not a random sample from the population , further reducing the value of database information . Properties of conditional distributions for other VRS values and growth models are given in S3 Table ( VRS = 0 . 2 , constant population size ) , S4 Table ( VRS = 1 , constant population size ) , S5 Table ( VRS = 0 , constant population size ) , S6 Table ( VRS = 0 . 2 , population growth ) , S7 Table ( VRS = 1 , population growth ) , S8 Table ( VRS = 0 , population growth ) .
Although the distribution of |Ω| varies with demographic parameters such as VRS and population growth rate , which cannot be known exactly for a specific court case , we have shown ( Fig 4 ) that the distributions are insensitive to major changes in parameter values , relative to the precision necessary for a juror’s reasoning: whether the number of matching individuals in the population is 40 or 50 or 60 is unlikely to have much impact on a juror’s decision , but orders of magnitude may well be important . Based on the results from simulations such as those underlying Fig 4 , an expert could propose a suitable summary of the distribution of |Ω| over a range of plausible parameter values , and this could be presented at court along the following lines: If there is database frequency information , the court could be further advised , for example: The impact of this information may be minimal , and it could perhaps be omitted except that courts may be expecting to hear database information . Depending on the circumstances of the case , a judge might further instruct members of the jury: It should be clear from these considerations that a matching Y profile , taken alone , can never suffice to establish convincingly that Q is a source of the crime-scene DNA . However , it remains very powerful evidence that can reduce the number of alternative sources from perhaps several millions , for a crime in a large city , down to just a few tens . If an autosomal DNA profile is also available that includes Q then the remaining task for the prosecution of establishing that Q is the source , rather than one of the 40 or so potential Y-matchers , will usually be readily achievable even if the autosomal profile is complex , for example due to DNA from multiple sources . Alternatively , non-DNA evidence can suffice to complete the task of convincing a jury that Q is the source of the Y profile . We have here only considered DNA samples with a single male contributor . A similar approach can be applied for multiple male contributors , see S1 Text , S3 Fig and S9 Table . For autosomal DNA profiles a coancestry adjustment is generally recommended to allow for remote shared ancestry between Q and X , the hypothetical alternative source of the DNA . Similarly if Q and X might be close relatives , a match probability can be computed using explicit kinship parameters . Sampling adjustments are also sometimes recommended , based on database size . None of these adjustments is needed for the method proposed here , because all matches with related individuals are modelled in the simulations , as is the size of the database . Forensic weight-of-evidence is often best quantified using a ratio of likelihoods under prosecution and defence hypotheses , which in simple settings reduces to a match probability . We support that approach in general , but there are specific difficulties applying it to Y profiles , because the match probability for an alternative to Q will depend strongly on Δ , the number of meioses separating that individual from Q . We therefore propose a different approach , reporting to the court an estimate of the number of males with matching Y profiles . Estimating the number of matching individuals in the population has been recommended in the past for autosomal DNA profiles . In the mid-1990s , when autosomal DNA profile match probabilities were not as minuscule as they have become , the England and Wales Court of Appeal recommended that , instead of a match probability , courts be informed of the expected number of matching individuals in a relevant population [3] ( see Section 11 . 4 . 3 ) . This recommendation was followed for some time , until autosomal match probabilities became too small for the approach to be helpful to jurors . In addition , for complex profiles the concept of the number of matching individuals is problematic , because the “match” may only be partial . For older Y-profiling kits with lower profile mutation rate , or when only a few loci generate usable results due to poor sample quality , it may be appropriate to use a standard match probability approach . This is because there will be many matching individuals in the population , so that profile population fractions are larger and so can be better estimated from databases , with sampling biases being less important than when matching individuals are predominantly closely related to Q . Despite their lack of discriminatory power relative to autosomal or even Y profiles , mitochondrial DNA ( mtDNA ) profiles can be invaluable when matrilineal relatedness is of interest or when nuclear DNA is unavailable , for example in old bones , teeth or hair shafts , or for some highly-degraded samples [22] . Similar issues arise for mtDNA profiles as for Y profiles , but because the mutation rate for the entire mtDNA genome is an order of magnitude lower than for current Y profiling kits , the sets of matching individuals tend to be much larger . This makes the problems of relatedness of matching individuals and database sampling biases less severe , though still important . Our open source ( Apache License ) R [23] package malan ( MAle Lineage ANlysis ) available at https://github . com/mikldk/malan performed the analyses described in this paper . A vignette demonstrating the functionality of malan is available in the package . We also provide an online demonstration app based on malan but with limited functionality , available at https://mikldk . shinyapps . io/ychr-matches/ . Mutation count data are provided for the Yfiler , PowerPlex 23 and Yfiler Plus kits . | Y-chromosome DNA profiles are important in forensic science , particularly when a male has been accused of assaulting a female . However , unlike for autosomal profiles , the problem of evaluating weight-of-evidence for Y profiles has not been satisfactorily resolved despite many attempts . The key idea missing from current approaches is that Y-profile matches are due to patrilineal relatedness that is typically too remote to be recognized , but close compared with the relatedness of random pairs from the population . We focus on approximating the number of males with matching Y profiles , rather than a population fraction or match probability . We describe a simulation model of Y profile evolution , implemented in open-source software , for approximating the number of males sharing a Y profile . We extend our simulation method to also model database selection . Even under the optimistic assumption that the database has been sampled randomly in the relevant population , we show that database counts don’t help much . The reason is that modern profiling kits with high profile mutation rates imply that almost all profiles are rare relative to typical database sizes . We discuss how to use our approach to present evidence in court in a way that is both fair and easy to understand . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"crime",
"clinical",
"laboratory",
"sciences",
"sociology",
"geographical",
"locations",
"social",
"sciences",
"genetic",
"mapping",
"simulation",
"and",
"modeling",
"mutation",
"criminology",
"mutation",
"databases",
"population",
"biology",
"research",
"and",
"analysis",
"methods",
"law",
"and",
"legal",
"sciences",
"biological",
"databases",
"people",
"and",
"places",
"population",
"metrics",
"haplotypes",
"population",
"size",
"diagnostic",
"medicine",
"heredity",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"europe",
"forensics",
"population",
"growth"
] | 2017 | How convincing is a matching Y-chromosome profile? |
Rhodococcus equi causes fatal pyogranulomatous pneumonia in foals and immunocompromised animals and humans . Despite its importance , there is currently no effective vaccine against the disease . The actinobacteria R . equi and the human pathogen Mycobacterium tuberculosis are related , and both cause pulmonary diseases . Recently , we have shown that essential steps in the cholesterol catabolic pathway are involved in the pathogenicity of M . tuberculosis . Bioinformatic analysis revealed the presence of a similar cholesterol catabolic gene cluster in R . equi . Orthologs of predicted M . tuberculosis virulence genes located within this cluster , i . e . ipdA ( rv3551 ) , ipdB ( rv3552 ) , fadA6 and fadE30 , were identified in R . equi RE1 and inactivated . The ipdA and ipdB genes of R . equi RE1 appear to constitute the α-subunit and β-subunit , respectively , of a heterodimeric coenzyme A transferase . Mutant strains RE1ΔipdAB and RE1ΔfadE30 , but not RE1ΔfadA6 , were impaired in growth on the steroid catabolic pathway intermediates 4-androstene-3 , 17-dione ( AD ) and 3aα-H-4α ( 3′-propionic acid ) -5α-hydroxy-7aβ-methylhexahydro-1-indanone ( 5α-hydroxy-methylhexahydro-1-indanone propionate; 5OH-HIP ) . Interestingly , RE1ΔipdAB and RE1ΔfadE30 , but not RE1ΔfadA6 , also displayed an attenuated phenotype in a macrophage infection assay . Gene products important for growth on 5OH-HIP , as part of the steroid catabolic pathway , thus appear to act as factors involved in the pathogenicity of R . equi . Challenge experiments showed that RE1ΔipdAB could be safely administered intratracheally to 2 to 5 week-old foals and oral immunization of foals even elicited a substantial protective immunity against a virulent R . equi strain . Our data show that genes involved in steroid catabolism are promising targets for the development of a live-attenuated vaccine against R . equi infections .
Rhodococcus equi is a nocardioform Gram-positive bacterium and a facultative intracellular pathogen that causes fatal pyogranulomatous bronchopneumonia in young foals aged up to five months . R . equi is also an emerging opportunistic pathogen of immunocompromised humans , particularly HIV infected patients [1]–[3] . Like Mycobacterium tuberculosis , the causative agent of tuberculosis ( TB ) in man , R . equi is able to infect , survive and multiply inside the host cells mainly in alveolar macrophages [4]–[7] . R . equi and M . tuberculosis are both members of the class of Actinomycetales and share physical , biochemical and cell biological characteristics [2] . Antibiotic treatment of R . equi infections is not consistently successful and is costly due to the necessity of treatment for a prolonged period of time [8] . More importantly , there is currently no safe and effective vaccine against R . equi infections . Virulence of R . equi is dependent on the presence of a plasmid ( approx . 85–95 kb ) which is essential for R . equi to survive and grow in macrophages [9]–[13] . This virulence plasmid carries a pathogenicity island , encoding a number of related virulence associated proteins ( Vaps ) that includes the immunodominant surface expressed protein VapA [9] , [10] , [14] . Following infection with R . equi , the presence of the VapA-expressing virulence-associated plasmid is believed to promote necrotic damage to the host , which is strongly pro-inflammatory [15] , [16] . VapA is not required for host cell necrosis , but has been implicated in early phagosome development [17] . Consistent with this role , mutational analysis showed that vapA , unlike vapG , is indispensable for multiplication of R . equi in macrophages and its persistence in mice [12] , [18] . Indeed , VapA has been most widely investigated in vaccine studies for the prevention of R . equi infections . Oral vaccination of mice with an attenuated Salmonella enterica Typhimurium vaccine strain expressing VapA protein , for example , has been shown to confer protection against virulent R . equi [19] , [20] . DNA vaccines encoding vapA have also been shown to stimulate cell-mediated immunity [21] , [22] . Besides the vap genes , only a limited number of other virulence genes have been identified in R . equi to date . Random transposon mutagenesis using Himar1 transposition in R . equi identified a metabolic gene essential for riboflavin biosynthesis . The riboflavin auxotrophic mutant was shown to be fully attenuated in immunocompromised mice and could be safely administered to young foals [23] , [24] . Immunization of young foals with the riboflavin auxotrophic mutant , however , did not afford protection against a virulent R . equi challenge [24] . choE , encoding the extracellular cholesterol oxidase in R . equi , is believed to be involved in macrophage destruction [25] , but is not essential for virulence [26] , [27] . Isocitrate lyase , a key enzyme of the glyoxylate bypass encoded by aceA , was shown to be important for virulence of R . equi . An aceA mutant was unable to proliferate in macrophages , was attenuated in mice and , when administrated to 3-week-old foals , did not induce pneumonic disease [28] . Crucially , a choE aceA double mutant in some cases was still able to induce severe pneumonia in 1-week-old foals , indicating that the mutant was not fully safe [27] . Attenuated mutants of R . equi were also obtained by targeted mutagenesis of htrA , narG , or pepD [29] . pepD in M . tuberculosis H37Rv is controlled by the two-component regulatory system MprA-MprB [30] . Consistent with this , the sensor kinase MprB of R . equi 103 was recently found to be required for intracellular survival [31] . So far , however , none of the strategies or identified virulence factors has resulted in the development of a safe vaccine capable of providing protective immunity against R . equi infection in young foals . In addition to its pathogenic life-style , R . equi also thrives as a soil-dwelling microorganism capable of rapid growth in soil and manure using steroids , such as cholesterol , as sole carbon and energy sources [32]–[34] . Microbial steroid degradation of cholesterol proceeds via the formation of 4-androstene-3 , 17-dione ( AD ) , methylhexahydroindane-1 , 5-dione propionate ( HIP; 3aα-H-4α ( 3′-propionic acid ) -7aβ-methylhexahydro-1 , 5-indanedione ) and 5-hydroxy-methylhexahydro-1-indanone propionate ( 5OH-HIP ) as pathway intermediates ( Fig . 1 ) [35]–[36] . The cholesterol catabolic pathway has been implicated in the pathogenicity of M . tuberculosis H37Rv [36]–[39] . Inactivation of the Mce4 cholesterol transporter in R . equi RE1 , however , did not reveal an essential role of cholesterol catabolism in R . equi macrophage survival [34] , [40] . Transposon mutagenesis had previously defined a subset of genes required for the survival of M . tuberculosis in murine macrophages . Amongst several others , rv3551 and rv3552 were predicted to be essential for the survival of M . tuberculosis H37Rv in vitro in macrophages [41] . Interestingly , rv3551 and rv3552 are part of the cholesterol catabolic gene cluster ( [36]; Fig . S1 ) . The close phylogenetic relationship between M . tuberculosis and R . equi prompted us to hypothesize that the predicted critically important genes of the cholesterol catabolic pathway in M . tuberculosis H37RV also are important for the pathogenicity of R . equi RE1 . In this study , we identified the orthologs of rv3551 and rv3552 , designated ipdA and ipdB , respectively , within the cholesterol catabolic gene cluster of R . equi 103S . The ΔipdAB mutant of R . equi RE1 was impaired in growth on the steroid catabolic pathway intermediates AD and 5OH-HIP . We also observed that RE1ΔipdAB was attenuated in vitro in macrophages . RE1ΔipdAB could be safely administered to 2–5 week-old foals intratracheally and oral immunization provided a substantial protection against R . equi infection . The data suggests that genes important for methylhexahydroindanone propionate ( HIP , 5OH-HIP ) degradation , as part of the steroid catabolic pathway , are promising targets for the development of a live-attenuated vaccine against R . equi infections .
Bioinformatic analysis of the sequenced genome of R . equi 103S [42] revealed the presence of a cholesterol catabolic pathway ( Fig . S1 ) . Within the cholesterol catabolic gene cluster , two genes , i . e . ipdA ( REQ_07170 ) and ipdB ( REQ_07160 ) , encode proteins that are highly similar to Rv3551 ( 69% identity ) and Rv3552 ( 67% identity ) of M . tuberculosis H37Rv , respectively . The similarities of IpdA and IpdB are comparable to those observed between other homologous proteins of the cholesterol catabolic gene clusters of R . equi 103S and M . tuberculosis H37Rv ( Table S1 ) . The operonic structure of rv3551 and rv3552 in strain H37Rv was conserved in R . equi 103S ( Fig . S1 ) . Unlike H37Rv , the genome of R . equi 103S encoded a second set of paralogous proteins , designated IpdA2 and IpdB2 , respectively , with highest protein sequence similarities to IpdA ( 55% identity ) and IpdB ( 51% identity ) , respectively . This second set of genes , designated ipdA2 ( REQ_00850 ) and ipdB2 ( REQ_00860 ) , was located outside of the cholesterol catabolic gene cluster and , unlike the ipdAB operon , was not clustered with an echA20 paralog . IpdA carries the PF01144 signature motif of heterodimeric coenzyme A transferases ( http://pfam . sanger . ac . uk; [43] ) as well as the COG1788 signature ( http://www . ncbi . nlm . nih . gov/Structure/cdd/cdd . shtml ) of AtoD , the α subunit of acetoacetyl-CoA transferase of E . coli . Moreover , IpdB contained the COG2057 signature motif of AtoA , the β subunit of acetoacetyl-CoA transferase of E . coli . Highest overall amino acid sequence similarity of IpdA and IpdB with characterized proteins in databases was observed with ORF1 ( 41% identity ) and ORF2 ( 36% identity ) of Comamonas testosteroni TA441 , representing the putative α and β subunits of a CoA-transferase , respectively , involved in testosterone catabolism [44] . Mutational analysis in C . testosteroni TA441 suggested that ORF1 is probably involved in the steroid degradation pathway at a step after ring cleavage into HIP and 2-hydroxyhexa-2 , 4-dienoic acid ( Fig . 1; [44] ) . Since tesE , tesF and tesG are thought to encode the enzymes necessary to degrade 2-hydroxyhexa-2 , 4-dienoic acid [45] , ORF1 is likely to play a role in HIP degradation . Thus , we hypothesized that IpdA and IpdB of R . equi most likely constitute the α-subunit and β-subunit , respectively , of a heterodimeric coenzyme A transferase involved in steroid catabolism , more specifically in methylhexahydroindanone propionate degradation . To substantiate the predicted roles of ipdAB and ipdA2B2 in steroid catabolism , we constructed R . equi unmarked gene deletion mutant strains RE1ΔipdAB , RE1ΔipdA2B2 and RE1ΔipdABΔipdA2B2 using the two-step homologous recombination strategy with 5-fluorocytosine counter-selection [34] . Deletion of the target genes ipdAB and/or ipdA2B2 was confirmed by PCR for all three mutant strains ( Table S2 , data not shown ) . PCR amplicons of the expected sizes were obtained for RE1ΔipdAB mutant ( 296 bp ) , RE1ΔipdA2B2 ( 123 bp ) and RE1ΔipdABΔipdA2B2 ( 296 bp and 123 bp , respectively ) . Analyses of the upstream and downstream regions of the deleted loci by PCR further confirmed genuine gene deletions and the absence of aberrant genomic rearrangements for all three mutants ( Table S2 ) . The presence of vapA as a marker for the virulence plasmid was also confirmed by PCR in each of the mutants ( Table S2 ) . The growth of all three mutant strains on standard acetate mineral media was comparable to wild type strain RE1 ( data not shown ) . Wild type strain RE1 also showed good growth on the steroid substrate AD as a sole carbon and energy source . By contrast , mutant strain RE1ΔipdAB was severely impaired in growth on AD ( Fig . 2A ) . RE1ΔipdAB displayed an extensive lag-phase in growth of more than 24 h compared to wild type strain RE1 . This growth phenotype of RE1ΔipdAB was fully complemented following the introduction of a 4 , 453 bp DNA fragment carrying wild type ipdAB under its native promoter ( Table S2 ) , restoring growth on AD to levels comparable to the wild type ( Fig . 2C ) . Since RE1ΔipdABΔipdA2B2 showed complete blockage of growth on AD , the observed growth of RE1ΔipdAB following the lag-phase appeared to be due to the presence of the paralogous gene set ipdA2B2 partly complementing the ipdAB mutation ( Fig . 2A ) . RE1ΔipdA2B2 on the other hand was not impaired in growth on AD and grew comparably to wild type strain , indicating that ipdAB , located within the cholesterol gene cluster , is the dominant ipd gene set involved in steroid catabolism . Next , we investigated whether ipdAB and ipdA2B2 were involved in the catabolism of one of the predicted methylhexahydroindanone propionate intermediates of steroid degradation , i . e . 5OH-HIP ( Fig . 2B ) . The growth phenotype of the mutants in the presence of 5OH-HIP as the sole carbon and energy source was comparable to those observed for AD: an extensive lag-phase in bacterial growth on 5OH-HIP was observed for strain RE1ΔipdAB , whereas hardly any impairment in growth was observed for RE1ΔipdA2B2 . Finally , mutant strain RE1ΔipdABipdA2B2 displayed no growth on 5OH-HIP at all . To further substantiate the predicted involvement of ipdAB and ipdA2B2 in methylhexahydroindanone propionate degradation , we performed whole cell biotransformation experiments with cell cultures grown in mineral acetate medium and incubated with AD . Wild type strain RE1 fully degraded AD ( 0 . 5 g/l ) within 24 hours without the accumulation of HIP or 5OH-HIP ( data not shown ) . Accumulation of HIP from AD , however , was observed for mutant strain RE1ΔipdABΔipdA2B2 ( Fig . 3 ) . A temporary accumulation of HIP , 24 hours after the addition of AD , was also detected in biotransformations with strain RE1ΔipdAB ( data not shown ) . HIP formed by RE1ΔipdAB was , however , fully degraded after 120 hours of incubation ( Fig . 3 ) , consistent with its growth phenotypes on AD and 5OH-HIP . Thus , the ipdAB genes , encoding a heterodimeric CoA transferase in R . equi RE1 , are important for growth on steroids and fulfil a role in the lower part of the steroid catabolic pathway , more specifically in methylhexahydroindanone propionate degradation . To investigate whether the ipdAB genes of R . equi RE1 are important for survival in macrophages , analogously to the predicted important role of rv3551 and rv3552 in M . tuberculosis H37Rv [41] , in vitro macrophage infection assays were performed . Macrophage infection experiments showed that strain RE1ΔipdAB and strain RE1ΔipdABΔipdA2B2 were significantly attenuated , comparable to the avirulent R . equi strain 103- lacking the virulence plasmid [10] ( Fig . 4A ) . Control experiments with virulent wild type strain R . equi RE1 showed that the parent strain was able to infect macrophages ( Fig . 4A ) . Inactivation of ipdAB was sufficient to significantly impair macrophage infection by R . equi RE1 and additional deletion of ipdA2B2 had no further attenuating effect ( Fig . 4A ) . Consistent with this result , inactivation of ipdA2B2 alone did not result in attenuation , indicating that ipdAB is the dominant gene set involved in R . equi RE1 pathogenicity ( Fig . 4B ) . The attenuation of RE1ΔipdAB was fully complemented by the introduction of wild type ipdAB ( Fig . 4C ) , excluding the possibility that the attenuation was due to a mutation unrelated to ipdAB . To investigate whether other genes with a role in steroid catabolism are important for macrophage infection by R . equi RE1 we constructed additional gene deletion mutants . We chose to inactivate two other genes that were located in close proximity to ipdAB within the cholesterol catabolic gene cluster and had been predicted as important for survival of M . tuberculosis H37Rv in macrophages , i . e . fadE30 ( REQ_07030 ) and fadA6 ( REQ_07060 ) ( Fig . S1; [36] , [41] ) . Mutant strains RE1ΔfadA6 and RE1ΔfadE30 were subsequently tested for growth on AD and 5OH-HIP as sole carbon and energy sources . RE1ΔfadE30 was severely impaired in growth on AD and growth on 5OH-HIP was fully blocked ( Fig . 2 ) . The growth phenotype of RE1ΔfadE30 was fully complemented following the introduction of wild type fadE30 under its native promoter ( Table S2 ) , restoring growth on AD and 5OH-HIP to levels comparable to wild type ( Fig . 2C and 2D ) . Consistent with the growth phenotypes of RE1ΔfadE30 , cell cultures of mutant strain RE1ΔfadE30 accumulated 5OH-HIP during biotransformation of AD ( Fig . 3 ) . Thus , fadE30 plays an essential role in steroid catabolism at the level of methylhexahydroindanone propionate degradation . By contrast , RE1ΔfadA6 was not affected and able to rapidly grow on both 5OH-HIP and AD , comparable to parent strain RE1 ( Fig . 2 ) . This suggests that fadA6 of R . equi RE1 is not essential for AD and 5OH-HIP catabolism . However , further analysis revealed that the genome of R . equi 103S codes for an apparent paralog of FadA6 ( REQ_21310 ) with 70% protein sequence identity . The possibility that fadA6 of RE1 is involved in steroid catabolism , but is not essential due to the presence of the gene paralog , therefore cannot be excluded at this point . Macrophage infection assays revealed that strain RE1ΔfadE30 was significantly attenuated , comparable to that of the attenuated mutant strains RE1ΔipdAB and RE1ΔipdABΔipdA2B2 , and the avirulent control strain R . equi 103- ( Fig . 4A ) . The attenuation of RE1ΔfadE30 could be fully reversed by the introduction of wild type fadE30 , indicating that attenuation was solely due to fadE30 gene inactivation ( Fig . 4C ) . Interestingly , RE1ΔfadA6 was not attenuated and showed survival curves similar to parent strain RE1 ( Fig . 4B ) , consistent with our hypothesis that R . equi RE1 mutant strains impaired in growth on methylhexahydroindanone propionate are attenuated . The attenuated phenotype of RE1ΔipdAB in our in vitro macrophage infection model suggested that strain RE1ΔipdAB also might be attenuated in foals . This prompted us to perform an in vivo intratracheal challenge experiment in young foals . In vivo attenuation of the RE1ΔipdAB mutant was tested in foals aged 3–5 weeks . The foals were equally divided into two groups of three ( n = 3 ) . One group was challenged intratracheally with mutant strain RE1ΔipdAB ( 7 . 1×106 CFU ) and the other group with wild type strain RE1 ( 4 . 3×106 CFU ) as a control . During a period of 3 weeks post-challenge the foals were clinically scored . None of the foals challenged with RE1ΔipdAB developed signs of respiratory disease and no increase in rectal temperatures of these foals was observed ( Fig . 5 ) . By contrast , two out of three foals in the wild type infected group developed severe clinical signs of respiratory disease , coinciding with increased rectal temperatures from 14 days post-challenge onwards . One wild type infected foal showed only mild clinical signs post-challenge . Mean daily weight gains post-challenge were substantially higher for foals challenged with RE1ΔipdAB ( 27 . 9 ± 5 . 2% ) compared to those challenged with RE1 ( 18 . 9 ± 1 . 3% ) . Serum blood analyses revealed that the RE1ΔipdAB mutant strain was able to elicit a substantial serum antibody titer against R . equi , although the titers were lower than those observed in foals challenged with strain RE1 ( Fig . 6 ) . At 3 weeks post-challenge all foals were euthanized and subjected to a complete post-mortem examination . Foals challenged with wild type strain RE1 had developed typical pyogranulomatous pneumonia from which wild type R . equi successfully was re-isolated ( Table 1 ) . The lungs of the foals challenged with the mutant strain , on the other hand , did not reveal pneumonic areas and R . equi could not be isolated ( Table 1 ) . Consistent with these observations , the mean percentage lung-to-body weight of foals challenged with wild type RE1 ( 2 . 0 ± 0 . 6% ) was twice as high as those challenged with mutant strain RE1ΔipdAB ( 1 . 0 ± 0 . 06% ) . The challenge experiments indicated that RE1ΔipdAB was attenuated in young foals and able to induce an immunological response . To test RE1ΔipdAB as a live-attenuated vaccine-candidate in providing protective immunity against an intratracheal challenge with virulent R . equi , we performed an immunization experiment . Eight 2 to 4-week-old foals were used for this experiment and divided into two groups of four foals ( n = 4 ) . At T = 0 and at T = 14 days ( booster ) one group was vaccinated orally ( 1 ml ) with strain RE1ΔipdAB ( 5×107 CFU/animal ) and the other group was left as unvaccinated control . After vaccination , all foals remained healthy and no vaccine-related abnormalities were observed . Rectal temperatures remained normal in all foals ( data not shown ) . Strain RE1ΔipdAB could not be re-isolated from rectal swabs , indicating that the mutant strain did not massively colonize the alimentary tract . Serum blood analyses revealed substantial serum antibody titers against R . equi following vaccination ( Fig . 7 ) . These post-vaccination results were consistent with the results obtained from the challenge experiment and confirmed that mutant strain RE1ΔipdAB was attenuated in vivo and can be safely administered to young foals . All foals were subsequently challenged intratracheally with virulent strain R . equi 85F ( 5×106 CFU ) , displaying strong cytotoxicity [16] , two weeks after the booster vaccination ( T = 28 days ) . During a period of 3 weeks post-challenge the foals were clinically scored . Then foals were euthanized and subjected to a complete post-mortem examination with special attention to the lungs and respiratory lymph nodes as well as the gut and associated lymph nodes . All four foals in the control group showed increasing signs of respiratory disease from day 7 to 10 post-challenge onwards ( Fig . 8; T = 35–38 days ) . The control foals were euthanized 14 days post challenge ( T = 42 days ) for humane reasons . Post-mortem macroscopic and microscopic analysis confirmed pyogranulomatous pneumonia in the control foals with severe pulmonary consolidations from which wild type R . equi was re-isolated as identified by PCR ( Tables 2 and 3; Fig . 9 ) . Wild type R . equi was also isolated in high numbers from swollen mediastinal lymph nodes and in one foal from a caecal lymph node . By contrast , the vaccinated foals had much milder clinical signs or virtually no clinical signs at all ( Fig . 8 ) . Two vaccinated foals remained completely healthy and post-mortem macroscopic analysis did not reveal any signs of pyogranulomatous pneumonia . Two other vaccinates had locally developed pyogranulomatous pneumonia with pulmonary consolidations in the accessory and caudal lobes from which wild type R . equi was isolated ( Tables 2 and 3 ) . Overall , the numbers of wild type R . equi isolated from the lungs of the vaccinated foals were substantially lower than those found in the control group . We conclude that vaccination of young foals with strain RE1ΔipdAB is safe and induces a substantial protective immunity against a severe intratracheal challenge with a virulent R . equi strain .
The current study identified the cholesterol catabolic gene cluster in R . equi and showed that ipdAB and fadE30 located within this cluster are important for the pathogenicity of R . equi RE1 . Interestingly , R . equi RE1 mutants that displayed attenuated phenotypes in an in vitro macrophage infection assay were also impaired in steroid catabolism , i . e . RE1ΔipdAB , RE1ΔipdABΔipdA2B2 and RE1ΔfadE30 . Conversely , mutants that had AD growth phenotypes comparable to wild type strain RE1 , i . e . RE1ΔipdA2B2 and RE1ΔfadA6 , were not attenuated . Both fadE30 and ipdAB were also shown to be important for 5OH-HIP catabolism . Biochemical and physiological studies previously showed that the degradation of the propionate moiety of HIP and 5OH-HIP likely occurs via a cycle of β-oxidation [35] , [46]–[47] ( Fig . 1 ) . ATP-dependent CoA activation was suggested to be the first step in the degradation of HIP in R . equi ATCC14887 [46] . Protein sequence analysis revealed that IpdA and IpdB represent the α and β-subunit of a heterodimeric CoA-transferase . The heterodimeric CoA-transferase encoded by ipdAB thus might be involved in the removal of the propionate moiety of methylhexahydroindanone propionate intermediates ( i . e . HIP , 5OH-HIP ) by β-oxidation during steroid degradation ( Fig . 1 , step 1 ) . Consistent with such a role , HIP accumulation was observed in biotransformation experiments with cell cultures of RE1ΔipdABΔipdA2B2 incubated with AD ( Fig . 3 ) . FadE30 belongs to the family of acyl-CoA dehydrogenases and might catalyze the second step in the β-oxidation cycle that removes the propionate moiety following CoA activation by IpdAB , i . e . the dehydrogenation of 5OH-HIP-CoA ( Fig . 1 , step 3 ) . Accumulation of 5OH-HIP indeed was observed in biotransformation experiments with cell cultures of RE1ΔfadE30 incubated with AD ( Fig . 3 ) . Interestingly , ipdA and ipdB appear to be part of an operon encompassing echA20 ( Fig . S1 ) , encoding a putative enoyl-coA hydratase that might catalyse the subsequent step in the β-oxidation cycle during the degradation of the propionate moiety ( Fig . 1 , step 4 ) . However , functions of ipdAB , fadE30 and echA20 further down in the degradation pathway of these compounds cannot be excluded and need further investigation . A second set of paralogous genes , designated ipdA2 and ipdB2 , was additionally identified in R . equi RE1 which do not play an important role in AD or 5OH-HIP catabolism . Still , ipdA2B2 are involved in growth on AD and 5OH-HIP , since ipdA2B2 are able to support the growth of mutant strain RE1ΔipdAB on AD and 5OH-HIP , albeit after an extensive lag-phase ( Fig . 2 ) . The data suggests that the primary role of ipdA2B2 is not in AD or 5OH-HIP catabolism , but that they are recruited in the ΔipdAB mutant , perhaps through a genetic mutation . Protein sequence similarities between IpdA and IpdA2 and between IpdB and IpdB2 are relatively low , which suggests that IpdAB and IpdA2B2 are related proteins , but have different physiological functions . This is further supported by the genomic location of ipdA2 and ipdB2 in a region distant from the cholesterol catabolic gene cluster and with no apparent clustering of steroid genes . Consistently , ipdA2B2 does not appear to be involved in pathogenesis . Due to the likely different physiological function of ipdA2B2 in R . equi these genes may be expressed differently relative to ipdAB , or even not at all , during R . equi infection . Overall , our results strongly imply that the pathogenicity of R . equi correlates with the steroid catabolic pathway , in particular with methylhexahydroindanone propionate ( HIP , 5OH-HIP ) degradation . Several other examples of virulence-associated genes important for microbial steroid ring degradation have been reported . The kshA and kshB genes of M . tuberculosis H37Rv , for example , were shown to be essential for pathogenicity of H37Rv [39] . These genes encode the two-component iron-sulfur protein 3-ketosteroid 9α-hydroxylase , which is a key-enzymatic step in microbial steroid ring opening [48] . The steroid ring-cleaving dioxygenase HsaC , catalyzing the further breakdown of steroids towards methylhexahydroindanone propionate pathway intermediates , also contributes to the pathogenicity of M . tuberculosis H37Rv [38] . We do not yet understand why genes of the steroid catabolic pathway are important for the pathogenicity of R . equi . Considering that many steroids are known to have immuno-regulatory properties , steroids could play an important role during R . equi infection . In vivo , β-androstenes , such as 3β-hydroxy-5-androstene-17-one ( DHEA ) and 5-androstene-3β , 17β-diol , have been associated with immune-homeostasis during bacterial infection [49] . Thus , ipdAB , fadE30 and other genes involved in steroid ring degradation may help R . equi to disrupt the immune-homeostasis in a yet unknown way , favouring infection of the macrophage . Intriguingly , attenuated mutant strains RE1ΔipdAB , RE1ΔipdABΔipdA2B2 and RE1ΔfadE30 consistently showed significantly lower bacterial counts in our macrophage infection assay at T = 4 h post-infection ( Fig . 4A ) compared to wild type strains RE1 and avirulent strain 103− , which suggests that the attenuated mutants are affected in processes that occur early in the infection . Whether these processes are involved in immune-homeostasis or are related to some other process , such as impaired adherence or uptake of R . equi by the macrophage , remains to be elucidated . It is noteworthy to mention that , for reasons unknown , wild type R . equi strains RE1 and 103+ do not appear to replicate well in the human macrophage cell line U937 when compared to the replication of wild type R . equi in murine or equine primary macrophages . A subset of genes of the cholesterol gene cluster present in Mycobacterium smegmatis mc2155 , designated the kstR2 regulon , was recently shown to be controlled by the TetR-type transcriptional regulator kstR2 [50] . An apparent orthologue of kstR2 of M . smegmatis mc2155 was also found present in the cholesterol gene cluster of R . equi 103S , encoding a protein with 56% amino acid sequence identity and located between fadE30 and fadA6 ( Fig . S1 ) . Interestingly , the fadA6 , fadE30 and ipdAB orthologues in M . smegmatis mc2155 all are part of the kstR2 regulon [50] . Most likely , the kstR2 regulon of M . smegmatis mc2155 is involved in methylhexahydroindanone propionate catabolism . The presence of a putative kstR2 regulon in R . equi 103S raises the intriguing question whether all genes belonging to this regulon are important for R . equi pathogenicity . Several vaccination strategies have been explored to date in an attempt to prevent infection by the opportunistic horse pathogen R . equi . So far , these have not resulted in the development of a safe and effective vaccine against R . equi infection . Indeed , protection has been observed when wild type virulent R . equi was administrated orally [51]–[53] . However , this vaccination approach cannot be used due to the high risk of provoking disease and contamination of the environment . Immunization procedures using avirulent ( plasmid-less ) or killed R . equi cells , on the other hand , do not induce a protective immune response [52] and underline the importance of developing a live-attenuated vaccine strain . The administration of specific hyperimmune plasma currently has been the only method providing a positive effect in avoiding foals of an endemic farm to develop R . equi pneumonia [54]–[56] . The method , however , is expensive , labour intensive and not consistently effective [57]–[59] . Our strategy targeted genes in the cholesterol catabolic gene cluster of R . equi to develop a live-attenuated vaccine . Our data revealed that RE1ΔipdAB is a highly promising candidate for a live-attenuated vaccine strain providing substantial protective immunity . Full immunity following oral immunization with RE1ΔipdAB was not yet observed in the vaccination experiment , since two foals showed mild signs of pneumonic disease following a severe challenge with R . equi 85F ( Tables 2 and 3 ) . However , re-isolation of wild type R . equi was several log10 fold lower in lungs of immunized foals compared to those of non-vaccinated controls ( Table 3 ) , strongly suggesting that protection had not yet fully developed . Further optimization of the vaccination protocol to increase its efficacy , as well as field trials , is currently on the way to develop the first safe and effective live-attenuated vaccine against R . equi infection in young foals . The incidence of R . equi infection in humans has increased markedly with human immunodeficiency virus ( HIV ) infection , as well as with the development of organ transplantations and chemotherapy for malignancies [1] , [60]–[61] . The infection mortality rate is still high ( 20–25% ) , especially for AIDS patients ( 50–55% ) , and disease relapses are common [60] , [62] . The steroid catabolic pathway of R . equi therefore may provide interesting novel targets for drug development to treat R . equi infection in humans , as many of the catabolic enzymes have no human homolog .
R . equi RE1 was isolated from a foal with pyogranulomatous pneumonia in the Netherlands in September 2007 [34] . Strains R . equi 103+ [14] , R . equi 103− [63] and R . equi 85F [52] , [64] have been previously described . R . equi cell cultures were routinely grown at 30°C ( 200 rpm ) in Luria-Bertani ( LB ) medium consisting of Bacto-Tryptone , Yeast Extract and 1% NaCl , or mineral acetate medium ( MM-Ac ) containing K2HPO4 ( 4 . 65 g/l ) , NaH2PO4·H2O ( 1 . 5 g/l ) , sodium acetate ( 2 g/l ) , NH4Cl ( 3 g/l ) , MgSO4·7H2O ( 1 g/l ) , thiamine ( 40 mg/l , filter sterile ) , and Vishniac stock solution ( 1 ml/l ) . Vishniac stock solution was prepared as follows ( modified from Vishniac and Santer [65] ) : EDTA ( 10 g/l ) and ZnSO4 . 7H2O ( 4 . 4 g/l ) were dissolved in distilled water ( pH 8 . 0 using 2 M KOH ) . Then , CaCl2 . 2 H2O ( 1 . 47 g/l ) , MnCl2 . 7 H2O ( 1 g/l ) , FeSO4 . 7 H2O ( 1 g/l ) , ( NH4 ) 6 Mo7O24 . 4 H2O ( 0 . 22 g/l ) , CuSO4 . 5 H2O ( 0 . 315 g/l ) and CoCl2 . 6 H2O ( 0 . 32 g/l ) were added in that order maintaining pH at 6 . 0 and finally stored at pH 4 . 0 . For growth on 4-androstene-3 , 17-dione ( AD , 0 . 5 g/l ) or 3aα-H-4α ( 3′-propionic acid ) -5α-hydroxy-7aβ-methylhexahydro-1-indanone ( 5OH-HIP , 1 g/l ) as sole carbon and energy source sodium acetate was omitted from the medium . Stock solutions of 5OH-HIP ( 100 mg/ml ) , prepared by dissolving 100 mg 3aα-H-4α ( 3′-propionic acid ) -5α-hydroxy-7aβ-methylhexahydro-1-indanone-δ-lactone ( HIL ) in 1 ml 0 . 5M NaOH , and AD ( 50 mg/ml in dimethylsulfoxide ( DMSO ) ) were used . Cultures ( 50 ml ) were inoculated ( 1∶100 ) using pre-cultures grown in MM-Ac . Growth was followed by regular turbidity measurements ( OD600nm ) . Turbidity measurements of AD grown cultures could not be accurately determined due to high background of the AD suspension . Protein content of the culture was therefore used as a measure for biomass formation and was determined as follows . A sample ( 0 . 5 ml ) of the culture was pelleted by centrifugation ( 5 min 12 , 000 x g ) , thoroughly resuspended in 0 . 1 ml 1 M NaOH and boiled for 10 min . Then , 0 . 9 ml distilled water was added and the suspension was vortexed . An aliquot of 100 µl was mixed with 300 µl of distilled water and 100 µl of protein assay reagent ( BioRad ) . Protein content of the sample was determined using bovine serum albumin ( BSA ) as a standard as described by the manufacturer . For growth on solid media Bacto-agar ( 15 g/l; BD ) was added . 5-Fluorocytosine stock solution ( 10 mg/ml ) was prepared in distilled water , dissolved by heating to 50°C , filter-sterilized and added to autoclaved media . Escherichia coli DH5α was used as host for all cloning procedures . Restriction enzymes were obtained from Fermentas GmbH . Chromosomal DNA of cell cultures was isolated using the GenElute Bacterial Genomic DNA Kit ( Sigma-Aldrich ) according to the instructions of the manufacturer . PCR was performed in a reaction mixture ( 25 µl ) consisting of Tris-HCl ( 10 mM , pH 8 ) , 1x standard polymerase buffer , dNTPs ( 0 . 2 mM ) , DMSO ( 2% ) , PCR primers ( 10 ng/µl each ) and High-Fidelity DNA polymerase enzyme ( Fermentas ) or Pwo DNA polymerase ( Roche Applied Science ) . For colony PCR , cell material was mixed with 100 µl of chloroform and 100 µl of 10 mM Tris-HCl pH 8 , vortexed vigorously and centrifuged ( 2 min , 14 , 000 x g ) . A sample of the upper aqueous phase ( 1 µl ) was subsequently used as template for PCR . A standard PCR included a 5 min 95°C DNA melting step , followed by 30 cycles of 45 sec denaturing at 95°C , 45 sec annealing at 60°C and 1–3 min elongation at 72°C . The elongation time used depended on the length of the expected PCR amplicon , taking 1 . 5 min/1 kb as a general rule . Cells of R . equi strains were transformed by electroporation essentially as described [34] . Briefly , cell cultures were grown in 50 ml LB at 30°C until OD600 reached 0 . 8–1 . 0 . The cells were pelleted ( 20 min at 4 , 500 x g ) and washed twice with 10% ice-cold glycerol . Pelleted cells were re-suspended in 0 . 5–1 ml ice-cold 10% glycerol and divided into 200 µl aliquots . MilliQ-eluted plasmid DNA ( 5–10 µl; GenElute Plasmid Miniprep Kit , Sigma-Aldrich ) was added to 200 µl cells in 2 mm gapped cuvettes . Electroporation was performed with a single pulse of 12 . 5 kV/cm , 1000Ω and 25 µF . Electroporated cells were gently mixed with 1 ml LB medium ( R . equi ) and allowed to recover for 2 h at 37°C and 200 rpm . Aliquots ( 200 µl ) of the recovered cells were plated onto selective agar medium . R . equi transformants were selected on LB agar containing apramycin ( 50 µg/ml ) and appeared after 2–3 days of incubation at 30°C . Unmarked gene deletion mutants of R . equi RE1 were constructed essentially as described previously [34] . All oligonucleotide primers used in the construction of plasmids necessary for the construction of the mutants are shown in Table S2 . Plasmid pSelAct-ipd1 , for the generation of an unmarked gene deletion of the ipdAB operon in R . equi RE1 , was constructed as follows . The upstream ( 1 , 368 bp; primers ipdABequiUP-F and ipdABequiUP-R ) and downstream ( 1 , 396 bp; primers ipdABequiDOWN-F and ipdABequiDOWN-R ) flanking regions of the ipdAB genes were amplified by PCR . The obtained amplicons were ligated into EcoRV digested pBluescript ( II ) KS , rendering plasmids pEqui14 and pEqui16 for the upstream and downstream region , respectively . A 1 . 4 kb SpeI/EcoRV fragment of pEqui14 was ligated into SpeI/EcoRV digested pEqui16 , generating pEqui18 . A 2 . 9 kb EcoRI/HindIII fragment of pEqui18 , harboring the ipdAB gene deletion and its flanking regions , was treated with Klenow fragment and ligated into SmaI digested pSelAct suicide vector [34] . The resulting plasmid was designated pSelAct-ipd1 for the construction of ipdAB gene deletion mutant R . equi ΔipdAB . Complementation of mutant strain RE1ΔipdAB was performed by introduction of a 4 . 4 kb DNA fragment obtained by PCR using primers ipdABequiContrUP-F and ipdABequiContrDOWN-R . The PCR product obtained was cloned into pSET152 and the resulting construct was introduced into RE1ΔipdAB by electroporation [34] . Mutant strain RE1ΔipdA2B2 was constructed by unmarked gene deletion of the ipdA2B2 operon from R . equi RE1 using plasmid pSelAct-ΔipdAB2 . Double gene deletion mutant R . equi RE1ΔipdABΔipdA2B2 was made in R . equi RE1ΔipdAB mutant strain . Plasmid pSelAct-ΔipdAB2 was constructed as follows . The upstream ( 1 , 444 bp; primers ipdAB2equiUP-F and ipdAB2equiUP-R ) and downstream ( 1 , 387 bp; ipdAB2equiDOWN-F , ipdAB2equiDOWN-R ) regions of ipdAB2 were amplified by PCR using genomic DNA as template ( Table S2 ) . The amplicons were ligated into SmaI digested pSelAct , resulting in plasmids pSelAct-ipdAB2equiUP and pSelAct-ipdAB2equiDOWN , respectively . Following digestion with BglII/SpeI of both plasmids , a 1 , 381 bp fragment of pSelAct-ipdAB2equiDOWN was ligated into pSelAct-ipdAB2equiUP , resulting in pSelAct-ΔipdAB2 used for the construction of a ΔipdA2B2 gene deletion . Plasmid pSelAct-fadE30 for the generation of an unmarked gene deletion of fadE30 in R . equi RE1 was constructed as follows . The upstream ( 1 , 511 bp; primers fadE30equiUP-F and fadE30equiUP-R ) and downstream ( 1 , 449 bp; primers fadE30equiDOWN-F and fadE30equiDOWN-R ) flanking genomic regions of fadE30 were amplified by a standard PCR using High Fidelity DNA polymerase ( Fermentas GmbH ) . The obtained amplicons were ligated into the pGEM-T cloning vector ( Promega Benelux ) , rendering pGEMT-fadE30UP and pGEMT-fadE30DOWN . A 1 . 4 kb BcuI/BglII DNA fragment was cut out of pGEMT-fadE30DOWN and ligated into BcuI/BglII linearized pGEMT-fadE30UP , resulting in pGEMT-fadE30 . To construct pSelAct-fadE30 , pGEMT-fadE30 was digested with NcoI and BcuI and treated with Klenow fragment . A 2 . 9 kb blunt-end DNA fragment , carrying the fadE30 gene deletion , was ligated into SmaI digested pSelAct [34] . The resulting plasmid was designated pSelAct-fadE30 and used for the construction of mutant strain R . equi RE1ΔfadE30 . Complementation of mutant strain RE1ΔfadE30 was performed by the introduction of a 2 . 8 kb DNA fragment obtained by PCR using primers fadE30equiUP-F and fadE30Contr-R ( Table S2 ) . The PCR product obtained was cloned into EcoRV digested pSET152 and the resulting construct was introduced into RE1ΔfadE30 by electroporation [34] . Plasmid pSelAct-fadA6 for the generation of an unmarked gene deletion of fadA6 in R . equi RE1 was constructed as follows . The upstream ( 1 , 429 bp; primers fadA6equiUP-F and fadA6equiUP-R ) and downstream ( 1 , 311 bp; primers fadA6equiDOWN-F and fadA6equiDOWN-R ) flanking genomic regions of fadA6 were amplified by a standard PCR using High Fidelity DNA polymerase ( Fermentas GmbH ) . The obtained amplicons were ligated into the pGEM-T cloning vector ( Promega Benelux ) , rendering pGEMT-fadA6UP and pGEMT-fadA6DOWN . A 1 . 4 kb SpeI/BglII DNA fragment was cut out of pGEMT-fadA6UP and ligated into SpeI/BglII linearized pGEMT-fadA6DOWN , resulting in pGEMT-fadA6 . To construct pSelAct-fadA6 , pGEMT-fadA6 was digested with EcoRI and a 2 . 7 kb DNA fragment , carrying the fadA6 gene deletion , was ligated into EcoRI digested pSelAct [34] . The resulting plasmid was designated pSelAct-fadA6 and used for the construction of mutant strain R . equi RE1ΔfadA6 . Strains were pre-grown ( 30°C , 200 rpm ) in LB medium ( 10 ml ) overnight and subsequently inoculated ( 1∶100 ) in 50 ml MM-Ac and incubated ( 30°C , 200 rpm ) for 36 hours . AD ( 0 . 5 ml of 50 mg/ml stock in DMSO ) was then added . Samples ( 0 . 25 ml ) for GC analysis were collected and acidified with 5 µl 10% H2SO4 at several intervals . Progesterone ( 10 µl of a 5 mg/ml stock in ethylacetate ) was added as an internal standard and samples were subsequently extracted using ethylacetate ( 1 ml ) . GC analysis was performed on a GC8000 TOP ( Thermoquest Italia , Milan , Italy ) equipped with an EC-5 column measuring 30 m by 0 . 25 mm ( inner diameter ) and a 0 . 25 µm film ( Alltech , Ill . , USA . ) and FID detection at 300°C . Chromatographs obtained were analysed using Chromquest V 2 . 53 software ( Thermoquest ) . HIP ( 200 mg/L ) and 5OH-HIP ( 50 mg/L ) , supplied by MSD Oss , The Netherlands , were used as authentic samples . The human monocyte cell line U937 [66] was grown in RPMI 1640 ( Invitrogen ) + NaHCO3 ( 1 g/L ) + sodium pyruvate ( 0 . 11 g/L ) + glucose medium ( 4 . 5 g/L ) ( RPMI 1640 medium ) , buffered with 10 mM HEPES ( Hopax fine chemicals , Taiwan ) and supplemented with penicillin ( 200 IU/ml ) , streptomycin ( 200 IU/ml ) and 10% fetal bovine serum ( FBS ) . The cells were grown in suspension at 37°C and 5% CO2 . For the macrophage survival assay , monocytes were grown for several days as described above . The culture medium was replaced with fresh culture medium and the cells were activated overnight with phorbol 12-myristate 13-acetate ( 60 ng/ml , PMA , Sigma-Aldrich ) to induce their differentiation to macrophages . The differentiated cells were spun down ( 5 min at 200 x g ) and the pellet was re-suspended in fresh , antibiotic free RPMI 1640 medium with 10% FBS . For each strain to be tested , a tube containing 10 ml of a cell suspension ( approximately 106 cells/ml ) was inoculated with R . equi , pre-grown in nutrient broth ( Difco , Detroit , MI . , USA ) at 37°C , at a multiplicity of infection ( MOI ) of approximately 10 bacteria per macrophage . The bacteria were incubated with the macrophages for 1 h at 37°C and 5% CO2 . The medium was replaced with 10 ml RPMI1640 medium supplemented with 10% FBS and 100 µg/ml gentamycin and incubated again for 1 h to kill any extra-cellular bacteria . In assays with complemented mutant strains of RE1ΔipdAB and RE1ΔfadE30 ampicillin ( 100 µg/ml ) was added in addition to gentamycin ( 100 µg/ml ) , since the apramycin cassette conferred gentamycin resistance . The minimal inhibitory concentration ( MIC ) for ampicillin was determined at 1 . 5–2 µg/ml ampicillin for wild type and all mutant and complemented mutant strains using an ampicillin Etest strip ( AB Biodisk/bioMérieux , Solna , Sweden ) . The macrophages ( with internalized R . equi ) were spun down ( 5 min at 200 x g ) and the pellet was re-suspended in 40 ml RPMI1640 medium , buffered with 10 mM HEPES and supplemented with 10% FBS and 10 µg/ml gentamycin , plus 10 µg/ml ampicillin in assays with the complemented mutant strains . This suspension was divided over four culture bottles ( 10 ml each ) and incubated at 37°C and 5% CO2 . After 4 , 28 , 52 and 76 h the macrophages ( one culture bottle per strain per time point ) were spun down ( 5 min at 200 x g ) and the pellet washed twice in 1 ml antibiotic free RPMI1640 medium . Finally the pellet was lysed with 1% Triton X-100 ( Sigma-Aldrich ) in 0 . 01M phosphate buffered saline , followed by live count determination ( plate counting ) . Six 3 to 5-week-old foals were allotted with mare to two groups of three foals , ensuring an even distribution of age over the groups . At T = 0 all foals were challenged with 100 ml suspension of RE1ΔipdAB or R . equi RE1 ( control ) by trans-tracheal injection . Bacterial suspensions of R . equi strains RE1 or RE1ΔipdAB were made by plating onto blood agar ( Biotrading Benelux , Mijdrecht , The Netherlands ) and incubation for 24 h at 37°C . Bacteria were then harvested with 4 ml of sterile isotonic PBS per plate and diluted with sterile isotonic PBS to a final concentration of approximately 5×104 CFU/ml . Live count determination by plate counting was performed post-challenge . Infectivity titers were determined at 4 . 3×104 CFU/ml for RE1 and 7 . 1×104 CFU/ml for RE1ΔipdAB . Foals were examined daily post-challenge until necropsy for clinical signs using a numerical clinical scoring system with 13 parameters ( Table S3 ) . The clinical score was calculated as the sum of clinical scores of the 13 different parameters . At day 21 post-challenge a post-mortem examination was performed . The foals were euthanized by anaesthesia with xylazine ( 100 mg/100 kg ) and ketamine ( 500 mg/100 kg ) and subsequent bleeding to death . The lungs were weighed in order to calculate the lung to body weight ratio . Details of these examinations are described below for the immunization experiment . Oral immunization of foals was based on a study done by Hooper McGrevy et al . ( 2005 ) [53] with modifications . Eight 2 to 4-week-old foals were allotted to two groups of four foals each , ensuring an even distribution of age over the groups . During the experiment the foals suckled and the mares were fed according to standard procedures . R . equi strain RE1ΔipdAB was administered orally ( 1 ml ) to the foals for vaccination at T = 0 and a booster at T = 14 days . The infectivity titer of RE1ΔipdAB was determined by plate counting ( 8 . 7×107 CFU/ml and 4 . 1×107 CFU/ml for the first and second vaccination , respectively ) . R . equi strain 85F ( CNCM I-3250; [52] , [64] ) was used as challenge strain and plated onto blood agar and incubated for 24 h at 37°C . Bacteria were harvested with 4 ml of sterile isotonic PBS per plate and diluted with sterile isotonic PBS to a final concentration of approximately 5×104 CFU/ml . At T = 28 days all foals were challenged with 100 ml R . equi 85F by trans-tracheal injection . Live count determination by plate counting was performed post-challenge in order to confirm the infectivity titer . Foals were examined daily for clinical signs using the numerical clinical scoring system described above ( Table S3 ) . Foals were weighed and blood was sampled at day of vaccination , day of challenge and at day of necropsy . Serum antibody titers against R . equi were determined as follows . R . equi strain 85F cell wall extract was prepared by resuspension of cells in 2% Triton X-114 . The detergent phase containing VapA and other surface molecules ( 13 . 5 mg protein/ml ) was diluted 2000x in 40 mM PBS and coated to microtiter plates during 16 h at 37°C . After washing with 40 mM PBS + 0 . 05% Tween , serial dilutions of test sera were made in the wells . After incubation for 1 h at 37°C and subsequent washing , the bound antibodies were quantified using HRP-rec protein G conjugate and 3 , 3′ , 5 , 5′-tetramethylbenzidine ( TMB ) as substrate . The antibody titers in sera were calculated using a positive standard serum with a defined titer of 9 ( log2 ) as reference . Rectal swabs for bacterial re-isolation were sampled just before each vaccination and on frequent days after vaccination . The swab samples were serially diluted in physiological salt solution and plated on blood agar and incubated at 37°C for 16–24 h . R . equi colonies were initially identified by the typical non-hemolytic mucoid colony morphology , enumerated and expressed as CFU/ml . At day 14 ( controls; T = 42 days ) or day 17–20 ( vaccinates; T = 45–48 days ) post-challenge foals were euthanized . The lungs were weighed in order to calculate the lung to body weight ratio . A complete post-mortem examination was performed with special attention to the lungs and gut with associated lymph nodes . Tissue samples ( 1 cm3 ) were excised from seven standard sites representative of the lobes of each half of the lung ( 3 sites per half and the accessory lobe ) ; diseased tissue was preferentially selected for each site . The mirror image samples ( the two samples of the equivalent lobe on each half ) were pooled to give three samples per foal and a sample of the accessory lobe . Each ( pooled ) sample was homogenized , serially diluted and inoculated on blood agar plates and then incubated at 37°C for 16–24 h . R . equi colonies were enumerated and expressed as CFU/ml homogenate . This study was carried out in strict accordance with the recommendations of the “Dutch Experiments on Animal Act” . The protocol was approved by the Committee on the Ethics of Animal Experiments of Intervet International bv ( Permit Number: REV 07060 ) . The R . equi counts ( log10 CFU/ml ) after incubation with macrophages for 4 , 28 , 52 and 76 h , reflecting the survival rate , were statistically analysed by ANOVA using a linear mixed model for repeated measurements and including time zero counts as covariate in the model Verbeke and Molenberghs [67] . Advanced statistical methods were applied for the ordinal scores over time of the daily clinical score using Generalized Estimating Equations ( GEE with p-values based on empirical standard error ) and ANOVA for repeated measurements for continuous outcomes of rectal temperature , lung scores ( % consolidation ) and the quantitative re-isolation of R . equi from the different lung lobes . In these methods the correlation of the repeated measurements on subjects ( i . e . animals ) is taken into account . Statistical methods were conducted in SAS V9 . 1 ( SAS Institute Cary , NC , USA ) using two-sided tests and a significance level ( α ) of 0 . 05 . | Rhodococcus equi causes fatal pyogranulomatous bronchopneumonia in young foals and is an emerging opportunistic pathogen of immunocompromised humans . Despite its importance , there is currently no safe and effective vaccine against R . equi infections . Like Mycobacterium tuberculosis , the causative agent of human tuberculosis , R . equi is able to infect , survive and multiply inside alveolar macrophages . Recently we have shown that essential steps in the cholesterol catabolic pathway ( encoded by the rv3551 , rv3552 , fadE30 genes ) are involved in the pathogenicity of M . tuberculosis . We hypothesized that the orthologous genes in the cholesterol catabolic gene cluster of R . equi also are essential for its virulence mechanism . Analysis of the respective R . equi strain RE1 mutants revealed that they were impaired in growth on intermediates of the steroid catabolic pathway and had attenuated phenotypes in a macrophage infection assay . Mutant RE1ΔipdAB , carrying a deletion of the orthologs of rv3551 and rv3552 , could be safely administered to 2–5 week-old foals intratracheally and oral immunization provided a substantial protection against infection by a virulent R . equi strain . Our data show that genes important for methylhexahydroindanone propionate degradation , part of the steroid catabolic pathway , are promising targets for the development of a live-attenuated vaccine against R . equi infections . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biotechnology",
"medicine",
"infectious",
"diseases",
"veterinary",
"diseases",
"veterinary",
"microbiology",
"genetics",
"biology",
"genomics",
"microbiology",
"genetics",
"and",
"genomics",
"veterinary",
"science"
] | 2011 | The Steroid Catabolic Pathway of the Intracellular Pathogen Rhodococcus equi Is Important for Pathogenesis and a Target for Vaccine Development |
The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors . Although knowledge of various genetic and cellular aspects of development is accumulating rapidly , there is less systematic understanding of how these various processes play together in order to construct such functional networks . Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ( ‘winner-take-all’ , WTA ) network architecture can arise by development from a single precursor cell . This precursor is granted a simplified gene regulatory network that directs cell mitosis , differentiation , migration , neurite outgrowth and synaptogenesis . Once initial axonal connection patterns are established , their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns . We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks , and compare our simulation results with biological data .
In this paper we address the question of how progenitor cells of the neocortical subplate can give rise to large functional neuronal sub-networks in the developed cortex . We choose winner-take-all ( WTA ) [1] , [2] connectivity as the target of this self-construction and -configuration process because these sub-networks are consistent with the observed physiology [3] , [4] and connectivity [5] , [6] of neurons in the superficial layers of neocortex , and because they are powerful elements of computation [7] , [8] . WTA networks actively select the strongest of multiple input signals , while suppressing the weaker ones . This fundamental characteristic is applicable in various contexts , and so many studies modeling cortical function are based on WTA modules [8]–[15] . The idealized WTA network architecture is shown in Fig . 1A . Excitatory neurons are recurrently connected to each other and also with one or more inhibitory neurons , which project back to the excitatory neurons . This architecture does not in itself guarantee WTA functionality . The degree of recurrent excitation , excitation of inhibitory neurons , and inhibition of excitatory neurons need all to lie within preferred ranges [8] in order for the network to exhibit effective WTA behavior . The appropriate neural architecture must be grown , and then the weights of the many synapses must be tuned to fall within the necessary ranges . Such neuronal growth and synapse formation are subject to variability ( 1B , C ) , for which the homeostatic learning mechanisms must compensate . The behavior of a WTA network depends on the ratios of the effects of its various excitatory and inhibitory connection paths . In its high excitatory gain regime a WTA network will report only the strongest of its feed-forward inputs , and suppress the remainder of the excitatory neurons , which are weakly activated . In a more relaxed regime ( soft-WTA , sWTA ) the network will return a pattern of winners that best conforms to its input . In this sense the sWTA performs a pattern based signal restoration , which is a crucial mechanism for resisting degradation of processing in neural systems across their many computational steps . In this paper we choose to have the developmental process grow and tune these sWTA networks . Our goal is to demonstrate how plausible genetic developmental mechanisms can combine with homeostatic synaptic tuning to bring networks of neurons into sWTA functionality ( Fig . 1 ) . Our demonstration is based on simulations of the development and growth of neural tissue in 3D physical space using Cx3D [16] . The simulation begins with a single precursor cell . This cell encodes gene-like instructions that are sequentially and conditionally expressed through a gene regulatory network ( GRN ) . By controlling the expression of different genes , this GRN gives rise to pools of differentiated excitatory and inhibitory neurons . These neurons , which are placed randomly in 3D space , extend axons and dendrites and make synapses according to a proximity rule . This process results in a synaptically connected network that matches well experimentally obtained connectivity statistics . During this neurite outgrowth , the synaptic weights calibrate themselves homeostatically using experimentally established synaptic scaling [17] and BCM learning rules [18] . This synaptic learning is conditioned by coarsely patterned neuronal activity similar to that of retinal waves or cortico-thalamic loops [19]–[23] . We compare these grown networks with biological data , and demonstrate WTA functionality . This comparison is done also in the context of cortical functionality , such as orientation selectivity . Importantly , the overall behavior stems solely from local processes , which are instantiated from internally encoded and developmental primitives [24] . Hence , we provide a model that explains the developmental self-construction and -configuration of a neocortical WTA network in a biologically plausible way .
Cell proliferation and differentiation into different cell types is specified implicitly in the genetic code of a single precursor cell . This code determines how a given number of excitatory and inhibitory neurons is produced . During the unfolding process of this code , each cell contains the same genetic code , but because of its local environment can follow different developmental trajectories . We model the molecular mechanisms that regulate cell differentiation by a dynamical gene regulatory network ( GRN ) . This GRN is defined by a set of 5 variables ( , , , , and ) that represent substance concentrations , where each substance is the expression of a gene . Importantly , all cells have their own instantiations of these variables . The secretion , interaction , and decay of substances , is regulated by the laws of kinetics . The differential equations specifying these dynamics are shown in Methods . During the evolution of the substance concentrations , also cell growth and division is simulated . The cell cycle time and model parameters of the differential equations are fixed and independent of the substance dynamics . Initially , all concentrations are set to zero . At this stage , only the “starter” substance is produced , which reaches high concentration levels in the first time step , and triggers the production of a second gene . is produced according to a prespecified intrinsic production constant . This value determines how many cell divisions will occur until the concentration of reaches a value of . When this threshold is reached , a probabilistic decision is induced: or , responsible for activating the excitatory and inhibitory cell phenotypes , are triggered with probability or , respectively . Such a GRN network configuration would enable us to generate cells , where is the number of symmetric divisions . However , the target number of cells might not be an exponential of 2 . Therefore , we have introduced a second gene that is ( probabilistically ) activated by high concentrations of , and that leads to a second round of symmetric division . As for , activates or in a probabilistic manner . The probability to enter into this secondary cell cycle is given by , which is computed based on the target number of cells . The evolution of the GRN across cell types is depicted in Fig . 2 . By setting the production rate constant of gene and the probabilistic activation of , we can control the final number of cells produced . The equations for computing the probabilities for either differentiating into neurons by induction ( ) or by induction ( ) , depending on the target number of cells , are shown in Methods . Overall , the GRN is designed so that a desired total number of cells is reached , and that the distribution of excitatory vs . inhibitory cells follows the approximate 4∶1 ratio observed in cortex [25]–[27] ( S1 Figure ) . Fig . 3 ( A-D ) shows the evolution of an initial cell giving rise to a number of cells which eventually grow out neurites based solely on their genetic encoding . Neurite growth and arborization is caused by growth cone traction and bifurcation . The growth cone is able to sense the presence and gradient of morphogens and other signal molecules , and also able to actively explore the local extracellular space . Importantly , neurite growth is steered via a growth cone model instantiated at the tip of the axon or dendrite , and so is a local process . Diffusable signal molecules are secreted by the cell somata . In these simulations excitatory and inhibitory neurons secrete two characteristic signals , that enable excitatory and inhibitory axons to find inhibitory and excitatory neurons , respectively . The axonal growth cones initially grow out of the somata in random directions . However , they retract whenever the concentration they sense falls below a threshold . The retraction stops and growth recommences when a second higher threshold is exceeded . In this way the axons remain close to substance secreting sources . Retraction is an efficient strategy for establishing connections because axons grow only into regions containing a potential target , and is commonly observed in developing neurons [28]–[31] . A video of a developing neural network with axonal retraction ( simulated in Cx3D ) is included in the Supporting Information ( S1 Video ) and on Youtube ( http://www . youtube . com/watch ? v=il2uc-ZUZQ4 ) . Axons deploy boutons . Whenever these boutons are sufficiently close to a potential post-synaptic site on a dendrite a synapse is created between them . Consequently , the final synaptic network connectivity depends on the nearly stochastic arrangement of regions of spatial proximity of the outgrowing axons and dendrites . We adapted the parameters of the neurite outgrowth ( see Table 1 ) so that the connectivity of the simulated neuronal growth matched our experimental observations in layers II/III of cat visual cortex [5] , [32] ( see Fig . 4A ) . Overall , we found that connectivity was robust to reasonable variation of the growth parameters and the random location of somata . The absolute numbers of synapses simulated here are smaller than observed in biology , due to constraints on computational resources . However , there is no inherent restriction on scalability using our methods , and so we expect that realistic numbers of cells and synapses could if necessary be simulated using supercomputers . Fig . 4B shows the distribution of the percentage of excitatory input synapses to the neurons , across the whole population . The average percentage of excitatory inputs to a neuron in this network is 84% , which is in good agreement with the experimental data . This result is consistent with observations across species and cortical areas that some 15% of all the synapses are GABAergic [5] , [33]–[35] , irrespective of neuronal densities . Importantly , this good agreement arises naturally out of the growth model , and did not require extensive tuning of the model parameters . The self-configuration of electrophysiological processing depends on the tuning of network synaptic weights and neuronal activity . In order to simulate this aspect of the developing networks , we must model also the electrical activity of neurons . However , the time scales of morphological growth and electrophysiological dynamics are many orders of magnitude different , and this difference makes for substantial technical problems in simulation . For simplicity , and for minimizing computational demands we have used a rate-based approach to modeling neuronal activity . We approximate the neuronal activation by a linear-threshold function [36] that describes the output action potential discharge rate of the neuron as a function of its input . This type of neuronal activation function is a good approximation to experimental observations of the adapted current discharge relation of neurons [37] , [38] and has been used in a wide range of modeling works [8] , [39]–[41] . The linear-threshold activation function is: ( 1 ) where denotes the firing rate of a neuron with index i , is the neuronal time constant , is the spontaneous activity , is the feed-forward input to neuron i , is the weight of the connection from neuron j to neuron i ( can be positive or negative , depending on the presynaptic neuron's type ) , and is the neuron's threshold . For simplicity , and are set to 1 and 0 . Exploratory simulations where yielded very similar results . For computational efficiency , the electrophysiology simulator is implemented as a global process that acts on the total weight matrix of the neuronal network , rather than performing these frequent computations locally . We chose this global methodology because it leads to a significant speed-up compared with a local version that had been used initially . The total weight matrix is obtained by summation of the weights of all synapses in the Cx3D simulation . Using these connection weights , neuronal activity is computed as described in Eq . 1 . Connection weight changes resulting from the learning and adaptation ( explained below ) are computed based on this summed weight matrix and the activities of the two respective connected neurons , which are saved at each electrophysiology time step . The same connection weights ( and neuronal activities ) would be computed if only local processes at the synapses were simulated , because the synaptic learning and adaptation dynamics ( Eq . 2 and 3 ) are dependent on the ( locally available ) neuronal activities , and linearly dependent on the synaptic weight . Hence , the dynamics of the summed synaptic weights match the sum of the individual synapse weight changes . For reasons of biological plausibility , the electrophysiology simulator incorporates a maximum connection weight . This maximum weight for the functional connection strength between two neurons is determined by counting the number of synapses involved . This number , multiplied by the maximal weight of a single synapse , is defined as the maximum of the total connection weight . Hence , neurons that are connected by few synapses can not establish a strong functional link . In our model , self-configuration of the weights towards sWTA functionality occurs during sequential developmental phases . Sequential phases of electrical adaptation and learning during development have been observed experimentally [42] , [43] , and have also been applied in previous models [44] , [45] . During the first , homeostatic phase neurons adapt the synaptic weights of their own input in order to maintain a target output activity . The effect of this phase is to bring the neuronal firing rates into a balanced regime , and so allow for a reliable synaptic learning without interference by unresponsive neurons or run-away excitation . During the second , specification phase the neurons structure their individual responses by correlation-based learning on their inputs . We investigated whether our developmental model can account for experimental findings on orientation selectivity in visual cortex; for example , differences in tuning between excitatory and inhibitory neurons . In order to address this question , we assumed that the hills of activity in the input layer correspond to oriented stimuli ( e . g . bars ) , which are smoothly and periodically rotating between 0 and 180 degrees . As anticipated from the previous results , excitatory neurons become highly orientation selective ( Fig . 9 ) , in contrast to inhibitory neurons . These results are in line with biological data . For example , [63] have analyzed orientation selectivity of excitatory and inhibitory neurons in mouse visual cortex . They report inhibitory neurons to be more broadly tuned and hence less selective than excitatory , pyramidal neurons . Similar findings were reported by [64]–[68] . We also quantified the orientation tuning based on the orientation selectivity index ( OSI ) , which specifies the degree to which a neuron is selective for orientation . The value of this index lies between 0 ( non-selective ) and 1 ( selective to a single , specific orientation ) . Fig . 9B shows the distribution of the OSI for excitatory and inhibitory neurons in a WTA network , demonstrating the discrepancy of orientation selectivity also on a population level . We conducted additional simulations , which demonstrated that when inhibitory neurons follow the same learning rule as excitatory neurons , they exhibit more narrowly tuned orientation selectivity ( Fig . 9C ) . Hence , experimental findings of orientation selective inhibitory neurons in cat visual cortex [69]–[72] can also be accounted for by our model . We have analyzed the consequences of our model on the nature of the inhibition of excitatory neurons . As mentioned above , inhibitory synapses onto excitatory neurons are subject to the BCM learning rule ( Eq . 3 ) . The competition between excitatory neurons depends on the common input that they all receive from inhibitory neurons . This common input must reflect the overall activity of the network , so that the competition is suitably normalized . However , the inhibition of the excitatory neurons stems from multiple inhibitory neurons , which should partition their common inhibitory task amongst each other in a self-organizing way . We investigated this partitioning , and how an excitatory neuron is inhibited during stimulation . In order to quantify the impact of a neuron j on another neuron i for a given stimulus , we calculate a value that we will call the recursively effective exertion ( REE ) . It is obtained by multiplying the activity of neuron j ( under a given stimulus ) with the total connection weight from neuron j to i: ( 5 ) The REE value is therefore stimulus-dependent , and dependent on the recurrent network connectivity . Fig . 10 shows that inhibition is distributed non-uniformly: A few inhibitory neurons dominate the suppression of an excitatory neuron . This dominance is due to the BCM learning by inhibitory synapses: Strongly and weakly correlated inhibitory connections to excitatory neurons are strengthened or weakened , respectively . These inhibitory connection strengths converge because of the homeostatic activity regulation , which is part of the BCM learning rule . The nature of inhibition of excitatory neurons is interesting in the context of the anatomy of inhibitory basket cells . These neurons predominantly target locations close to the soma or the proximal dendrites , where they can strongly influence the excitatory neuron [73] . Therefore , it is plausible that the recruitment of a small number of inhibitory neurons is sufficient to inhibit an excitatory neuron . Electrophysiological experiments could in principle validate this hypothesis by showing that only a small proportion of the inhibitory neurons projecting to a pyramidal neuron are predominantly responsible for its suppression .
In this paper we have demonstrated by simulation of physical development in a 3D space , how an autonomous gene regulatory network can orchestrate the self-construction and -calibration of a field of soft-WTA neural networks , able to perform pattern restoration and classification on their input signals . The importance of this result is that it demonstrates in a systematic and principled way how genetic information contained in a single precursor cell can unfold into a functional network of neurons with highly organized connections and synaptic weights . The principles of morphological and functional development captured in our model are necessarily simplified with respect to the boundless detail of biology . Nevertheless , these principles are both strongly supported by experimental data , and sufficiently rich in their collective expression to explain coherently the complex process of expansion of a genotype to a functional phenotypic neuronal circuit . In this way our work offers a significant advance over previous biological and modeling studies which have focused either on elements of neuronal development , or on learning in networks whose initial connectivity is given . Therefore we expect that methods and results of the kind reported here will be of interest both to developmental biologists seeking a modeling approach to exploring system level processes , as well as to neuronal learning theorists who usually neglect the genetic-developmental and homeostatic aspects of detailed learning in favor of an initial network that serves as a basic scaffold for subsequent learning [74]–[76] . It is relatively easy to express a well-characterized biological process through an explicit simulation . That is , one in which the simulation simply recapitulates the process by expanding some data through a simple model , without regard for physical and mechanistic constraints . By contrast , the simulation methods [16] that we have used here are strictly committed to physical realities such as 3D space , forces , diffusion , gene-expression networks , cellular growth mechanisms , etc . Our methods are also committed to local agency: All active processes are localized to cells , can only have local actions , and have access to only local signals . There is no global controller with global knowledge , able simply to paint the developmental picture into a 3D space . Instead , the ability of a precursor cell to expand to a functional network is the result of collective interaction between localized cellular processes . And overall , the developmental process is the expression of an organization that is encoded only implicitly , rather than explicity , in the GRN of the precursor cell . Thus , our GRN encodes constraints and methods rather than explicit behaviors . In previous work [77] , [78] we have shown how this approach can be used to explain the development of neocortical lamination and connectivity . In that case we did not consider also the electrophysiological signaling between cells and so the self-configuration of their computational roles , as we have done here . However , the incorporation of electrophysiological signaling into the growth model brings substantial technical difficulties , such as those arising out of the large differences in spatio-temporal scales between cellular developmental and electrophysiological signaling processes , as well as the supply and management of sufficient computational resources . Therefore we have chosen to keep these problems tractable in this first functional study , by restricting our question to a sub-domain of cortical development: How could neuronal precursors expand into functional circuits , at all . Even then , we must be satisfied for the moment with a rate based model of neuronal activity , rather than a fully spiking one . The emphasis of this paper is on the process whereby a precursor expands to some useful network function . The particular function is less relevant , and in any case the functional/computational details of cortical circuits are as yet not fully understood . We have chosen to induce WTA-like function because our previous work has been focused on the likely similarity between the WTA motif and the neuronal types and their inter-connectivity in the superficial layers of cortex [6] . Moreover these WTA networks are intriguing from both the biological , and computational perspective [3] , [6]–[15] , [41] . The strong recurrent excitation available in the superficial layers of cortex , and their critical dependence on feedback inhibition has been clearly demonstrated by intracellular recordings in the presence of ionophoretic manipulation of GABA agonists and antagonists [4] . These relationships are crucial for WTA-like processing , because they offer the network induced gain that is crucial for providing the signal restoration , signal selection , and process control that support systematic computation . Recent optogenetic studies appear to confirm the presence of circuit induced gain , in the input layers of mouse visual cortex [79] , [80] . Taken together these experimental and theoretical results support the hypothesis that at least some fundamental WTA functionality is embedded in the processing architecture of superficial neuronal circuits , and so makes the WTA motif a worth target of the developmental process that we have described here . Our model predicts that neurons form specific subgroups , or cell assemblies [81] , [82] . There is indeed strong evidence from biological data for this clustered connectivity [83]–[85] , which ( as in our simulations ) , appears to be grounded in the similarity of functional selectivity [86] . We did not allow dynamic rearrangement of synapses in these first simulations . However , it is plausible that weak synapses are pruned away , freeing synaptic resource to explore for more correlated partners . Peters' rule [87]–[89] proposes that connectivity can be estimated by the product of the random overlap of pre- and postsynaptic sites . This rule may be true for average connectivity , but specific functionality obviously calls for more specific low level connectivity within the average . One opinion is that such specificity is explicitly genetic , and so accounts for example for the diversity of cortical interneurons [90] , [91] . Instead , our result speaks for an implicit rather than explicit genetic specificity . That is , the apparently specific wiring of the WTA network arises by neurons collectively satisfying genetically expressed constraints . This concept is in stark contrast to the view that network functionality emerges from individual processes that do not coordinate with potential interaction partners . In our simulations , a neuron's morphology and the functional strengths of its synapses depend on the collective behavior of the other neurons . Hence , the structure and function of a neuron grown in isolation is different from a neuron with the same genetic code , but that interacts and coordinates with its environment during development . Our learning rule requires that input projections are ordered in such a way that their collective input patterns provide at least a coarsely structured signal against which the presumptive WTA layer of neurons can successfully deploy a BCM-like learning mechanism . This ordering is not a stringent requirement . For example , provided that there is some degree of coherent axonal mapping of axons from input neurons of the subplate or thalamus into the target WTA layer , then even metabolically induced travelling waves of activity across the developing input population could provide a sufficiently structured signal for learning . Traditionally , many modeling studies have been based on the assumption that the limited lateral extent of the neuronal axonal and dendritic tree naturally leads to a properly configured 2D neighborhood topology [92]–[94] . However , it is unclear how more realistic anatomical properties ( anisotropy , variation of neurite extent , irregular locations of somata etc . ) affect these topologies . Our work addresses this problem by demonstrating how neurons can self-calibrate in a stimulus-induced way , within a non-uniform and irregular neuronal setting . Hence , our work provides a better understanding of how developmental mechanisms can generate a neighborhood topology , and so is complementary to the classical approach . As development of input neurons proceeds , the degree of structuring is likely to improve also , so that input neurons projecting to the same targets share similar features ( for example , their ON- and OFF-subfields ) . This is in line with studies on thalamo-cortical projections [95] , as well as cortico-cortical projections from layer IV to II/III [96] . However , it should be noted that this input specificity does not play onto inhibitory targets , which is in accordance with our work . Since the input to the neurons shapes the functional connectivity in the network , it follows from our model that neurons which receive common input are more likely to connect with each other ( assuming that structural connectivity is adjusting to functional connectivity ) . The studies of [96] and [97] provide evidence for this input-dependent intra-network specificity . Our results predict that only a few inhibitory neurons provide the major part of WTA-relevant inhibition , i . e . a relatively small subset of all the inhibitory basket cells projecting to a single pyramidal cell is responsible for its WTA suppression . These results suggest that WTA inhibition might not be very redundant , so that de-activation of only a few inhibitory neurons could result in very different electrophysiological behavior of pyramidal cells . Our networks employ Hebbian-type learning for both excitatory and inhibitory synapses onto excitatory postsynaptic neurons . It is known that inhibitory synapses can undergo long-term potentiation ( LTP ) as well as long-term depression ( LTD ) [52]–[54] , and learning by inhibitory synapses has been used in previous modeling studies [45] , [98] . Non-Hebbian synaptic scaling of synapses onto inhibitory neurons results in orientation-nonselective inhibitory neurons . This distinction with respect to pyramidal neurons has been observed in mouse visual cortex , where the tuning of inhibitory neurons is broader than that of excitatory neurons [63]–[67] , [99]–[101] . There is evidence for broadly tuned thalamo-cortical input to inhibitory neurons [95] , as well as cortico-cortical input to those of layer II/III of mouse cortex [100] . Therefore we propose that at least some types of inhibitory neurons ( e . g . fast-spiking ( FS ) , PV-expressing interneurons ) do not selectively adjust their inputs , but uniformly adapt the electrophysiological properties of their inputs for homeostasis . Orientation-selective inhibitory neurons are found in cat visual cortex [69]–[72] . Since we do not model orientation maps , our findings are not directly applicable to the cat . However , we argue that it is the spatial location in the orientation map that determines the tuning curve of inhibitory neurons . Most cortical interneurons have a small horizontal dendritic extent [56] , and so they likely receive inputs from similarly tuned excitatory neurons within an orientation map . Inhibitory neurons located close to orientation pinwheels are expected to have relatively broad orientation tuning , as reported in the above studies . The unbiased pooling of surrounding activity by inhibitory neurons is also supported by experimental results across species and sensory modalities [63] , [101] . By contrast , we have shown that inhibitory neurons become orientation-selective when they follow the same ( BCM ) learning rule as excitatory neurons . Our learning model provides a computational explanation for why most interneurons are smooth , i . e . have very few dendritic spines . It is believed that spines , by compartmentalizing biochemical signals , provide the molecular isolation required for independent synaptic learning [102]–[104] . The nonspecific and homogeneous adaptation of inhibitory neurons , which in our model are homogeneously scaling the input efficacies , is therefore well in line with this suggested function of dendritic spines . This model also provides an explanation for the finding that inhibitory , but not excitatory neurons exhibit structural remodeling of dendrites in the adult rat [105] . Changes in excitatory morphology at the level of dendritic branches ( rather then spines ) could have detrimental effects on already consolidated memories . Inhibitory neurons may retain their potential for dendritic restructuring , because their homeostatic adaptation does not interfere with learning of sensory experience . We believe our findings to be robust also with respect to models incorporating spikes , because the main features of the adaptation and learning behavior have been demonstrated also on this more detailed level of electrophysiology . Along these lines , the studies of [106] , [107] have explored spike-based WTA network functionality . Spike-dependent plasticity ( STDP ) is a Hebbian learning rule [108] and can yield synaptic homeostasis [109] . In particular , the BCM learning rule has been related to STDP mechanisms [109]–[112] . The robust self-organization of the WTA network is remarkable in that it arises out a single precursor cell , by simple genetically encoded rules . In future , this genetic developmental approach to functional circuit construction could be extended to larger networks composed of multiple WTA networks . For example , it has been hypothesized that by cooperation of multiple WTA circuits , the superficial layers of cortex could perform context-dependent processing [8] . Along these lines , [78] provide a model for the development of long-range projections connecting multiple columns , arranged on an hexagonal grid , as is observed in the superficial patch system [113]–[116] . It also remains to integrate these computational aspects into the context of a laminated cortical structure , which has already been simulated in Cx3D [24] , [77] .
The GRN is defined by a set of variables that represent genes and the corresponding substance concentrations . Changes in substance concentration are described by the rate equation: ( 6 ) where is the concentration of a protein encoded by the gene ( i . e . or ) , and the corresponding concentration vector . The function expresses how the synthesis rate of the protein encoded by gene depends on the cooperative binding of all the substances , and , represent the production and degradation rates ( , ) . is a vector of Hill functions , which compute the binding probability of a substance to a regulatory region given the affinity constant , cooperativity and binding bias : ( 7 ) Gene substances can regulate gene expression by binding to specific sites in the genomic cis-regulatory regions . Substances that regulate each others' transcription are called transcription factors . Many genes are controlled by a number of different transcription factors and different arrangements of binding sites can compute logic operations on multiple inputs . Here , the function takes the form of a logical combination of interacting substances and is defined by the elementary operations: ( 8 ) ( 9 ) ( 10 ) More information on this description of GRN dynamics can be found in [117] , [118] . Although abstract , this formalism can be directly translated into the corresponding mechanistic , kinetic differential equations . For our computational model based on 5 genes , we have used the following equations: ( 11 ) ( 12 ) ( 13 ) ( 14 ) ( 15 ) with: ( 16 ) The probabilities of either differentiating into neurons by induction ( ) or by induction ( ) are computed as follows: ( 17 ) ( 18 ) ( 19 ) ( 20 ) where is the number of divisions in the first division cycle , is the difference between the target number of neurons ( ) and the number of neurons resulting from the first division cycle , and denotes the floor function for rounding to integers . The intrinsic production constant determines the number of cell divisions until differentiation into excitatory and inhibitory neurons can occur . The higher it is , the faster the gene reaches the threshold of 0 . 99 . was adjusted manually in order for divisions to occur in the cycle . Initially , neuronal cell bodies are assigned uniformly random positions in 3D unprepared space . In Cx3D , these cell bodies are modeled as physical spheres . The neuronal cell density was in agreement with values derived from experimental data , i . e . in the range of 40'000 to 86'900 per mm [119]–[121] . We found 250 neurons ( 200 excitatory and 50 inhibitory ) to be appropriate for the available computer resources . For the establishment of neuronal connectivity , the somata were placed randomly in a cube with side length 160 m . A smaller network of 150 neurons in a cube with side length 140 m was used for simulations where the second developmental phase was simulated , in order to decrease simulation time . 3 of these 150 neurons did not get inhibitory inputs after the initial outgrowth and were not included for the simulation of learning , such that the analyzed network consisted of 117 excitatory and 30 inhibitory neurons . Standard Cx3D parameters for the physical properties of the cells ( e . g . mass or adherence ) were used [16] . The somatic diameters were set to 8 m . Variation of these parameters had only minimal effects on the simulation results . Axonal and dendritic growth were encoded with the instruction language G-code [24] . We used the following mechanisms , which are executed by such G-code “modules” located in the growth cone , for axonal and dendritic growth , as well as synapse formation: The computation of the electrical activity was implemented in Java , to allow a direct interfacing with Cx3D . All the synaptic weights in the Cx3D simulation are summed up , which yields a weight matrix . Based on this weight matrix , the input activity and the spontaneous activity , the firing rate of a neuron is computed according to Eq . 1 . The numerical solution of the differential equations was computed using the explicit Euler integration method . The network's activity is computed with 3000 iterations and integration step . The maximal firing rate is set to 250 Hz . Analyses of the simulated networks were performed with MATLAB ( Mathworks Inc . ) . In order to assess WTA functionality , electrical activity was computed in the same way as in the Java implementation of the Cx3Dp simulation , namely using the rate-based model ( Eq . 1 ) and the explicit Euler method . The integration step was decreased to for minimizing integration errors . The ordering of neurons for visualization , such as for Fig . 6A , was done using the genetic algorithm “ga . m” from the Global Optimization Toolbox of MATLAB . The energy to be minimized was defined as the sum of weighted topological distances between neurons , i . e . , where are the summed synaptic weights from neuron j to neuron i . The topological distances are inferred from a discrete 1-dimensional position vector of the neurons , which is initialized randomly and optimized . The ordering for the matrix visualization is then given by the locations of the neurons in this vector ( i . e . neighbors in this vector are also neighbors in the matrix ordering ) . Note that the topological position is unrelated to the physical position of the neurons , and is only used for the optimization process . The visualization of the clustering was done with CytoScape [126] , an open-source framework that is downloadable from http://www . cytoscape . org/ . We used the “dynnetwork” plugin implemented by Sabina Pfister , which clusters weighted networks based on the Kamada-Kawai algorithm [127] . The neurite outgrowth has several parameters , which depend on the neuronal type ( excitatory/inhibitory ) and also on the neurite type ( axon or dendrite ) . Table 1 lists all these parameters . The 2 substances which are secreted by the cell bodies and used by the axons as guidance cues both have a diffusion coefficient of 50 and a degradation constant of 5 . | Models of learning in artificial neural networks generally assume that the neurons and approximate network are given , and then learning tunes the synaptic weights . By contrast , we address the question of how an entire functional neuronal network containing many differentiated neurons and connections can develop from only a single progenitor cell . We chose a winner-take-all network as the developmental target , because it is a computationally powerful circuit , and a candidate motif of neocortical networks . The key aspect of this challenge is that the developmental mechanisms must be locally autonomous as in Biology: They cannot depend on global knowledge or supervision . We have explored this developmental process by simulating in physical detail the fundamental biological behaviors , such as cell proliferation , neurite growth and synapse formation that give rise to the structural connectivity observed in the superficial layers of the neocortex . These differentiated , approximately connected neurons then adapt their synaptic weights homeostatically to obtain a uniform electrical signaling activity before going on to organize themselves according to the fundamental correlations embedded in a noisy wave-like input signal . In this way the precursor expands itself through development and unsupervised learning into winner-take-all functionality and orientation selectivity in a biologically plausible manner . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"neuroscience",
"neurogenesis",
"cellular",
"neuroscience",
"synaptic",
"plasticity",
"computer",
"and",
"information",
"sciences",
"neural",
"networks",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"computerized",
"simulations",
"neuroscience",
"neural",
"circuit",
"formation",
"cortical",
"neurogenesis"
] | 2014 | Developmental Self-Construction and -Configuration of Functional Neocortical Neuronal Networks |
Allelic imbalance ( AI ) is a phenomenon where the two alleles of a given gene are expressed at different levels in a given cell , either because of epigenetic inactivation of one of the two alleles , or because of genetic variation in regulatory regions . Recently , Bing et al . have described the use of genotyping arrays to assay AI at a high resolution ( ∼750 , 000 SNPs across the autosomes ) . In this paper , we investigate computational approaches to analyze this data and identify genomic regions with AI in an unbiased and robust statistical manner . We propose two families of approaches: ( i ) a statistical approach based on z-score computations , and ( ii ) a family of machine learning approaches based on Hidden Markov Models . Each method is evaluated using previously published experimental data sets as well as with permutation testing . When applied to whole genome data from 53 HapMap samples , our approaches reveal that allelic imbalance is widespread ( most expressed genes show evidence of AI in at least one of our 53 samples ) and that most AI regions in a given individual are also found in at least a few other individuals . While many AI regions identified in the genome correspond to known protein-coding transcripts , others overlap with recently discovered long non-coding RNAs . We also observe that genomic regions with AI not only include complete transcripts with consistent differential expression levels , but also more complex patterns of allelic expression such as alternative promoters and alternative 3′ end . The approaches developed not only shed light on the incidence and mechanisms of allelic expression , but will also help towards mapping the genetic causes of allelic expression and identify cases where this variation may be linked to diseases .
In a diploid cell , each gene is present in two copies . The vast majority of microarray-based or RNA sequencing-based gene expression studies do not distinguish between the two copies and measure the sum of the expression of the two alleles . This hides the fact that the two alleles are not necessarily expressed at equal levels , a phenomenon called allelic imbalance ( AI ) [1] . The complete shut down of one allele results in monoallelic expression ( ME ) . The most drastic example of ME is X-chromosome inactivation , where , in females , one of the two copies of the X chromosome is inactivated and packaged into heterochromatin [2] . Less drastic is random monoallelic expression , whereby a randomly selected copy of a gene or chromosomal region is silenced by epigenetic mechanisms ( e . g . methylation ) . In contrast , imprinting results in parent-of-origin specific inactivation of the maternal or paternal allele , depending on the locus . While monoallelic expression completely silences one of the two alleles , less drastic allelic expression differences can result from a heterozygous regulatory site . For example , allele of a transcription factor binding site may allow binding and result in normal expression of the target gene on that chromosome , while allele may disrupt the binding site , resulting in lower expression . While the lower expression of allele may be compensated by an increased transcription rate at allele in heterozygous individuals , this may not be the case for individuals who are homozygous , which may result in phenotypic variation . Researchers have tried to identify causative regulatory variants by measuring the total expression ( i . e . expression of both copies ) of a particular gene across multiple individuals , treating this as a Quantitative Trait Locus ( eQTL ) , and mapping nearby cis-regulatory regions to the gene expression ( reviewed in [3] ) . A key problem with this type of approach is that environmental differences across individuals can affect gene expression , making the mapping problem very challenging . Instead , a focus on the relative expression of two alleles within the same cell has been suggested to factor out environmental sources of variation , allowing for more sensitive and specific detection of epigenetic and genetic phenomena related to local control of gene expression [4] . Combining AI measurements obtained from a set of individuals with genotyping information about these same individuals , one can map cis-regulatory variants [5]–[8] or detect epigenetic variation in allelic expression [9] , [10] . Past studies with the goal of detecting AI have typically relied upon panels of SNPs with relatively low density , located in only a subset of transcribed genes of the genome [10]–[12] . A simple threshold for the ratios of expression of the two alleles at a heterozygous locus is usually established ( e . g . 1 . 5 or 2-fold ) and a gene is called as imbalanced based upon whether or not the SNP ( s ) within it exceed this threshold . Optimal AI profiling in a genome-wide manner would require high-density sampling of expressed heterozygous sites in the genome . We recently generated the first large-scale , high-resolution assay of allelic expression [13] . In this study , Illumina genotyping arrays were used to measure differential allelic expression at 755 , 284 polymorphic sites in lymphoblastoid cell lines ( LCL ) derived from 53 CEU samples included in the HapMap project [14] . Because of the noise in single point AI measurements made at each heterozygous locus , sophisticated analytical methods are required to make the most out of this data . In this paper , we develop signal processing approaches for the accurate identification and delineation of transcripts with allelic imbalance , either in a single individual at a time , or in a collection of samples . To our knowledge , no hypothesis-free computational approaches have been proposed for the analysis of this type of data . Detection of AI in Ge et al . [13] relied heavily upon RefSeq , Vega , and UCSC gene annotations , and SNPs were first partitioned into windows corresponding to these annotated regions as well as intergenic regions and windows with significant AI were reported . Sophisticated bioinformatics approaches have been developed for a related , but simpler , problem in the past , that of detecting Copy Number Variants ( CNV ) or Loss Of Heterozygosity ( LOH ) in cancer cells using array-based Comparative Genomic Hybridization ( CGH ) [15]–[18] or genotyping arrays [19]–[25] . These include the PennCNV program [26] and the QuantiSNP program [27] , that use a Hidden Markov Model related to one of the approaches considered here . However , CNV or LOH regions have properties that make them easier to detect than regions of allelic imbalance: ( i ) the signal , coming from genomic DNA is generally quite strong , whereas gene expression can be very low; ( ii ) the number of copies of an allele is a small integer , whereas the allelic expression ratio is a real number; ( iii ) the regions affected are typically quite large , whereas AI can affect a single , short gene , or even only part of a gene . The approaches listed above are thus not easily applicable to the detection of AI in gene expression . An alternate family of statistical approaches called changepoint methods has been proposed for segmenting array CGH data into regions exhibiting consistent signals [28] , [29] . These non-parametric , model-free approaches have the benefit of segmenting real-numbered data without enforcing discretization . However , they are difficult to generalize to a situation like ours , where signals come from a mixture of discrete ( sites with no expression , sites with expression but no imbalance ) and continuous ( sites with real-valued imbalance ) state space . In this paper , we introduce a family of signal processing approaches for the analysis of AI data obtained from genotyping arrays . We consider both statistical approaches ( Z-score computation ) and machine learning approaches ( Hidden Markov Models ) to identify transcripts that show AI and to quantify the latter . We introduce a new type of left-to-right HMM for the joint prediction of allelic imbalance in the 53 samples considered . Our algorithms are evaluated using permutation testing and succeed at identifying regions with known AI . Our approaches reveal that more than 25% of transcripts ( coding or non-coding ) are subject to differential expression between the two alleles and that patterns of AI are varied and complex . The tools and data sets described here will help biologists and geneticists to identify regions of allelic imbalance , understand the mechanisms at play , identify the genetic or epigenetic causative agents , and associate expression polymorphisms with disease susceptibility .
Allelic imbalance was assayed using Illumina Infinium Human1M/Human1M-Duo SNP bead microarrays . These arrays , originally designed for genotyping , have probes for approximately 1 . 1 Million polymorphic sites from HapMap , of which 755284 where used for this study . Each probe estimates the abundance of each of the two possible alleles in the sample . Normally , genomic DNA is hybridized onto the chip and the genotypes are easily inferred from the probe intensities . We have previously described how one can take advantage of this technology to measure allelic expression in a high-resolution , genome-wide manner [13] . Briefly , total RNA is extracted and cDNAs are synthesized based on a protocol on heteronuclear RNA , allowing us to measure unspliced primary transcripts [8] . The cDNA sample is hybridized onto the array and each probe estimates the abundance of each of the two alleles in the sample . In parallel , genomic DNA from the same cell line is hybridized , which provides the basis for normalization of the cDNA hybridization while providing us with the genotype of each sample . Details for the full process of experimentally obtaining the raw imbalance information , as well as the sample information , can be obtained from [13] . Data obtained from technical replicates show that although the total expression level ( sum of RNA abundance in both alleles ) measured at a given SNP is highly reproducible ( = 0 . 864 ) , single point allelic expression ratios are much more noisy ( = 0 . 632 ) , especially for low expression levels ( see 9 ) . This suggests that careful data analysis is required to extract as much information as possible . Let be the set of two alleles present at polymorphic site in the population , for ( the rare cases where three or more alleles exist at the same site are ignored in this study ) . For notational simplicity , we assume that the genome consists of a single pair of chromosomes . In reality , the analysis that follows is repeated separately for each autosome . Genotype phasing consists of the decomposition of the genotype of an individual into its two homologous chromosomes . For individual , let and , be these two chromosomes , where . Phasing remains a computationally and statistically challenging problem [30] . In the case of HapMap individuals , phased genotypes are available , although they are not error free . Removal of SNPs not phased in CEU HapMap release R22 resulted in 755284 SNPs which were utilized in our study . Let and be the intensity read outs obtained from the probes interrogating site when hybridizing the genomic DNA of individual . If individual is heterozygous at site ( i . e . ) , then we expect both and to be large . When it is homozygous , say for , ( i . e . ) , we expect to be large and to be small . The genotype of an individual can thus be deduced from the ratio of the two measurements . Consider now and , the intensity read outs obtained from the probes interrogating site when hybridizing cDNA obtained from whole cell RNA extraction . When heterozygous site sits in a transcribed region with no allelic imbalance , both and will be relatively large . Any difference between the two may indicate allelic imbalance . Regions that are not transcribed will obtain low values for both alleles . We consider the following pair of observations at each site :measures the total transcript abundance , andwhich measures the fold imbalance between the expression of the two alleles . Normalization with the DNA sample , which , for heterozygous sites , is known to be balanced , normalizes for probe sensitivity and biases . Values for and were collected at 755284 sites . Those sites are not uniformly distributed in the genome , with genic regions ( exonic and intronic ) having roughly 1 . 3 times the SNP density as intergenic regions ( one SNP per 3 . 5 kb in genic regions , one SNP per 4 . 5 kb in intergenic regions ) . Figure 1 ( a ) shows the distribution of over all genic and intergenic positions . The distribution of expression levels in gene regions is clearly bimodal: a good fraction of genes are not transcribed in LCL , and most but not all intergenic sites are not transcribed . Assuming that 50% of genes and 10% of intergenic sites are expressed , we can deconvolve these distributions to obtain the distribution of for expressed and non-expressed regions ( Figure 1 ( b ) ) . For two individuals , experiments were done in triplicates . As seen in Figure S1 ( a ) and ( b ) , the technical noise in the measurement of both and is quite significant . As expected , values are particularly noisy at low expression levels . The main problem addressed in this study is the statistically robust identification of genomic regions with significant and consistent allelic imbalance . We start by noting that the data is too noisy to accurately call imbalance based on each SNP individually ( e . g . by simply using on ) , especially for regions whose expression level is relatively low . We thus consider approaches that take advantage of the fact that most regions with AI are relatively long and are expected to contain more than one SNP . Four main approaches were designed , implemented and compared . Each method aims to robustly assign a score to each SNP , so that SNPs that belong to transcripts with significant allelic imbalance obtain large ( positive or negative ) scores . In all our AI detection algorithms , AI is detected without reference to any kind of gene annotation , contrasting with the annotation-driven approach used by Ge et al . [13] , which allows us to identify regions of AI whose boundaries does not necessarily correspond to annotated genes . The first three approaches consider data from each sample individually while the last considers data from all samples jointly in order to improve the detection of AI in individual samples . The four approaches considered are first summarized below and then described in details . The code implementing each algorithm is available at http://www . mcb . mcgill . ca/~blanchem/AI/code . zip . Consider heterozygous site and define window W ( ) to be the set consisting of heterozygous sites to the left of , heterozygous sites to the right of , and itself . The simple smoothing approach estimates . Any site with would then be reported as having imbalance , for some appropriate threshold . Based on False Discovery Rate assessment ( described below ) , a value of was determined to be the optimal window size and was used for all results reported . At sites with no allelic imbalance , the value of is modeled adequately using a normal distribution centered at 0 . However , the variance is inversely correlated with the total expression , as AI is difficult to estimate when the total expression is low ( see Figure S1b ) . The range of possible values of are subdivided into 100 bins of equal size and the mean and variance of values were determined for SNPs belonging to every expression level bin . A site-specific Z-Score is assigned to heterozygous site as . Homozygous sites , being uninformative with respect to allelic ratios , are excluded from the analysis . Consider now a collection of consecutive heterozygous ( ignoring possibly intervening homozygous sites ) SNPs . We define the regional Z-score as . Assuming the normality of noise in measurements , follows a Normal ( 0 , 1 ) distribution under the null hypothesis of absence of allelic imbalance . Regional Z-Scores are first computed for every possible window of heterozygous sites . The region with the highest regional Z-score ( in absolute value ) , is selected first and we set for all sites heterozygous within the region . This region is then masked out and the next highest scoring non-overlapping window is selected . The process is repeated until all heterozygous sites have a Z-Score assigned . We note that because the is obtained based on the best window that contains site , there is an complex issue of multiple hypothesis testing that makes that this measure will not follow a Normal ( 0 , 1 ) distribution under the null hypothesis ( i . e . absence of AI ) . In consequence , one cannot easily translate into a p-value . We also considered a variant of the Z-Score approach where each SNP is assigned the Z-Score of the fixed-size window centered around it . This approach , which can be seen as an improved version of our simple smoothing approach , indeed improves on the latter ( based on permutation testing and comparison to transcripts with known AI - see below ) , but is far from being as accurate as the proposed Z-Score approach , because it leads to bleeding edges at transcript boundaries . We also investigated a version of the Z-Score approach where SNPs are not binned by expression level prior to Z-Score computation; this resulted in a small but significant decrease in accuracy , showing that the appropriate modeling of the dependency between the noise in allelic ratio and the total expression level is an important feature of our approach . The linear nature of the data in question lends itself well to a Hidden Markov Model ( HMM ) in which each data point corresponds to a particular SNP , the hidden states correspond to qualitative descriptions of the allelic imbalance ( e . g . positive imbalance , negative imbalance , no imbalance ) , and emissions correspond to the total expression and the allelic log-ratio observed at site . We built an HMM consisting of a total of eight hidden states ( see Figure 2a ) . Seven of these states correspond to SNPs take belong to expressed transcripts in the LCL sample in question , with various levels of imbalance: , corresponding to strongly positive imbalance ( ) , moderately positive imbalance ( ) , slightly positive imbalance ( ) , balance ( ) , slightly negative imbalance ( ) , moderately negative imbalance ( ) and strongly negative imbalance ( ) . There is also a state ( ) that corresponds to SNPs located in regions that are predicted not to be transcribed , and for which allelic imbalance is meaningless . The emission probability for each state is modeled with a pair of normal distributions for the and values , with parameters ( , ) , and ( , and ) respectively . Whereas both total expression and allelic imbalance measurements are observed at heterozygous sites , only the expression is measured at homozygous sites . In the latter case , the imbalance data is left unobserved ( i . e . all 8 states are equally likely to have generated the observation ) . Homozygous SNPs can thus be included in the model training and predictions , and can help delineating regions of based on expression levels . An HMM with a realistic correspondence to the data can in principle be built with states , where represents the number of levels of positive ( and negative ) imbalance that the model represents . Larger values of should in principle be favorable as they allow a finer discretization of allelic ratios . Models with were trained and the false discovery rate measured and compared ( see section 0 ) . It was found that performed better than and , and similarly to ( Figure S2 ) , so this value was used for both the ergodic and left-to-right models . Certain parameters of the HMM are trained using the Baum-Welch algorithm , while others are fixed . For , the emission probability distribution for is modeled non-parametrically by the histogram of Figure 1 ( b ) ( black curve ) whereas all expressing states share the same total expression distribution from Figure 1 ( b ) ( red curve ) . These emission probability distributions are kept constant during the training procedure . The Baum-Welch algorithm [31] is used to find maximum likelihood estimators for and , for , as well as all transition probabilities and the initial state probability . The Baum-Welch algorithm is an expectation-maximization ( EM ) [32] approach that alternates between the Expectation step ( or E-step ) , in which the posterior probability over states is computed for each site using the Forward-Backward algorithm , and the Maximization step ( or M-Step ) where the parameters of the emission and transition probability distributions are adjusted to best reflect the observed data given these posterior probabilities . Formulas for updating the emission probability parameters and transition probabilities are adapted straightforwardly from Mitchell [33] . We considered training one HMM per individual ( which would allow the flexibility to model inter-experiment variation in noise , for example ) , or to train a single HMM based on the data from all individuals ( which would have the benefit of being based on more data ) . The latter option produced slightly better results and this is the strategy we used for the rest of the study . We also considered filtering out sites with low total expression , as their allelic expression ratio may be less reliable . However , slightly better results were obtained without any filtering ( allowing non-expressed SNPs to naturally be classified as belonging to state ) . Training on the whole data set took less than Baum-Welch 20 iterations and 3 hours to converge on a standard desktop computer ( convergence is defined as two consecutive iterations where no parameter or transition probability changed by more than or 1% of their value ) . Restarts from different initial values converged to nearly the same values . The Viterbi algorithm [34] can then be used to identify , in each individual , predicted regions of different levels of positive or negative imbalance . The Forward-Backward algorithm [35] yields an estimate of the posterior probability of each state at each site . In the latter case , a useful summary score for each site is the posterior expected allelic expression log-ratio , which we use as AI predictor: . Until now we have assumed homogenous transition probabilities , regardless of the distance in base pairs between consecutive SNPs along the chromosome . However , a more accurate model would factor in the distance between neighboring SNPs , to increase the probability of self-loops ( i . e . staying in the same state ) when the two sites are nearby but increase the probability of state change for two distant sites . Such an approach has been used previously in HMMs designed to detect CNVs [27] . We obtained a unit transition probability matrix as the -th root of the transition matrix obtained via Baum-Welch training of the homogeneous model , where is the average distance ( in base pairs ) between two consecutive SNPs in our data . Then , the transition probability matrix used for a pair of sites separated by base pairs will be , which is efficiently computed using the eigenvalue decomposition of . To ensure that our training procedure was not subject to overfitting , we used 2-fold cross validation ( dividing the 53 samples into one 26-sample data set and one 27-samples data set ) and trained our 8-state ergodic HMM separately on each half the samples . The parameters and transition probabilities obtained were nearly identical , and so were the FDR estimates obtained by running each HMM on the complementary data set , indicating that overfitting is not an issue . The previous HMM is called ergodic because it models an ergodic , homogeneous Markov chain over the state space ( i . e . the set of transition probabilities is independent of the position along the genome ) . One limitation of this HMM is that it does not take full advantage of the fact that data exists for multiple individuals and that , while not all individuals are expected to have AI in exactly the same regions , one does expect AI hotspots where a significant fraction of the individuals would have imbalance . That would be the case , for example , for genes where one allele is commonly or always silenced via epigenetic mechanisms , or when AI is due to a common regulatory variant . The approach proposed in this section aims at predicting AI regions separately in each individual , while taking into consideration the data observed in all individuals . In doing so , we still want to be able to identify AI regions that are unique to a given individual , but are hoping to improve the detection of regions with common AI . For example , AI regions containing only a few SNPs , or those where the imbalance is only moderate , may be missed when present in a single individual , but may be detectable if present in a large fraction of the population . In addition , we may be able to detect boundaries of AI regions with more accuracy when they are shared among individuals . The approach utilized to address this is termed the left-to-right HMM [35] ( see Figure 2 ( b ) ) , similar to profile HMMs [36] . Each site has its own copy of the set of states and transitions can only occur between states associated with neighboring sites , from left to right . Each copy of a given state shares the same emission probability distributions that are modeled the same way as with the ergodic HMM . However , transition probabilities will vary across positions , making the model non-homogeneous ( in contrast to our ergodic HMM approach ) . This configuration allows for greater fine tuning at the level of each individual SNP or region , though at the cost of a substantially larger set of transition probabilities to be learned . The training of our left-to-right HMM is a two stage process . In the first stage , emission probabilities , transition probabilities , and start probabilities are estimated for the ergodic version of the HMM using the Baum-Welch algorithm described above , using all available individuals . The parameters of the emission probabilities of the states in the left-to-right HMM will be set to those obtained on the ergodic training and will not be re-estimated . The obtained ergodic non-homogeneous distance-corrected transition probabilities will be used as prior for those of the left-to-right HMM . In the second stage , we now switch to learning the transition probabilities of the left-to-right HMM . We assume that the data set from each individual is the result of an independent run of the HMM: , and we seek to identify the set of transition probabilities of the left-to-right HMM that maximizes this joint likelihood . Consider a site that is not imbalanced in any individual but where site is positively imbalanced in a large fraction of the individuals . The maximum likelihood estimator for the transition from state to state will be higher than at other positions where few individual enter an imbalanced region . Now consider an individual where there is only weak evidence of AI starting at position . When using an ergodic HMM for our predictions , the weak AI region will probably not be detected . However , in the left-to-right HMM , with the increased transition probability , the AI path becomes more likely , so provided that there is sufficient imbalance , the most likely path may now to go through one of the imbalanced state . Estimating transition probabilities between two sites separated by base pairs is done using a simple modification to the standard Baum-Welch algorithm , where the update rule for transitions is: where is the -th power of the unit transition probability obtained previously and indicates the pseudocount weight described in the following paragraph . The regularization obtained by using the ergodic transition probability as prior reduces the risks of overfitting while improving the convergence of the training procedure . In practice , based upon permutation tests and resulting FDR scores , a parameter of was determined to be optimal ( data not shown ) . Once the left-to-right HMM is trained using the data from all 53 individuals ( which took 161 Baum-Welch iterations - less than 4 hours on a standard desktop computer ) , the standard Viterbi or Forward-Backbward algorithms are used to identify AI regions separately for each individual . As with the case of the ergodic HMM , we use the posterior expected allelic expression log-ratio to summarize AI evidence at SNP . Overfitting is a possible issue with our left-to-right HMM , as the number of parameters estimated is much larger than for the ergodic HMM . We performed 5-fold cross-validation , training on 4/5 of the data and predicting on 1/5 . Thanks to our regularization procedure , the predictions obtained were very similar to those obtained by training and testing on the full data set , with only a marginal decrease in FDR . Upon study of some of the regions where AI was predicted in most or all individuals but where not known imprinted regions existed , we found that nearly half were a likely artifact of cross-hybridization . All these suspicious regions were the results of a segmental duplication , where a fragment of a gene was duplicated . Because the fragments still matches the genic region , sites within them will appear to be expressed ( as they match the transcript of the paralogous region ) , and polymorphisms will cause mismatches between the probe and the true transcript , which will result in apparent AI . We thus used the human Blastz self-alignment from the UCSC Genome Browser [37] , [38] to filter out regions corresponding to recent duplications . A possible alternate approach would consist of using the results of the genomic DNA hybridization to identify probes that match more that one location in the genome , with the possible added benefit of detecting DNA possible copy-number variation . Due to the relatively small number of “gold standard” regions known to exhibit AI , the best available option for comparison of the various models is through permutation tests . The goal was to preserve some of the structure of the genome such that only SNPs with approximately equal expression levels and heterozygosity would be swapped , i . e . , the only factor that is swapped freely is that of the allelic imbalance ratio . Permuted data sets were generated as follows . Sites were partitioned into five levels based on the number of individuals in which they are heterozygous . Five bins were also assigned based on the average level of expression seen across all individuals . Each SNP was then finally assigned to one of 25 bins , with one bin for each of the possible combinations of heterozygosity frequency and expression levels . Sites were randomly permuted within each bin , preserving the correspondence between sites in different individuals ( in the case of the left-to-right HMM , the first stage of training of global HMM parameters was first done on non-permuted data , and then the second stage of model training was done on permuted data ) . Preserving expression levels and heterozygosity is important to create permuted data sets that are as realistic as possible , in particular with respect to the fact that expressed sites are found in contiguous genomic regions rather than dispersed randomly in the genome . Each of the prediction methods described produces one AI score per site and per individual . For each method , the number of regions of consecutive SNPs exceeding a given score threshold , and was determined in the real and permuted data , resulting in a False-Discovery Rate of .
We use two examples to highlight the features of the data and the methods developed . Figure 3 gives a sample of the raw data and predictions made by each method in the BLK locus . BLK is a gene that has previously been described as allelically imbalanced in LCL [13] . Interestingly , in this individual , two other neighboring genes have strong allelic imbalance , with FAM167A showing expression on the opposite allele compared to BLK and GATA4 also obtaining strong an consistent signals . Although in this example the boundaries of allelic expression domains align nicely with known gene boundaries , this is not the case in general . As is obvious from the figure , the raw expression and allelic ratio data are quite noisy . The simple smoothing approach succeeds at identifying the main regions of allelic imbalance but does so much less reliably and precisely than the other three approaches . Notice that this individual has no heterozygous sites in the 5′ end of FAM167A . This results in different behaviors for each method . The ergodic approach assigns gradually decreasing expected allelic log-ratios in that region , while the Z-Score approach only predicts imbalance in the 3′ end of the gene . However , the left-to-right HMM has the benefit of considering data from other individuals , which have some heterozygous sites in the 5′ region of the gene , which allows it to predict strong and consistent negative allelic log-ratios over the whole gene , and a sharp transition entering the BLK transcript . A similar phenomenon is observed for GATA4 . Figure 4 shows the set of predictions made by the Viterbi algorithm using the left-to-right HMM on the extended GATA3 locus , in all 53 samples . The region exhibits a large diversity of patterns of AI . In some cases , the region of AI closely matches an annotated gene ( e . g . SFTMBT2 in several individuals ) . Often , AI regions do not overlap any known gene ( e . g . the region located upstream of SFMBT2 ) . Such regions , especially when they abut an annotated gene , may reflect the presence of alternative allele-dependent promoters . They may also represent completely novel unannotated transcripts . Another frequently observed pattern is the presence of AI within annotated transcripts , near the 5′ or 3′ end ( e . g . the 3′ end of the ITIH5 gene ) . Finally , AI regions often encompass one or more complete genes ( e . g . GATA3 and NM_207423 ) , possibly because of epigenetic modification of one of the two alleles . We note based on analysis done in [13] that SFTMBT2 and ITIH5 show evidence of heritable allelic expression , whereas GATA3 does not show correlation with common genetic variants and could represent epigenetic modification of expression in LCLs . The accuracy of the AI predictions made by each method was evaluated using both permutation testing ( in order to assess the false discovery rate ) and comparison to previously characterized AI transcripts . We first estimated the false-discovery rate ( FDR ) of each method using a permutation test where genomic sites are randomly permuted , subject to some constraints ( preservation of heterozygosity and expression level; see Methods ) . This randomized data set preserves the level of imbalance observed at each site , but randomly disperses sites in such a way that few regions are expected to exhibit strong and consistent allelic ratios over several consecutive sites ( as real AI transcripts should ) . For each algorithm , the number of genomic regions with AI score above some threshold in the real data was compared to the corresponding number on the permuted data - the ratio of these two numbers is an estimate of the FDR of the algorithm ( note that the FDR could also be estimated at the individual SNP level , rather than at the region level; the conclusions are the same ) . Figure 5 shows the FDR curves obtained for each method , as a function of the number of predictions made . All methods are able to detect the most obvious cases of AI ( roughly 200 regions per individual , where all methods have near-zero FDR ) . However , as our threshold decreases and the number of regions predicted increases , the performance of the four approaches become quite different . Setting 5% as an acceptable FDR , the simple smoothing , Z-Score , ergodic HMM , and left-to-right HMMs result in 360 , 622 , 662 , and 954 predicted regions with AI . In other words , at that FDR level , the best approach , left-to-right HMM , is 160% more sensitive than the simple smoothing approach and 45% more sensitive than the second best approach , which is the ergodic HMM . Similar observations hold for other FDR thresholds . Therefore , the information obtained from the total expression levels , as well as the added site-specific transition probabilities are beneficial in terms of obtaining reliable AI predictions . This is particularly noteworthy for regions whose AI is weaker ( those ranking between the 500 to 1000th per individual ) , for which the FDR remains quite low with the left-to-right HMM but quickly increases with all other methods . Although no comprehensive set of validated AI transcripts exists to date , a set of 62 imprinted genes ( containing 1099 SNPs in our data set ) have been collected from the literature and posted on www . geneimprint . com . Most imprinted regions are easily detected by most methods , as they affect relatively large genomic regions and their allelic expression ratios are extremely large . Figure 6 shows how the enrichment of the overlap between imprinted genes and the predictions made by each of the four methods varies as a function of the number of sites being predicted with AI . ( The enrichment of the overlap between a set of predicted AI regions and a set of annotated regions is the ratio of the size of the overlap to the expected size of the overlap if AI regions had been selected randomly in the genome . ) Imprinted SNPs are enriched 5 to 20-fold among the top predictions made by each algorithm ( except the Z-Score approach , which assigns high scores to other types of regions ) . Focussing on the left-to-right HMM AI predictions at a 5% FDR threshold ( which consist of roughly 40 , 000 SNPs per individual ) , we find that 67% ( resp . 35% ) of SNPs in imprinted regions are predicted to have AI in at least one ( resp . five ) individual . Manual inspection of imprinted genes that have gone undetected by any of our methods reveals genes that are short , contain few heterozygous SNPs , or are expressed at a very low levels in LCL . Allelic imbalance resulting from cis-regulatory variation typically have allele ratios less extreme than imprinted genes and are thus more difficult to detect . A set of 61 transcripts ( containing 1596 SNPs in our data set ) with AI resulting from cis-regulatory variation in LCL have been identified and validated by Verlaan et al . [8] . Figure 6 ( b ) shows the fold-enrichment of these SNPs among those predicted as AI SNPs by each of our methods . Here , the predictions made by the two types of HMMs perform significantly better than the Z-Score and smoothing approaches , detecting approximately 50% and 100% more validated SNPs . Overall , our best approach is again the left-to-right HMM , which predicts 87% ( resp . 70% ) of the 1596 validated SNPS as imbalanced in at least one ( resp . five ) individual ( s ) . Inspection of AI genes that were undetected showed that they exhibited little evidence of allelic imbalance by our method ( see Figure S3 ) . These represent likely false positives in earlier study as well as more localized effects caused by few independent AI measurements and driving the association tests in previous analyses [13] . Our predictions allow a first glimpse into the diversity of allelic expression patterns in the human genome , although a comprehensive analysis of AI regions is beyond the scope of this study . We first observe that AI in LCL samples is widespread , with on average 9 . 7% ( resp . 5 . 6% ) of an individual's genes containing at least one ( resp . all ) imbalanced SNP ( using the left-to-right HMM with a threshold corresponding to an FDR of 5% ) . Considered in total , 54 . 4% of genes show at least one imbalanced SNP in at least one individual , and 45 . 6% of genes have all of their SNPs showing allelic imbalance in at least one individual . Note that only approximately 50% of genes in total are detectably expressed in LCL [39] , and hence candidates for being allelically imbalanced . Thus , the majority of expressed genes show AI in one or more individuals . Figure 7 reports the distribution of AI regions across various types of genomic regions . While a substantial fraction ( 19% ) of AI regions closely match annotated gene boundaries , most exhibit more complex relationships to annotated protein-coding gene transcripts , a larger portion of AI regions ( 28% ) are within annotated genes but cover only a fraction of the transcript . In nearly half of those , allelic expression is found toward the 3′ end of the gene , possibly because of allele-specific transcription termination or mRNA degradation , or the presence of an allele-specific alternate transcription start site within the annotated gene . The presence of AI regions at the 5′ end of the transcript appears somewhat less frequent . 22% have little or no overlap with protein-coding genes , although this fraction is enriched for other types of transcripts such as LINC-RNAs [40] . Our data set affords a first glimpse into the commonality of allelic imbalance at a given site across individuals . We calculated the number of individual showing AI ( based on the Viterbi predictions; see Figure 8 ) . The very long tail of this distribution indicates that a lot of AI is shared among a portion of the population . In fact , 65% of an individual's AI regions are found in at least 10 other individuals . Allelic imbalance , whether caused by genetic or epigenetic causes , is thus highly structured in the human population . On the other hand , rare AI , defined as that seen in at most 10% of our individuals , constitutes approximately 20% of an individual's AI regions , while 4% are unique to that individual . We note however that because AI regions found in a large number of samples are easier to detect than those that are less common in the population , we may underestimate the proportion of AI that is found in a small number of individuals . We note that the left-to-right HMM predictions used for this analysis are potentially biased towards over-predicting sites with common AI and under-predicting those with rare AI . We thus repeated the analysis with the ergodic HMM approach , which does not suffer from this bias . The results were very similar , with only a very slight shift toward less frequent AI .
The recent development of a genome-wide high-density assay of allelic imbalance based on genotyping arrays has resulted in a vast improvement in our understanding of this type of variation and in our ability to map this variation to causative regulatory SNPs [13] . A relatively simple gene-based analysis was sufficient to identify a significant number of genes with allelic imbalance [13] . However , taking full advantage of this technology requires advanced signal processing approaches to accurately detect , delineate and quantify allelic expression . Furthermore , relying too heavily on known gene annotation may hide the fact that most AI does not perfectly align with gene boundaries . Indeed , the approaches proposed here , which do not make use of gene annotations , reveal that allelic imbalance is widespread and exhibits complex patterns in relation to annotated genes . Although our approach was specifically applied to the analysis of data obtained from high-density genotyping arrays , it should be readily applicable to studies based on data obtained next generation RNA sequencing . Detection of AI based on data from genotyping arrays proves challenging because of the significant noise in the allelic ratio measured at individual SNPs and because of the complex patterns of AI . To our knowledge , our study represents the first in-depth , statistical and computational analysis of a large scale , genome-wide allelic imbalance data set . Because of the noise level in allelic expression ratios at individual SNPs , one must rely on the fact that transcripts with allelic imbalance will generally contain several SNPs that are expected to show imbalance . Our Z-Score approach identifies regions where the allele ratio is significantly different from the expected one-to-one ratio . An aspect of the data that is not exploited by the Z-Score approach is that the total expression and allelic ratio are expected to be consistent across the transcript . Our two HMM approaches model this explicitly , and obtain better results in part because of this . An additional improvement in accuracy of AI detection is obtained by our left-to-right HMM , which considers jointly the data from all individuals to serve as prior for the detection of AI in each one . This approach yields improved detection of AI regions that are shared among many individuals , while being able to detect those present in only one or a few samples . This new type of machine learning problem , where a collection of sequences of observation are expected to have been derived from a common ( but unknown ) model but where each individual can significantly deviate from that model is a situation that may arise in a number of other situations where our left-to-right HMM approach may be useful , including for comparative genomics based gene predictions [41] ( where different species are expected to share some but not all of their exon structure ) . Although a detailed biological analysis of allelic imbalance and its phenotypic consequences is beyond the scope of this paper , our predictions reveal that AI is widespread , with roughly 10% of genes showing evidence of AI in a given individual , and with the majority of genes expressed in LCLs showing AI in at least one of our 53 samples . Although roughly 60% of AI regions are clearly related to an annotated transcript , they often reflect the presence of alternative promoters , splicing , or transcription termination . An increasing proportion of the genetic burden of disease is being associated with differences in gene regulation [42] . At the same time greater complexity of gene regulation and the transcriptome are being uncovered [43] . Therefore , hypothesis-free methods detecting allelic imbalance are a prerequisite to advancing our understanding of population variation in cis-regulatory control by heritable or epigenetic mechanisms . | Measures of gene expression , and the search for regulatory regions in the genome responsible for differences in levels of gene expression , is one of the key paths of research used to identify disease causing genes , as well as explain differences between healthy individuals . Typically , experiments have measured and compared gene expression in multiple individuals , and used this information to attempt to map regulatory regions responsible . Differences in environment between individuals can , however , cause differences in gene expression unrelated to the underlying regulatory sequence . New genotyping technologies enable the measurement of expression of both copies of a particular gene , at loci that are heterozygous within a particular individual . This will therefore act as an internal control , as environmental factors will continue to affect the expression of both copies of a gene at presumably equal levels , and differences in expression are more likely to be explicable by differences in regulatory regions specific to the two copies of the gene itself . Differences between regulatory regions are expected to lead to differences in expression of the two copies ( or the two alleles ) of a particular gene , also known as allelic imbalance . We describe a set of signal processing methods for the reliable detection of allelic expression within the genome . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"genetics",
"and",
"genomics/genomics",
"computational",
"biology/population",
"genetics",
"genetics",
"and",
"genomics/gene",
"expression",
"computational",
"biology/molecular",
"genetics",
"computational",
"biology/genomics",
"genetics",
"and",
"genomics/epigenetics",
"genetics",
"and",
"genomics/bioinformatics"
] | 2010 | Computational Analysis of Whole-Genome Differential Allelic Expression Data in Human |
Neurons spike when their membrane potential exceeds a threshold value . In central neurons , the spike threshold is not constant but depends on the stimulation . Thus , input-output properties of neurons depend both on the effect of presynaptic spikes on the membrane potential and on the dynamics of the spike threshold . Among the possible mechanisms that may modulate the threshold , one strong candidate is Na channel inactivation , because it specifically impacts spike initiation without affecting the membrane potential . We collected voltage-clamp data from the literature and we found , based on a theoretical criterion , that the properties of Na inactivation could indeed cause substantial threshold variability by itself . By analyzing simple neuron models with fast Na inactivation ( one channel subtype ) , we found that the spike threshold is correlated with the mean membrane potential and negatively correlated with the preceding depolarization slope , consistent with experiments . We then analyzed the impact of threshold dynamics on synaptic integration . The difference between the postsynaptic potential ( PSP ) and the dynamic threshold in response to a presynaptic spike defines an effective PSP . When the neuron is sufficiently depolarized , this effective PSP is briefer than the PSP . This mechanism regulates the temporal window of synaptic integration in an adaptive way . Finally , we discuss the role of other potential mechanisms . Distal spike initiation , channel noise and Na activation dynamics cannot account for the observed negative slope-threshold relationship , while adaptive conductances ( e . g . K+ ) and Na inactivation can . We conclude that Na inactivation is a metabolically efficient mechanism to control the temporal resolution of synaptic integration .
Action potentials are initiated when the membrane potential exceeds a threshold value , but this value depends on the stimulation and can be very variable in vivo [1]–[4] , which has triggered a recent controversy about the origin of this variability [5]–[7] . This phenomenon has been observed in many areas of the nervous system: visual cortex [1]–[3] , somatosensory cortex [4]; prefrontal cortex [8]; neostriatum [9] , neocortex [10] , [11] , hippocampus [12] , [13] , and auditory brainstem [14]–[17] . Experimental studies have shown that the spike threshold is correlated with the average membrane potential [2] , [8] , inversely correlated with the preceding rate of depolarization [1]–[4] , [9] , [12] , [14] and inversely correlated with the preceding interspike interval [13] , [18] . Thus , threshold dynamics participate in the input-output properties of neurons: it enhances coincidence detection and gain modulation properties [1] , [2] , it contributes to feature selectivity in sensory processing [2] , [4] , [19] , contrast invariance [2] , [20] and temporal coding [17] , [21] , [22] . Among the mechanisms that can modulate the spike threshold [23] , two are thought to be especially relevant: inactivation of sodium channels [1] , [2] , [4] , [8] , [12] , [17] and activation of potassium channels [2] , [10]–[12] , [14]–[16] . In this study , we chose to focus on the role of sodium channel inactivation because it specifically impacts spike initiation without changing the membrane potential , and because of the extensive voltage-clamp data available for Na channels . Our first goal was to check whether Na channel inactivation , given their measured properties , can account for significant threshold variability and for the qualitative properties of the spike threshold dynamics , as listed above . Our second goal was to evaluate the consequences of threshold dynamics on the integration of postsynaptic potentials ( PSPs ) . We analyzed the influence of Na inactivation on spike threshold in a model , in which we were able to express the spike threshold as a function of Na channel properties and variables [23] . We collected previously published voltage clamp measurements of Na channel properties and found that Na inactivation by itself can account for substantial threshold variability , with the same qualitative properties as experimentally observed . To investigate the implications for synaptic integration , we derived a dynamical equation for the spike threshold and defined effective PSPs as the difference between the PSP and the threshold . We found that , with threshold adaptation as implied by Na inactivation , effective PSPs are briefer than PSPs and that their shape depends on membrane depolarization . Finally , we discuss the potential contribution of other mechanisms of threshold modulation .
We previously derived a formula , the threshold equation , which relates the instantaneous value of the spike threshold to ionic channels properties [23]:where Va is the half-activation voltage of Na channels , ka is the activation slope factor , gNa is the total Na conductance , gL is the leak conductance , ENa is the Na reversal potential , h is the inactivation variable ( 1-h is the fraction of inactivated Na channels ) . Here the spike threshold is defined as the voltage value at the minimum of the current-voltage function in the membrane equation ( we compared various threshold definitions in [23] ) . This formula is derived from the assumption that the Na activation curve is well described by a Boltzmann function , which implies that the Na current below spike initiation is close to an exponential function of voltage ( see Text S1 for the derivation ) . This approximation of the Na current is the basis of the exponential integrate-and-fire model ( EIF ) [24] . In this paper , we focus on the impact of Na inactivation and therefore we ignore the last term of the threshold equation , which simplifies to:where VT is a constant term , corresponding to the minimum spike threshold ( when Na channels are not inactivated ) . We call the EIF model with Na inactivation the inactivating exponential integrate-and-fire model ( iEIF; see Methods ) . After a spike , the voltage is reset to the resting potential EL , and h is unchanged . Thus , when the neuron is depolarized , Na channels inactivate ( h decreases ) and the threshold increases: the threshold adapts to the membrane potential . We start by studying the steady-state threshold , which is the value of the spike threshold for a fixed voltage V0 . It corresponds to the threshold measured with the following experiment . The cell is clamped at a voltage V0 ( Figure 1A ) , and a fraction of Na channels inactivates . In the Hodgkin-Huxley formalism , this fraction is , where is the steady-state inactivation function ( h is the fraction of non-inactivated channels ) . If the clamp is relaxed and a current is injected , the neuron may produce a spike if the current is large enough ( Figure 1A ) . The steady-state threshold corresponds to the maximum voltage that can be reached without triggering an action potential , and it depends on the fraction ( 1-h ) of inactivated Na channels: when the membrane is depolarized , Na channels inactivate , which raises the spike threshold . One way to understand threshold adaptation is to look at how the excitability curve changes with h ( and therefore with depolarization ) . The excitability curve ( Figure 1B ) shows the value of dV/dt vs . V for a fixed value of h , as given by the membrane equation ( which is equivalent to the I-V curve , if the current is scaled by the membrane capacitance ) . When h decreases ( Na channels inactivate ) , the entire excitability curve shifts towards higher voltages and the threshold shifts accordingly . As in [23] , we define the threshold as the voltage at the minimum of the excitability curve , but since the entire curve is shifted by Na inactivation , other definitions would produce similar results . The membrane potential V is always below threshold , unless the cell spikes . Therefore the observable threshold values cannot be larger than the intersection between the threshold curve and the diagonal line , if these two curves intersect ( Figure 1C ) . Thus , the spike threshold may vary between the minimum steady-state threshold VT and the solution of . When there is no such solution , the threshold can be arbitrarily large , meaning that a very slow depolarization would not elicit a spike ( Figure 1C , top dashed curve ) . Thus , the range of threshold variability can be derived from the steady-state threshold curve . Using the threshold equation , we can calculate the steady-state threshold as a function of V: , where is the Na inactivation curve , which is generally well fitted by a Boltzmann function [25]:where is the half-inactivation voltage , and is the inactivation slope factor . When we substitute this function in the threshold equation , we find that the steady-state threshold has a horizontal asymptote ( VT ) for large negative potentials and a linear asymptote for large positive potentials , because the inactivation function is close to exponential ( Figure 2A ) . Thus , the steady-state threshold can be approximated by a piecewise linear function ( see Text S1 ) : In other words , the minimum threshold is VT , which is determined by the maximum Na conductance ( Figure 2B ) , the threshold increases above the half-inactivation voltage Vi , and the slope is the ratio of activation and inactivation slope factors . Regarding threshold variability , we can distinguish three cases , depending on Na channel properties: Figure 2C-E illustrates case 2 in a single-compartment model with fluctuating inputs ( note that the membrane potential can exceed the threshold without triggering a spike because spike initiation is not sharp , unlike in real cortical neurons and in multicompartmental models; see the discussion in [23] ) . We started by examining these conditions in the dataset collected in the literature by Angelino and Brenner [25] about the properties of the 9 Nav1 channel types . These properties were obtained from voltage clamp measurements of Na channels expressed in exogenous systems . Figure 3A shows the distribution of Vi in this dataset , which is rather wide ( −90 mV to −25 mV ) . Central neuron channel types , i . e . , Nav1 . [1] , [2] , [3] , [6][27] , are shown in red . Since the minimum threshold VT depends on the maximal Na conductance , it cannot be deduced from channel properties alone . Considering that VT should lie between −55 and −45 mV [28] , a substantial part of the channels fall into the first case , i . e . , constant threshold , while the rest can fall into the second ( moderate threshold variability ) or third case ( unbounded variability ) , depending on whether ka>ki . Figure 3B shows that , while this latter condition is never met for channel types expressed in sensory neurons ( blue dots ) , about half of those expressed in central neurons ( red ) and muscles ( green ) satisfy ka>ki . Thus , it seems that all three cases occur in similar proportions for channel types expressed in central neurons . However , not all Na channels are involved in spike initiation . In particular , in central neurons , spike initiation is mediated by Nav1 . 6 channels while Nav1 . 2 channels are involved in axonal backpropagation [8] . This first dataset contained only 4 Nav1 . 6 channels , for which Vi<−50 mV in all cases ( −61±8 . 4 mV ) , suggesting significant threshold variability , but this is a small sample . Besides , this first dataset was somewhat artificial , because channels , some of which had mutations , were artificially expressed in an exogenous system , which might alter their properties . Therefore we looked at a second dataset , consisting of in situ measurements in intact central neurons that we collected in the literature ( see Table S1 ) . These measurements may combine the properties of several channel types expressed at the same site , e . g . Nav1 . 1 , Nav1 . 2 , or Nav1 . 6 . In some of these studies , the threshold was also measured and found to be variable [8] , [17] , [29] , [30] . In this dataset , as shown in Figure 3C , the half-inactivation voltage was always lower than −50 mV , which implies that most channels induce threshold variability ( cases 2 and 3 ) . About half of them met the condition ka>ki ( Figure 3D ) . Thus , in this dataset , Na inactivation induces unbounded threshold variability in about half cases and moderate variability in the other half . We have shown that Na channel properties , i . e . , parameters , , , , allow us to determine whether Na inactivation can make the spike threshold variable and we found that the answer is positive in central neurons . While this analysis gives an estimate of potential threshold variability , the observed variability and its properties depend on the stimulation . The instantaneous value of the spike threshold depends on the value of the inactivation variable h through the following formula [23]: . We now assume that h evolves according to a standard Hodgkin-Huxley equation with first order kinetics:where is the inactivation time constant . By differentiating the threshold equation and substituting the differential equation for h , we obtain a differential equation for as function of the membrane potential ( see Text S1 A ) , which can be approximated by:with . To simplify the calculations , we assume in the following that the inactivation time constant does not vary significantly with V , but we examine the effect of this voltage-dependence later . This equation describes how the threshold changes with the membrane potential , and therefore with the stimulation , and is entirely determined by Na channel properties . Since the steady-state threshold increases with V ( Figure 2 ) , it appears that the threshold adapts to the membrane potential with characteristic time . Thus , we readily see that 1 ) the threshold increases with the membrane potential and 2 ) the threshold is lower for faster depolarization , because it has less time to adapt to the membrane potential . Before we describe threshold dynamics in more details , we need to make an important remark . As is seen in Figure 2E , which describes the dynamics of an iEIF model with fluctuating inputs , the membrane potential can exceed the threshold without triggering a spike , if the fluctuation is fast enough . This reflects the fact that spike initiation in this model , as in any biophysical single-compartment model , is not sharp: since there is no well-defined voltage threshold , what we describe as threshold variations are more accurately described as voltage shifts of the excitability curve . This makes the definition of a dynamic threshold a little ambiguous . However , spike initiation in cortical neurons is much sharper than in single-compartment models [5] , because of the active backpropagation of spikes from the initiation site [6] . A direct in vitro measurement of the slope factor in cortical neurons ( characterizing spike sharpness ) gave ΔT≈1 mV [18] ( compared to ka ≈ 6 mV ) , meaning that spike initiation is almost as sharp as in an integrate-and-fire model . This phenomenon is well captured by multicompartmental models [8] , [23] and it affects spike sharpness independently of threshold variability: in Figure 7H of ref . [23] , spikes are initiated as soon as the membrane potential exceeds the dynamic threshold , which is determined according to the threshold equation . This motivates us to introduce a new model , the inactivating integrate-and-fire model ( iLIF , see Methods ) , which is simply an integrate-and-fire model with an adaptive threshold given by the differential equation above ( after a spike , the voltage is reset to the resting potential EL , and the threshold is increased - see Methods ) . This phenomenological model is not only simpler , but also seemingly more realistic than the iEIF model for the present problem , in that it reproduces both the sharpness of spike initiation and the variability of spike threshold . We use this model in the remainder of this paper . The threshold also increases with each action potential [23] ( see also Text S1 A ) , as was recently demonstrated in vitro [18] . This can be described as simple additive shift: , where is the average value of the time constant during the action potential and is the spike duration ( typically , a few ms ) . If the inactivation time constant is short compared to the typical interspike interval , then this shift results in a relative refractory period , but has negligible influence on the subsequent dynamics of the model . If it is long , it results in spike-frequency adaptation and explains in vivo observations where the threshold was found to be inversely correlated with the previous interspike interval [13] . This phenonemon can be seen in the noise-driven iLIF model when Na inactivation is slow ( not shown ) . In the following , we focus on the impact of fast Na inactivation . Quantitatively , the relationship between average membrane potential and threshold depends on the steady-state threshold function . Figure 4 shows this relationship in a neuron model with adaptive threshold ( defined by the dynamical equation above ) and fluctuating inputs of varying mean . As expected , the average threshold increases with the average membrane potential , and the slope is steeper above half-inactivation voltage Vi . In these simulations , the slope of the steady-state threshold curve was ka/ki = 1 , close to experimental values , but we note that the average threshold only increases as about 2/3 the average membrane potential in the depolarized region . This is because the membrane potential is very variable ( about 6 mV in this figure ) and therefore the threshold is not constantly in the sensitive region ( V>Vi ) . This is consistent with previous measurements in the visual cortex in vivo , where Azouz and Gray ( 2003 ) found a linear correlation with a slope of 0 . 5 . To calculate the relationship between the slope of depolarization and the threshold , we consider a linear depolarization with slope s ( i . e . , V ( t ) = V0+st ) and calculate the intersection with the threshold ( Figure 5A ) . By linearizing the steady-state threshold as previously described , we find that the slope s and the threshold are related by the following equation ( see Methods ) : Unfortunately , this implicit equation does not give a closed formula for as a function of s , except when : In this particular case , the threshold diverges to infinity at , i . e . , no spike is produced if the depolarization is slower than s* ( Figure 5B , dashed line ) . This phenomenon can occur more generally when ( unbounded variability , case 3 ) and has been observed in neurons of the cochlear nucleus [16] ( where it is described as a "rate threshold" ) . In all cases , for large s ( fast depolarization ) , the threshold tends to VT , i . e . , to the lowest possible threshold , and it increases for smaller s , i . e . , slow depolarization ( Figure 5B , solid line ) . The equations show that the slope-threshold relationship depends on the half-inactivation voltage Vi and on the threshold time constant ( = ) . The relationship is more pronounced when Vi is low compared to the minimum threshold VT ( Figure 5C; VT was −55 mV ) . The role of the threshold time constant can be seen as a scaling factor for slopes , i . e . , the threshold depends on the product of the slope and threshold time constant . The slope-threshold relationship is more pronounced when the threshold time constant is short ( Figure 5D ) . In experiments in vivo , the slope-threshold relationship was measured using linear regression on the membrane potential preceding each spike [2] , [4] . We simulated the adaptive threshold model with a fluctuating input ( Figure 5E ) and performed a similar analysis , by calculating the depolarization slopes over a duration equal to the threshold time constant . The resulting slope-threshold relationship matches our previous calculation ( which only uses Na channel properties ) , but with more variability ( Figure 5F ) , as is also observed in experiments . Finally , we measured the slope-relationship in the multicompartmental model of Hu et al . [8] with fluctuating inputs , for which we previously showed that the threshold equation accurately predicted the measured threshold [23] . The slope-threshold relationship also matched our prediction ( Figure S1 ) . These dynamical properties of the threshold imply that the threshold should be variable for fluctuating inputs ( typical of in vivo regimes ) but not for constant DC inputs ( typical of in vitro stimulations ) . More generally , it implies that the threshold distribution depends on the membrane potential distribution , as shown in Figure 6 with a neuron model with adaptive threshold driven by fluctuating inputs with different statistics . The average threshold depends mainly on the average membrane potential ( Figure 6A ) , but the standard deviation is correlated with both the average and the standard deviation of the membrane potential ( Figure 6B ) . This could underlie the observed difference in threshold variability between spontaneous activity ( <σ> = 1 . 4 mV ) and visual responses ( <σ> = 2 . 3 mV ) [1] , because in visual responses the membrane potential is presumably both more depolarized and more variable . Interestingly , fast spiking cells showed lower threshold variability together with a lower mean threshold , which is also consistent with our results . These results have two main implications for synaptic integration: 1 ) threshold adaptation reduces the impact of the input mean , relative to its variance , and 2 ) the negative correlation between threshold and depolarization rate shortens the timescale of synaptic integration .
A recent debate about the validity of the Hodgkin-Huxley model for cortical neurons has highlighted the fact that , for central neurons , spikes are initiated in the axon while in vivo measurements of the spike threshold were done at the soma , which could be an artifactual cause of threshold variability [5]-[7] . However , it is unclear whether distal initiation could account for the inverse correlation between the threshold and the preceding slope of depolarization . To address this question , we consider a simplified situation where spikes are initiated in the axon hillock when the potential is above a fixed threshold VT ( Figure 11A ) . Suppose the membrane potential increases linearly in the soma ( blue line ) and spreads to the spike initiation site with a delay ( black line ) . A spike is initiated when the propagated potential reaches threshold ( dashed red line ) , and backpropagated to the soma with a delay . As a result , the spike “threshold” ( in fact , spike onset ) is higher when measured at the soma , by an amount of , where s is the slope of depolarization . This has two consequences: 1 ) threshold variability is increased for fluctuating inputs , 2 ) the threshold is positively correlated with the slope of depolarization . Based on passive cable properties , the forward delay can be estimated as and the backward delay as , where C is the specific membrane capacitance , ( resp . ) is the membrane surface of the spike initiation site ( resp . soma ) and is the coupling conductance between the two sites [23] . Considering active conductances would reduce these values , but these estimations are already close to experimental measurements [42] . Thus , the total delay ( forward + backward ) is smaller than 1 ms . We confirmed this reasoning by simulating the response of the multicompartmental model of Yu et al . ( 2008 ) [7] to fluctuating inputs and measuring the slope-threshold relationship both at the soma and at the axon initial segment ( AIS ) ( Figure 11B ) . As we expected , we found that this relationship was more pronounced at the AIS than at the soma , meaning that the net effect of backpropagation is a positive correlation between slope and threshold . More precisely , the net effect corresponds to a total delay of ( difference between the two slopes of the linear regressions ) , in accordance with the estimation above . Thus , since distal spike initiation predicts the opposite relationship between depolarization rate and threshold than experimentally observed , it cannot be the dominant cause of threshold variability and cannot account for the properties of threshold dynamics . The Hodgkin-Huxley formalism describes the dynamics of the macroscopic average of many sodium channels , but individual channels have stochastic dynamics [43] , [44] . It results in threshold variability which is not significantly correlated with input properties [45] , [46] , [43] , [47] , [48] . As previously , we examine whether this mechanism may account for the slope-threshold relationship in a simplified model . We consider an integrate-and-fire model with a threshold that fluctuates randomly , according to an Ornstein-Uhlenbeck process:where is the mean voltage threshold , is the standard deviation of the threshold distribution , is a gaussian white noise and is the time constant of fluctuations ( related to the time constant of Na activation ) . When depolarization is very slow , spikes will be initiated lower than on average , because the stochastic threshold has time for many excursions below its mean , i . e . , the threshold reaches the membrane potential rather than the converse ( Figure 11C , left ) . In fact if the membrane is not depolarized ( zero slope ) , a spike will be initiated at resting potential ( although after a potentially very long time ) because there is a positive probability that reaches that potential . On the contrary , if depolarization is very fast , spike initiation occurs at , where t is near the time of depolarization , and therefore the distribution of the threshold at spike times is the same as the distribution of ( at all times ) , with mean ( Figure 11C , right ) . Therefore , the threshold is positively correlated with the slope of depolarization . We confirmed this reasoning with a numerical simulation of the model for different depolarization slopes ( Figure 11D ) . Thus , as for distal spike initiation , channel noise produces threshold variability but induces a ( weak ) positive slope-threshold relationship , which is contrary to experimental findings . The spike threshold increases with the total non-sodium conductance , because spike initiation requires more Na channels to be open in order to counteract a larger total conductance . Thus , fluctuating synaptic conductances could be a source of threshold variability . We previously estimated the effect of total conductance on spike threshold through the following formula [23]:where is the total conductance , including excitatory ( ge ) and inhibitory ( gi ) conductances , and we ignored the effects of Na inactivation . Threshold variability is determined by the variability of total conductance at spike time . In low-conductance states ( in vitro or down states in vivo ) , spikes are preferentially triggered by increases in excitatory conductance ge [49] . In this case , the depolarization rate is positively correlated with ge , and therefore with the threshold . Besides threshold variability can only be mild because the total conductance is low ( relative to the leak conductance ) . In high-conductance states ( up states in vivo ) , spikes are preferentially triggered by decreases in inhibitory conductance gi [49] . In this case , the depolarization rate is negatively correlated with gi , and therefore with the threshold . Therefore , in high-conductance states but not in low-conductance states , the slope-threshold relationship induced by synaptic conductances is qualitatively consistent with experimental observations in vivo . However , with the same reasoning , the membrane potential increases when inhibition decreases and therefore , if inhibition is the main source of variability , the threshold should be negatively correlated with the preceding membrane potential , which contradicts experimental observations in vivo . Therefore , synaptic conductances cannot simultaneously account for the slope-threshold relationship and for the dependence on membrane potential observed in vivo . In our analysis , we assumed that Na activation is instantaneous . Voltage clamp measurements indeed show that its time constant is only a fraction of millisecond [50] , [29] , [51] , [52] . However , with this approximation , we might have neglected a source of threshold variability . As previously , let us examine the potential contribution of this cause of threshold variability to the slope-threshold relationship . If depolarization is slow ( compared to the activation time constant ) , then the proportion of open channels is given by the steady-state activation curve and our analysis applies . If depolarization is very fast , fewer channels are opened than at steady state and therefore the threshold is higher . Thus , non-instantaneous activation of Na channels contributes a positive correlation between depolarization rate and threshold , contrary to experimental findings . In the same way as synaptic conductances , voltage-gated channels may also modulate the spike threshold [23] . In particular , the delayed-rectifier potassium channel ( e . g . Kv1 ) has been previously proposed by several authors as the source of threshold variability [2] , [10] , [11] , [14]–[16] , [21] . Indeed , a similar model to our iLIF model was previously introduced in the context of threshold accommodation by potassium channels [36] . To account for the positive correlation between membrane potential and threshold , the conductance must increase with depolarization , i . e . , the activation curve must be an increasing function of the voltage . We only consider this case in this discussion . The threshold depends on the voltage-gated conductance gK through the following formula:where we ignored the effect of Na inactivation . To account for significant threshold variability , two conditions must be met: 1 ) the maximal conductance must be large ( compared to the leak ) and 2 ) the half-activation voltage must be low enough . In this case , the spike threshold adapts to the membrane potential , which implies a positive correlation between membrane potential and threshold and a negative correlation between depolarization rate and threshold , as experimentally observed . It is also possible to differentiate the threshold equation and obtain a differential equation that describes the threshold dynamics as for Na inactivation , although it takes a different form [23] . However , there are several differences with threshold modulation induced by Na inactivation . Firstly , the threshold is always bounded by the value obtained with the maximal conductance . Secondly , the relationship between membrane potential and threshold is in general sigmoidal and can only be linear in a limited range , where the voltage is below half-activation but the conductance is still very large ( the slope of this relationship is then kaNa/kaK ) . The impact on synaptic integration is also different , because the conductance impacts not only the threshold but also the PSPs and effective membrane time constant . Finally , we discuss below the possible interactions of several Na channel subtypes and of slow and fast Na inactivation . We assumed that a single Na channel type ( e . g . Nav1 . 6 ) was present . It is possible to extend our analysis to the case of multiple subtypes . Suppose the Na current is made of two components corresponding to two channel types: To simplify , we assumed that the two channels have the same activation Boltzmann factor ka , which is not unreasonable . Then the Na current can be equivalently expressed as: where: In other words , when several subtypes are present , inactivation in the threshold equation is replaced by a linear combination of inactivation variables of all subtypes . For example , Nav1 . 2 and Nav1 . 6 are both found in the axon initial segment [8] , and Nav1 . 2 channels activate and inactivate at more depolarized potentials than Nav1 . 6 [53] . According to the threshold equation above , at hyperpolarized voltages , threshold modulation should be mainly determined by Nav1 . 6 ( the inactivation variable h2 for Nav1 . 2 is less voltage-dependent and its threshold is higher ) ; at more depolarized voltages ( assuming the threshold has not been reached ) , Nav1 . 6 channels inactivate ( h1≈0 ) and threshold modulation is then determined by Nav1 . 2 channels . Note however that with several channel subtypes , it is not possible to express threshold dynamics as a single kinetic equation for anymore ( without the use of the hidden variables h1 and h2 ) . In the present study , we focused on fast Na inactivation . We have briefly mentioned that the threshold equation applies when Na inactivation is slow , and implies that the threshold increases after each spike , which induces a negative correlation between threshold and preceding inter-spike interval . This effect is expected , but it gets more interesting when the interaction between slow and fast components is considered . One way to model this interaction is to consider two Na currents , as in the previous section . But since inactivation in the same channel can show slow and fast components , it might be more relevant to include this interaction in the gating variables . The simplest way is to consider these components as independent gating processes , that is:where the gating variables hslow and hfast have slow and fast dynamics , respectively [54] , [55] . Since the interaction is multiplicative for the Na current , it is additive for the threshold: In this case , it is possible to write a kinetic equation for each component of the threshold ( and ) , in the same way as before ( note that increases after each spike , whereas this effect can be neglected for since its impact on subsequent spikes is negligible ) . Here , the effect of slow inactivation can be thought of as a slow change of an effective minimal threshold with firing activity . Interesting interactions appear because , as we have seen , threshold variability depends on the value of that minimal threshold ( relative to Vi ) . Suppose for example that VT<Vi . At low firing rates ( when interspike intervals are larger than the slow inactivation time constant ) , and the threshold is not variable . If the firing rate is high enough , then and the threshold becomes variable with fast inactivation . In the same way , the time constant of synaptic integration should be larger at low rates than at high rates . Thus , slow inactivation controls threshold modulation by fast inactivation . In summary , many mechanisms may contribute to the variability of the spike threshold , but only two can account for its observed adaptive properties: Na inactivation and adaptive conductances ( most likely K channels ) . Although threshold dynamics is qualitatively similar for both mechanisms , they can be distinguished by the fact that Na inactivation has no subthreshold effect on the membrane potential . Specifically , if the threshold is mainly modulated by adaptive conductances , then we can make two predictions: In a few experimental studies , the application of α-dendrotoxin , a pharmacological blocker of low-voltage-activated potassium channels , greatly reduces threshold variability [16] , which suggests a strong role for these channels in threshold adaptation . Our results suggest an alternative interpretation of these observations . The application of a blocker reduces the total conductance , which also reduces the minimum threshold VT ( see the threshold equation with voltage-gated channels ) , possibly below half-inactivation voltage Vi , where there is no threshold adaptation due to Na inactivation . Thus , it could be that threshold adaptation was due to Na inactivation , but that suppressing K conductances shifted the minimum threshold out of the operating range of this mechanism . This hypothesis could be tested by simultaneously injecting a fixed conductance in dynamic clamp , to compensate for the reduction in total conductance of the cell . Although we cannot draw a universal conclusion at this point , and while it is possible that either or both mechanisms are present in different cells , we observe that Na inactivation is a metabolically efficient way for neurons to shorten and regulate the time constant of synaptic integration . Indeed , Na inactivation implies no charge movement across the membrane while K+ conductances modulate the threshold by counteracting the Na current , which implies a large transfer of charges across the membrane ( Na+ inward and K+ outward ) in the entire region where the threshold is variable . Recently , it was found in hippocampal mossy fibers that K+ channels open only after spike initiation , in a way that minimizes charge movements [56] . Since energy consumption in the brain is a strong evolutionary pressure [57]-[59] , we suggest that Na inactivation may be the main source of threshold variability when this variability has functional benefits .
Near spike initiation , the Na current can be approximated by an exponential function of the voltage [18] , [24] . If the inactivation variable h is not discarded ( see Text S1 A ) , we obtain the following model ( membrane equation and inactivation dynamics ) : ( 1 ) ( 2 ) where V is the membrane potential , h is the Na inactivation variable , I is the input current , C is the membrane capacitance , ( resp . ) is the leak conductance ( resp . the reversal potential ) , is the Na activation slope factor , VT is the threshold when Na channels are not inactivated , is the Na steady-state inactivation function , and is the Na inactivation time constant , which we consider constant for simplification ( except in Figure 10D ) . Since the model does not include K+ channels and the exponential approximation is not valid beyond spike initiation , action potentials are not realistically reproduced , but we only focus on spike initiation . We call this model iEIF ( inactivating exponential integrate-and-fire model , equations ( 1–2 ) ) . The membrane potential is reset to when it crosses 0 mV ( h is unchanged ) . In Figure 2 , we used , ( typical membrane time constant in vivo [34] ) , VT = −58 mV , ka = 5 mV , , and the inactivation function was a Boltzmann function with parameters Vi = −63 mV and ki = 6 mV . A very good approximation of the Na current is an exponential function of V [18] , [24] , [61] . The spike threshold can then be expressed with the threshold equation [23]: ( 3 ) where ( 4 ) is the minimum threshold , i . e . , when Na channels are not inactivated ( h = 1 ) . By differentiating the threshold equation and substituting the differential equation for h , we obtain a differential equation for as function of the membrane potential ( see Text S1 ) , which can be approximated by: ( 5 ) with , where is the steady-state threshold , which can be approximated by a piecewise linear function ( see Text S1 ) : ( 6 ) ( 7 ) We refer to the differential equation of together with the expression of above as the adaptive threshold model . In simulations , we used this model with a passive membrane equation: ( 8 ) where R is the membrane resistance and I is the input current , and a spike is produced when . The membrane potential is then reset to EL . Refractoriness is implemented either by maintaining V at resting potential for 5 ms ( Figure 10 ) or by increasing the threshold by 3 . 6 mV ( Figures 4 , 6–8 ) , corresponding to a spike duration of 3 ms and ka = 6 mV ( see Text S1 A , effect of output spikes on threshold ) . We call this model iLIF ( inactivating leaky integrate-and-fire model , equations ( 5–8 ) ) . In Figure 10 we used and Na parameters from a recent study of the role of Na inactivation in the temporal precision of auditory neurons [17]: ; ; ; . For Figures 4–8 , we used VT = −55 mV , Vi = −63 mV ( average value in the in situ dataset ) , . Unless otherwise specified , we chose ka/ki = 1 ( average in the dataset: 1 . 05 ) . In Figure 10D , the time constant of Na inactivation is voltage-dependent , as in [17]: Fluctuating inputs ( Figures 2C–E , 6–10 ) were generated according to Ornstein-Uhlenbeck processes: where is the mean , is the standard deviation , is the autocorrelation time constant , and is a gaussian white noise of zero mean and unitary variance . We chose in Figure 2 and in other figures . To measure spike onset in models with no explicit threshold ( Figures 2 , 10 , 11 ) , we used the first derivative method [62] , which consists in measuring the membrane potential V when its derivative dV/dt crosses an empirical criterion . Since the input is not controlled , it measures spike onset and is an overestimate of the spike threshold . These two quantities can be related in simple models [23] . To calculate the relationship between the slope of depolarization and the threshold , we consider a linear depolarization with slope s: V ( t ) = st , and we calculate the intersection with the threshold ( Figure 5A ) , described by the adaptive threshold model . By integrating the dynamic threshold equation , we find that when ( ) , the threshold is implicitly determined by the following equation: For low values of s , this equation may have no solution ( i . e . , the neuron does not spike ) . Using the piecewise linear approximation of the steady-state threshold , we obtain:which simplifies to: This is also an implicit equation for , but it can be easily ( numerically ) calculated with a nonlinear solver . A closed formula can be obtained in the case when : | Neurons spike when their combined inputs exceed a threshold value , but recent experimental findings have shown that this value also depends on the inputs . Thus , to understand how neurons respond to input spikes , it is important to know how inputs modify the spike threshold . Spikes are generated by sodium channels , which inactivate when the neuron is depolarized , raising the threshold for spike initiation . We found that inactivation properties of sodium channels could indeed cause substantial threshold variability in central neurons . We then analyzed in models the implications of this form of threshold modulation on neuronal function . We found that this mechanism makes neurons more sensitive to coincident spikes and provides them with an energetically efficient form of gain control . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neuroscience/neuronal",
"signaling",
"mechanisms",
"neuroscience/theoretical",
"neuroscience",
"computational",
"biology/computational",
"neuroscience"
] | 2011 | Impact of Fast Sodium Channel Inactivation on Spike Threshold Dynamics and Synaptic Integration |
The shift from outcrossing to self-fertilization is among the most common evolutionary transitions in flowering plants . Until recently , however , a genome-wide view of this transition has been obscured by both a dearth of appropriate data and the lack of appropriate population genomic methods to interpret such data . Here , we present a novel population genomic analysis detailing the origin of the selfing species , Capsella rubella , which recently split from its outcrossing sister , Capsella grandiflora . Due to the recency of the split , much of the variation within C . rubella is also found within C . grandiflora . We can therefore identify genomic regions where two C . rubella individuals have inherited the same or different segments of ancestral diversity ( i . e . founding haplotypes ) present in C . rubella's founder ( s ) . Based on this analysis , we show that C . rubella was founded by multiple individuals drawn from a diverse ancestral population closely related to extant C . grandiflora , that drift and selection have rapidly homogenized most of this ancestral variation since C . rubella's founding , and that little novel variation has accumulated within this time . Despite the extensive loss of ancestral variation , the approximately 25% of the genome for which two C . rubella individuals have inherited different founding haplotypes makes up roughly 90% of the genetic variation between them . To extend these findings , we develop a coalescent model that utilizes the inferred frequency of founding haplotypes and variation within founding haplotypes to estimate that C . rubella was founded by a potentially large number of individuals between 50 and 100 kya , and has subsequently experienced a twenty-fold reduction in its effective population size . As population genomic data from an increasing number of outcrossing/selfing pairs are generated , analyses like the one developed here will facilitate a fine-scaled view of the evolutionary and demographic impact of the transition to self-fertilization .
Most flowering plants are hermaphroditic , but many have evolved elaborate mechanisms to avoid self-fertilization and the associated costs of inbreeding [1] , [2] . However , an estimated of flowering plant species are predominantly self-fertilizing [3] , [4] and many of these species have evolved floral morphologies that promote this means of reproduction . This shift from outcrossing to inbreeding by self-fertilization is among the most common transitions in flowering plants [5] , [6] , and can occur when the short-term benefits of selfing ( e . g . assured fertilization [7] , the ‘automatic' transmission advantage [8] , and the maintenance of locally adapted genotypes [9] ) overwhelm the immediate costs of inbreeding depression [10] , [11] . However , in the longer term , limited genetic diversity and difficulty in shedding deleterious mutations are thought to doom selfing lineages to extinction [12]–[14] . While the causes and consequences of plant mating system evolution have long fascinated evolutionary biologists , the paucity of population genomic data for species with a recent shift in mating system and an absence of a framework in which to interpret such data have prevented the development of a genome-wide understanding of this transition . Here , we introduce a novel approach that utilizes patterns of variation in a recently derived selfing population to partition diversity within and among founding haplotypes . By partitioning two sources of sequence diversity – incompletely sorted ancestral polymorphisms and de novo mutations which occurred since the population origin – we generate a novel view of the selective and demographic history of a recently derived selfing population . In particular , we can distinguish two factors that can lead to low diversity in selfers: the loss of ancestral polymorphism that occurred at the transition to selfing and a long term small effective population size since the transition . We apply this framework to the selfing species , Capsella rubella , for which we make use of a recently available population genomic dataset [15] consisting of eleven resequenced transcriptomes – six of C . rubella and five of a closely related , obligately outcrossing species , C . grandiflora , to generate a well-resolved , genome-wide view of the transition from outcrossing to selfing and its immediate consequences . While the origin of C . rubella has received significant attention [15]–[18] , our understanding of C . rubella's history has been hampered by the small number of independent loci examined in previous studies and by the lack of methods tailored to understand the somewhat unusual haplotype structure of genetic variation within recently derived selfing species . Similarly , while C . rubella contains relatively elevated levels of putatively deleterious variation [15]–[17]; previous analyses could not partition the extent to which this was due to a long-term relaxation of the efficacy of purifying selection , or extreme sampling variance at the founding of the species . Perhaps the most intriguing ‘origin story' for C . rubella argues that at the last glacial maxima , a single individual capable of selfing may have became isolated and gave rise to the entire species [17] . Evidence for this hypothesis comes from the observation of only one or two distinct haplotypes per a locus in a sample of 17 loci examined in 25 C . rubella individuals [17] . Here , we use our novel framework and coalescent modeling to investigate the origin of C . rubella focusing on: testing the hypothesis that it was founded by a single individual , estimating the timing of its founding , comparing patterns of variation across its distribution , estimating its long-term effective population size , and documenting the weakening of purifying selection associated with the shift to selfing . A major result of our analyses is that we need not invoke an extreme bottleneck of a single founder , rather the data are consistent with high levels of drift in a population with a small effective size potentially founded by a large number of individuals . The novel haplotype-based method developed herein allows us to partition polymorphism patterns between variation inherited from the ancestral outcrossing population and new diversity introduced after the bottleneck . By partitioning these sources of variation , our approach allows us to more clearly detail the relaxation in purifying selection associated with the transition to selfing . This partitioning also facilitates coalescent-based approaches to the demographic history of selfing populations and can therefore help infer the extent of a founding bottleneck , identify population subdivision , and document recent population growth and geographic spread . Therefore , beyond the application to Capsella , the framework developed here can be used in other pairs of outcrossing/selfing species in order to build a broad comparative view of the shift from outcrossing to self-fertilization . More generally , the ideas developed herein could be applied to many recently diverged species pairs in which one has gone through an extreme demographic bottleneck , leaving only a few recognizable founding haplotypes , regardless of mating system .
Before presenting our haplotype-based analyses , we briefly summarize patterns of sequence variation within and among species . These results , which are consistent with previous analyses and are strongly concordant with Slotte et al . 's [15] analysis of the same data , are summarized here for completeness . To generate empirical confidence intervals , we calculate the upper and lower 2 . 5% of tails of focal summary statistics by resampling kb blocks with replacement . We now describe our novel haplotype-based analysis , which focuses on identifying haplotypes that founded C . rubella . By identifying these distinct founding haplotypes , we can divide variants in the extant C . rubella population into those present in its founding lineages and new mutations . This information will allow us to infer a coalescent based model of the recent demography of C . rubella .
While we have sequence data from only six C . rubella samples ( and often make use of three to four genomes to control for population structure ) , these transcriptomic data provide information about hundreds to thousands of genealogical histories as we move along the genome . Therefore the small number of sequenced individuals provides plentiful information about population history . A recent demonstration of this principle is the development of coalescent methods to infer population history from a single individual's genome [40] . In particular , our findings about the small number of founding haplotypes are likely generalizable to the population , since much of the common diversity ( i . e . that contained in the deep parts of the genealogy ) in large samples is expected to be found in small samples [41] . This view is supported by the consistency of our findings and those of Guo et al . [17] , who usually found one or two distinct haplotypes at each of 17 loci in a survey 25 C . rubella individuals . While it is likely that our analyses , based on small sample sizes , have captured many aspects of the founding of C . rubella , larger samples will provide a fuller view of recent events . For example , additional genome-wide samples would provide access to lower frequency variants ( i . e . more novel mutations ) , providing information about more recent population growth [42] , [43] , and finer resolution of population structure . Additionally , sequence data from more individuals would provide a finer resolution to the frequency spectrum of ancestral polymorphisms , and would help clearly identify genomic regions with more than two founding haplotypes . Therefore , additional samples could facilitate a more refined view of C . rubella's initial founding , and could potentially narrow the confidence intervals on our estimates of founding time , population growth rates , and population size . Our haplotype-based approach provides a rough characterization of the history of the selfing species , C . rubella . We note that since we have sequence data for only a handful of samples , we cannot provide fine resolution of recent demographic events in the history of the species . Assuming a mutation rate of [25] , we infer that approximately 50 kya , a C . grandiflora-like ancestral population of unknown size became largely selfing and gave rise to C . rubella . Much of the ancestral diversity present in the founding population has since been lost due to subsequent drift and selection . In fact , two C . rubella individuals inherit different founding haplotypes for on average only of their genome . Despite this , the diversity maintained from the founding population makes up roughly 90% of extant pairwise sequence diversity in C . rubella , since little diversity has arisen since its founding . We now turn to discuss some of the specifics of the founding and subsequent history of C . rubella . High levels of autozygosity associated with selfing can reduce the effective population size of a selfing species to less than of the same outcrossing population [44] , [45] . Therefore , all else being equal , neutral diversity in selfing taxa should be no less than half of that observed in their outcrossing relatives . As selfing species often exhibit a greater than two-fold reduction in diversity , severe founding bottlenecks are often presented to explain this discordance ( e . g . in C . rubella [16] , [17] ) ; however , alternative explanations , including the greater reach of linked selection in selfing populations have also been proposed [46]–[50] ( see below ) . Such founding bottlenecks are seen as evidence supporting the idea that selfing species are often founded by a small number of individuals , consistent with reproductive assurance favoring the evolution of selfing [8] , [51] . The very low levels of diversity within C . rubella seemed initially to be consistent with this view [17] . Indeed , we find that for a given genomic region , few founding lineages drawn from a C . grandiflora - like population contributed ancestry to present day C . rubella . However , this reduction in C . rubella's diversity relative to C . grandiflora , and the observation of only one or two extant founding haplotypes in most genomic regions ( as previously observed [17] ) is due to an extreme loss of variation subsequent to the founding of C . rubella , and does not necessarily imply an extreme founding bottleneck . This loss of variation is likely due to an extreme reduction in C . rubella's effective population size , the potential causes of which we discuss shortly . The high level of drift due to this small confounds our ability to estimate the actual number of founding chromosomes , because the genetic contribution of founders has been lost ( see [28] , [29] further discussion ) . We therefore caution that low long-term effective population sizes in selfing plants may erode historical signals of their founding . Our likelihood based inference as well as our evidence for more than two founding haplotypes in some genomic regions argues against the hypothesis that C . rubella was founded by a single plant with no subsequent secondary contact from C . grandiflora ; however , we lack sufficient information to pinpoint the founding population size . The patterns of diversity that have arisen since C . rubella's founding are consistent with a population at approximately mutation-drift equilibrium with a small long-term effective population size . In fact , we estimate a twenty-fold reduction in C . rubella's effective number of chromosomes from the ≈600 , 000 in C . grandiflora . Although the causes of this reduced effective population size are unclear , numerous forces , including frequent oscillations in population size , linked selection , etc . may be responsible [49] , [52]–[54] , and future work on the determinants of in selfing species will clarify this issue . This small effective population size has led to a rapid loss of diversity since C . rubella's founding . While some genomic regions maintain multiple extant founding lineages and high levels of pairwise sequence diversity , if this small size persists C . rubella will quickly lose much of its genetic variation . For example , currently two individuals inherit the same founding haplotype for approximately of the genome , resulting in a profound lack of diversity . At the current rate , it will take only another ky for of the genome of two individuals to be homozygous for all ancestral variation . This would reduce genome-wide in C . rubella to , severely limiting the pool of standing variation available for a response to selection . Perhaps it is this low diversity that limits the adaptive evolution [55] of selfing species and contributes to their eventual demise [12]–[14] . Viewing C . rubella's founding haplotypes as a random draw from an ancestral C . grandiflora -like population , we expect ( and indeed observe – Figure 3A ) comparable values among C . rubella's founding haplotypes and within C . grandiflora . Therefore , the founding of C . rubella did not itself facilitate the accumulation of deleterious mutations , contrary to expectations from a model where an extreme reduction in at the species founding allowed deleterious mutations to markedly and suddenly increase in frequency . Rather , the long-term reduction in C . rubella's effective population size lessened the efficacy of purifying selection , as is reflected by the threefold increase in within founding haplotypes as compared to between species , founding haplotypes , or within C . grandiflora . Our view of the origin of deleterious mutations in C . rubella can reconcile two seemingly contradictory observations – that within selfing species is large but between selfers and close relatives is unremarkable ( e . g . [56] ) . The unremarkable between selfers and their relatives reflects the fact that since selfing species are generally young , an overwhelming portion of their divergence from outcrossing relatives is simply the sorting of ancestral variation . By contrast , the high observed within selfing species reflects the rapid homogenization of most initial variation in selfing taxa , and the weakening of purifying selection against novel non-synonymous mutations , which can make up a substantial portion of intraspecific variation while hardly contributing to interspecific divergence . With our haplotype-based approach , we provide a reasonable sketch of C . rubella's history . However , numerous questions remain . Future work on the population genomics of selfing will identify the cause ( s ) of the reduced effective population size often observed in selfing populations , highlight the role of rare introgression in the evolution of selfing , identify recent fluctuations in the size of selfing populations , and inform the geographic spread of selfing lineages . While full sequence data from more individuals will further illuminate these issues , our result highlight the vast information about species' origin present in population genomic data . Future analyses like the one presented here will help further refine our genomic understanding of the evolutionary transition to selfing .
We utilized genotype data from 38 bp paired-end sequencing of RNA extracted from flower bud tissue of 11 samples ( 6 C . rubella and 5 C . grandiflora ) . These reads were then mapped to the C . rubella reference genome using Tophat [57] ( v . 1 . 3 . 0 ) as described previously [15] ( using an inner distance between reads ( -r ) of 100 , and minimum and maximum intron length of 40 and 1000 respectively ) . To call SNPs from the RNA data , we utilized the GATK pipeline on the BAM files [58] , [59] . We instituted straightforward QC steps , and treated all genotypes with coverage less than 10× , quality scores ( from the GATK pipeline ) less than 30 , and/or heterozygous sites in putatively autozygous regions as missing data . To validate our calls we compared our genotype data to sites of Sanger sequencing and found very little discordance ( see Text S1 , Table S1 ) , and nearly identical diversity measures ( , , for 72 , 066 and 71 , 645 pairwise comparisons between base pairs , respectively ) . We analyzed all loci where individual genotypes passed quality control standards allowing us to utilize sites with partially missing data , a slight departure from the initial presentation of this data set [15] , which only examined sites where all individuals passed QC . We focus on divergence and diversity at fourfold degenerate ( i . e . synonymous ) and zero fold degenerate ( i . e . nonsynonymous ) sites to view patterns of neutral and putatively deleterious variation within and among species . Given the high selfing rate in C . rubella , [18] the genome of a C . rubella individual is expected to be mostly autozygous . However , some allozygous regions are expected in field-collected samples of a species with a non-zero outcrossing rate . Indeed , we observe heterozygous sites in our C . rubella samples . Such sites could be caused by genotyping and/or alignment error , de novo mutations , or residual heterozygosity retained since a lineage's most recent outcrossing event ( i . e . heterozygosity in allozygous regions ) . Since allozygous loci will be clustered in the genome due to the limited number of generations for recombination since the most recent outcrossed ancestor , while sequencing errors will be distributed relatively uniformly across the genome , we utilize the distribution of heterozygous sites across the genome to separate allozygous regions from sequencing error in C . rubella . More specifically , we identify allozygous regions by examining the local density of heterozygous sites . These regions are generally quite obvious ( Figure S9A–F ) , so we visually identified the beginning and ends of these allozygous stretches of the genome within an individual . We treat these allozygous regions of an individual's genome as missing data . Reassuringly , the average heterozygosity within an individual in these allozygous regions ( ) closely matches the pairwise diversity between individuals ( see Figure S7 ) . This gives us confidence that by treating these allozygous regions as missing data for an individual we are not biasing ourselves away from interesting genomic regions of high diversity . By contrast , nearly all heterozygous sites in putatively autozygous regions should be artifacts ( e . g . sequencing error , misalignment , etc . ) , and very few should represent de novo mutations that have arisen since the region was last made homozygous by descent due to inbreeding . In inferred autozygous regions on average 0 . 13% of synonymous sites are heterozygous . This error rate varies across individuals ( see Text S1 , Figure S8 ) , corresponding to sequencing lane . We treat these heterozygous sites in allozygous regions as missing data in our population genomic analyses . Since C . rubella and C . grandiflora have recently split , much variation within each species is incompletely sorted variation inherited from a population ancestral to both . In C . rubella , this ancestry can persist for long physical distances , due to its recent founding and low effective recombination rate . We can therefore hope to infer the haplotypes that contributed to the founding of extant C . rubella diversity . In doing so , we do not attempt to assign founding haplotypes in regions between informative data , therefore minimizing our uncertainty in founding haplotype assignment . One of the strengths of this approach is that even ancestrally polymorphic alleles that are missing from our small sample of extant C . grandiflora diversity , but by chance are found in our C . rubella sample , are likely to be correctly identified as differences among founding haplotypes , rather than contributing to difference within founding haplotypes . This follows from the fact that such sites will often be flanked by jointly polymorphic sites that were common in the ancestral population , allowing us to correctly assign the status of founding haplotype sharing . Preliminary haplotype assignment: In some genomic regions , all of our samples will carry the same founding haplotype . Thus , we assign all C . rubella samples to the same founding haplotype in long regions ( kb and polymorphisms in C . grandiflora ) where all C . rubella samples ( with non-missing data ) are identical at positions polymorphic in C . grandiflora . We next focus on pairwise comparisons in regions where polymorphisms are jointly segregating , since such variation likely represents incompletely sorted ancestral variation . In regions of the genome where a pair of C . rubella individuals have inherited the same founding haplotype , they must have identical alleles at ancestrally polymorphic sites . We labeled all sites polymorphic in both species as a ‘same site’ if both individuals were homozygous for the same allele , and as a ‘different site’ if both individuals were homozygous for different alleles . We labeled sites as missing data if at least one of the pair did not pass QC at this site . We identified runs of haplotype sharing between two samples beginning with a ‘same site’ and ending at the last ‘same site’ before a ‘different site , ’ ignoring sites with missing data . When these runs of ‘same’ sites extended more than 1 . 5 kb and consisted of at least 4 jointly polymorphic sites , we preliminarily assigned these individuals to the same founding haplotype . In regions with ancestry from exactly two founding haplotypes ( e . g . the left hand side of Figure1B ) , alternative founding haplotypes must differ at sites polymorphic in both species – that is , with two extant founding haplotypes , differences at jointly polymorphic sites are necessary and sufficient for assigning individuals to alternate founding haplotypes . In regions with more than two extant founding haplotypes , differences at jointly segregating sites are sufficient but not necessary for assigning individuals to alternate founding haplotypes , because two distinct founding haplotypes could be identical at the same jointly polymorphic allele . We explore alternative founding haplotype labeling rules in Text S1 , and show that our results hold under most reasonable criteria . Higher order haplotype assignment: Building on pairwise founding haplotype assignments , we aim to identify alternative founding haplotypes across the C . rubella genome . To do so , we broke the genome into windows of differing sizes corresponding to points in which runs of pairwise ( same vs different ) founding haplotype assignment begin and end across individuals . We then assigned individuals to founding haplotypes in each window as follows: At the conclusion of this algorithm every individual was assigned to a founding haplotype ( or labelled as ambiguous ) for every genomic window where an individual was autozygous . We do not use these ambiguous regions when comparing within or among founding haplotypes , and we examine the possibility of regions with more than two founding haplotypes in the main text . We used the nj function in the R [60] package ape [61] to construct neighbor-joining trees ( presented in Figure 3C ) from distance matrices containing subsets of our SNP data set at synonymous sites . For the entire transcriptome ( Figure 3C1 ) we constructed the distance matrix where each off-diagonal element was the fraction of pairwise sequence differences between the pair of individuals ( and ) at synonymous sites , where and refer to rows and columns of the distance matrix . For the tree constructed within C . rubella's founding haplotypes ( Figure 3C2 ) , we calculated the fraction of pairwise sequence differences between the pair of C . rubella individuals ( and ) where we inferred and to have inherited the same founding haplotype . For the tree constructed among C . rubella's founding haplotypes ( Figure 3C3 ) , we calculated the fraction of pairwise sequence differences between the pair of C . rubella individuals ( and ) where we inferred and to have inherited different founding haplotypes . In both cases , entries in the distance matrix between pairs of C . grandiflora and C . rubella , and within C . grandiflora pairs where constructed by using all synonymous sites . We note that numerous recombination events clearly occurred during the history of these samples , and we therefore caution against interpreting this neighbor joining tree as a phylogenetic statement . To infer the history of C . rubella , we simulated a coalescent model where at time , chromosomes founded a population that instantaneously grew to effective chromosomes ( Figure 1A ) . To avoid potential confusion with the definition of the effective population size in selfers ( see [62] for recent discussion ) , we directly used the effective number of chromosomes , , as our coalescent units , so that the rate of coalescence of a pair of lineages equaled . We note that our inference of the number of founding chromosomes was inspired by two recent papers [28] , [29] that addressed this question using small numbers of micro-satellite and PCR amplified loci , respectively . To infer the demographic parameters of interest ( , , and ) , we made use of the frequency with which all samples are assigned to the same founding haplotype , , and the allele frequency spectrum in these regions , . In our four exchangeable individuals ( three Greek and one Out-of-Greece ) , , and . We aimed to estimate the composite likelihood of our data given our parameters , , via coalescent simulation . As this likelihood depends on only – the coalescent-scaled founding time , and not on and separately , we estimated the likelihood surface as a function of this compound parameter . We then resolved these two parameters by considering nucleotide diversity within founding haplotypes ( below ) . For inference , we use a composite likelihood framework . Composite likelihoods approximate the full likelihood of the data as the product of the likelihoods of a set of correlated observations – ignoring their dependance . This facilitates inference in cases where obtaining the full likelihood is computationally prohibitive ( see [42] , [63] , [64] for earlier population genetic applications ) . In making this approximation , composite likelihoods make the likelihood surface overly peaked , but do not produced a bias in the maximum likelihood estimate ( MLE ) [65] , [66] . | While many plants require pollen from another individual to set seed , in some species self-pollination is the norm . This evolutionary shift from outcrossing to self-fertilization is among the most common transitions in flowering plants . Here , we use dense genome sequence data to identify where in the genome two individuals have inherited the same or different segments of ancestral diversity present in the founders of the selfing species , Capsella rubella to obtain a genome-wide view of this transition . This identification of founding haplotypes allows us to partition mutations into those that occurred before and after C . rubella separated from its outcrossing progenitor , C . grandiflora . With this partitioning , we estimate that C . rubella split from C . grandiflora between 50 and 100 kya . In this relatively short time frame , an extreme reduction in C . rubella's population size is associated with a massive loss of genetic variation and an increase in the relative proportion of putatively deleterious polymorphisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Genomic Identification of Founding Haplotypes Reveals the History of the Selfing Species Capsella rubella |
Zoonoses are increasingly recognized as an important burden on global public health in the 21st century . High-resolution , long-term field studies are critical for assessing both the baseline and future risk scenarios in a world of rapid changes . We have used a three-decade-long field study on hantavirus , a rodent-borne zoonotic pathogen distributed worldwide , coupled with epidemiological data from an endemic area of China , and show that the shift in the ecological dynamics of Hantaan virus was closely linked to environmental fluctuations at the human-wildlife interface . We reveal that environmental forcing , especially rainfall and resource availability , exert important cascading effects on intra-annual variability in the wildlife reservoir dynamics , leading to epidemics that shift between stable and chaotic regimes . Our models demonstrate that bimodal seasonal epidemics result from a powerful seasonality in transmission , generated from interlocking cycles of agricultural phenology and rodent behavior driven by the rainy seasons .
Most emerging infectious diseases are zoonotic , and more than 70% of these originate among wildlife [1] . Zoonotic disease emergence and reemergence has been hypothesized to be driven by environmental and anthropological variability at the human-wildlife interface [2–4] . However , recent reviews of our understanding of the determinants of spillover have shown that a critical knowledge-gap [5] exists as we lack empirically validated models of the ecological interactions between humans , wildlife reservoirs and key environmental drivers [6 , 7] . In order to untangle the complexity of zoonotic spillover , combined field surveillance and modeling approaches that link the contacts between humans and wildlife with disease dynamics within the wildlife reservoir are essential [8] . However , such comprehensive investigations are still lacking for almost all zoonotic disease systems [9] . Hantaviruses are rodent-borne zoonotic pathogens within the Bunyaviridae family that cause hundreds of thousands of hospitalizations annually on a global scale . Depending on the viral strain in question , which may cause hantavirus pulmonary syndrome ( HPS ) or hemorrhagic fever with renal syndrome ( HFRS ) , case fatality rates range between 0 . 5–40% [10] . Hantavirus is responsible for numerous significant zoonotic outbreaks , including the outbreak of HFRS due to Hantaan virus ( HTNV ) during the Korean War [11] , and HPS due to Sin Nombre virus ( SNV ) in the Four Corners region of the United States in 1993 [12] and more recently in Yosemite National Park , California , in 2012 [13] . Hantaan virus , which is in the clade of hantaviruses that causes HFRS , was first isolated in 1978 [14] . Previous analyses of hantavirus infection dynamics suggest that changes in climate [15–17] , environmental condition and/or agricultural activity affect the risk of zoonotic transmission via changes in reservoir dynamics [18 , 19] , exposure risk [20–22] , or virus stability in the environment [23–25] . However , HPS/HFRS epidemics do not appear to simply track environmental conditions or rodent dynamics [26 , 27] . An integrated picture of host-environment interactions and the resulting hantavirus transmission and spillover is far from clear . China has the highest incidence of HFRS worldwide . Our study area in central China , Hu County , is one of the main epidemic areas and serves as a national surveillance site to monitor the HFRS situation . Since 1984 , a unique longitudinal field study of hantavirus in rodents with additional epidemiological tracking of human incidence has been conducted in the area ( Fig 1A ) . From 1994 onwards , an attempt has been made to control hantavirus transmission through the targeted routine vaccination of adults aged 16 to 60 yrs . old . Despite these control efforts , the dominant hantavirus ( HTNV ) continues to infect humans , with dynamics exhibiting clear seasonal and interannual variability as outbreaks invariably coincide with the end of the two rainy seasons ( Fig 1B ) . In this study , we used a Bayesian state space approach to demonstrate how natural seasonal patterns interact with anthropogenic environmental changes to drive the temporal dynamics of host-virus infection and the consequent risk of HFRS in human populations .
In Hu County , a total of 9 , 626 HFRS cases were reported from 1984–2014 , with the highest incidence of 0 . 3% occurring in 1984 . During the study , 10 , 598 rodents were captured in 247 , 408 trap-nights , with a capture rate of 4 rodents per hundred trap-nights . Of the rodent species captured , striped field mice ( Apodemus agrarius , mean capture rate of 2 . 1 ) was the most frequently captured species , with 48% ( 5079/10598 ) of total captures , followed by brown rats ( Rattus norvegicus , mean capture rate of 0 . 9 ) , buff-breasted rats ( Rattus flavipectus , mean capture rate of 0 . 7 ) , Gansu hamsters ( Cansumys canus , mean capture rate of 0 . 3 ) , and house mice ( Mus musculus , mean capture rate of 0 . 2 ) [28] . Hantavirus antigen-positive captures were also found in the rodent species: A . agrarius , R . norvegicus , R . flavipectus , C . canus , and M . musculus with positive rates of 6 . 8% ( 346/5079 ) , 0 . 5% ( 12/2237 ) , 0 . 2% ( 4/1702 ) , 0 . 3% ( 2/677 ) , and 0 . 2% ( 1/493 ) , respectively . Complete S segments of Hantaan virus ( HTNV ) were obtained from A . agrarius and HFRS patients from 1984–2012 ( Fig 1C , S1 Table ) as described both previously and in this study [29 , 30] . This result indicates that HTNV , carried by A . agrarius , is primarily associated with the HFRS cases in Hu County . While other rodent species were relatively rarely infected , these sequences were closely clustered with little antigenic variation from sequences obtained from A . agrarius even over long periods of time ( S1 Fig ) , indicating that these are spillover infection of HTNV . We therefore chose to explore the epidemics of HFRS by considering its dynamics solely within A . agrarius , using field and laboratory studies . We found that HFRS epidemics increased after the rainy seasons , even after the mass vaccination program was initiated in 1994 , which was associated with a decrease in the mean number of cases ( Fig 1B ) . This suggests powerful seasonality in wildlife-to-human transmission . However , while the incidence indicates a clear intra-annual variability with a strong correspondence to A . agrarius dynamics ( R = 0 . 80 , P < 0 . 05 ) prior to the vaccination program ( Fig 1C–1E ) , the incidence during the vaccination era testifies to highly erratic outbreaks . Changes in interannual patterns of epidemics may be linked to changes in potential environment drivers . Environmental variability has cascading effects on wildlife population dynamics [31–33] , through breeding success ( S2 Fig ) and mortality ( resource availability and carrying capacity ) , which may further affect hantavirus dynamics [34] . We propose a mechanistic mathematical model to explore the response of hantavirus dynamics to environmental fluctuations . The model includes the logistic growth of the rodent reservoir [35] , where A . agrarius population dynamics are influenced by environmental factors affecting both birth and death rates , which are in turn determined by amount of rainfall in the breeding season and the carrying capacity of farmland , respectively ( see Materials and Methods ) . The model supports our dynamical hypothesis , and captures the qualitative pattern of rodent population dynamics ( Fig 2A ) . In particular , the model accurately predicts the unusually low abundance between 2002 and 2005 . Our analysis reveals that the exceptional 2002 population crash in autumn breeding could be traced back to a significant food shortage , as crops growing after the spring harvest in our study area were affected by extreme drought in 2002 . The extent of this catastrophic drought in the area is illustrated by the temperature vegetation dryness index ( TVDI ) ( S3 Fig ) . In addition , the mean normalized difference vegetation index ( NDVI ) for farmland in this region was significantly lower during the drought year of 2002 , compared with other years ( Fig 2B ) . Significantly , drought may be associated with low breeding rate ( S2 Fig ) , and the resulting food shortage may increase mortality for most rodents ( Fig 2C ) , except the brown rat ( R . norvegicus ) which lives in close association with humans and does not rely on farm crops ( S4 Fig ) . The dynamics of A . agrarius normally undergoes biennial cycles , which was especially the case in the high population densities of 1984 and 2012 . However , these population oscillations collapsed in 2002 , initiating a population decline ( Fig 1D ) . We infer that the life cycle of A . agrarius in our study area is affected by rainfall and resource availability during the breeding season , and our model was therefore constructed to represent these dynamics ( S5 Fig ) . Bifurcation analysis demonstrates that our model produces stable population dynamics ( i . e . stable equilibria , in which population numbers remain constant ) at low density under low rainfall or drought scenarios , oscillations with 1–2 yrs . periodicity under normal rainfall , and aperiodic and chaotic dynamics ( i . e . chaos , in which population numbers change erratically ) for strong environmental forcing and abundant rainfall ( Fig 3 , S6 Fig ) . The magnitude of transmission rate varies with time and corresponds to time-varying contacts between susceptible and infected hosts [36] . Thus , environmental changes lead wildlife hosts to a critical density threshold , below which the virus cannot invade ( S7 Fig ) [34 , 37] . In addition , a decreased carrying capacity is associated with loss of farmland area over time ( Fig 4 ) . This moves hantavirus dynamics into an environmentally forced regime with large fluctuations in infection rate . Intervention can explain the discrepancy between rodent dynamics and human infections at interannual timescales . In times of high rodent abundance , the response in number of HFRS cases would be expected to be large . However , the number of HFRS cases was low for 2001 and 2012 , despite favorable transmission conditions due to high rodent density ( Fig 1 ) . Our results indicate that these episodes were concurrent with a vaccination-induced reduction in human susceptibility . This in turn reduced the number of human hantavirus infections and controlled the susceptible population size , even though the overall human population increased in these decades ( see Supporting Information ) . Most notably , a significant decrease in the number of susceptible individuals was observed after the mass vaccination in 2011 ( S8 Fig ) , and the measures implemented successfully averted further epidemics . In addition , the number of HFRS cases were clustered on an annual basis during two time periods: June to July , and October to November ( Fig 5A ) . The cases peaked and coincided with two important annual agricultural events , the spring and autumn harvest . Maize is sown in late October for the spring harvest at the end of May , and wheat is sown in June for the autumn harvest at the end of September and October . These agricultural activities coincide with the two rainy seasons ( Fig 5B ) . A second important cycle ( shown in Fig 5B ) involves the pregnancy rate of A . agrarius [38] , which closely matches the NDVI curve . Apodemus agrarius , which tends to live in agricultural fields , initiates its spring breeding season in April–May , and the autumn breeding season starts in August–September [38] . It is interesting to note that the breeding season is closely associated with agricultural activity in Hu County . To summarize , the incidence of HFRS cases peaked during the harvests , when the risk of exposure of farmers to infected rodents in the farmland areas would have increased ( Fig 5C , as the average incubation period for HFRS is approximately 3 weeks , ranging from 10 days to 6 weeks [10] ) . We estimated the seasonal variation in the transmission rate explicitly by applying the discrete-time susceptible-infected-recovered model to 30 yrs . of monthly data from the area , and the fit of the full model accounts for 88% of the variability in the HFRS cases ( Fig 5D ) .
Public health scientists and epidemiologists are increasingly challenged to understand how environmental change and anthropogenic trends affect zoonotic disease dynamics at the wildlife-human interface [39–42] . An effective prevention and control method of zoonotic disease is required , which integrates ecological principles of animal , human , and environmental factors [2 , 9] . Our study of how shifts in disease ecology can be forced by environmental and anthropogenic processes sheds critical light on zoonotic dynamics and the persistence of disease [2] . We have shown the ecological drivers responsible for the cascading effects of environmental variability on HFRS , using a mechanistic mathematical model integrating longitudinal field surveillance , environmental change and epidemiological data . Once the wildlife and virus dynamics are taken into account , a clear picture emerges of the role of environmental variability in zoonoses [43] . We found support for intra-annual disease cycles driven by seasonal interactions between humans and wildlife , and by an environmentally induced cascade which can switch the dynamics of A . agrarius abundance between stable and oscillatory [44] . This in turn affects seasonality in HFRS incidence . Our finding adequately explains the complexity and interrelatedness of the environmental , biological , and anthropogenic dimensions of zoonotic pathogen dynamics . We have provided support for the hypothesis that environmental forcing , rainfall and related vegetation growth , may induce strong chain reactions [45 , 46] in wildlife dynamics and zoonotic epidemics [47] . Environmental conditions influence survival of the animal reservoir [48 , 49] and affect transitions between stability , cycles and chaotic dynamics . This is consistent with numerous field studies showing that an increase in resources would allow the rodent host to survive and reproduce [50–52] , possibly leading to a higher prevalence of infection [26 , 53–55] and a higher transmission rate among rodent populations with an older age structure [56] as hantavirus infection is life-long in natural hosts [57] . In turn , this could lead to a greater chance of spillover to humans . Various studies cite HFRS as an example of a zoonotic disease which is linked to climate variability and environmental factors [58–60] . However , general predictions and a supporting model of the effects of environmental change on HFRS dynamics have not yet been empirically tested [27 , 61 , 62] . Our results in this study suggest that the proposed mechanism would be valuable in broadening our understanding of human exposure to hantaviruses in general . Anthropogenic forcing has been linked to disease dynamics and relations between wildlife hosts , humans , and pathogens [63 , 64] . While prior research has traditionally focused on land-use change and zoonosis emergence [65–67] , growing evidence indicates that the expansion of ecotones ( transitional areas between adjacent ecological systems ) can provide opportunities for pathogen spillover [64 , 68–70] . Our work provides an improved understanding of the mechanistic processes linking anthropogenic environmental change ( for instance , land-use change ) and disease dynamics . During the study , farmland loss was found to be associated with host resources and carrying capacity , both of which affect wildlife abundance . In addition , over the past three decades there has been a decline in the abundance of A . agrarius , coinciding with a long-term trend showing a decrease in the incidence of HFRS . Our results suggest not only the role of environmental seasonality in shaping population fluctuations [71 , 72] , but also 1 ) the critical role of human activities , which shape the seasonal dynamics of A . agrarius by deeply influencing the local rodents’ activity and their life cycles , as well as 2 ) the role of seasonality in influencing contact between humans and the reservoir host . Apodemus agrarius has adapted to thrive in the ecological landscape created by agriculture . This adaptation amplifies seasonality in both transmission and spillover , which alter the spread of infectious diseases [73–75] . The estimated seasonality in the transmission rate shows a bimodal distribution , consistent with the seasonal timing of HFRS cases . Given the distinct roles of wildlife and agricultural activity in transmission , a reasonable explanation for this seasonal pattern is the increase in potential contact between rodents and humans in the dry season due to seasonal agricultural activities . Overall , the combination of both agricultural and seasonal environmental forcing generates a setting in which irregular epidemics arise intrinsically . These findings not only provide evidence for the long-standing hypothesis that environmental change is associated with zoonotic persistence and amplification , but also indicate that the dynamical effects of human-wildlife interactions are dependent on environment-linked processes ( Fig 5E ) . The HFRS vaccination strategy has been effective and has played an important role in reducing the incidence of HFRS in Hu County [76 , 77] . Despite this , challenges still remain regarding the prevention and control of HFRS outbreaks . It should be noted that the incidence of HFRS evidently rebounded after 2010 , even with high vaccination coverage [19] . This may be attributed to many factors and requires a deeper understanding of the drivers of zoonotic disease risk [78 , 79] . Taken together with the empirical data on demography and epidemiology , the results suggest that such erratic HFRS epidemics in the study area may have been generated by high amplitude wildlife oscillations interacting with environmental stochasticity and vaccination coverage . All of this demonstrates that wildlife monitoring and modeling may not only help us to retrospectively understand the dynamics of the system , but may also provide advance warning of an outbreak . Several important limitations of this study should also be acknowledged . First , there is no surveillance data available before 1984 , and it is therefore difficult to provide a possible mechanistic explanation for the rodent population peaks in 1984 and 1985 . Second , although our rodent surveillance involved constant effort over time , the capture rate was estimated using an unequal number of traps between months . Third , the relationship between environmental variability and the infection rate of wildlife was not considered in the present analysis due to constraints in data availability , and this relationship may have accounted for unexpected outbreaks , e . g . the outbreaks in 1988 and 2011 . Future surveillance efforts should include more detailed and frequent sampling of wildlife and hantavirus to improve our knowledge of the association between virus transmission and environmental variability . Zoonotic diseases significantly impact human health globally , with approximately 1 billion cases and millions of deaths reported each year [63] , and are persistent public issues around the world . Our longitudinal survey provides evidence that the key to HFRS epidemic control is critical monitoring of wildlife and the environment , combined with mathematical models to forecast outbreaks and the vaccination of farmers at risk .
The study was located in Hu County ( 108° E , 34° N ) on the Loess Plateau of central China , an area of 1 , 255 km2 and a population of approximately 600 , 000 people ( according to the 2013 Chinese national census ) . We used the official monthly notification data of HFRS cases from Shaanxi Provincial Center for Disease Control and Prevention and associated demographic data available for Hu County between 1984 and 2014 . All HFRS cases were confirmed according to the standard diagnosis set by the Ministry of Health of the People’s Republic of China [80] , then confirmed by detecting antibodies against hantavirus in human serum samples . Serum samples were sent to the Shaanxi Centre for Disease Control and Prevention ( CDC ) for the detection of hantavirus-reactive antibodies . Between 1994 and 2014 , a vaccination campaign was conducted in the study area . To assess both the vaccine efficacy and the loss of vaccine efficacy with time elapsed since vaccination , we randomly selected a total of 29 , 359 people from epidemic and non-epidemic villages in Hu County and monitored them [77] . The health records of each person were investigated , and blood was collected and analyzed by ELISA for the presence of anti-hantavirus IgG specific antibodies . Starting in 1984 , surveillance of the rodent host population density in Hu County has been carried out on a monthly basis ( Fig 1B ) . In each month between 1984 and 2014 , rodent trapping was carried out in the fields ( farmland or wasteland , 3 km away from villages , which are the habitats for the important rodent reservoirs ) in Hu County for three consecutive nights at 9 trapping sites , according to standard protocol from the Chinese Center for Disease Control and Prevention . Snap-traps ( medium-sized steel rodent trap , brand name: Golden Cat , Guixi Mousetrap Factory , Jiangxi , China ) were baited with peanuts , set each night , and recovered in the morning . At the trapping site , traps were set as 4 parallel lines of 25 traps each and were spaced at 5 m intervals . The trapped rodents were identified to species by species identification experts according to previously described criteria [81] . All rodents were accessioned to the Shaanxi CDC [84HX001-13HX141] , and retained as voucher specimens for each species . Lung tissues were removed from the trapped rodents and stored immediately at –196°C , and then transported to the biosafety level-2 ( BSL-2 ) laboratory of Shaanxi CDC for processing . The frozen lungs were sliced with a cryostat ( Leica CM1950 ) and preserved in a refrigerator at –80°C . Tissues and serum specimens for serological or molecular tests were handled during the various laboratory procedures in class II type A2 biosafety cabinets . The average monthly NDVI , an index of the amount and productivity of vegetation , was derived from satellite data and was generated as follows: NDVI = ( NIR—VIS ) / ( NIR + VIS ) , where VIS and NIR stand for the spectral reflectance measurements acquired in the visible ( red ) and near-infrared regions , respectively [82] . NDVI values for farmland were obtained from 9 sampling sites ( Fig 1A ) during 1984–2014 using AVHRR GIMMS 15-day composite NDVI products [83] . The TVDI , based on an empirical parameterization of the relationship between surface temperature and NDVI , was used in monitoring soil moisture and drought regionally [84] . The TVDI is estimated using the following equation: TVDI = ( Ts—Ts min ) / ( Ts max—Ts min ) , where Ts is the observed land surface temperature at a given pixel , and Ts min is the minimum surface temperature in the triangle ( Ts min = a1 + b1 ( NDVI ) ) . Ts max is the maximum surface temperature observation for a given NDVI ( Ts max = a2 + b2 ( NDVI ) ) . a1 and a2 are the intercepts , and b1 and b2 are the slopes for the dry and wet edges . The value for TVDI is higher for dry conditions and lower for wet conditions , and varies between 0 and 1 . Climatic data , including temperature and rainfall , were obtained from a local meteorological station from 1984–2014 . The study’s protocol was conducted according to the guidelines of animal welfare set by the World Organization for Animal Health , and approved by the institutional ethics committee of the Shaanxi CDC ( Permit numbers: 2014–2 and 2013–005 ) . The species captured in this study were not protected wildlife and were not included in the China Species Red List , therefore a permit to collect wildlife was not required from an official wildlife/conservation agency . During the study , the major crop production in Hu County was spring wheat and autumn maize , occupying most of the local farmland . We used the phenology of local crop production , together with the spectral features of satellite images , to select Landsat images ( with a resolution of 30 m ) from March to May and September to October in order to extract the areas of cropland in the Hu area in 1984 , 1995 , 2001 , 2004 , 2010 and 2014 . A support vector machine ( SVM ) of supervised classification was applied to perform the classification process in ENVI v4 . 3 [85] . The accuracy assessment of land cover classification using ground truth images by region of interest tools ( ROI ) indicated a Kappa coefficient of 0 . 98 on average . Viral antigens in lungs were detected by using direct immuno-fluorescent assay as described previously [86] . Lung tissue samples were cut into 7–8 μm sections on a freezing microtome and fixed in acetone after air drying for at least 30 min . 100μl of FITC-labeled anti-SEOV/L99 or HTNV/76–118 hantavirus nucleoprotein typing monoclonal antibody [87] was pipetted onto each slide . Tissues were incubated at 37°C for 1 hour and washed five times with 0 . 02 M phosphate buffered saline ( PBS ) . The samples were considered positive when yellow-green fluorescing hantavirus particles were seen under fluorescence microscopy . Total RNA was extracted from rodent lung tissue with the TRIzol reagent ( Invitrogen , USA ) and RNeasy mini kit ( Qiagen , Germany ) , the viral RNAs from the sera of patients were extracted using the QIAamp viral RNA mini kit ( Qiagen , Germany ) according to the manufacturer’s instructions . cDNAs were synthesized from 5 μg total RNA with the RevertAid first strand cDNA synthesis kit ( Fermentas , Canada ) in the presence of random hexamers primer according to the manufacturer’s instructions . The partial S segment sequences were obtained by PCR as described previously [29] . The PCR products were gel purified using QIAquick Gel Extraction kit ( Qiagen , Hilden , Germany ) , according to the manufacturer’s instructions . DNA sequencing was performed with the Big Dye Termination Sequencing kit on the ABI-PRISM3730 genetic analyzer ( Applied Biosystems , Carlsbad , CA , USA ) and sequences ( S1 Table ) were submitted to GenBank ( accession nos . KY357322–KY357327 , KY283955–KY283956 ) . For a detailed description of the laboratory methods used , see Ma et al . [29 , 30] . Human serum samples were tested for IgG antibodies against hantavirus by ELISA . Briefly , the serum samples were diluted 1:10 in PBS and incubated on a microtiter plate containing hantavirus recombinant nucleoprotein . After incubation at 37°C for 1 hour , the plate was washed six times with PBS containing 0 . 05% Tween 20 ( PBST ) , then peroxidase-labeled goat anti-human IgG ( Millipore , Bedford , MA ) at a dilution of 1:10 , 000 was added . After the incubation and washing steps ( described above ) , tetramethylbenzidine ( TMB ) and hydrogen peroxide ( H2O2 ) substrate was added and incubated at 37°C for 10 mins . The reaction was stopped by adding 1 M H2SO4 and the plates were read on a microplate reader at 450 nm . A net absorbance value of > 0 . 15 was considered positive . Neighbor-joining trees of hantaviral S segment sequences were constructed using a GTR + I + Γ4 model in PAUP v4 . 0b10 [88] . The best-fit phylogenetic model was determined by Modeltest v3 . 7 [89] . To assess the robustness of the tree topology , a set of 100 pseudoreplicates was generated and used in bootstrap analyses with the maximum likelihood ( ML ) method implemented in PHYML [90] and the neighbor-joining method implemented in PAUP v4 . 0b10 . A Bayesian phylogenetic tree ( 10 million generations ) was also constructed using MrBayes v3 . 2 [91] . Trees were highly congruent to those produced above . We modelled the HFRS epidemics in Hu County ( 1984–2014 ) using a discrete-time susceptible-infected-recovered ( TSIR ) model with age structure [92] . We estimated the seasonality for the HFRS transmission rate by fitting the 30-year-long time series of observed monthly cases using this TSIR model in a Bayesian state-space framework to account for uncertainty in the observation [93] . New infections were drawn from the pool of susceptible individuals , along with information on births , deaths , and vaccinations . As the natural time scale for the disease is ~1 month [94] , we used this as the time interval in our model . The number of people susceptible to disease in month t+1 is given as St+1 = Bt + St−Dt−Vt−It + λRt . Bt and Dt represent the number of human births and deaths during the time period , respectively . V is the number of vaccinated individuals based on medical records , and R is the number of immune individuals . λ is the proportion of vaccinated people who lost their immunity per month , based on 20 years of surveillance . Susceptible individuals were divided into three different age groups ( 0–15 yrs . , 16–60 yrs . , and 61–100 yrs . ) according to disease characteristics , and each individual was also kept track of over the study period ( Fig 5A ) . The number of people aged 16–60 yrs . accounted for more than 90% of the total cases in the study area , and as the vaccine was only provided to this group ( S9 Fig ) we assumed that the highest risk of infection was for this age group . Additional information is given in the Supporting Information . In the model , the force of infection can be expressed as: βt ( NRt/Nt ) ( IRt ) , where Nt is the current human population size at time t , IR and NR indicate the number of infected and total rodent hosts , and βt the month-specific transmission rate from rodents to human beings . The overall human HFRS epidemic dynamics are thus given by: It+1= ( NRt+τ1 ) Nt ( IRt+τ2 ) αStβt ( 1 ) where α allows for the nonlinearities generated by the heterogeneity of the contacts between rodents and humans [95] . The parameter τ represents low , random abundances when no animals were caught or no infected animals were caught . Here βt = β0 ( 1 + β1cos ( 2πt ) ) , where β0 is the average transmission rate and β1 denotes the amplitude of variation around β0 [96] . To represent the roles of intrinsic feedbacks from environmental forcing , we proposed an environment-based wildlife dynamic model . The dynamic change of host population can be mathematically represented as: NRt+1=NRt+bseasrt ( 1−NRtKt ) NRt−dseasNRt ( 2 ) Hosts grow and die seasonally at rates bseas and dseas respectively , which are time-varying parameters influenced by extrinsic drivers . bseas and dseas contain both the basic components bcons and dcons , and an environmental component ( influenced by rainfall and the NDVI for farmland ) . Seasonality has been observed in the birth and pregnancy rates of A . agrarius [97] . An increase in the number of births during the wet season has been suggested [38] , and this was observed in the Chencang district from 1984 to 1987 , 10 km away from Hu County , corresponding to a greater percentage of pregnant females during the wet seasons ( S2 Fig ) . Assuming that an increased pregnancy rate is associated with rainfall , we estimated the time-varying seasonal birth rates by monthly rainfall , and set the seasonal birth index , rt , at a value of 0 ( non-breeding season ) or 1 ( breeding season ) according to data provided by reference [38] . Kt is the time-varying carrying capacity , determined by the area of farmland ( see Supporting Information ) . To reduce the dimensionality of the model , we ignored sex and age heterogeneity among A . agrarius . | Pathogens shared with wildlife cause more than 60% of human infectious diseases . However , there is a scarcity of comprehensive modeling of zoonotic disease dynamics at the wildlife-human interface . Here , we use 30 years of monthly rodent-hantavirus monitoring to show that the complex seasonality in human spillover results from the interplay between an ecological cascade that shapes reservoir infection dynamics and seasonal agricultural cycles that in turn determine human-wildlife contact patterns . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"animal",
"types",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"population",
"dynamics",
"immunology",
"microbiology",
"vertebrates",
"animals",
"mammals",
"viruses",
"preventive",
"medicine",
"seasons",
"hemorrhagic",
"fever",
"with",
"renal",
"syndrome",
"hantavirus",
"rna",
"viruses",
"population",
"biology",
"vaccination",
"and",
"immunization",
"zoology",
"bunyaviruses",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"zoonoses",
"medical",
"microbiology",
"microbial",
"pathogens",
"rodents",
"wildlife",
"viral",
"pathogens",
"earth",
"sciences",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"amniotes",
"organisms"
] | 2017 | Anthropogenically driven environmental changes shift the ecological dynamics of hemorrhagic fever with renal syndrome |
Conflict destabilizes social interactions and impedes cooperation at multiple scales of biological organization . Of fundamental interest are the causes of turbulent periods of conflict . We analyze conflict dynamics in an monkey society model system . We develop a technique , Inductive Game Theory , to extract directly from time-series data the decision-making strategies used by individuals and groups . This technique uses Monte Carlo simulation to test alternative causal models of conflict dynamics . We find individuals base their decision to fight on memory of social factors , not on short timescale ecological resource competition . Furthermore , the social assessments on which these decisions are based are triadic ( self in relation to another pair of individuals ) , not pairwise . We show that this triadic decision making causes long conflict cascades and that there is a high population cost of the large fights associated with these cascades . These results suggest that individual agency has been over-emphasized in the social evolution of complex aggregates , and that pair-wise formalisms are inadequate . An appreciation of the empirical foundations of the collective dynamics of conflict is a crucial step towards its effective management .
We begin by asking whether fight sizes are correlated in time . An example time-series , from a single eight-hour observation period and showing fight size and duration , is in Fig . 1; one may construct from this various autocorrelation functions . Surprisingly , the sizes of fights are nearly uncorrelated over the course of the day . Larger-than-average fights do not , for example , predict the appearance of larger-than-average fights later . This is discussed in greater detail in the Supporting Information . We then ask whether there are correlations across fights in membership – does the the appearance of individual in a fight at time step one predict the appearance of in the next fight ? For simplicity , the only within-fight information we use is individual identity data; we do not take into account individual behavior ( e . g . , aggressor , recipient , intervener , and so forth – see Empirical Methods ) , nor do we consider which individuals interacted within fights . Given this , the simplest correlations we can observe are correlations in membership across fights separated by one peace bout . We write these , estimated as : the number of fights involving that followed a fight involving , divided by the number of fights involving . Informally , gives the probability of observing in a conflict given that one has just observed a conflict involving . The probabilities will vary for different pairs of individuals . In order to remove time-independent effects on individual participation in fights , we compute ; the difference between the null-expected and that measured from the data: ( 1 ) where is the average from a large Monte Carlo set of null models generated by time-shuffling the series but not shuffling identities within fights . Fig . 2 shows some of the strongest correlations of this form found between the 48 individuals , in the form of a directed graph . Correlations across fights can also be generated by subgroups deciding to fight in response to other subgroups fighting previously . There are several possible variations in subgroup-generated correlations . We consider only the two computationally simplest correlational structures . Correlations of the form reveal the extent to which the presence or absence of a pair of individuals at one time predicts the appearance of a particular individual at the next step . They can be defined: ( 2 ) as can , the extent to which the presence of an individual at the previous step predicts the presence of a pair at the next step: ( 3 ) Using this combinatorial Monte Carlo technique , we find significant correlations for both these structures . Plots of the distribution of these three correlations can be found in the Supporting Information . As one measures higher-level correlations , between , for example , triplets and individuals , the effective sample size – number of relevant observations ( the conditional and ) – drops , while the number of parameters to estimate rises combinatorically . This leads to a rapid decrease in signal-to-noise on any one observable . Extracting overall significance levels for measurements requires caution . For example , since individuals are correlated within fights , and these correlations are maintained by the null model , the various measurements are not independent of each other . A Monte Carlo simulation of the expected numbers of correlations confirms that the excess of positive values in the observed data is significant at ; these issues are discussed in greater detail in the Supporting Information . A similar analysis can be done for two-step correlations between named individuals and groups; while individual detections can be made , Monte Carlo simulations of the expected noise properties of the measurement suggest that such correlations , should they exist , are too weak to detect even in the full sample of 1096 fights . Given the observed correlations , we now consider the causal mechanisms underlying the detectable individual and subgroup correlations . To do so , we introduce a class of minimal models for social reasoning , or “strategy space . ” Full specification of these models is in the Supporting Information section , “Simulation Specification . ” We suppose that each individual or subgroup decides whether to join a fight based on composition of the previous fight . The space of possible strategies can then be written as , where is the size of the relevant group in the previous fight , and is the number of individuals making the decision . We allow decisions to be probabilistic ( “mixed , ” in the game theory terminology ) , so that a particular fight composition can lead , with some probability distribution , to different kinds of subsequent fights . Each element of a strategy is a number between and , specifying the probability that the appearance of a particular -tuple leads to a recommendation that a particular -tuple join , or avoid , the next fight . These probabilities derive directly from the data , using the equations given in the previous section to determine whether there is a significant identify correlation across fights between two individuals or pairs . A negative value can be interpreted as repulsion or inhibition , and a positive value can be interpreted as attraction or stimulation . In the case that a particular -tuple receive multiple , possibly incompatible , recommendations to join or avoid , which is always possible because each fight has a minimum of two individuals involved , the decision to join or avoid can be resolved by introducing a temperament parameter , which we call a “combinator . ” We choose AND and OR to capture the two ends of the spectrum of individual temperaments . Under the conflict averse , or conservative AND combinator , an -tuple must receive recommendations to join from all relevant -tuples . Under the maximally conflict-prone OR combinator , a single recommendation to join is sufficient . We begin with a randomly generated , spontaneous “seed” pair . These seeds can trigger a subsequent series of fights ( a “cascade” ) that in our simulation build up a time series . At some point , a particular fight may lead to no recommendations to join , or a recommendation that only a single individual join; at this point , the cascade ends , and a new seed pair is chosen . This is a ( one-step ) Markov model; the restriction to single , as opposed to multi-step models can be justified in part by the absence of detectable correlations at two steps , discussed above , and by the reproduction of this absence in the outputs of the one-step model . Different and combinator choices amount to constraining the transition matrix . The estimation of maximum-likelihood transition probabilities in ( hidden ) Markov models is often accomplished with a variant of the EM algorithm [30]; however , even in the simplest model , , the number of parameters ( ) to be estimated is larger than the number of events ( 1096 fights ) , and so such iterative methods are unlikely to reliably converge . On the other hand , determining the parameters directly by searching the full parameter space is impossible . In this exploratory work , we instead make a convenient Ansatz . Specifically , we take the elements of to be equal to the corresponding measurement of the between the relevant - and -tuple . In the discussion of results ( “How Specific are the Strategies” ) , we consider a number of alterations from this first guess as a way to assess the flatness of the likelihood and thus to suggest , for future investigations , how to reduce the dimensionality of the parameter space . Our choice should be reasonably close to the maximum of the likelihood when fights are small and do not grow or shrink too quickly . We find that some choices of strategy class both “validate” ( approximately reproduce the measurements used to specify them ) and “predict” ( reproduce other features of the data that do not directly influence the values of their parameters . ) As shown in Fig . 3 we can define a systematic , discrete space of models that stand in hierarchical relation to one another . Increases in correspond to an increase in the memory capacity of decision makers . Increases in correspond to an increase in coordination among individuals . Hence the space defines an hierarchy of memory and information processing requirements . We confine the space of models we consider to those in which and , as the strategies of higher-order models are unlikely to be within the cognitive capabilities of the individuals . In principle , can be extended systematically to ( i ) larger values of and , ( ii ) include a combinator with more complicated functional dependence , and ( iii ) accommodate longer timescales , for example , by expanding the dimension of the strategy space from to , and even , where and respectively refer to the second and third time steps from the initial fight . Later in the Results section , we consider how factors like power [4] affect strategy use by individuals . Such factors can be incorporated into the IGT framework but caution is warranted as it is nontrivial to do so systematically; we defer this question to future work . We consider , and , with either combinator . Thus each hypothesis has two variants . includes only pair-wise decision making strategies that do not require any coordination between conflict participants fighting in the second time-step . We call this the Rogue Actor Hypothesis – an individual's involvement in a conflict provokes others to become involved in subsequent conflicts . Rejection of this model would suggest that individuals – by either appearing in many fights themselves or by repeatedly provoking others – are not the primary cause of fights or cascades . means that an individual decides to participate in a subsequent conflict based on the presence or absence of a particular pair of individuals in the previous bout . This model includes triadic-decision making strategies . Rejection of this model would rule out what we call the Triadic Discrimination Hypothesis – individuals make strategic decisions about whether to engage in the present conflict based on who fought with whom previously , and their strategic relation to that pair . means that the decision of a pair of individuals to participate in a subsequent conflict is based on the presence or absence of a particular individual in the previous bout . This model includes triadic decision-making strategies that additionally require coordination of participants in the second time-step . Rejection of this model would rule out what we call the Triadic Coordination Hypothesis – individuals jointly decide to fight in a subsequent bout based on the presence of a particular individual in the previous bout , and their strategic relation to that individual . Higher-order strategies are in general irreducible – not decomposable into the products of lower-order strategies . In the language of statistical inference , is nested within the other two strategies; imposing equality constraints allows them to approximate . With these three hypotheses in hand , we can produce simulations of the empirical time series , whose predictions we analyze below .
We test these hypotheses against each other by simulating conflict dynamics using the models . We run one simulation for each +combinator model . We ask how well each of the resulting simulated distributions of fight sizes fits the empirical distribution; the total number of simulated fights is at least 100 times larger than that observed , allowing Monte Carlo estimates of the statistical properties of observable parameters . The simulations tell us three things . One is the implication of each model and its associated strategies for conflict dynamics , including cascade severity . Another is which of the models better reproduces the data , and thus which of the strategies individuals and subgroups are more likely to be playing in the group . A third insight given by the simulations is how much information the individuals are using , when playing a particular strategy , about other individuals and their interactions . We operationalize conflict size using a measure we call the “long fraction” ( Fig . 4 ) . The long fraction is the number of fights of size , divided by the total number of fights larger than two; formally , ( 4 ) where is the number of fights of size ; the maximum fight size of 48 comes from the total number of socially-mature individuals in the group . The long fraction is a measure of cascade severity , showing how large fights can grow due to the combined strategies of individuals and subgroups . We consider only fights larger than two in size in order to reduce the influence of seed pair composition on the analysis . As shown in Fig . 4 , the most striking feature of the simulations is the vastly different conflict sizes generated by the different strategies . In the cases considered , these differences allow us to quickly rule out certain simple models . Two of the variants we consider , + AND and + OR , lead to “anomalous quiescence” – few fights are sufficiently motivating to the group to be consequential . Even if a small conflict manages to double in size , it is rarely able to double again . We find that in three cases the models lead to “forest fires” – conflict expands in a cascade that engulfs the group , with nearly all individuals participating , and refuses to die down . These are + OR , + OR , and + AND . These strategies do not reproduce the data . Since neither combinator for works , we rule out the Triadic Coordination Hypothesis . Only + AND reproduces the distribution of fight sizes . This supports the Triadic Discrimination Hypothesis – individuals decide to fight based on their relation to pairs in previous fights . A small surplus of fights in the data at the very largest fight sizes ( ) suggests that strategies of other models might come into play at these extremes . This might happen , for example , if during turbulent periods individuals form coalitions in response to the perceived coalitions of others – . However , the frequency with which this model is used is likely to be low given it requires a level of coordination made difficult by constraints imposed by spatial considerations and limited capacity for communication [31] among the individuals in individual societies . Model on the other hand does not require coordination . The Triadic models , and + AND in particular , have ( formally ) many more parameters than the Rogue Actor Hypotheses . A study of the comparative Akaike Information Criterion ( AIC ) values , an information theoretic criterion that includes a penalty for model complexity , shows that the improvement in goodness-of-fit is sufficient to compensate; this is discussed in detail in Supporting Information . As we noted earlier , the autocorrelation function finds no significant fight size correlations; our model also reproduces this feature . Below we consider a wider range of observables to see how well the Triadic model performs . We find in our simulations that the different strategies have different implications for cascade size – the pairwise strategies produce small cascades , whereas the triadic strategies produce longer cascades , with the conflict prone variants producing the longest . Here we show , using data taken simultaneously with the time series , that in addition to the assumed costs and benefits to individuals from playing a particular strategy ( e . g . , that triadic strategies allow individuals to strategically respond to the interactions of others , whereas pair-wise strategies allow no such social “tuning” ) , there is a group cost to playing strategies that produce large fights . ( We refer the reader to the Empirical Methods for important operational definitions and statistical methods used in this section . ) We consider two measures of group cost . These measures capture how likely an individual is to receive aggression given the eruption of a conflict in size class . The first is the ( population ) mean frequency of contact aggression ( e . g . , tumbling , wrestling , biting ) received by group members during fights in size class , . The second is the ( population ) mean frequency of redirected aggression ( e . g . , aggression directed by a conflict participant to a third party ) received by group members during fight in size class , . The total number of fights in size class is given by . The total number of fights in size class in which individual receives contact aggression is and redirected aggression , . The aforementioned population-level means are then , and . For all large fights ( fights size ) , and the means are given by , and . For contact aggression received , the fight sizes are 2 , 3 , 4 , and 4 . For redirected aggression received , the fight sizes are 3 , 4 , 4 . By definition , there can be no redirected aggression in fights of size two . Measuring cost with respect to all individuals in the population rather than conditioning the calculation only on the individuals who fight allows us to capture the consequences to the group of variation in the individual proclivity to fight as well as in strategy variation . All else being equal , the population cost of 10 individuals fighting in a group of 10 is higher than the cost of 10 individuals fighting in a group of 100 . Second , by considering redirected aggression , we capture how conflict size affects the likelihood that an individual uninvolved in the dispute will be drawn in . As shown in Figs . 5 and 6 , we find , using a paired Wilcoxon signed ranks test , significantly more contact aggression is received by group members when fights are of size 3 than when fights are of size 2 ( one-tailed , ) , when fights are of size 4 than when fights are of size 3 ( one-tailed , ) , and when fights are of size 4 than when they are of size 4 ( one-tailed , ) . Note that the relation between contact aggression received and fight size is nontrivial: aggressors need not use contact aggression and some individuals participate without using or receiving aggression ( Methods ) . Consequently , contact aggression received does not necessarily increase with increasing fight size . Using a paired Wilcoxon Signed Ranks Test we also find significantly more redirected aggression is received by group members when fights are of size 4 than when they are of size 3 ( one-tailed , ) , and when fights are of size 4 than when they are of size 4 ( one-tailed , ) . These results , in conjunction with the results reported in Fig . 4 , suggest that conflict decision-making strategies based on triadic memory are associated with a higher population cost than decision-making strategies based on pair-wise memory . Whether this cost is outweighed by the direct benefits of playing these strategies is a question for future work . The model is triadic; an individual makes a decision to join the present fight depending on the participation of a particular pair of individuals in the previous fight . For each individual , our simulations associate a particular probability with every single pair . The actual strategies are likely to be far less specific . Cognitive and perceptual constraints mean that a pair might have been perceived as “Fred and Mary” or – at a much lower degree of specificity – as “Any Male and Mary . ” A decision-maker's response might also not be so fined graded; instead of a continuum of probabilities , only a finite number of distinct probabilities might be allowed . In addition to showing the effect of cognitive and biological constraints , studying strategy specificity is important for future work , since by reducing the dimensionality of the space , it could allow direct maximum likelihood searches ( see , e . g . , [32] . ) We consider two variants of + AND that are less specific . These are Shuffled and Coarse-Grained . For clarity , we will sometimes refer to the original model as Base . The Shuffled models are alterations of Base that re-assign strategies to the group . As with the base model , each individual maintains a static set of strategies from fight to fight . However , the sets used are shuffled compared to the base; we consider three kinds of shuffles . A Total Shuffle takes all the combinations , and randomly reassigns values to them from the original set . An Outgoing Shuffle is shown schematically in Fig . 7 . For each of the incoming pairs , it randomly swaps the associated with two outgoing elements . The distribution of the 48 values for any particular pair remains constant . When possible , the swaps are done between pairs with strategies of opposite sign . An Incoming Shuffle is similar , but for incoming pairs; a particular outgoing individual has the same distribution of , but they are now randomly associated with different pairs than in the original set . The Coarse-Grained models are , like the base and Shuffled , also + AND . The particular associations between pairs and individuals are maintained , but the values of are now coarse-grained to the nearest of a limited set of values . For of one , only three values are allowed: equal to the average of all the negative , equal to zero , or equal to the average of all the ( strictly ) positive values . The example of of two , with two negative and two positive values of allowed , is shown as dotted lines in panel two of Fig . 4 in the Supporting Information . Given the data indicating that Macaque perceptual systems have a logarithmic bias [33] , we space the bins logarithmically between the min and max of the positive and negative ranges . Testing the coarse-grained models gives a sense of how calibrated an individual's response needs to be to reproduce the data . As gets larger , the coarse-grained models are closer and closer to the base model in terms of the underlying values that dictate the responses of individuals to different pairs . One can consider a measure of how “graded” an individual's responses to a particular pair might be . If is two , for example , it suggests that individuals class pairs into five categories – “don't care” ( zero ) , “avoid” and “strongly avoid” , and “join” and “strongly join” – with no finer distinction . Earlier in this section , the long fraction alone was sufficient to rule out alternative strategies . The long fractions for the different shuffled strategies , shown in Fig . 8 , also have worse values . There are , of course , many more observables than simply the fight size distribution , and we now consider a large set of them . They are ( see the Supporting Information ) and , individual and ( connected ) pair appearance probability; and , average fight size conditional on individual or pair appearance; and . In Table 1 , we show the Pearson cross-correlation between the observed data , and the simulations , for the different shuffles and coarse-grainings . We may also make preliminary estimates of the change in likelihood from the data; we find that the overall likelihood for the parameters drops with either shuffling or coarse-graining . The use of shuffled models also allows us to make a ( very preliminary ) assessment of the “true” number of free parameters in the model , and to penalize the more complicated models; this is discussed in the Model Complexity section of the Supporting Information . The base model for which we find support assumes every individual relies solely on + AND . Although it is likely that some of the inconsistency with the data can be removed iteratively through corrections to the 's as part of a high-dimensional search using an approach similar to Ref . [34] , it is worthwhile asking whether some subset of individuals and pairs , chosen in a biologically-principled fashion , are better reproduced than others . In other words , are there subsets of individuals that are particularly triadic , and other subsets that either care less about triadic relations or make poorer discriminations ? We illustrate here how our methods allow one to investigate this question . Individual properties ( e . g . sex , age , power scores , etc . ) can be used to group individuals into categories . We can then ask how well individuals in a particular category are fit by the Base model . This can be done by considering for all individuals in the category of interest the two and measurements , the 94 and measurements , and the 95 measurements , and estimating the goodness of fit by computing the associated . By sorting the individuals into groups based on various extrinsic characteristics , we can determine whether there is evidence for the employment of strategies other than the triadic model of + AND . Here , as an illustration of the method , we sort individuals by power score . The power score , discussed in detail in Ref . [4] , is an estimate of how much ‘consensus’ there is among individuals in the group about whether the receiver is capable of using force successfully during fights . Power structure changes the cost of social interactions , facilitating the evolution of intrinsically costly interactions , like policing [9] , by supporting a proto-division of labor in which powerful individuals police and low-power individuals do not . Power structure can thus change the strategies individuals play . We expect this variation to influence the extent to which individuals play . We find that the highest power individuals , and the lowest power individuals , are the least-well fit by the data , suggesting that they are using different strategies from those in + AND that reproduce much of the behavior of the intermediate-power individuals . This is shown graphically in Fig . 9 , where the individuals are sorted into groups of eight in order of decreasing power score .
Macaque societies are characterized by social learning at the individual level , social structures that arise from nonlinear processes and feedback to influence individual behavior , frequent non-kin interactions and multiplayer conflict interactions , the cost and benefits of which can be quantified at the individual and social network levels [4] , [5] , [9] , [28] , [29] , [44] , [49] , [55] . These properties coupled to highly resolved data make this system an excellent one for drawing inferences about critical processes in social evolution as well as for developing new modeling approaches that are intended to apply more broadly . In this study we focus on one species in the genus , the pigtailed macaque ( Macaca nemestrina ) . The data set , collected by J . C . Flack , is from a large , captive , breeding group of pigtailed macaques that was housed at the Yerkes National Primate Research Center in Lawrenceville , Georgia . Pigtailed macaques have frequent conflict and employ targeted intervention and repair strategies for managing conflict [9] . The study group had a demographic structure approximating wild populations . Subadult males were regularly removed to mimic emigration occurring in wild populations . The group contained 84 individuals , including 4 adult males , 25 adult females , and 19 subadults ( totaling 48 socially-mature individuals used in the analyses ) . All individuals , except 8 ( 4 males , 4 females ) , were either natal to the group or had been in the group since formation . The group was housed in an indoor-outdoor facility , the outdoor compound of which was 125×65 ft . Pigtailed macaques are indigenous to south East Asia and live in multi-male , multi-female societies characterized by female matrilines and male group transfer upon onset of puberty [56] . Pigtailed macaques breed all year . Females develop swellings when in Œ strus . During observations all individuals were confined to the outdoor portion of the compound and were visible to the observer . The hours of observations occurred for up to eight hours daily between 1 , 100 and 2 , 000 hours over a twenty-week period from June until October 1998 and were evenly distributed over the day . Provisioning occurred before observations , and once during observations . The data were collected over a four-month period during which the group was stable ( defined as no reversals in status signaling interactions resulting in a change to an individual's power score , see [49] ) . Conflict and power ( subordination signal ) data were collected using an all-occurrence sampling procedure [57] in which the compound was repeatedly scanned from left to right for onset of conflict or the occurrence of silent-bared teeth displays ( used to measure power , see below ) . The entire conflict event was then followed , including start time , end time , and the identity of individuals involved as aggressors , recipients , or interveners ( see below for operational definitions ) . Although conflicts in this study group can involve many individuals , participation is typically serial , making it possible to follow the sequence of interactions . A nearly complete time-series of conflict events is available for each observation period . Breaks in data collection during the day occurred sufficiently rarely ( seldom more than once a day ) , and were sufficiently short ( seldom more than fifteen minutes ) , that results changed little from when correlations were computed assuming no activity during breaks , to not including any fight pairs separated by a break in correlation estimators . We avoided altogether using fight pairs with fights on different days . Instantaneous scan sampling [57] occurred every 15 min for state behaviours ( here , grooming ) . Grooming: passing hands or teeth through hair of another individual or plucking the hair with hands or teeth for a minimum of five seconds . Conflict: includes any interaction in which one individual threatens or aggresses a second individual . A conflict was considered terminated if no aggression or withdrawal responses ( fleeing , crouching , screaming , running away , submission signals ) occurred for two minutes from the last such event . A conflict can involve multiple pairs if pair-wise conflicts result in aggressive interventions by third parties or redirections by at least one conflict participant . In addition to aggressors , a conflict can include individuals who show no aggression ( e . g . recipients or third-parties who either only approach the conflict or show affiliative/submissive behavior upon approaching , see [58] . ) Because conflicts involve multiple players two or more individuals can participate in the same conflict but not interact directly . Contact aggression: aggression received by one group member from another that involves grappling , tumbling , hitting , slapping , or biting . Power-disparity: difference between two individuals in their power scores . Power scores for each individual in this study were calculated using a procedure described in [49] . In brief , the total frequency of peacefully-emitted subordination signals received by an individual over a given duration ( in this case , the study duration , which was approximately four months ) is corrected for the uniformity ( measured using Shannon entropy ) of its distribution of signals received from its population of potential senders ( all socially-mature individuals ) . This equation quantifies how much consensus there is among individuals in the group about whether the receiver is capable of using force successfully during fights . Redirected aggression: aggression or threat directed from a conflict participant towards a third-party during or within 5 seconds of the conflict . Subordination signal: the subordination signal in the pigtailed macaque communication repertoire is the silent bared-teeth display [58] . Bared-teeth ( BT ) displays are marked by a retraction of the lips and mouth corners such that the teeth are partially bared . In pigtailed macaques , the SBT occurs in two contexts: peaceful and agonistic SBT see [58] ) Signals in both contexts are highly unidirectional . The agonistic SBT encodes submission . The peaceful variant signals agreement to primitive social contract in which the signaler has the subordinate role [58] . The network of SBT interactions encodes information about power structure [49] . In the results of the main paper , we presented results obtained using Wilcoxon Signed Ranks Tests on two measures of cost , contact aggression received and redirected aggression received . We preformed multiple ( three for contact aggression received and two for redirected aggression ) independent Wilcoxon tests per cost measure instead of one overall Friedman test ( nonparametric version of repeated measures ) per measure because the post hoc planned comparison tests associated with the Friedman test typically do not have enough power to detect differences across treatments . We performed nonparametric tests rather than parametric tests because our data violated the homogeneity of variance assumption . The data collection protocol was approved by the Emory University Institutional Animal Care and Use Committee and all data were collected in accordance with its guidelines for the ethical treatment of nonhuman study subjects . | Persistent conflict is one of the most important contemporary challenges to the integrity of society and to individual quality of life . Yet surprisingly little is understood about conflict . Is resource scarcity and competition the major cause of conflict , or are other factors , such as memory for past conflicts , the drivers of turbulent periods ? How do individual behaviors and decision-making rules promote conflict ? To date , most studies of conflict use simple , elegant models based on game theory to investigate when it pays to fight . Although these models are powerful , they have limitations: they require that both the strategies used by individuals and the costs and benefits , or payoffs , of these strategies are known , and they are tied only weakly to real-world data . Here we develop a new method , Inductive Game Theory , and apply it to a time series gathered from detailed observation of a primate society . We are able to determine which types of behavior are most likely to generate periods of intense conflict , and we find that fights are not explained by single , aggressive individuals , but by complex interactions among groups of three or higher . Understanding how memory and strategy affect conflict dynamics is a crucial step towards designing better methods for prediction , management and control . | [
"Abstract",
"Introduction",
"Results",
"Methods"
] | [
"evolutionary",
"biology",
"evolutionary",
"biology/animal",
"behavior",
"computational",
"biology"
] | 2010 | Inductive Game Theory and the Dynamics of Animal Conflict |
It has long been recognized that oncogenic viruses often integrate close to common fragile sites . The papillomavirus E2 protein , in complex with BRD4 , tethers the viral genome to host chromatin to ensure persistent replication . Here , we map these targets to a number of large regions of the human genome and name them Persistent E2 and BRD4-Broad Localized Enrichments of Chromatin or PEB-BLOCs . PEB-BLOCs frequently contain deletions , have increased rates of asynchronous DNA replication , and are associated with many known common fragile sites . Cell specific fragile sites were mapped in human C-33 cervical cells by FANCD2 ChIP-chip , confirming the association with PEB-BLOCs . HPV-infected cells amplify viral DNA in nuclear replication foci and we show that these form adjacent to PEB-BLOCs . We propose that HPV replication , which hijacks host DNA damage responses , occurs adjacent to highly susceptible fragile sites , greatly increasing the chances of integration here , as is found in HPV-associated cancers .
Papillomaviruses are an ancient group of viruses that establish a persistent infection in the host epithelium . To maintain such a long-term infection , the E2 protein from a subset of papillomaviruses binds to the viral genome and tethers it to the host chromosomes [1]–[3] . The bromodomain protein , BRD4 , binds to mitotic chromosomes with E2 [4] , [5] , is essential for regulation of viral transcription [6]–[9] and is recruited to early viral replication foci [10] , [11] . BRD4 is a mitotic chromosome-associated protein [12] that interacts with acetylated histone tails [13] and is a key regulator of the pTEF-b elongation factor [14] . There has been a recent explosion of data as BRD4 has been implicated in regulation of cell cycle , mitotic memory , transcription of MYC and regulation of viral gene expression [15]–[19] . BRD4 is highly enriched at super-enhancers that maintain expression of oncogenes in tumors [20] and is a promising therapeutic target for a number of cancers [21] . Most HPV infections result in benign lesions , but several are oncogenic and the causative agents of human cancer [22] . Almost all cervical cancer is associated with HPV infection , and oncogenic HPVs are responsible for many anal , penile , vaginal and oropharyngeal cancers [23] . The HPV genome is found integrated into the host genome in over 80% cancers and this promotes malignant progression . The integration event is accidental , but the resulting deregulation of expression of the E6 and E7 oncogenes gives cells a selective growth advantage [24] . There is a predilection for integration within the vicinity of fragile sites [25] , [26] . Papillomaviruses are adept at hijacking host functions and induce a host DNA damage response ( DDR ) in nuclear foci , resulting in an influx of repair factors that the virus exploits to amplify its own DNA [11] , [27]–[31] . We show that the HPV E2 protein binds with BRD4 to regions that are highly susceptible to replication stress and overlap many common fragile sites . Common fragile sites are hypersensitive to DNA damage and their replication is often incomplete in the G2 phase of the cell cycle [32] . Thus , they represent a vulnerable and very clever target for papillomavirus replication . Furthermore , replication adjacent to fragile sites may explain the high incidence of integration of oncogenic HPV genomes at these loci .
Many papillomavirus E2 proteins bind readily to host mitotic chromosomes with the BRD4 protein [9] . To identify the targets of these E2 proteins we analyzed chromatin binding sites of HPV1 E2 , a protein that binds BRD4 and host chromosomes with high affinity . In a natural infection E2 levels range from almost undetectable in basal cells to fairly high levels in differentiated cells [33]; thus we were careful to titrate E2 to low , but detectable , levels for the experiments presented ( Figure S1A and S1B ) . Chromatin was prepared from mitotic C-33 cells expressing HPV1 E2 ( C-33-1E2 ) , and analyzed by ChIP-chip analysis for binding to a portion of the human genome ( chromosomes 3 , 4 , 5 , 18 , 19 , 20 , 21 , 22 and X ) . We have previously shown by ChIP-chip analysis of 5 kb promoter regions that E2 and BRD4 bind to active promoters in interphase C-33 cells [34] . In the present study we used whole genome tiling arrays to study E2 and BRD4 binding . As shown in Figure 1A , in mitosis E2 was observed to bind to a few extremely broad peaks on several chromosomes . These peaks ranged in size from several hundred Kb to >1 Mb and , for the most part , overlapped coding regions . Two detailed examples of the genomic regions covered by the peaks are shown in Figure S1C . The mitotic E2 binding peaks were further validated by conventional ChIP assays ( Figure 1B ) with primers selected from eight of the peaks indicated in Figure 1A . E2 binding to these regions was strong in both asynchronous and mitotic cells , showing that it persisted throughout the cell cycle , consistent with the concept that E2 partitions the viral genome by linking it to mitotic chromosomes [1] . The levels of E2 bound to the broad mitotic regions were several-fold higher than those bound to active promoter regions . Furthermore , the levels of E2 bound to promoters dropped to almost background levels in mitotic cells ( Figure 1B ) , consistent with cessation of transcription and displacement of most transcription factors from promoters in mitosis [35] . Since E2 binds to mitotic chromosomes in complex with BRD4 [4] , [5] , [7] we carried out ChIP assays to determine whether BRD4 bound the same regions of mitotic chromatin . As shown in Figure S1D , S1E , and S1F ( and summarized in Figure 1C ) BRD4 bound to five sites selected from an E2 positive region from chromosome 5 , even in the absence of E2 . However , expression of E2 increased BRD4 binding at least two fold , consistent with the stabilization of BRD4 binding by E2 [7] . In contrast HPV31 E2 , which does not stabilize binding of BRD4 to chromatin [9] , had little effect on the binding of BRD4 to mitotic chromatin ( data not shown ) . Figure S1G shows a comparison of the size of these broad regions compared to promoter binding of BRD4 and E2 that we had detected previously using promoter microarrays . As we show in more detail below , E2 and BRD4 bind together to these exceptionally large regions of mitotic chromatin that likely correspond to the mitotic chromatin tethering target used by papillomaviruses for genome partitioning . Thus , we have named these regions Persistent E2 and BRD4-Broad Local Enrichments of Chromatin , or PEB-BLOCs . In C-33-1E2 cells , E2 colocalizes with BRD4 in approximately 50 punctate speckles on mitotic chromosomes ( data not shown ) and so we extended the pilot experiment described above to analyze BRD4 binding in the entire human genome . BRD4 binding was analyzed by ChIP-chip using whole genome arrays and the BRD4 binding profile is shown in Figure 2A ( for chromosome 4 ) and S2 ( for the entire genome ) . Almost all chromosomes showed large peaks similar in size to , and overlapping with , the E2 peaks identified in the subset of chromosomes shown in Figure 1A . A visual inspection showed that approximately 50 broad BRD4 binding regions were detectable on C-33 mitotic chromatin in the entire genome and about 100 regions were detected in the presence of E2 ( Figure S2 ) . Therefore , BRD4 binds to some PEB-BLOCs in the absence of E2 , but E2 enhances the BRD4 binding signal . In contrast , BRD4 is only detected on mitotic chromosomes by immunofluorescence in the presence of E2 [7] . This likely reflects differences in sensitivity between the techniques . We have shown previously that the dimerization property of E2 increases the ability of E2-BRD4 complexes to bind mitotic chromosomes , most likely by promoting the formation of higher order complexes [36] . The genomic localization and characteristics of 53 of the strongest PEB-BLOCs identified by visual inspection are listed in Table S1 . We computationally defined and identified the enriched binding regions for E2 and BRD4 ( shown in red in Figure 2A and S2 ) . The best algorithm was able to identify all of the visually identified binding peaks , with the exception of one on chromosome 20 ( Chr20-P3 in Table S1 ) . Using this algorithm , the overlap between E2 and BRD4 binding regions was calculated , as defined in Methods . Figure 2B shows the overlap among the three binding profiles for chromosomes 3 , 4 , 5 , 20 , 21 , 22 and X ( only a subset of chromosomes was analyzed for binding in these experiments ) . There was a complete overlap between the BRD4 binding regions in control and E2 expressing cells and >50% overlap with the E2 binding enriched regions and BRD4 binding regions . The overlap with the E2 binding enriched regions is underestimated because of the different resolution of the microarray chips used for the E2 and BRD4 binding studies . However , as can be seen visually in Figure 2A , there is a substantial overlap in the major binding peaks . Figure 2C shows the overlap of computationally defined BRD4 enriched regions , in the presence and absence of E2 expression , for all human chromosomes . Therefore , many PEB-BLOCs exist even without E2 expression and E2 stabilizes and increases Brd4 binding to a subset of PEB-BLOCs . Presumably , different stages of the viral life cycle the levels of E2 would determine which PEB-BLOCs were highly occupied by E2 and BRD4 . Two residues in the transactivation domain of E2 ( R37 and I73 ) are essential for interaction with BRD4 [5] , [37] . Therefore , we analyzed binding of an E2 R37A/I73A mutated protein to PEB-BLOCs by ChIP . Wild-type E2 and R37A/I73A E2 were expressed at equivalent levels and had no effect on the levels of BRD4 ( data not shown ) . Both wild-type E2 and BRD4 bound strongly to PEB-BLOC regions in asynchronous cells ( Figure 3A ) and while BRD4 bound to most PEB-BLOCs in the absence of E2 , binding was about two fold higher in the presence of E2 . However , E2 R37A/I73A was minimally recruited onto and did not augment BRD4 binding to PEB-BLOCs . While BRD4 and E2 colocalize as distinct speckles on mitotic chromosomes , in cells expressing the R37A/I73A protein , neither E2 nor BRD4 was detected on chromosomes ( Figure 3B ) . Therefore the interaction with BRD4 is essential for E2 binding to PEB-BLOCs , but in turn E2 stabilizes the binding of BRD4 to these regions . To confirm the requirement for BRD4 in E2 binding to mitotic PEB-BLOCs , BRD4 gene expression was downregulated with siRNA . In the absence of BRD4 , E2 no longer bound to mitotic chromosomes ( Figure 3C ) or colocalized in speckles with BRD4 in the nucleus of interphase cells ( data not shown ) . Small molecule inhibitors such as GSK525762A+ interfere with binding of the specific bromodomains of the family of BET proteins ( bromodomain plus extraterminal domain ) to their acetylated target [38] . In cells treated with GSK525762A+ , neither E2 , nor BRD4 , could be detected bound to PEB-BLOC regions by ChIP ( Figure 3D ) . Likewise , E2 and BRD4 speckles were no longer observed in the nuclei of interphase cells ( Figure 3E ) or on mitotic chromosomes ( data not shown ) after GSK525762A+ treatment . Therefore , E2 binding to PEB-BLOC regions is dependent on BRD4 and its interaction with acetylated histones . To further investigate the nature of PEB-BLOCs , histone modifications were analyzed by ChIP ( Figures 4A and S3 ) using the primers listed in Table S9 . PEB-BLOCs were highly acetylated at positions K9 , K14 , K18 , K23 , K27 , K56 , K9/14 , and K9/18 in histone H3 and K5 , K8 , K12 , and K5/8/12/16 in histone H4 . E2-BRD4 bound promoter regions showed higher acetylation levels than E2-negative regions , but the acetylation status of PEB-BLOCs was consistently several-fold higher than in active promoter regions , which are already acetylation-rich . Therefore , PEB-BLOCs are highly acetylated at many positions , consistent with the ability of BET inhibitors to abolish E2 and BRD4 binding . Histone methylation , especially of H3K4 , is also associated with active chromatin [39] . PEB-BLOCs have consistently high H3K4me1 and H3K4me2 , but low H3K4me3 . Conversely , promoter regions had high H3K4me2 and H3K4me3 , but low H3K4me1 ( Figure 4A ) . To validate these findings , we performed ChIP-chip analysis for binding of E2 , BRD4 , H4K8ac , and H3K4me1 on a subset of the genome ( chromosome 4 and part of chromosome 3 ) . Each PEB-BLOC overlapped with prominent peaks of H4K8ac and H3K4me1 modification ( Figure 4B and 4C ) . Therefore , PEB-BLOCs contain highly acetylated histones and high levels of H3K4me1 , a pattern similar to that described for enhancers [39] . Notably , as shown in Figure 4C , between 65% and 71% of H4K8ac and BRD4 broad enriched regions overlapped and the E2 bound regions were contained completely within this overlap . All E2 bound peaks also completely overlapped with enriched regions of H3K4me1 . To confirm these findings , mitotic and interphase C-33-1E2 cells were analyzed for global histone modification patterns by immunofluorescence ( Figure S4 and data not shown ) . E2-BRD4 speckles colocalized with acH4K8 and acH3K56 on mitotic chromosomes , and were also highly enriched in H3K4me1 and H3K4me2 , but not H3K4me3 . The E2-BRD4 speckles observed in interphase nuclei also showed an enrichment of acH4K8 , acH3K56 , H3K4me1 and H3K4me2 . To ascertain the histone acetyl transferase ( HAT ) responsible for acetylation of PEB-BLOCs , we downregulated expression of EP300 , CREBBP and KAT5 by siRNA treatment . In control cells , BRD4 speckles colocalized with H4K8ac , CREBBP and EP300 . However , siRNA downregulation of CREBBP or EP300 resulted in a great reduction in the appearance of BRD4 speckles as well as the focal regions of histone acetylation in the nucleus ( Figure 4D ) . In contrast , KAT5 only partially colocalized with BRD4 speckles and downregulation of KAT5 had no effect on the acetylation or localization of BRD4 to PEB-BLOCs . Therefore , CREBBP and EP300 are both recruited to PEB-BLOCs where they acetylate histones , thus providing binding sites for E2 and BRD4 . Notably , E2 proteins interact with CREBBP/EP300 [40] and this could enhance the formation and development of PEB-BLOCs in a natural infection . However , in C-33 cells these regions are already genetically unstable and highly acetylated , and acetylation is not obviously increased by E2 . Regions of chromatin that are methylated on H3K4 show highly dynamic acetylation mediated by CREBBP/EP300 , while H3K4 methylation remains more stable [41] . This is consistent with the histone modifications of PEB-BLOCs and the requirement for CREBBP/EP300 . To verify that BRD4 nuclear speckles correspond to the regions identified by ChIP-chip , we performed combined IF-FISH with a BRD4 antibody and FISH probes for PEB-BLOCs . In many cases , the BRD4 speckles colocalized with only one of the two PEB-BLOC FISH signals ( Figure S5 ) . BRD4 speckles were often observed as doublets on one chromosome , which in mitotic cells colocalized with a similar doublet of FISH signal ( Figure 5A and 5B ) . In contrast , the second PEB-BLOC allele was detected as a condensed FISH signal that didn't colocalize with BRD4 , indicating that BRD4 binds to PEB-BLOCs on one allele on mitotic chromosome . Analysis of the PEB-BLOC FISH signals in interphase cells revealed that the two alleles often replicated at different times . When this occurred , the early replicating allele was observed as a doublet FISH signal , while the late replicating allele was a single FISH signal . When these exist in the same nuclei due to asynchronous replication they are termed SD ( singlet-doublet ) FISH signals ( Figure 5C ) . We calculated the rate of asynchronous DNA replication for loci corresponding to PEB-BLOCs and non-PEB-BLOCs by counting the number of SD FISH signals in individual nuclei ( Figure 5C ) . Non-PEB-BLOC regions , displayed an SD pattern in ∼12% S-phase cells , as previously reported [42] . In contrast , the SD pattern was present in ∼30% PEB-BLOCS . There was no difference in the percentage of SD signals in control C-33 or C-33-1E2 cells , showing that this is an inherent property of the cells and not due to E2 expression . Notably , asynchronous replication is also a property of common fragile sites [43] . PEB-BLOCs span large chromosomal regions , which mostly contain annotated genes ( Table S1 ) . To determine whether these genes were transcriptionally active , RNA was prepared from control C-33 and C-33-1E2 cells and analyzed by microarray gene expression analysis ( data not shown ) . This showed that most genes located in the PEB-BLOCs were transcribed at low to moderate levels . To determine whether there was additional transcription ( perhaps non-mRNA ) from apparently non-coding regions , we conducted RNA seq analysis ( available at GEO: GSE52367 ) . This confirmed that most PEB-BLOC genes were transcriptionally active , and also identified ten long ( >0 . 2 Mb ) , previously un-annotated , genes in these regions . These novel genes are shown , along with RNA seq signals for 53 of the strongest PEB-BLOCs in Table S1 . About 35 cellular genes were differentially regulated by E2 expression in both microarray and RNA seq analysis . However , these genes were not associated with PEB-BLOCs and have no obvious connection to E2 function . There has been reported to be a strong correlation between transcription of very long genes and the expression of fragile sites resulting from a conflict in transcriptional and replication machineries [32] . To date , there are 56 annotated human genes that are >1 Mb and another 219 that are between 0 . 5–1 Mb long . In the 53 strong PEB-BLOC loci listed in Table S1 , there are ten known genes >1 Mb and 19 genes >500 kb . Therefore , there is a vast enrichment of long genes in the PEB-BLOC regions . This calculation does not include the transcriptionally active long segments in PEB-BLOCs that contain unknown genes . Figure 5D shows the size range of genes that overlap PEB-BLOCs . Many of the properties described above for PEB-BLOCs are also attributes of common fragile sites . These sites are genetically unstable ( reviewed in [44] ) and are common sites of viral genome integration [25] . Like PEB-BLOCs , common fragile sites often replicate asynchronously , have monoallelic expression and contain large genes [32] . Fragility can arise because of a conflict between transcription and replication of very long genes as a paucity of replication initiation sites can result in failure to complete replication before mitosis [45] . We compared the location of PEB-BLOCs with mapped common fragile sites in the human genome ( retrieved from HUGO , www . genenames . org ) and found that a subset of visually identified strong PEB-BLOCs ( 22 out of 53 ) contain 25 known fragile sites in the same chromosomal band ( Table S1 ) . However , most common fragile sites have been mapped cytogenetically and span large portions of the human genome , making it difficult to statistically correlate with the enriched binding regions . Furthermore , common fragile sites are cell type specific [46] and the majority have been mapped in lymphocytes . To further examine the association of PEB-BLOCs with common fragile sites we mapped aphidicolin-inducible fragile sites in C-33 cells . C-33 cells were treated with aphidicolin to cause mild replication stress and the resulting fragile sites were identified using a novel ChIP-chip method with an antibody to FANCD2 , which is involved in replisome surveillance and binds fragile sites [47]–[49] . Approximately 100 strong FANCD2 binding regions were visually identified and are shown aligned with the BRD4 binding profile in Figure S7 . Large , enriched FANCD2 binding regions were further defined by computational analysis and are shown as red blocks under the signal map ( Figure S7 ) . Figure 6A shows the alignment of PEB-BLOCs , FANCD2 binding sites and known common fragile sites for chromosome 4 and detailed alignments can be found for all strong PEB-BLOCs in Table S1 . It is clear that many PEB-BLOCs and FANCD2 binding peaks overlap precisely , others are slightly offset , and some prominent peaks do not overlap . As shown in Figure 6B , a significant subset of FANCD2 enriched binding regions ( ∼30% ) and PEB-BLOCs ( ∼36% ) overlap ( P<0 . 002 ) . Therefore , there is a strong association between PEB-BLOCs and sites of genomic instability . An absolute distance analysis showed that ∼27% PEB-BLOCs and ∼30% FANCD2 enriched binding sites are within 2 Mb of a known common fragile site ( Figure S8 ) . The association between PEB-BLOCs , FANCD2 binding sites and common fragile sites was further examined in three subsets of fragile sites that are most closely related to our biological system . As shown in Table 1 ( with details in Table S6 ) these consisted of: aphidicolin induced fragile sites recently mapped in epithelial cells [50]; fragile sites that have been cloned and therefore are of much higher resolution [51]; and fragile sites that have been mapped in cervical cancer cells [25] . This showed that there was a significant association between these fragile sites and the FANCD2 regions and a near-significant association between these fragile sites and the enriched PEB-BLOC regions . We noted evidence of deletion in several PEB-BLOCs as there was an abrupt loss of BRD4 signal in certain regions of the BRD4 ChIP-chip binding profiles . We found eight loci showing obvious loss of ChIP signals in PEB-BLOCs and/or FANCD2 binding regions ( Figures 6C and S6 ) . Five of these regions are located in the same chromosome bands as known fragile sites , four are in PEB-BLOCs and the others are in non-PEB-BLOC FANCD2 binding regions . To verify these deletions , we performed FISH using two adjacent FISH probes . One probe ( 245M5 ) was targeted to the putatively deleted region and the other ( 451M10 ) was derived from an adjacent , undeleted BRD4 binding region of the PEB-BLOC . As predicted , the 245M5 probe gave rise to only one FISH signal per cell due to the deletion of this locus on one chromosome ( Figure 6C ) . In contrast , the 451M10 probe showed two clear FISH signals , demonstrating that both chromosomal loci were intact . Because there was an abrupt and complete loss of BRD4 signal in these deleted regions ( despite the intact locus on the other chromosome ) we can conclude that only the BRD4 bound allele sustained the deletion . Thus , PEB-BLOCs sustain frequent deletions . This finding is supported by our previous observation that BRD4 is often bound to only one allele of PEB-BLOCs ( Figure 5A and 5B ) . Analysis of the RNAseq signal in these regions confirms that there are no detectable transcripts from the missing exons , reinforcing the hypothesis that the deletion is present in the transcribed allele ( Figure 6D ) . Four of the eight regions shown in Figure S6 also show transcription spanning a deleted allele , supporting this conclusion . Therefore , PEB-BLOCs frequently contain deletions in the transcriptionally active allele . The experiments described above used the E2 protein from HPV1 , a virus that causes benign papillomas . The HPV1 E2 protein binds BRD4 with high affinity , but E2 proteins from the Alpha genus have a relatively low affinity for BRD4 and host mitotic chromosomes [9] . Nevertheless , when expressed together with the E1 replication protein both alpha-PV E1 and E2 proteins colocalize in nuclear foci that recruit markers of a DNA damage response ( DDR ) and recruit BRD4 [11] , [29] . Because of the links among E2 , BRD4 , DDR , replication stress and fragile sites , we questioned whether these nuclear viral replication foci formed at PEB-BLOCs/fragile sites . HPV16 E1 and E2 proteins were transiently expressed in C-33 cells and chromatin was extracted for ChIP-chip analysis . Regions of E1–E2 binding were isolated with an antibody directed against an epitope tag on E1 . The resulting E1 binding profile was very similar to that of BRD4 ( in the presence of HPV1 E2 ) and thus to PEB-BLOCS ( Figure 7A , 7B and S7 ) . Computation of the E1 enriched regions showed a significant overlap ( p<0 . 002 ) among the PEB-BLOCs , HPV16 E1 ( in the presence of HPV16 E2 ) and FANCD2 ( aphidicolin treated cells ) ( Figure 7C and Table S5 ) . Therefore , PEB-BLOCs are also targets for alpha-HPV E1/E2 protein complexes and therefore there is a strong link among PEB-BLOCs , fragile sites and viral DNA replication proteins . Highly notable is the fact that HPV genomes are very often integrated close to fragile sites in HPV-associated cancers [25] . It has been noted for many years that HPVs ( and other oncogenic viruses ) are often found integrated close to common fragile sites [25] , [26] , [52] . However , most of the HPV integration sites have been mapped at low resolution , similar to the cytogenetically mapped common fragile sites . To allow for a more detailed analysis , we collated the precise HPV integration sites from several studies [53]–[55] as well as those listed in the DrVIS database [56] ( Table S7 ) . The overlap between these sites and the BRD4 and FANCD2 enriched regions is highly significant , as shown in Figure 7D and Table 2 . The human genome contains a number of hotspots for HPV integration . For example , chromosomes regions 8q24 . 21 ( the MYC locus ) and 13q22 . 1 contain many HPV integration sites [57]; notably these two regions overlap PEB-BLOCs . A recent high resolution study of HPV integration sites in cervical and head and neck cancers demonstrated focal genomic instability; cellular DNA flanking the viral integration site contained amplifications , rearrangements and translocations and concatameric viral DNA was often interspersed with host sequences [58] . Thus , genomic instability continues after the initial integration event . Since HPV16 E1 and E2 replication proteins associate with PEB-BLOCs , these are likely sites of viral replication . To verify this we studied the association of HPV genomes with PEB-BLOCs: HPV1 , HPV16 and HPV18 genomes were transfected into C-33 cells and the association of viral DNA with specific regions of host chromatin was analyzed by FISH . Transfected viral DNA often gave rise to a single nuclear signal that was closely associated with different PEB-BLOC regions more frequently than control regions ( Figure S9 ) . To further explore this association , we isolated C-33 cells containing replicating HPV16 genomes and analyzed the association between the resulting replication foci , PEB-BLOCs and control regions ( Figure 8A ) . Five of the six PEB-BLOCs tested associated with HPV16 replication centers in ∼9% cells while control regions were associated with replication foci in ∼2% cells ( Figure 8B ) . Of importance , the PEB-BLOC allele associated with the replication factory in Figure 8A appears to be late replicating ( two , still tightly linked , FISH signals ) compared to the “double-dot” pattern observed in the other allele . However , it was difficult to quantitate this observation as the PEB-BLOC signal adjacent to the replication factory was often disrupted and sometimes dispersed throughout the viral DNA . This made it difficult to determine whether it was a singlet or doublet . When one considers that we are only measuring the interaction of replication factories with one PEB-BLOC at a time ( and only a few PEB-BLOCs are likely to be associated with replication foci in any single cell ) , the observed association is noteworthy . Also of note , some PEB-BLOCs are only associated with HPV replication foci in certain cell lines; for example , Chr3-P4 does not show increased association in C-33 cells , but does in 9E cells ( Figure 8 ) . It is possible that the virus replication foci form only at the PEB-BLOC regions with highest affinity for E2 and BRD4 . We carried out a similar analysis in CIN-612 9E cells , which contain large numbers of HPV31 genomes . Large and small viral replication foci can be generated in these cells by differentiation with calcium [27] . Four out of six PEB-BLOCs tested were closely associated with HPV31 replication foci in ∼12% cells , compared to a ∼4% association with control regions ( Figure 8C and 8D ) . In a parallel study , we find that these large foci are frequently ringed with small BRD4 foci [11] that presumably represent additional PEB-BLOCs . In conclusion , replicating HPV genomes are commonly associated with PEB-BLOCs . Figure 8E shows an example of a small replication focus in CIN-612 9E cells stained by immunofluorescence for BRD4 , and by FISH for HPV31 DNA and a single PEB-BLOC . As shown , it appears that the replication foci “grow” from the PEB-BLOC foci and BRD4 is localized at the interface between viral and host DNA .
We show that HPV1 E2 , and the HPV16 E1/E2 protein complex , bind with BRD4 to common fragile sites in the human genome . Like other persistent viruses that form long-term associations with their host , HPVs are masters at hijacking cellular processes . The E2 proteins interact with BRD4 to regulate viral transcription , and associate with host chromatin to partition the viral genome in dividing cells . We demonstrate that this association is not random , and that the virus has taken advantage of very susceptible regions of the host genome that are prone to replication stress . Both oncogenic and non-oncogenic papillomaviruses induce a DDR and both probably replicate adjacent to these susceptible regions . Most likely , both oncogenic and non-oncogenic HPV types have the propensity to become integrated into unstable regions of the genome on rare occasions . However , only oncogenic HPVs could give the cells a selective growth advantage , in turn further increasing genetic instability , and eventually leading to carcinogenic progression . The precise role of the BRD4 protein in the HPV lifecycle remains somewhat elusive [19] . BRD4 binds to all PV E2 proteins and regulates viral transcription in an E2-dependent manner . The E2 proteins of viruses such as BPV1 ( and HPV1 ) bind BRD4 with high affinity and link the viral genome to mitotic chromosomes in complex with BRD4 , most likely to mediate genome partitioning [4] , [59] . However , alpha-PVs ( such as HPV16 and HPV31 studied here ) bind to BRD4 and chromatin with lower affinity and the role of the E2-BRD4 interaction in replication is enigmatic [10] , [11] . In fact , HPV31 genomes encoding an E2 protein that is unable to bind BRD4 in vitro , can replicate persistently , and induce late viral functions , in keratinocytes [60] , [61] . One explanation for these findings is that the interaction of E2 and BRD4 is important to establish an efficient infection when limiting amounts of genome are delivered to the nucleus by a viral particle rather than by DNA transfection . Also , the nucleation of viral replication factories at regions of the nucleus highly susceptible to replication stress could be important , but not absolutely required , for efficient viral replication in a natural infection . While the HPV replication proteins are sufficient to induce a DDR , the viral oncogenes can contribute . E7 induces a DDR by associating with ATM [27] and induces oncogenic replication stress by pushing cells into continual , unscheduled division [62] . This could increase replication stress at fragile sites and potentiate the association with the E2-BRD4-genome complex . Our experiments were carried out in C-33 cancer derived cells and we believe that PEB-BLOCs are very prominent in these cells because they are already very genetically unstable . Thus , under these circumstances E2 and BRD4 bind with high affinity to pre-existing fragile sites without the need for other viral factors to promote replication stress . Normal cells do not show much FANCD2 binding and fragile sites must be induced by replication stress such as that induced by low levels of aphidicolin . In a natural HPV infection , this replication stress could be induced by the viral E7 protein [62] . Common fragile sites are often caused by a paucity of replication origins and/or collisions of transcription and replication machinery in very long genes [32] , [63] . Often , fragile sites remain incompletely replicated as cells progress into mitosis . Papillomaviruses amplify their DNA in differentiated cells that are in G2 [64] , [65]; hijacking the DDR at this time allows the virus to replicate outside S-phase and without competition from host DNA synthesis . By associating with fragile sites that undergo replication stress at this stage , the virus has to do little but be “in the right place , at the right time” , simply amplifying the DDR response to generate a replication factory . Notably , almost 25 years ago when the correlation between viral integration and fragile sites was first recognized Popescu and DiPaolo predicted that “It is conceivable that because of their replication pattern at a certain point in the cell cycle fragile sites may be the only replicating regions available for the integration of viral DNA” [26] . The role of BRD4 in binding to fragile sites has not been completely defined . Previously , chromatin in fragile sites was reported to be hypoacetylated [66] , however , we find that the PEB-BLOC regions are highly acetylated and have an “enhancer-like” chromatin signature . It has recently been shown that BRD4 is enriched at super-enhancers that regulate key cell identity genes and tumor drivers [20] , [67] . However , despite a common chromatin signature ( high H3K4me1 and H3K27ac ) , PEB-BLOCs are much larger in size than super-enhancers and we do not detect a significant overlap in these elements . Since fragile sites are approaching mitosis with unreplicated regions of DNA , there needs to be a mechanism to keep the chromatin open and accessible to finish replication or repair and to resist the chromosome condensation required for mitosis . BRD4 might maintain an accessible chromatin environment conducive to the processes of DNA damage sensing and repair . Notably , while BRD4 can preserve chromatin acetylation , decompact chromatin and modulate higher-order chromatin structure [68] , a short isoform of BRD4 actually limits the DDR by compacting chromatin to insulate it from ATM signaling [69] . The image shown in Figure 8E is very compatible with the idea that BRD4 is protecting host chromatin from a full-blown viral-mediated DDR . If BRD4 assists in the repair of fragile sites in genetically unstable cells , inactivation of this function could result in a rapid accumulation of catastrophic DNA damage . Normal , genetically stable cells would not depend on this function , and this could help explain the sensitivity of cancer cells to BET inhibitors . In conclusion , show that the viral E2 and cellular BRD4 proteins associate with fragile regions of the human genome and nucleate replication foci at these sites . This is a resourceful strategy for a virus that uses the host DNA damage response to amplify viral DNA . However , the consequence could be increased accidental integration of viral DNA , which in the case of oncogenic viruses can promote carcinogenesis .
The pMEP4 expression vectors for FLAG-tagged E2 have previously been described [70] . E2 proteins containing alanine substitutions in residues R37 and I73 were described previously [9] . Standard mutagenesis procedures were used to substitute HPV1 E2 residues R37 and I73 with alanines in pTZ18U-FLAG HPV1 E2 . FLAG-HPV1 E2 ( R37A/I73A ) was subcloned into the Asp718 and blunted NheI sites of pMEP4 . The FLAG-HA tag was introduced into the HindIII and blunted NotI sites of pMEP4 to generate the control plasmid , pMEP-fh . Plasmids expressing HPV16 E1 and E2 proteins have been described previously [29] . RPCI-11 BAC clones were purchased from Empire genomics ( Table S10 ) . HPV1 , HPV16 , HPV18 and HPV31 genomes have been described previously [24] , [71]–[73] and sequences can be found at http://pave . niaid . nih . gov [74] . All antibodies are described in Table S8 . GSK525762+ or the inactive enantiomer GSK525762− were synthesized as described previously [11] , following the methods described [38] . C-33 cells [75] were cultured in DMEM , 10% FBS , 100 U/ml penicillin , and 100 µg/ml streptomycin . The HPV31 positive cell line , CIN-612 9E cells [76] were obtained from Lou Laimins ( Northwestern University , Chicago , Illinois , USA ) and was grown on irradiated 3T3-J2 feeder cells in F medium ( 3∶1 [v/v] F-12 [Ham]-DMEM , 5% FBS , 0 . 4 µg/ml hydrocortisone , 5 µg/ml insulin , 8 . 4 ng/ml cholera toxin , 10 ng/ml EGF , 24 µg/ml adenine , 100 U/ml penicillin , and 100 µg/ml streptomycin ) . Inducible E2 expressing cell lines were generated in an HPV-negative cervical carcinoma derived cell line , C-33 by transfecting with the pMEP4-E2 expression plasmids , using Fugene ( Roche ) . Cells containing the pMEP episomal plasmids were selected with 80 µg/ml of hygromycin B ( Roche ) . Drug-resistant colonies were pooled after 2 weeks . E2 protein expression was induced with CdSO4 for 4 h before harvest and the levels of E2 proteins were titrated and adjusted by differential CdSO4 concentration to ensure that binding to the identified chromatin regions increased in an E2-dependent and specific fashion . For transient HPV16 E1/E2 expression , C-33 cells were cotransfected with pMEP9/EE-HPV16 E1 and pMEP4/FLAG-HPV16 E2 . E2 expression was induced with 3 µM CdSO4 induction for 4 h before harvest at 24 h post-transfection . CIN-612 9E cells were differentiated with calcium , essentially as described previously [27] . Feeders and CIN-612 9E cells were seeded as described above . When 90% confluent , the medium was changed to Lonza Growth medium ( KBM plus supplement media ) . Twenty four h later , the medium was changed to Differentiation medium ( Lonza KBM/1 . 5 mM CaCl2/no supplements ) . Cells were cultured for the times indicted before harvest or fixation . C-33 cells were treated with 0 . 2 µM aphidicolin for 24 h before harvesting for ChIP-chip experiments described in other sections . ChIP experiments were performed as previously described [34] . For mitotic cells , C-33 cells were blocked by 2 mM thymidine overnight and released into medium without thymidine for 9 h . Four hours before harvesting , E2 expression was induced with 3 µM CdSO4 and mitotic cells were collected by mitotic shake off at which point cells were fixed in formaldehyde . For conventional ChIP assay , 0 . 5 mg of chromatin prepared from asynchronous or mitotic cells was incubated overnight with a specific antibody and collected with Dynabeads conjugated to Protein G ( Invitrogen ) . For ChIP-chip analysis , 2 mg of chromatin was incubated overnight with a specific antibody prebound to Dynabeads conjugated to Protein G . Further processing for ChIP-chip was as described by Jang et al . [34] . DNA isolated from immunoprecipitated chromatin was amplified using the whole genome amplification system ( WGA , Sigma ) . Two HG18 build whole genome arrays were used . C-33-1E2 amplified DNA was labeled and hybridized to the 385K Whole-Genome Tiling Array or the 2 . 1M Whole-Genome Tiling Array by NimbleGen . E2 binding signals on the arrays for ChIP DNA were normalized to the input signals for total DNA . The ratios were plotted against genomic position to identify regions where increased signal is observed relative to the control sample . All datasets are available at GEO: GSE52312 . Real-time Q-PCR was performed using the ABI Prism 7900HT Sequence Detector ( Applied Biosystems ) and SYBR Green PCR master mix ( Applied Biosystems ) . An aliquot of ChIP DNA was analyzed with 12 . 5 µl of SYBR Green PCR master mix and 0 . 3 µM each oligonucleotide primer in total volume of 25 µl . In each run , a four-fold dilution series of pooled input chromatin DNA was used to generate a standard curve of threshold cycle ( Ct ) versus log of quantity . PCR was performed at 95°C for 15 min , followed by 40 cycles of denaturation at 95°C for 10 sec and annealing and extension at 60°C for 60 sec . The specificity of each primer pair was determined by dissociation curve analysis . The data were analyzed with SDS 2 . 1 software ( Applied Biosystems ) . The primers used are listed in Table S9 . Cells were arrested in G1/S phase by culture in 2 mM thymidine overnight , washed to release , and grown for 9 h in the absence of thymidine to select for cells in mitosis . The metallothioneine promoter was induced by the addition of 3 µM CdSO4 for 4 h . Cells were fixed at room temperature in 4% paraformaldehyde ( PFA ) in PBS for 20 minutes , blocked and stained with mouse monoclonal anti-FLAG M2 antibody and FITC or Alexa 488 anti-mouse antibody; various primary rabbit antibodies and Texas Red or Alexa 596 anti-rabbit antibody . Cellular DNA was stained with DAPI . Images were collected using a Leica TCS-SP5 laser scanning confocal imaging system . Cells were seeded at a density of 1×106 cells per 10 cm dish , incubated for 24 h , and transfected with 750 ng of siRNA ( Table S11 ) using 40 µl of HiPerFect ( Qiagen ) . Cells were incubated for three days at which point E2 expression was induced by 3 µM CdSO4 for 4 h . The efficiency of BRD4 downregulation was verified by immunoblot analysis using specific antibodies for the target proteins . siRNA treated cells were fixed for immunofluorescence using specific antibodies , as described above . Cells were cultured on coverslips or glass slides . 9E cells were differentiated for 5 days in the KBM media with 1 . 5 mM CaCl2 . The cells were fixed three times with cold methanol∶acetic acid ( 3∶1 ) for 15 mins . For chromosome spreads , C-33 cells were prepared as described for indirect immunofluorescence , treated with 0 . 1 mg/ml of Colcemid Karyomax ( Invitrogen ) for 90 mins , and collected by mitotic shake-off . Cells were resuspended in 10 ml of hypotonic buffer ( 0 . 075 M KCl ) and incubated at 37°C for 20 mins . After pelleting , the cells were resuspended and fixed three times in 10 ml of cold methanol∶acetic acid ( 3∶1 ) for 15 mins . The fixed cells were resuspended in 0 . 5 ml methanol∶acetic acid , applied onto glass slide by dropping , and dried for O/N . The cells were treated with RNace-it cocktail for 1 h , dehydrated with 70% , 85% , and 100% ethanol , and dried for several hours . For combined immunofluorescence-FISH analysis , mitotic cells were collected as described above and treated with H1 buffer ( 10 mM Tris , pH 7 . 4 , 10 mM NaCl , 5 mM MgCl2 ) for 15 mins and H2 buffer ( 0 . 25× PBS ) for 15 mins . Cells were centrifuged at spun at 1500 rpm for 10 mins in a Cytocentrifuge 7620 ( Wescor ) and fixed at room temperature in 4% PFA/PBS for 20 minutes . After immunofluorescent detection as described above , cells were treated with methanol∶acetic acid ( 3∶1 ) for 10 min , 2% paraformaldehyde for 1 min , before dehydration through a series of 70% , 90% , and 100% ethanol . FISH probes were prepared using ULysis nucleic acid labeling kit ( Molecular Probes ) , purified through Illustra ProbeQuant G-50 micro column ( GE Healthcare ) , and resuspended in TE containing 0 . 3 µg/µl of Cot-1 DNA . For hybridization , 2 µl 5-fluorescein-labeled BAC probe ( Empire Genomics ) or 50 ng ULysis FISH probe was mixed with 8 µl FISH hybridization buffer ( Empire Genomics ) , applied to the slide , covered with coverslip , and sealed with rubber cement . The cells and probes were denatured at 75°C for 5 minutes and incubated overnight at 42°C . Cells were washed in 1× phosphate-buffered detergent ( Qbiogene ) for 5 min at room temperature , 1× wash buffer ( 0 . 5× SSC , 0 . 1% SDS ) for 5 min at 65°C , and 1× phosphate-buffered detergent ( Qbiogene ) for 5 min at room temperature . Cellular DNA was stained with DAPI . Images were collected using a Leica TCS-SP5 laser scanning confocal imaging system . Images were processed using Leica AS Lite software , or Bitplane Imaris software ( Zurich , Switzerland ) or deconvolved with Huygens Essential software ( Scientific Volume Imaging B . V . , VB Hilversum , Netherlands ) , where indicated . C-33 cells were seeded at a density of 1×106 cells per 10 cm dishes and grown for 2 days . E2 expression was induced by the addition of 3 µM CdSO4 for 4 h . Total RNA was purified using RNeasy ( Qiagen ) , and analyzed for integrity using the Agilent RNA 6000 nano kit on 2100 Bioanalyzer ( Agilent ) . Both polyadenylated and non-polyadenylated ( after rDNA subtraction ) RNA was sequenced . Two different libraries were constructed for each sample . For one library , total RNA was purified by poly A selection following manufacturer's instructions . For the second library , 1 . 5 µg total RNA was rRNA depleted using Ribo-Zero ( Epicentre , Madison , WI ) , followed by library generation using the Illumina TruSeq RNA protocol , beginning at the fragmentation step . Libraries were sequenced on an Illumina GAIIx . The adapters were trimmed from raw sequences and low quality reads were filtered out . Processed reads were mapped to human genome assembly hg19 using Tophat and differentially expressed gene analysis was performed using Cufflinks [77] . Data was visualized using the Integrative Genomics Viewer ( Broad Institute ) . The dataset can be accessed at GEO: GSE52367 . HPV genomes were removed from the plasmid vector by restriction digestion and religated as described [78] . C-33 cells expressing either HPV1 , HPV16 , or HPV18 E2 proteins were transfected using FuGene 6 with the corresponding recircularized HPV genome and incubated for 24 h . E2 expression was induced with 3 µM CdSO4 for 4 h and the cells were prepared for FISH experiments as described above . PEB-BLOCs were detected using 5-fluorescein or Alexa 488 labeled probes , produced from BAC clones by Empire Genomics ( Table S10 ) , and the HPV genomes were detected using HPV DNA , purified from vector sequences by PCR amplification and labeled by an Alexa 594 ULysis labeling kit ( Molecular Probes ) . | Papillomavirus cause persistent , but mostly self-limiting , infections of the host epithelium . However , a subset of oncogenic papillomaviruses is the causative agent of certain human cancers . In persistent infection the viral genomes are tethered to host chromosomes to maintain and partition the extrachromosomal viral genomes to daughter cells . However , in cancers viral DNA is often found integrated close to common fragile sites , regions prone to breakage , amplification and deletion . We show that the viral E2 and cellular BRD4 proteins are associated with fragile regions of the human genome and nucleate viral replication foci at these sites . This is a resourceful strategy for a virus that uses the host DNA damage response to amplify viral DNA . However , the outcome may be increased accidental integration of viral DNA , which in the case of the oncogenic viruses can promote carcinogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genome",
"expression",
"analysis",
"medicine",
"and",
"health",
"sciences",
"chromosome",
"structure",
"and",
"function",
"viruses",
"and",
"cancer",
"dna-binding",
"proteins",
"microbiology",
"cancers",
"and",
"neoplasms",
"genitourinary",
"tract",
"tumors",
"histone",
"modification",
"oncology",
"genome",
"analysis",
"bioassays",
"and",
"physiological",
"analysis",
"dna",
"epigenetics",
"molecular",
"genetics",
"epigenomics",
"chromatin",
"research",
"and",
"analysis",
"methods",
"genomics",
"chromosome",
"biology",
"proteins",
"gene",
"expression",
"viral",
"replication",
"microarrays",
"biochemistry",
"cell",
"biology",
"viral",
"persistence",
"and",
"latency",
"virology",
"transcriptome",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"dna",
"repair",
"computational",
"biology",
"molecular",
"cell",
"biology",
"chromosomes"
] | 2014 | Papillomavirus Genomes Associate with BRD4 to Replicate at Fragile Sites in the Host Genome |
Genome-wide association studies have identified hundreds of loci for type 2 diabetes , coronary artery disease and myocardial infarction , as well as for related traits such as body mass index , glucose and insulin levels , lipid levels , and blood pressure . These studies also have pointed to thousands of loci with promising but not yet compelling association evidence . To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping , we designed the “Metabochip , ” a custom genotyping array that assays nearly 200 , 000 SNP markers . Here , we describe the Metabochip and its component SNP sets , evaluate its performance in capturing variation across the allele-frequency spectrum , describe solutions to methodological challenges commonly encountered in its analysis , and evaluate its performance as a platform for genotype imputation . The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents , and provides the opportunity to compare results across a range of related traits . The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits .
Recent data emerging from theoretical models [1] , [2] and empirical observation through genome-wide association studies ( GWAS ) ( for example [3] , [4] ) demonstrate that hundreds of genetic loci contribute to complex traits in humans . These data prompt two questions: ( 1 ) can additional genetic loci be identified by follow-up of the most significantly associated variants after initial GWAS meta-analysis ? and ( 2 ) can further investigation via genetic fine-mapping refine association signals at established genetic loci ? Systematically addressing these two questions should help improve understanding of the genetic architecture of complex traits and their shared genetic determinants , and suggest hypotheses and disease mechanisms that can be tested in functional experiments or model systems [5] . Addressing these two questions requires genotyping thousands of individuals at many genetic markers . For most currently available genotyping technologies , this kind of characterization is cost-prohibitive . To address this need in the context of type 2 diabetes , coronary artery disease and myocardial infarction , and quantitative traits related to these diseases , we designed the Metabochip , a custom genotyping array that provides accurate and cost-effective genotyping of nearly 200 , 000 single nucleotide polymorphisms ( SNPs ) chosen based on GWAS meta-analyses of 23 traits ( Table 1 ) . Metabochip SNPs were selected from the catalogs developed by the International HapMap [6] and 1000 Genomes [7] Projects , allowing inclusion of SNPs across a wide range of the allele frequency spectrum . These included 63 , 450 SNPs to follow-up the top ∼5 , 000 or ∼1 , 000 ( see Methods ) independent association signals for each of the 23 traits , 122 , 241 SNPs to fine-map 257 loci which showed genome-wide significant evidence for association with one or more of the 23 traits , and 16 , 992 SNPs chosen for a variety of other reasons ( see Methods and Table 2 ) . In designing the array , we sought to maximize assay success rates as well as the number of variants that could be assayed; Illumina custom arrays include a fixed number of “beads” and some sites can be assayed with a single bead while others require two [8] . Here , we describe Metabochip array design , and evaluate performance of the array in common genetic analysis steps , including quality control steps such as genomic control calculations , identification of related individuals , and fine-mapping of known disease susceptibility loci . Our results provide practical guidance to investigators and show that for fine-mapping loci the Metabochip provides much greater resolution than prior GWAS arrays .
The Metabochip was designed by representatives of the Body Fat Percentage [9] , CARDIoGRAM ( coronary artery disease and myocardial infarction ) [10] , DIAGRAM ( type 2 diabetes ) [11] , GIANT ( anthropometric traits ) [3] , [12] , [13] , Global Lipids Genetics ( lipids ) [4] , HaemGen ( hematological measures ) [14] , ICBP ( blood pressure ) [15] , MAGIC ( glucose and insulin ) [16]–[18] , and QT-IGC ( QT interval ) [19] , [20] GWAS meta-analysis consortia . The array is comprised of SNPs selected across two tiers of traits ( Table 1 ) . Tier 1 is comprised of eleven traits deemed to be of primary interest: type 2 diabetes ( T2D ) , fasting glucose , coronary artery disease and myocardial infarction ( CAD/MI ) , low density lipoprotein ( LDL ) cholesterol , high density lipoprotein ( HDL ) cholesterol , triglycerides , body mass index ( BMI ) , systolic and diastolic blood pressure , QT interval , and waist-to-hip ratio adjusted for BMI ( WHR ) . Tier 2 is comprised of twelve traits of secondary interest: fasting insulin , 2-hour glucose , glycated hemoglobin ( HbA1c ) , T2D age of diagnosis , early onset T2D ( diagnosis age<45 years ) , waist circumference adjusted for BMI , height , body fat percentage , total cholesterol , platelet count , mean platelet volume , and white blood cell count . We included three design classes of SNPs on the Metabochip ( Table 2 ) : In total , 217 , 695 SNPs were chosen for the array ( Table 2 ) . 20 , 970 SNPs ( 9 . 6% ) failed during the assay manufacturing process , resulting in 196 , 725 SNPs available for genotyping . A summary file annotating each Metabochip SNP with ascertainment criteria , SNP assay , a list of unintended duplicate SNPs ( Supplementary Table S4 ) , and reference strand orientation for alleles is provided at http://www . sph . umich . edu/csg/kang/MetaboChip/ . We evaluated the utility of the Metabochip and accuracy of its genotype calls in three sample sets: ( 1 ) 15 , 896 northern European individuals from the FUSION , METSIM , HUNT , Tromsø , and Diagen studies [26]–[30] together with 67 HapMap samples genotyped at least two times each and called using Illumina GenomeStudio software by re-clustering these data; ( 2 ) 6 , 614 Sardinian individuals organized in 1 , 243 extended families from the SardiNIA study [31] , [32] called by GenomeStudio software using default cluster data; and ( 3 ) 9 , 715 Nordic individuals from the Malmø Preventive Project , the Scania Diabetes Registry , and the Botnia Study [33]–[35] genotyped using a modified version of the BIRDSEED genotype calling algorithm [36] . We applied standard SNP- and sample-based QC filters based on call rate , Hardy-Weinberg equilibrium deviations , duplicate genotype inconsistencies , and failures of Mendelian inheritance; in the Nordic sample , we also carried out checks based on plate-specific characteristics . These filters resulted in final data sets of 163 , 222 polymorphic SNPs genotyped in 67 HapMap samples , 142 , 812 polymorphic SNPs genotyped in 6 , 164 Sardinians , and 179 , 165 polymorphic SNPs genotyped in 8 , 473 Nordic individuals . Since Metabochip SNPs were selected to be associated with our 23 traits of interest , performing genomic control correction [37] requires some care . To select a set of ( near ) -independent SNPs that are not associated with an analysis trait of interest , we focused on SNPs selected to replicate signals unrelated to the trait of interest ( for example , QT interval SNPs for a T2D association analysis ) , also removing SNPs within 250 kb of SNPs previously associated with the trait of interest , and then LD-pruning the remaining SNPs so that no SNP pair is in strong LD ( r2> . 3 ) . To estimate kinship coefficients or to correct for population stratification using principal components analysis ( PCA ) or multidimensional scaling ( MDS ) covariates , we require SNPs that are not too rare and are not in strong pairwise LD . We found that taking SNPs with MAF> . 05 and LD-pruning them so that no SNP pair has r2> . 3 works well for PCA and MDS ( data not shown ) . The same subset of SNPs can be used for pairwise IBD estimation using the maximum-likelihood method of Milligan [38] implemented in PLINK [39] or the variance-components method of Balding and Nichols [40] implemented in EMMAX [41] . We carried out genotype imputation in the Sardinian data . We imputed variants observed in a reference set of 280 Europeans from the August 2010 1000 Genomes Project data into: ( a ) 6 , 164 individuals genotyped on the Metabochip [32] , ( b ) 1 , 097 individuals genotyped on the Affymetrix 6 . 0 array , and ( c ) 1 , 412 individuals genotyped on the Affymetrix 500 K array [42] . We evaluated mean estimated r2 within fine-mapping regions using minimac ( [43]; www . genome . sph . umich . edu/wiki/minimac ) , and empirically compared the imputation quality using the published Sanger sequencing data in five fine mapping loci [32] . In addition , we evaluated mean estimated r2 across different continental populations by leaving one individual out from the 1000 Genomes reference panel and imputing them using markers present in each platform across the fine mapping regions and a 1 Mb window flanking each region . We also compared association power obtained by imputation into GWAS and Metabochip samples in Metabochip fine-mapping regions by comparing LDL cholesterol association evidence in 2 , 342 of these individuals genotyped using both the Metabochip and one of the Affymetrix arrays .
Of 217 , 695 SNPs chosen for the Metabochip across all design categories , 196 , 725 ( 90 . 4% ) were successfully manufactured on the array ( Table 2 ) . The 48 , 846 previously manufactured SNPs had higher success rate ( 95 . 4% ) than the 168 , 849 new SNP assays ( 88 . 7% ) . Illumina design score was predictive of the quality of manufactured SNP assays . For example , 25% of SNPs with design score<0 . 6 failed to produce genotype calls due to poor clustering of the intensity data , compared to 3 . 1% of SNPs with design score between 0 . 6 and 1 . 0 ( Supplementary Figure S1 ) . We evaluated genotype calling accuracy for 67 HapMap samples genotyped multiple times using three different calling strategies: ( a ) Illumina GenomeStudio with reclustering the intensity data using >15 , 000 samples; ( b ) Illumina GenomeStudio based on default clusters provided by Illumina; and ( c ) GenoSNP [44] , which calls genotypes based on a within-sample-between-markers analysis of intensity data rather than a between-sample-within-marker analysis . The large majority of Metabochip SNPs yielded high quality genotypes . For the 67 HapMap samples called using GenomeStudio with reclustering , only 8 , 344 ( 4 . 2% ) of the 196 , 725 SNP assays had genotype call rates <95% , while another 25 , 958 SNPs ( 13 . 2% ) were monomorphic . Using GenomeStudio and default clusters , these numbers were 12 , 131 ( 6 . 2% ) and 25 , 311 ( 12 . 9% ) , while using GenoSNP , they were 18 , 107 ( 9 . 2% ) and 25 , 532 ( 13 . 0% ) . Using GenomeStudio with reclustering , genotype concordance between Metabochip genotypes for duplicate pairs was 99 . 998% overall and 99 . 990% for heterozygotes . Comparing Metabochip genotypes to HapMap 3 genotypes for the 59 , 935 SNPs in common , genotype concordance was 99 . 93% overall and 99 . 84% for heterozygotes , similar to the 99 . 87% Mendelian consistency rate reported in the HapMap3 data [45] . We observed similar concordance rates for these sample sets using the Illumina caller with default clusters ( 99 . 93% overall , 99 . 84% for heterozygotes ) , or using GenoSNP [44] ( 99 . 85% overall , 99 . 81% for heterozygotes ) . Genotype concordance for less common variants was slightly lower than for common variants . For example , among the singleton SNPs in the 67 HapMap samples , 98 . 9% of heterozygous genotypes were concordant with HapMap3 for the two GenomeStudio call sets and 97 . 8% for the GenoSNP set . Heterozygous genotype concordances for singleton SNPs between duplicate pairs were 99 . 76% , 99 . 70% , and 99 . 83% for the three call sets . We evaluated the allele frequency spectrum for Metabochip SNPs in the 67 HapMap samples ( Figure 2 ) . Mean MAF of Metabochip SNPs was . 152 overall , . 109 among fine-mapping SNPs , and . 224 among replication SNPs . Among these three SNP sets , 38% , 53% , and 12% of SNPs had MAF< . 05 , and 14% , 21% , and 2% were monomorphic . Within the 257 fine-mapping regions ( 45 . 52 Mb ) , 109 , 855 SNPs were catalogued by the 1000 Genomes Project [7] pilot studies and 240 , 805 SNPs are in the current Phase 1 release ( as of November 2011 ) . Of these , 122 , 241 fine-mapping SNPs were genotyped on the Metabochip ( Supplementary Table S2 ) . In the 1000 Genomes European samples , Metabochip SNPs tag 82 . 0% and 54 . 5% of all Pilot and Phase 1 1000 Genomes variants in these regions at r2≥ . 8 , compared to 61 . 3% and 40 . 3% coverage using HapMap 3 SNPs ( Figure 3 ) . Among SNPs with MAF< . 05 , Metabochip SNPs tag 61 . 9% and 33 . 8% at r2≥ . 8 , compared to 24 . 3% and 17 . 0% using HapMap 3 . Using genotype imputation , we can impute 82% of 1000 Genomes Phase 1 European SNPs with MAF>0 . 5% with an estimated r2≥0 . 8 . We next investigated accuracy of genotype imputation into the 257 Metabochip fine-mapping regions using the 280 Europeans from 1000 Genomes Project [7] as reference set and the 6 , 164 individuals in the Sardinian Metabochip sample as target . Figure 3 displays estimated r2 values in the Metabochip fine-mapping regions as a function of MAF . Also displayed are estimated r2 values for SNPs in these regions using the 280 European 1000 Genomes project samples as reference set and 1 , 412 Sardinians genotyped on the Affymetrix 500 K and 1 , 097 Sardinians genotyped on the Affymetrix 6 . 0 chips as targets . Imputation accuracy into the Sardinian Metabochip sample is greater in all allele frequency ranges than for the samples genotyped using the GWAS arrays . For example , among SNPs with . 02≤MAF< . 05 , mean estimated r2 for the Affymetrix 500 K , Affymetrix 6 . 0 , and Metabochip samples were . 47 , . 62 , and . 84 , respectively ( Figure 4 ) . The improved imputation accuracy for Metabochip compared to GWAS array is primarily due to increased marker density of the Metabochip in these regions . Imputation quality in the Metabochip fine-mapping regions using Metabochip is also improved for non-European individuals compared to imputation using GWAS platforms . Using a leave-one-sample-out approach , we evaluated the average r2 from the 1000 Genomes reference panel into Affymetrix 500 k , Affymetrix 6 . 0 , and Metabochip . For example , among SNPs with . 02<MAF< . 05 , mean estimated r2 across European individuals for the chips were . 78 , . 83 , and . 93 , respectively . For individuals with African ancestry , corresponding values were . 78 , . 85 , and . 94 , and for individuals of Asian ancestry , they were . 67 , . 72 , and . 89 ( Supplementary Figure S2 ) . The fact that imputation of rare variants in African ancestry populations is more accurate than in European populations is probably explained by noting that – in the short regions evaluated here – there will be only a limited number of common variant haplotypes in Europeans and , in some cases , these will not effectively tag specific rare variants . In African populations , with a larger variety of rare haplotypes , it is more likely ( relative to Europeans ) that at least one haplotype will capture rare variants of interest . In addition , we empirically evaluated the quality of experimentally determined and imputed SNPs within the five fine mapping regions by comparing individual genotypes with those obtained by Sanger sequencing . For 126 SNPs evaluated , the average r2 in analyses based on the Affymetrix 500 k and 6 . 0 arrays was . 46 and . 55 , respectively . Analyses based on Metabochip showed average r2 = . 79 . Focusing on 48 SNPs that were imputed in all three analyses , the average r2 was . 31 ( Affymetrix 500 K ) , . 41 ( Affymetrix 6 . 0 ) , and . 57 ( Metabochip ) ( Supplementary Figure S3 ) . To compare the power and resolution for association testing in the Metabochip fine-mapping regions to that of standard GWAS arrays , we revisited the LDL cholesterol association analysis from the SardiNIA study [32] in 2 , 342 individuals genotyped for both Metabochip and an Affymetrix ( 6 . 0 or 500 k ) GWAS chip . Here , we focus on five of the six most strongly associated loci from Willer et al . [46] , in and around PCSK9 , LDLR , APOE/APOC1/APOC2 , SORT1 , and APOB ( Figure 5A–J ) , all of which were designated for locus fine mapping by the Global Lipids Genetics Consortium . In the SORT1 and APOB regions , the peak association signals for the two data sets are similar ( Figure 5A–D ) . For PCSK9 , LDLR , and APOE/APOC1/APOC2 , Metabochip based analysis resulted in considerably stronger association signals . For PCSK9 and APOE/APOC1/APOC2 , the most strongly associated variants were low-frequency SNPs ( MAF = 1 . 1% for PCSK9 , MAF = 3 . 4% for APOE ) that were directly genotyped on the Metabochip but not on the Affymetrix chips ( Figure 5E–J ) . Although the signals from common variants are similar , the peak SNPs were not imputed accurately in the Affymetrix data ( estimated r2 = . 04 and . 08 , respectively ) . Within the LDLR region , there are 165 SNPs in the 1000 Genomes European panel . None of these SNPs are on the Affymetrix chips and only eight could be imputed at estimated r2≥ . 3 using the Affymetrix data; the locus is also hard to impute using HapMap 2 as a reference , with the peak association signals corresponding to r2 of ∼ . 40 . In contrast , 36 of the 165 SNPs were directly genotyped in Metabochip , and 122 were imputed at estimated r2≥ . 3 . As a result , imputation into the Metabochip data resulted in a substantial association signal ( p = 7 . 3×10−6 ) , while for the Affymetrix data , p> . 02 at all markers ( Figure 5I–J ) . These results demonstrate that dense genotyping may substantially improve imputation accuracy , increasing association power even for common variants . We carried out kinship estimation between pairs of individuals and calculated genotype-based principal components for inclusion as covariates in genetic association analysis using all Metabochip SNPs that passed QC , and then using the pruned subset of SNPs described in the Methods section . When using all QC-passing SNPs , estimates of pairwise kinship coefficients in the Sardinia sample had inflated variance ( Supplementary Table S5 ) , and kinship coefficient estimates for the Nordic sample calculated using PLINK suggested ( incorrectly ) that essentially all pairs of individuals were related ( Supplementary Figure S4 ) . For each analysis , using the pruned set of SNPs gave sensible results , reducing variance in estimated kinship coefficients in the Sardinia sample and removing the artifactual estimates of close relatedness in the Nordic sample . Because many Metabochip SNPs were included specifically due to prior evidence for association of T2D , CAD/MI and related traits , controlling for potential population stratification in Metabochip analysis requires some care . Not surprisingly , carrying out T2D association analysis in the Nordic sample on all SNPs passing QC without inclusion of genotype-based principal components resulted in a large genomic control inflation factor ( λGC = 1 . 44 ) . Including all SNPs that passed QC to estimate principal components ( PCs ) , and then including those PCs as covariates in the association analysis gave reduced but still substantial inflation ( λGC = 1 . 13 ) . When we instead estimated test statistic inflation based only on the 3 , 772 LD-pruned QT interval replication SNPs ( not expected to associate with T2D ) we obtained a genomic control inflation factor near unity ( λGC = 1 . 01 ) . We were interested whether the replication SNP sets submitted by the GWAS consortia for the different traits showed more or less overlap than expected by chance . To address this question , we counted the number of SNPs in common across pairs of traits , and used simulation to test whether the observed overlaps were different than expected under the null hypothesis of genetic independence of pairs of traits ( Supplementary Table S6 ) . Not surprisingly , we observed substantial SNP set overlaps ( and greater than expected assuming independence ) for multiple pairs of correlated traits , notably SBP and DBP ( 38% proportion of maximum possible overlap ) , HDL and TG ( 17% ) , and TC and LDL ( 87% ) . We also observed substantial genetic overlap ( 4% ) between LDL and SBP , which are nearly uncorrelated traits . Overall , we observed an excess of nominally significant SNP set overlaps , consistent with ( but in no way proof of ) the hypothesis a shared genetic etiology between these cardiometabolic traits .
We designed the Metabochip , a custom genotyping array for replication of the top association signals from the largest available GWAS meta-analysis for 23 T2D and CAD/MI related traits and for fine-mapping 257 genome-wide significant association signals for 15 of these traits ( Table 1 ) . The Metabochip also includes a set of SNPs representing genome-wide significant associations across a range of human traits; SNPs that tag known copy number polymorphisms , the MHC , and mitochondrial variants; X and Y chromosome SNPs for sex verification , fingerprint SNPs for sample tracking , and “wildcard” SNPs selected by the participating GWAS consortia ( Table 2 ) . The array has already been genotyped on DNA samples from hundreds of thousands of individuals and preliminary analyses across the contributing GWAS consortia have identified hundreds of new genome-wide association signals ( manuscripts being prepared by each of the consortia ) . In designing the Metabochip , 90 . 4% of chosen SNPs were successfully designed and manufactured onto the array , and of these , ∼82% passed QC filters in our three example studies , resulting in very complete coverage of variation in our 257 fine-mapping regions . Of course , as time passes and catalogs of SNPs expand , potential shortcomings in coverage should become apparent . Currently , coverage of 1000 Genomes Pilot Study European SNPs in the fine-mapping regions is 82 . 0% at a tagging threshold of r2≥ . 8 . Coverage of Phase 1 European SNPs in these regions is 54 . 5% , and the number increases to 73 . 7% for SNPs at MAF>0 . 5% . Using genotype imputation , we can impute 82% of 1000 Genomes Phase 1 European SNPs with MAF>0 . 5% with estimated r2≥0 . 8 . The resulting data are of high quality , with 99 . 99% duplicate consistency in heterozygotes and 99 . 77% Mendelian consistency in heterozygotes in our studies . Further , Metabochip fine-mapping regions provide an excellent target for genotype imputation from relevant reference sets , and in our experience can provide more complete coverage than provided by standard HapMap-based GWAS arrays ( Figure 3 ) for both common and less common variants . A key decision in the fine-mapping of any GWAS signal concerns the size of the region where genetic variation will be examined exhaustively . In designing the Metabochip , we focused on relatively small regions surrounding each lead SNP – these included all variants in strong linkage disequilibrium ( r2> . 5 ) and a small shoulder extending . 02 cM beyond that ( typically , ∼20 kb ) . This decision was informed by the observation that , in cases where GWAS signals and Mendelian disease loci overlap , they are typically very close together ( typically within ∼10 kb of each other and nearly always within <100 kb; see [4] for a discussion of the issue ) , although there are exceptions to this rule ( see [47] , for example ) . Within each fine-mapping region , we selected variants identified by the HapMap consortium and early analyses of the 1000 Genomes Consortium data . The 1000 Genomes Project and other sequence based catalogs of genetic variation are now more extensive that at the time of array design , but ( as noted above ) our analyses show that the SNPs selected for inclusion in the Metabochip form a useful reagent for genotyping imputation – not only for the imputation of newly discovered SNPs in the fine-mapping regions ( see above ) but also for the imputation of other types of variants , such as indel polymorphisms , that have become part of newer 1000 Genomes Project analyses ( unpublished data ) . Several other design choices for Metabochip were to some degree arbitrary: which traits to include; balance in numbers of SNPs for replication , fine mapping , and other purposes; and how to prioritize among SNPs available for each purpose . Were we to design a similar chip now , we would take advantage of the now available more extensive and deeply annotated SNP catalogs . In addition , we would likely include a set of randomly ascertained SNPs to facilitate analysis that control for population structure and other artifacts . Finally , with empirical evidence from this and other projects on the relationship between SNP design score and empirical probability of successful design , we would likely replace design score by probability of successful design . This approach would likely result in even higher call rates . Because Metabochip SNPs are highly enriched for trait-associated SNPs and >60% are clustered in the ∼1 . 5% of the genome that comprises the fine-mapping regions , Metabochip genotype data present some challenges to standard analyses such as relationship estimation , principal components analysis , and genomic control determination . However , as we demonstrated , these challenges can be overcome by focusing on replication SNPs expected to be unrelated to the trait of interest . An alternative approach is to use SNPs that were not associated with the trait ( s ) of interest in the corresponding GWAS ( for example , p-value> . 50 for all such traits ) and then to LD-prune the resulting set of SNPs to identify a near-independent set . An alternative that is also worthy of investigation in the analysis of case-control samples is the application of principal component factor loadings derived from a controls-only analysis to the combined sample of cases and controls . When this last alternative is considered , it is important to check that PCA axes derived from controls represent all relevant ancestries present in cases . The design of the array , focused on replication and fine-mapping and selecting SNPs from early releases of the HapMap and 1000 Genomes Projects , resulted in a highly non-random ascertainment of SNPs . Thus , we cannot recommend use of Metabochip SNPs for population genetic analyses that rely on unbiased , and/or comprehensive ascertainment schemes for SNPs . The need for follow-up genotyping is a frequent requirement of GWAS and sequencing studies of complex human traits . Approaching array design in a coordinated fashion across related studies and traits can be particularly cost-effective , since per array costs often drop dramatically with increasing numbers of individuals to be genotyped , and ( given sufficient numbers of individuals ) may increase only modestly with increasing numbers of SNPs . For example , a custom chip designed to genotype the ∼22 , 000 DIAGRAM-selected type 2 diabetes Metabochip SNPs in the ∼80 , 000 individuals genotyped on Metabochip by the DIAGRAM consortium studies would have cost ∼$55 compared to the Metabochip cost of $39 , delivering only 1/9 as many genotypes at >40% greater cost . Furthermore , examining the association between SNPs tentatively associated with one trait for other related traits can also be informative , highlighting pleiotropy across related traits and helping discover new association signals; for example , two of the ten novel type 2 diabetes loci identified to date by Metabochip analysis by the DIAGRAM consortium were placed on Metabochip for other traits [48] . In the case of the Metabochip , which is less expensive than many smaller trait specific arrays , this opportunity to collect more information and investigate the effects of SNPs associated with other traits actually comes with reduced costs ( compared to trait specific arrays ) , although with the need to organize across multiple consortia and to share the number of SNPs that can be cost-effectively genotyped . The “Immunochip” [49] follows this same paradigm and supports genotyping of ∼200 , 000 SNPs identified on the basis of GWAS meta-analyses for immunological disorders , while the recently designed “exome chip” ( Benjamin Neale , Gonçalo Abecasis , personal communication ) supports genotyping of ∼250 , 000 exonic SNPs identified via large-scale exome sequencing studies totaling >12 , 000 individuals . These and other similar array products represent valuable tools in ongoing efforts to understand the genetic architecture of complex human traits . | Recent genetic studies have identified hundreds of regions of the human genome that contribute to risk for type 2 diabetes , coronary artery disease and myocardial infarction , and to related quantitative traits such as body mass index , glucose and insulin levels , blood lipid levels , and blood pressure . These results motivate two central questions: ( 1 ) can further genetic investigation identify additional associated regions ? ; and ( 2 ) can more detailed genetic investigation help us identify the causal variants ( or variants more strongly correlated with the causal variants ) in the regions identified so far ? Addressing these questions requires assaying many genetic variants in DNA samples from thousands of individuals , which is expensive and timeconsuming when done a few SNPs at a time . To facilitate these investigations , we designed the “Metabochip , ” a custom genotyping array that assays variation in nearly 200 , 000 sites in the human genome . Here we describe the Metabochip , evaluate its performance in assaying human genetic variation , and describe solutions to methodological challenges commonly encountered in its analysis . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"diabetes",
"mellitus",
"type",
"2",
"mathematics",
"diabetic",
"endocrinology",
"statistics",
"genetics",
"endocrinology",
"genetics",
"and",
"genomics",
"biology",
"human",
"genetics",
"diabetes",
"and",
"endocrinology",
"metabolic",
"disorders",
"cardiovascular"
] | 2012 | The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits |
Understanding any brain circuit will require a categorization of its constituent neurons . In hippocampal area CA1 , at least 23 classes of GABAergic neuron have been proposed to date . However , this list may be incomplete; additionally , it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required . We studied the transcriptomes of 3 , 663 CA1 inhibitory cells , revealing 10 major GABAergic groups that divided into 49 fine-scale clusters . All previously described and several novel cell classes were identified , with three previously described classes unexpectedly found to be identical . A division into discrete classes , however , was not sufficient to describe the diversity of these cells , as continuous variation also occurred between and within classes . Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes , which correlated similarly with it across multiple cell types . Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons . These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes .
Cortical circuits are composed of highly diverse neurons , and a clear definition of cortical cell types is essential for the explanation of their contribution to network activity patterns and behavior . Cortical neuronal diversity is strongest amongst GABAergic neurons . In hippocampal area CA1—one of the architecturally simplest cortical structures—GABAergic neurons have been divided so far into at least 23 classes of distinct connectivity , firing patterns , and molecular content [1–6] . A complete categorization of CA1 inhibitory neurons would provide not only essential information to understand the computational mechanisms of the hippocampus but also a canonical example to inform studies of more complex structures , such as six-layered isocortex . CA1 GABAergic neurons have been divided into six major groups based on connectivity and expression patterns of currently used molecular markers . Parvalbumin ( PVALB ) -positive neurons ( including basket , bistratified , and axo-axonic cells ) target pyramidal cells’ somata , proximal dendrites , or axon initial segments , firing fast spikes that lead to strong and rapid suppression of activity [7 , 8] . Somatostatin ( SST ) -positive oriens/lacunosum-moleculare ( O-LM ) cells target pyramidal cell distal dendrites and exhibit slower firing patterns [9] . GABAergic long-range projection cells send information to distal targets and comprise many subtypes , including SST-positive hippocamposeptal cells; NOS1-positive backprojection cells targeting dentate gyrus and CA3; and several classes of hippocamposubicular cells , including trilaminar , radiatum-retrohippocampal , and PENK-positive neurons [10–14] . Cholecystokinin ( CCK ) -positive interneurons are a diverse class characterized by asynchronous neurotransmitter release [15 , 16] that have been divided into at least five subtypes targeting different points along the somadendritic axis of pyramidal cells [17–21] . Neurogliaform and Ivy cells release GABA diffusely from dense local axons and can mediate volume transmission as well as conventional synapses [22 , 23] . Interneuron-selective ( I-S ) interneurons comprise at least three subtypes specifically targeting other inhibitory neurons and expressing one or both of Vasoactive intestinal polypeptite ( VIP ) and calretinin ( CALB2 ) [2 , 24–26] . Finally , additional rare types , such as large SST/NOS1 cells [27] , have been described at a molecular level , but their axonal targets and relationship to other subtypes is unclear . This already complex picture likely underestimates the intricacy of CA1 inhibitory neurons . Currently defined classes likely divide into several further subtypes , and additional neuronal classes likely remain to be found ( e . g . , [28] ) . Furthermore , it is unclear whether a categorization into discrete classes is even sufficient to describe the diversity of cortical inhibitory neurons [29 , 30] . For example , several CCK interneuron classes have been described , targeting pyramidal cells at multiple locations ranging from their somata to distal dendrites , and the molecular profile and spiking phenotype of these cells correlates with their synaptic target location , with fast-spiking cells more likely to target proximal segments of pyramidal neurons [18 , 19 , 21] . Do such cells represent discrete classes with sharp interclass boundaries , or do they represent points along a continuum ? Finally , while a cell’s large-scale axonal and dendritic structure likely remains fixed throughout life , both gene expression and electrophysiological properties can be modified by factors such as neuronal activity [31–34] . To what extent is the observed molecular diversity of interneurons consistent with activity-dependent modulation of gene expression ? Single-cell RNA sequencing ( scRNA-seq ) —which can read out the expression levels of all genes in large numbers of individual cells—provides a powerful opportunity to address these questions . This method has successfully identified the major cell classes in several brain regions [35–46] . Nevertheless , identifying fine cortical cell classes has not been straightforward because of both incomplete prior information on the underlying cell types and complicating factors , such as potential continuous variability within these classes . The large body of prior work on CA1 interneurons provides a valuable opportunity to identify transcriptomic clusters with known cell types in an important cortical circuit , enabling confident identification of known and novel classes and investigation of questions such as continuous variability . Here , we describe a transcriptomic analysis of 3 , 663 inhibitory neurons from mouse CA1 . This analysis revealed 49 clusters , of which we could identify 41 with previously described cell types , with the remaining 8 representing putative novel cell types . All previously described CA1 GABAergic classes could be identified in our database , but our results unexpectedly suggest that three of them are identical . The larger number of clusters occurring in our transcriptomic analysis reflected several previously unappreciated subtypes of existing classes and tiling of continua by multiple clusters . Our data suggest a common genetic continuum exists between and within classes , from faster-firing cells targeting principal cell somata and proximal dendrites , to slower-firing cells targeting distal dendrites or interneurons . Several classes previously described as discrete represent ranges along this continuum of gene expression .
We collected cells from six Slc32a1-Cre;R26R-tdTomato mice , three of age p60 and three of age p27 . Cells were procured using enzymatic digestion and manual dissociation [46] , and data were analyzed using the 10X Genomics “cellranger” pipeline , which uses unique molecular identifiers ( UMIs ) to produce an absolute integer quantification of each gene in each cell . The great majority of cells ( 4 , 572/6 , 971 cells total; 3 , 283/3 , 663 high-quality interneurons ) came from the older animals . Because we observed no major difference in interneuron classes between ages , data were pooled between them ( S1 Fig ) . Fluorescence-activated cell sorting ( FACS ) yielded an enriched but not completely pure population of GABAergic neurons . A first-round clustering ( using the method described below ) was therefore run on the 5 , 940 cells passing quality control , identifying 3 , 663 GABAergic neurons ( as judged by the expression of genes Gad1 and Slc32a1 ) . We analyzed the data using a suite of four novel algorithms derived from a probabilistic model of RNA distributions . All four methods were based on the observation that RNA counts within a homogeneous population can be approximated using a negative binomial distribution ( see Methods , [47 , 48] ) . The negative binomial distribution accurately models the high variance of transcriptomic read counts ( S2A and S2B Fig ) . As a consequence , algorithms based on this distribution weight the presence or absence of a gene more than its numerical expression level—for example , this distribution treats read counts of 0 and 10 as more dissimilar than read counts of 500 and 1 , 000 ( S2C Fig ) . The algorithm we used for clustering was termed ProMMT ( Probabilistic Mixture Modeling for Transcriptomics ) . This algorithm fits gene expression in each cluster k by a multivariate negative binomial distribution with cluster-specific mean μk . The mean expression levels of only a small subset of genes are allowed to vary between clusters ( 150 for the current analysis; S3 Fig ) ; these genes are selected automatically by the algorithm by maximum likelihood methods . The use of such “sparse” methods is essential for probabilistic classification of high-dimensional data [49] , and the genes selected represent those most informative for cluster differentiation . The number of clusters was chosen automatically using the Bayesian Information Criterion ( BIC ) [50] . The ProMMT algorithm also provides a natural measure of the distinctness of each cluster , which we term the isolation metric ( see Methods ) . The ProMMT algorithm divided CA1 interneurons into 49 clusters ( Fig 1 ) . We named the clusters using a multilevel scheme after genes that are strongly expressed at different hierarchical levels; for example , the cluster Calb2 . Vip . Nos1 belongs to a first-level group characterized by strong expression of Calb2 ( indicating I-S interneurons ) ; a second-level group Calb2 . Vip; and a third-level group distinguished from other Calb2 . Vip cells by stronger expression of Nos1 . This naming scheme was based on the results of hierarchical cluster analysis of cluster means , using a distance metric based on the negative binomial model ( Methods; Fig 1 ) . To visualize cell classes in two dimensions , we modified the t-stochastic neighbor embedding ( tSNE ) algorithm [51] for data with negative binomial variability , terming this approach nbtSNE ( negative binomial tSNE ) . In conventional tSNE , the similarity between data points is defined by their Euclidean distance , which corresponds to conditional probabilities under a Gaussian distribution . We obtained greater separation of clusters and a closer correspondence to known cell types by replacing the Gaussian distribution with the same negative binomial distribution used in our clustering algorithm ( see Methods; S4 Fig ) . The nbtSNE maps revealed that cells were arranged in 10 major “continents” ( Fig 2 ) . The way expression of a single gene differed between classes could be conveniently visualized on these maps by adjusting the symbol size for each cell according to that gene’s expression level . Consistent with previous transcriptomic analyses , we found that classes were rarely , if ever , identified by single genes but rather by combinatorial expression patterns . Thanks to the extensive literature on CA1 interneurons , 25 genes together sufficed to identify the main continents with known cell classes ( Fig 3 ) , and it was also possible to identify nearly all the finer subclasses using additional genes specific to each class ( S1 Text ) . Previous work has extensively characterized the connectivity , physiology , and firing patterns of CA1 inhibitory neurons , and these cellular properties have been related to the expression of large numbers of marker genes . We next sought to identify our transcriptomic clusters with previously defined cell types , taking advantage of the “Rosetta stone” provided by this extensive prior research . Explaining how the identifications were made requires an extensive discussion of the previous literature , which is presented in full as S1 Text , online . Here , we briefly summarize the major subtypes identified ( summarized in Fig 4 ) . Continent 1 was identified with the Sst-positive hippocamposeptal and O-LM cells of stratum oriens ( so ) . These cells all expressed Sst and Grm1 , and were further divided into two Npy+/Ngf+ clusters identified as hippocamposeptal neurons [52] and three Pnoc+/Reln+/Npy− clusters identified with O-LM cells [9] . In addition , continent 1 contains a previously undescribed subclass positive for Sst , Npy , and Reln . Continent 2 was identified as basket and bistratified cells . These were all positive for Tac1 ( the precursor to the neuropeptide Substance P ) , as well as Satb1 and Erbb4 , but were negative for Grm1 . They were divided into two Pvalb+/Sst− clusters identified with basket cells , two Pvalb+/Sst+/Npy+ clusters identified with bistratified cells ( Klausberger et al . , 2004 ) , and three Pvalb− clusters identified with Oriens-Bistratified ( O-Bi ) cells [53] . Continent 3 was identified as axo-axonic cells because of their expression of Pvalb but not Satb1 [54] . This continent’s three clusters were Tac1 negative but positive for other markers , including Snca , Pthlh , and C1ql1 , which have also been associated with axo-axonic cells in isocortex [43 , 44] . We note that this dichotomy of Pvalb interneurons into Tac1-positive and -negative subclasses is likely homologous to previous observations in isocortex [55] . Continent 4 was identified as Ivy cells and medial ganglionic eminence ( MGE ) -derived neurogliaform cells . These cells expressed Cacna2d1 , which we propose as a unique identifier of hippocampal neurogliaform/ivy cells , as well as Lhx6 and Nos1 [56] . They were divided into a Reln+ cluster identified with MGE-derived neurogliaform cells and a Reln−/Vwa5a+ cluster identified with Ivy cells [23] . This continent is homologous to the isocortical Igtp class defined by Tasic and colleagues [44] , which we hypothesize may represent isocortical neurogliaform cells of MGE origin; this hypothesis could be confirmed using fate mapping . Continent 5 was identified as caudal ganglionic eminence ( CGE ) -derived neurogliaform cells . Its three clusters contained Cacna2d1 and many other genes in common with those of continent 4 , but lacked Lhx6 and Nos1 [56] . Similar to isocortical putative neurogliaform cells , this continent expressed Ndnf and contained a distinct subtype positive for Cxcl14 [44] . As with continent 4 , continent 5 mainly expressed Reln but also contained a small Reln-negative cluster , which we suggest forms a rare and novel class of CGE-derived ivy cell . Continent 6 was identified with Sst-negative long-range projection interneurons . It divided into two distinct clusters , both of which were strongly positive for Ntng1 . The first strongly expressed Chrm2 but lacked Sst and Pvalb , identifying them as trilaminar cells [12 , 57] . The second subgroup lacked most classical molecular markers; this fact , together with their inferred laminar location at the stratum radiatum / stratum lacunosum-moleculare ( sr/slm ) border , identified them as putative radiatum-retrohippocampal neurons that project to the retrosplenial cortex [12 , 58] . Continents 7 and 8 were identified as what are traditionally called Cck interneurons . This term is somewhat unfortunate: while these cells indeed strongly express Cck , many other inhibitory classes express Cck at lower levels , including even Pvalb+ basket cells [59] . Continents 7 and 8 cells comprised 13 highly diverse clusters but shared strong expression of Cnr1 , Sncg , Trp53i11 , and several other novel genes . Continent 8 is distinguished by expression of Cxcl14 , which localizes these cells to the sr/slm border . This continent comprised a continuum ranging from soma-targeting basket cells , identified by their Slc17a8 ( vGlut3 ) expression , to dendrite-targeting cells , identified by expression of Calb1 or Reln [19 , 21] . Continent 7 , lacking Cxcl14 , was identified as Cck cells of other layers and contained multiple subtypes characterized by the familiar markers Calb1 , Vip , and Slc17a8 [21] as well novel markers such as Sema5a and Calca . Associated with continent 8 were several apparently novel subtypes: a rare and distinct group positive for both Scl17a8 and Calb1 and marked nearly exclusively by Lypd1; a Ntng1+/Ndnf+ subgroup related to cells of continent 6; and a group strongly expressing both Vip and Cxcl14 , which therefore likely corresponds to a novel Vip+/Cck+ interneuron at the sr/slm border . Continent 9 was identified as I-S interneurons . Its eight clusters fell into three groups: Calb2+/Vip− neurons identified as IS-1 cells , Calb2−/Vip+ neurons identified as IS-2 cells , and Calb2+/Vip+ neurons identified as IS-3 cells [2 , 24 , 26 , 60] . All expressed Penk [61] . These cells contained at least two novel subgroups: an IS-3 subtype positive for Nos1 and Myl1 , homologous to the Vip Mybpc2 class defined in isocortex [44] , and a rare subclass of IS-1 cells positive for Igfbp6 . Continent 10 contained a single highly distinct cluster located in an “island” off continent 1 . It contained cells strongly positive for Sst and Nos1 [27] , whose expression pattern is consistent with that of both backprojection cells [13] and PENK-positive projection cells [10] , suggesting that these three previously identified classes reflect a single cell type . Our finding of 49 clusters in a sample of 3 , 663 CA1 cells contrasts with a previous study of isocortical area V1 ( primary visual cortex ) , which found 23 clusters from a sample of 761 inhibitory neurons [44] . One can imagine three reasons for the greater number of clusters found in the present study: the larger sample size used here may have resulted in our resolving more clusters , the use of a different clustering algorithm may have allowed the current study to reveal finer cell types , or area CA1 might genuinely contain more diverse inhibitory neurons than isocortex . To address these questions , we performed two analyses . First , we applied our clustering algorithm to the data of Tasic and colleagues ( 2016 ) , and second we reanalyzed subsamples of the data of both the current study and of Tasic and colleagues ( 2016 ) to see how the number of clusters found varies with cell count and with sequencing depth . Applying the ProMMT algorithm to the Tasic dataset yielded 30 clusters ( S5A and S5B Fig ) . The cluster assignments almost completely overlapped as far as top-level groupings but showed some more subtle distinctions in finer-level clusters ( S5C Fig ) . We examined three of these novel classes’ differences in more depth , to ask whether the finer distinctions found by the ProMMT algorithm could correspond to genuine biological cell classes . The most notable of these was cluster 11 , which contained neurons that had previously been assigned to the neurogliaform clusters Ndnf Cxcl14 , Ndnf Car4 , but lacked common neurogliaform markers such as Lamp5 and Gabrd . Instead , cells in these clusters expressed Calb2 and Penk but not Vip , suggesting cells homologous to hippocampal IS-1 cells and potentially matching the Vip-negative I-S layer 1 “single-bouquet cells” ( SBCs ) described by Jiang and colleagues [62 , 63] . To test whether cluster 11 indeed corresponds to SBCs , we took advantage of a Patch-seq study [64] that contrasted gene expression in anatomically identified layer 1 SBCs and neurogliaform cells ( S5D Fig ) . We found that the genes that Cadwell and colleagues had reported as distinguishing SBCs from neurogliaform cells indeed occurred in almost entirely nonoverlapping populations of cells; furthermore , these populations closely matched the ProMMT clusters identified with SBCs and neurogliaform cells . Examination of two further subdivisions found by the ProMMT algorithm again revealed genes uniquely expressed in nonoverlapping subpopulations of the Sst Cbln4 and Vip Parm1 clusters ( S6 Fig ) . We conclude that the larger number of clusters identified by the ProMMT algorithm at least in part results from its ability to distinguish subtle variations in gene expression between related cell types . To ask whether the greater number of clusters found in the current study might in part arise from its larger sample size , we reran the cluster analysis on randomly selected subsets of cells from our dataset . We found a strong linear increase in the number of clusters found with increasing sample size ( S7A Fig ) . To investigate what effect sequencing depth may have had , we resampled our dataset to simulate lower read counts for the same cells , and again found an approximately linear increase in the number of identified clusters with read count ( S7B and S7C Fig ) . We performed similar analyses on Tasic and colleagues’ data and obtained similar results ( S7D and S7E Fig ) . We therefore conclude that the larger number of clusters found by the current study is more likely to reflect a combination of larger sample size and more sensitive clustering algorithms than a greater number of biological cell types in CA1 than in V1 . Furthermore , we expect that an even larger sample size or greater sequencing depth would have revealed yet more , finely distinguished cell types . Although the major continents of the expression map were clearly separated , clusters within these continents often appeared to blend into each other continuously . This suggests continuous gradation in gene expression patterns: while our probabilistic mixture model will group cells from a common negative binomial distribution into a single cluster , it will tile cells of continuously graded mean expression into multiple clusters . Although visualization methods such as nbtSNE can suggest whether classes are discrete or continuously separated , they are not sufficient to confirm the suggestion . Such methods exhibit local optima , raising the possibility that apparent continuity only occurs for particular initialization conditions . Furthermore , as nbtSNE is based on a subset of genes , it is conceivable that discrete/continuous patterns occur only for this subset . To confirm the apparent continuity or discreteness of these groups , we therefore employed a novel method of negative binomial discriminant analysis that is independent of nbtSNE and considers all genes . Given a pair of cell classes , this method compares how close each cell’s whole-genome expression pattern is to each class , using a cross-validated likelihood ratio statistic . For two classes identified as basket and axo-axonic cells , the histogram of likelihood ratios was clearly bimodal ( Fig 5A , top ) , indicating that every cell exhibited a much stronger fit to its own class than to the other , and confirming the discrete separation of these classes . A second example of clusters identified with Ivy and MGE-neurogliaform cells , however , showed different behavior ( Fig 5A , middle ) : a unimodal likelihood ratio histogram indicated that the two clusters ran smoothly into each other , tiling a continuum of gene expression patterns . The bimodality of the likelihood ratio can be captured by a d’ statistic , which for these two examples was 7 . 2 and 1 . 5 , respectively . Perhaps ironically , the degree to which two neighboring classes are discrete or continuous was itself a continuous variable . For example , Slc17a8-expressing Cxcl14/Cck neurons showed largely continuous overlap with their neighboring Cck/Cxcl14 cells , but with some small indication of bimodality , characterized by a d’ of 3 . 1 ( Fig 5A , bottom ) . We conclude that while truly discrete cluster separations do exist , the dataset is not fully described as a set of discrete classes , and that many clusters tile continuous dimensions ( Fig 5B ) . The existence of continuous variation in gene expression suggests that cluster analysis is not giving a complete picture of neuronal gene expression patterns . To further study the biological significance of continuously varying gene expression , we therefore applied a complementary method , latent factor analysis . Cluster analysis can be viewed as an attempt to summarize the expression of all genes using only a single discrete label per cell ( the cell’s cluster identity ) ; the value this label takes for each cell is not directly observed but is “latent” and inferred from the data . Latent factor analysis also attempts to predict the expression of all genes using only a single variable ( the “latent factor” ) , but now with a continuous rather than discrete distribution . As with cluster analysis , the latent factor is not directly observed but is inferred for each cell . Latent factor analysis operates without knowledge of cluster identity and therefore requires that the same rules be used to predict gene expression from the latent factor for cells of all types . Clearly , one should expect neither method to precisely predict the expression of all genes from a single variable , but the rules of cellular organization they reveal may provide important biological information . As expected , latent factor analysis produced a complementary view to cluster analysis ( Fig 6A ) . Knowing a cell’s cluster identity did not suffice to predict its latent factor value , and vice versa . For example , the ranges of latent factor values for cells in the clusters identified with Cck and Pvalb basket cells overlapped . Nevertheless , the range of possible latent factor values was not identical between clusters , and the mean latent factor value of each cluster differed in a manner that had a clear biological interpretation . The mean latent factor value of each cluster correlated with the axon target location of the corresponding cell type ( Fig 6A ) . The clusters showing largest mean latent factor values were identified with soma-targeting basket cells ( both Pvalb and Cck expressing ) and with axo-axonic cells . Lower values of the latent factor were found in clusters identified with dendrite-targeting Cck cells and with bistratified , Ivy , and hippocamposeptal cells , which target pyramidal cells’ proximal dendrites [14 , 23] . Still lower values of the latent factor were found in clusters identified with neurogliaform and O-LM cells , which target pyramidal distal dendrites . The lowest values of all were found in clusters identified with cells synapsing on inhibitory interneurons: the IS cells of continent 9 and the Sst/Penk/Nos1 cells of continent 10 , whose local targets are Pvalb cells [10] . While mean values of the latent factor differed between continents , there was also substantial variability within cells of a single continent . For example , a gradient of latent factor values was seen within continent 8 ( identified with Cck-positive neurons at the sr/slm border ) , with larger values in the west smoothly transitioning to smaller values in the east ( Fig 6B ) . Comparison of gene expression patterns in continent 8 to previous work again suggested that this gradient in latent factor values correlates with axon target location . Indeed , immunohistochemistry has demonstrated that CCK-positive cells expressing SLC17A8 ( expressed in western continent 8 ) project to the pyramidal layer [21] , while those expressing CALB1 ( expressed in the east ) target pyramidal cell dendrites [3 , 18 , 65] . The cannabinoid receptor Cnr1 , which is more strongly expressed in soma-targeting neurons [66 , 67] , was also more strongly expressed in western cells with larger latent factor values . As expected , the expression levels of many individual genes correlated with the latent factor; furthermore , the directions of these correlations were consistent , even within distantly related cell types . We investigated the relationships of genes to the latent factor by focusing initially on the Pvalb- and Cck-expressing cells of continents 2 and 8 ( Fig 6C ) . Most genes correlated similarly with the latent factor in both classes . For example , the Na+/K+ pump Atp1b1 and the GABA synthesis enzyme Gad1 correlated positively with the latent factor for multiple cell types , while 6330403K07Rik , a gene of unknown function , correlated negatively . Some genes’ expression levels depended on both cell type and latent factor value . For example , the ion channel Kcnc1 ( which enables rapid action potential repolarization in fast-spiking cells ) correlated positively with the latent factor in both Pvalb and Cck cells , but its expression was stronger in Pvalb cells , even for the same latent factor value . Other genes showed correlations with the latent factor , but only within the specific classes that expressed them . For example , expression of Pvalb correlated with the latent factor within cells of continent 2 , but the gene was essentially absent from cells of continent 8; conversely , Cnr1 expression correlated with the latent factor in continent 8 but was essentially absent in cells of continent 2 . Thus , the latent factor value is not alone sufficient to predict a cell’s gene expression pattern but provides a summary of continuous gradation in the expression of multiple genes in multiple cell types . The relationship of genes to latent factor values was statistically similar across cell types . To demonstrate this , we computed the Spearman correlation of each gene’s expression level with the latent factor , separately , within cells of each continent ( S1 Table ) . As expected from the scatterplots ( Fig 6C ) , the correlation coefficients for Atp1b1 , Gad1 , and 6330403K07Rik were similar between continents 2 and 8 ( Fig 6D ) . Also as expected , Pvalb and Cnr1 showed strong positive correlations with the latent factor within the continent where these genes were expressed , but correlations close to zero within the continent where they were barely expressed . In general , the correlation coefficients of genes with the latent factor were preserved between continents 2 and 8 ( Fig 6D; Spearman rank correlation ρ = 0 . 58 , p < 10−100 ) . A similar relationship was found across all continents ( Fig 6D , inset; p < 10−100 in each case ) , although cells of continents 9 and 10 showed less similarity than continents 1–8 . Furthermore , similar results were obtained when analyzing isocortical data , most notably in isocortical Pvalb cells ( S8 Fig ) . In summary , the expression of many genes correlates with a single continuous variable , the latent factor value assigned to each cell . While this latent factor does not provide a complete summary of a cell’s gene expression pattern , the direction and strength of the correlation of individual genes to the latent factor is largely preserved across cell types . Furthermore , while a cell’s latent factor value was not simply a function of its cell class , mean latent factor values differed between clusters , being largest for clusters identified with cell types whose axons target pyramidal somata or axon initial segments and smallest for clusters identified with cell types targeting pyramidal distal dendrites or interneurons . The above results suggest that the expression of a large set of genes is modulated in a largely consistent way across multiple cell types in a manner that correlate with their axonal targets . What biological functions might these genes serve ? While one might certainly expect structural genes be differentially expressed between soma- and dendrite-targeting interneurons , these cells also differ in their physiology . Indeed , Pvalb-expressing basket cells are known for their fast-spiking phenotype , which produces rapid , powerful perisomatic inhibition and is mediated by a set of rapidly acting ion channels and synaptic proteins , including Kcnc1 , Kcna1 , Scn1a , Scn8a , and Syt2 [8] . Although most other interneurons show regular-spiking phenotype , CCK-expressing basket cells with a fast-spiking phenotype have also been reported [18 , 20] . We therefore hypothesized that genes responsible for the fast-spiking phenotype might be positively correlated with the latent factor , because of increased expression in soma-targeting cells of all classes . Consistent with this hypothesis , genes associated with fast-spiking phenotype ( Kcnc1 , Kcna1 , Scn1a , Scn8a , Syt2 ) were amongst the genes most positively correlated with the latent factor in both Pvalb and Cck basket cells ( Fig 6D ) . However , this positive correlation was not restricted to these cell types: in an ordering of the correlations of all genes with the latent factor ( taking into account cells of all types ) , these genes ranked in the 99 . 9th , 98 . 3rd , 99 . 5th , 98 . 9th , and 95th percentiles , respectively ( S1 Table ) . Other gene families positively correlated with the latent factor included genes associated with mitochondria ( e . g . , mt-Cytb ) , ion exchange and metabolism ( e . g . , Atp1b1; Slc24a2 ) , GABA synthesis and transport ( e . g . , Gad1 , Slc6a1 ) , vesicular release ( e . g . , Syp , Sv2a , Cplx2 , Vamp1 ) , and fast ionotropic glutamate and GABA receptors ( e . g . , Gria1 , Gabra1 ) as well as GABAB receptors ( e . g . , Gabbr1 , Gabbr2 , Kcnj3 , Kctd12 ) . The genes correlating negatively with the latent factor were less familiar but included Atp1b2 , a second isoform of the Na+/K+ pump; Fxyd6 , which modulates its activity; Nrsn1 , whose translation is suppressed after learning [68] , as well as many neuropeptides ( e . g . , Sst , Vip , Cartpt , Tac2 , Penk , Crh; exceptional neuropeptides such as Cck showed positive correlation ) . Genes associated with neurofilaments and intermediate filaments ( e . g . , Nefh , Nefl , Krt73 ) tended to show positive weights , while genes associated with actin processing ( e . g . , Gap43 , Stmn1 , Tmsb10 ) tended to show negative weights . Many other genes of as yet unknown function correlated positively and negatively with the latent factor ( for example , 6330403K07Rik ) . Relating the latent factor correlations of each gene to their gene ontology ( GO ) annotations ( which are not granular enough to list annotations such as fast-spiking physiology ) suggested that negatively correlated genes tended to be associated with translation and ribosomes , while positively correlated genes were associated with diverse functions , including transcription , signal transduction , ion transport , and vesicular function and with cellular compartments , including mitochondria , axons , and dendrites ( S2 Table ) . We therefore suggest that cells with large values of the latent factor not only target more proximal components of pyramidal cells but also express genes enabling a faster spiking firing pattern , more synaptic vesicles , and larger amounts of GABA release; receipt of stronger excitatory and inhibitory inputs; and faster metabolism . These are all characteristics of Pvalb-expressing fast-spiking interneurons [8]; however , a similar continuum was observed within all cell types , suggesting that these genes are commonly regulated in all CA1 interneurons . The fact that the latent factor differs systematically between cells with different axonal targets suggests that this property is in good measure fixed , as it seems unlikely that neurons would make major changes to their axonal targets in adulthood . Nevertheless , interneuronal gene expression can be modulated by activity , and some of the genes that were most strongly correlated with the latent factor ( Pvalb , Kcna1 ) are amongst those with activity-dependent modulation [31–34 , 69] . To investigate whether the genes correlated with the latent factor might also be partially modulated by neuronal activity , we correlated each gene’s latent factor score with that gene’s modulation by in vivo light exposure after dark housing , using data from three classes of visual cortical interneurons ( made available by Mardinly and colleagues [33] ) . We observed a moderate relationship of latent factor weighting to activity modulation in Sst neurons ( r = 0 . 26; p < 10−12; S9 Fig ) , suggesting that activity-dependent modulation of Sst cells may cause them to move along the continuum of latent factor values . A weaker but still significant correlation was observed for Pvalb neurons ( r = 0 . 11; p < 0 . 002 ) , whereas no significant relationship was found for Vip neurons ( p = 0 . 17 ) . These data therefore suggest that a portion of the continuous variability of gene expression observed in CA1 interneurons may arise from activity-dependent modulation but that such modulation is unlikely to be a full explanation for the genetic continua revealed by latent factor analysis . The transcriptomic classification we derived makes a large number of predictions for the combinatorial expression patterns of familiar and novel molecular markers in distinct CA1 interneuron types . To verify our transcriptomic classification , we set out to test some of these predictions using traditional methods of molecular histology . Our first tests regarded the very distinct Sst . Nos1 cluster of continent 10 . This cluster’s expression pattern matched three previously reported rare hippocampal inhibitory cell types: large SST-immunopositive cells that are intensely immunoreactive for NOS1 throughout the cytoplasm , revealing their full dendrites [27]; PENK-positive projection cells [10]; and strongly NADPH diaphorase-labeled ( i . e . , NOS1-positive ) backprojection cells [13] . We therefore hypothesized that these cell types , previously regarded as separate , may in fact be identical . To test this hypothesis , we performed a series of triple and quadruple immunoreactions , focusing on the intensely NOS1-positive neurons ( n = 3 mice , n = 70 cells: 39% in so/alveus; 10% in stratum pyramidale ( sp ) ; 27% in sr; 24% at the sr/slm border ) . Similar to previously reported PENK-projection , backprojection , and SST/NOS1 cells [10 , 13 , 27]—but unlike SST-positive O-LM cells [9]—these neurons all showed spiny or sparsely spiny dendrites . As expected from the Sst . Nos1 cluster , we found that they were all SST/NPY double positive ( n = 20/20 ) and were virtually all weakly positive for CHRM2 ( n = 36/38 ) and GRM1 ( n = 17/17 ) in the somatodendritic plasma membrane , strongly positive for PCP4 ( n = 19/21 ) in the cytoplasm and nucleus , and strongly positive for PENK ( n = 35/42 ) in the Golgi apparatus and granules in the soma and proximal dendrites ( Fig 7 ) . By contrast , the more numerous moderately NOS1-positive cells ( which include many interneuron types such as ivy , MGE-neurogliaform , and a subset of IS-3 neurons ) were mostly immunonegative for CHRM2 , PCP4 , and PENK , although some were positive for GRM1 . Our results are therefore consistent with the hypothesis that all three previously reported classes correspond to the Sst1 . Nos1 cluster . A second prediction of our classification was the expression of Npy in multiple subclasses of Cck cell , most notably the Slc17a8- and Calb1-expressing clusters of continent 8 . This was unexpected , as NPY ( at least at the protein level ) has instead been traditionally associated with SST-expressing neurons and ivy/neurogliaform cells ( Fuentealba et al . , 2008a; Katona et al . , 2014 ) . Nevertheless , no studies to our knowledge have yet examined immunohistochemically whether the neuropeptides NPY and CCK can be colocalized in the same interneurons . We therefore tested this by double immunohistochemistry in sr and slm ( Fig 8A , n = 3 mice ) . Consistent with our predictions , 119 out of 162 ( 74% ± 6% ) of the cells immunopositive for pro-CCK were also positive for NPY ( an additional 73 cells were positive for NPY only , which , according to our identifications , should represent neurogliaform and radiatum-retrohippocampal cells ) . A subset ( 176 cells ) of NPY and/or pro-CCK immunopositive neurons were further tested for CALB1 in triple immunoreactions . As expected , nearly all CALB1-positive neurons were pro-CCK positive ( 89% ± 2% ) , and CALB1 immunoreactivity was seen in a subset of the cells containing both pro-CCK and NPY ( 27% ± 3% ) . Additional triple immunohistochemistry for NPY , pro-CCK , and SLC17A8 ( VGLUT3 ) revealed triple positive cells in sr and particularly at the sr/slm border , as predicted by the class Cck . Cxcl14 . Slc17a8 ( Fig 8B ) . Because of the low level of somatic immunoreactivity for SLC17A8 ( which , as a vesicular transporter , is primarily trafficked to axon terminals ) , we could not count these cells reliably; however , of the cells that were unambiguously immunopositive for SLC17A8 , in a majority we detected NPY . Additional analysis combining double in situ hybridization for Slc17a8 and Npy with immunohistochemistry for pro-CCK ( Fig 8C , n = 3 mice ) confirmed that the great majority of Slc17a8-expressing cells were also positive for Npy and pro-CCK ( 84% ± 3% ) . As predicted by our identifications , the converse was not true: a substantial population of Npy/pro-CCK double-positive cells ( 57% ± 7% of the total ) did not show detectable Slc17a8 , which we identify with dendrite-targeting neurons in the east of continent 8 . Several cell types in our classification expressed Cxcl14 , a gene whose expression pattern in the Allen Brain Atlas shows localization largely at the sr/slm border . The Cxcl14-positive population includes all clusters of continent 8 , which express Cck and contain subclusters expressing Npy , Calb1 , Reln , and Vip; a subtype of CGE-derived neurogliaform cell that expresses Reln and Npy but lacks Nos1 and expresses Kit at most weakly; as well as IS-1 , IS-2 , and radiatum-retrohippocampal cells . However , as all Cxcl14-positive clusters lacked Lhx6 , we conclude they should be distinct from all MGE-derived neurons , including MGE-derived neurogliaform cells . To test these predictions , we performed in situ hybridization for Cxcl14 simultaneously with in situ hybridization or immunohistochemistry to detect Reln , Npy , CALB1 , CCK , PVALB , Sst , Nos1 , and Kit ( n = 3 mice; Fig 9 ) . In addition , we combined fluorescent in situ hybridization for Cxcl14 with immunohistochemistry for yellow fluorescent protein ( YFP ) in Lhx6-Cre/R26R-YFP mice , which allows identification of developmental origin by marking MGE-derived interneurons ( Fogarty et al . , 2007 ) . The results of these experiments were consistent with our hypotheses . We found that within CA1 , Cxcl14-expressing cells were primarily located at the sr/slm border ( 71% ± 3% ) , although a subpopulation of cells were also found in other layers . We found no overlap of Cxcl14 with YFP in the Lhx6-Cre/R26R-YFP mouse , confirming the CGE origin of Cxcl14-expressing neurons ( Fig 9A ) . The majority of Cxcl14-positive cells expressed Reln ( 72% ± 4% ) , accounting for 42% ± 9% of Reln-expressing neurons ( substantial populations of Reln+/Cxcl14− cells located in so and slm likely represent O-LM and MGE-neurogliaform cells , respectively ( Fig 9B ) . Indeed , although less than half of Reln cells were located at the R-LM border ( 44% ± 1% ) , the great majority of Reln+/Cxcl14+ cells were found there ( 88% ± 6% ) . Consistent with the expected properties of continent 8 cells , a large fraction of the Cxcl14 population were immunoreactive for pro-CCK ( 62% ± 6%; Fig 9C ) , while substantial minorities were positive for CALB1 ( 29% ± 2%; Fig 9D ) or Npy ( 25% ± 5%; Fig 9E ) . However , as expected from the lack of Cxcl14 in MGE-derived neurogliaform and IS-3 cells , we observed no overlap of Cxcl14 with Nos1 ( 0 out of 209 cells; Fig 9F ) and very weak overlap with Kit , which is primarily expressed in clusters Cacna2d1 . Ndnf . Npy and Cacna2d1 . Ndnf . Rgs10 , associated with the Cxcl14-negative CGE-neurogliaform population ( 1 of 264 cells , respectively , from all mice; Fig 9G ) . The cluster Cck . Cxcl14 . Vip presented a puzzle , because Cxcl14 is located primarily at the sr/slm border , whereas immunohistochemistry in rat has localized CCK/VIP basket cells to sp [24] . Because Cxcl14 expression can sometimes also be found in sp , we tested whether this cluster reflects sp cells by combining in situ hybridization for Cxcl14 with immunohistochemistry against VIP in mouse CA1 ( Fig 10 ) . This revealed frequent co-expression at the sr/slm border ( 8% ± 1% Cxcl14 cells positive for Vip; 23% ± 1% Vip cells positive for Cxcl14 ) but very few Cxcl14 cells in sp , and essentially no double labeling ( 1 of 147 Vip cells in sp was weakly labeled for Cxcl14 ) . We therefore conclude that this cluster indeed represents a novel cell type located at the sr/slm border , expressing Cck , Vip , and Cxcl14 .
The molecular architecture of CA1 interneurons has been intensively studied over the last decades , leading to the identification of 23 inhibitory classes . Our transcriptomic data showed a remarkable correspondence to this previous work , with all previously described classes identified in our database . Our analysis also revealed a continuous mode of variability common across multiple cell types , eight hypothesized novel classes , as well as additional molecular subdivisions of previously described cell types . Surprisingly , these data suggest that three previously described CA1 cell groups in fact represent a single cell class , a fact previously overlooked because of the limited combinations of molecules tested in prior work . The Sst . Nos1 class is strongly positive for Nos1 and also expresses Sst , Npy , Chrm2 , Pcp4 , and Penk , but unlike Penk-positive I-S cells of continent 9 , it lacks Vip . This class is homologous to the “Int1” and “Sst Chodl” classes defined in isocortex , which have been identified with long-range projecting sleep-active neurons [44 , 46 , 70 , 71] . The three previously described classes identified with Sst . Nos1 are PENK-immunopositive neurons with projections to subiculum , which were shown to be VIP negative , but not tested for SST or NOS1 ( Fuentealba et al . , 2008b ) ; the NADPH diaphorase-labeled ( i . e . , strongly NOS1-positive ) axons reported by Sik and colleagues ( 1994 ) as projecting to CA3 and dentate , but not tested for SST or PENK; and the SST/NOS1 cells identified by Jinno and Kosaka ( 2004 ) in mouse , which were not tested for long-range projections or for PENK . While it remains possible that a larger transcriptomic sample of these rare neurons would reveal subclasses , our present data suggest that Sst . Nos1 cells are a homogeneous population: the nbtSNE algorithm , BIC criterion , and further manual exploration failed to reveal any finer distinctions . We therefore suggest that they constitute a class of inhibitory neurons with diverse long-range projection targets . Interestingly , the targets of PENK-positive projection cells are most commonly PVALB-positive interneurons , unlike conventional IS cells , which preferentially target SST cells [10] . As these cells are identified as sleep active , this fact may provide an important clue to the mechanisms underlying sleep in cortical circuits . The match between our transcriptomic analysis and previous immunohistochemical work ( primarily in rat ) is so close that it is simpler to describe the few areas of disagreement than the many areas of agreement . First , ACTN2 has been used as a neurogliaform marker in rat [72] but was almost completely absent from any cell type of our database . We suggest this reflects a species difference , as previous attempts with multiple ACTN2 antibodies have been unsuccessful in mouse ( J . H . -L . , unpublished observations ) , and Actn2 labeling is not detectable in the Allen atlas [73] . Second , we observed Calb2 in a subset of putative O-LM cells; these Calb2-expressing neurons typically also expressed Calb1 . Such O-LM cells have not been described in rat [9] , but CALB2/SST neurons have been observed in mouse isocortex [44 , 74] . A third inconsistency regards NCALD , which in rat was reported not to overlap with PVALB , SST , or NPY [75] , but did so in our data . Finally , it has previously been reported that a subset of O-LM cells show Htr3a expression [76] . In our data , we observed at most weak expression of Htr3a in Sst cells , and the cells showing it belonged to clusters identified as hippocamposeptal rather than O-LM cells . Our analysis revealed several rare and presumably novel cell groups , although we cannot exclude that some of these were inadvertently included from neighboring areas such as subiculum ( S10 Fig ) . Sst . Npy . Serpine2 and Sst . Npy . Mgat4c , which simultaneously expressed Sst , Npy , and Reln , fit the expected expression pattern of neither O-LM nor hippocamposeptal cells; Sst . Erbb4 . Rgs10 is a distinct group related to Pvalb basket and bistratified cells; Cck . Lypd1 formed a rare and highly distinct class expressing Cck , Slc17a8 , and Calb1; Ntng1 . Synpr showed an expression pattern with features of both sr/slm Cck neurons and projection cells; and Cck . Cxcl14 . Vip represents a cell class strongly positive for both Cck and Vip located at the sr/slm border that appears to be a pyramidal- rather than interneuron-targeting class . The analysis also revealed subdivisions of known types , such as the division of IS-3 cells into Nos1-positive and -negative groups , and the division of CGE-NGF cells into Car4- and Cxcl14-expressing subtypes . Finally , our data suggested that with more cells or deeper sequencing , even rarer types are likely to be found , as subsetting analysis showed a linear increase in the number of clusters with cell count and read depth , with little sign of saturation as yet . The data appeared to contain several novel cell types not containing enough cells to overcome the algorithm’s parsimony penalty , such as a small group of cells with features of both basket and axo-axonic cells located off the coast of continent 3; such cells have indeed been rarely encountered by quantitative electron microscopic analysis of synaptic targets in the rat ( P . S . , unpublished observations ) . Latent factor analysis revealed a common continuum of gene expression across the database , suggesting a large “module” of genes that are coregulated in multiple types of hippocampal interneuron . The latent factor differed between clusters , and clusters with larger latent factor values were identified with interneuron types targeting pyramidal cell somas or proximal dendrites ( such as Pvalb or Cck/Slc17a8 expressing basket cells ) , while those with low mean values were identified with interneurons targeting pyramidal distal dendrites ( such as Sst or Cck/Calb1 expressing dendrite-targeting cells ) or targeting other interneurons . Subtler differences in latent factor were found within clusters , suggesting that a similar continuum exists within cells of a single type . Genes positively correlated with the latent factor are associated with fast-spiking phenotype , presynaptic function , GABA release , and metabolism . Consistent with this expression pattern , perisomatic inhibitory cells show fast-spiking phenotypes and deliver powerful , accurately timed inhibition [8] , but interneurons targeting distal dendrites show slower-spiking patterns; presumably because distal inputs are subject to passive dendritic filtering , their presynaptic vesicle release does not need to be so accurately timed . I-S cells had the lowest mean values of the latent factor , consistent with their small axonal trees and metabolic machinery [77] . The stronger expression of many neuropeptides in cells of low latent factor suggests that these slower , distal-targeting interneurons may also rely more heavily on neuropeptide signaling , for which slow firing rates support outputs transduced by slower G-protein–coupled receptors . Interestingly , a study conducted independently of the present work identified enriched expression of a gene module similar to our latent factor in isocortical Pvalb neurons [43] and suggested it is controlled by the transcription factor PGC-1α [78 , 79] . Our results suggest that Cck-expressing basket cells have a similar expression pattern and that , more generally , expression of this module correlates with a neuron’s axonal target location . Several novel genes correlating with the factor appear to be interesting candidates for future research , such as Trp53i11 , Yjefn3 , and Rgs10 , associated with faster-spiking Cck cells; Zcchc12 and 6330403K07Rik , both associated with slower-firing cells of all classes; and Fxyd6 , associated with slow spiking , which may modulate ion exchange . Intriguingly , genes for neurofilaments and other intermediate filaments were positively correlated with the latent factor , while genes involved in actin processing were negatively correlated; we hypothesize that this might reflect a different cytoskeletal organization required for somatic- and dendritic-targeting neurons . The question of how many cell classes a given neural circuit contains is often asked of transcriptomic analyses , but we argue this question will not have a clearly defined answer . For example , our data indicate no sharp dividing line between ivy cells and MGE-derived neurogliaform cells . Yet cells at the two ends of the continuum are clearly different: not only do their gene expression patterns differ substantially , but their different axonal targets indicate different roles in circuit function [23] . In statistics , multiple criteria can be used to define how many clusters should be assigned to a dataset; a common approach ( which is used by the ProMMT algorithm ) is to consider a cluster indivisible if within-cluster fluctuations cannot be distinguished from random noise . Using this criterion , the number of clusters of CA1 interneurons increased with the number of cells and read depth analyzed , showing no sign of saturation in the current dataset . Furthermore , we observed several apparent rare classes that were too small to be assigned their own clusters at present , together with further subtle gradations within currently assigned clusters . The fact that we observed more clusters in CA1 than the 23 previously identified in isocortex [44] should therefore not be taken as implying that CA1 is a more complex circuit but simply that our larger sample size and different clustering algorithm were able to detect finer distinctions . Indeed , our data suggest that while the divisions between the 10 major “continents” are unambiguous , the organization of gene expression within these continents is complex and subtle , and likely far more detailed than characterized by our present 49 clusters . An understanding of this multiscale variability in gene expression in CA1 interneurons will be a key tool to understanding the function of this circuit .
Six adult ( 20 wk old ) male C57BL/6J mice ( Charles River , Oxford , UK ) were perfusion fixed following anesthesia and tissue preparation for immunofluorescence ( Katona et al . , 2014 ) and analysis using wide-field epifluorescence microscopy [21] was performed as described . The following primary antibodies were used: anti-calbindin ( goat , Fronteir Inst , Af104 ) ; anti-pro-CCK ( rabbit , 1:2 , 000 , Somogyi et al . , 2004 ) ; anti-metabotropic glutamate receptor 1a ( GRM1 , rabbit , 1:1 , 000; guinea pig , 1:500; gifts from Prof . M . Watanabe , Frontier Institute ) ; anti-muscarinic acetylcholine receptor 2 ( CHRM2 , rat , 1:400 , EMD Millipore Corporation , MAB367 ) ; anti-NOS1 ( rabbit , 1:1 , 000 , EMD Millipore Corporation , AB5380; mouse , 1:1 , 000 , Sigma-Aldrich , N2280 ) ; anti-NPY ( mouse , 1:5 , 000 , Abcam , #ab112473 ) ; anti-Purkinje cell protein 4 ( PCP4 , rabbit , 1:500 , Santa Cruz Biotechnology , sc-74816 ) ; anti-pre-pro-enkephalin ( PENK , guinea pig , 1:1 , 000 , gift from Takahiro Furuta , Kyoto University , Japan; rabbit , 1:5 , 000 , LifeSpan Biosciences , LS-C23084 ) ; anti-SST ( sheep , 1:500 , Fitzgerald Industries International , CR2056SP ) ; anti-VGLUT3 ( guinea pig , Somogyi et al 2004 ) . Secondary antibodies were raised in donkey against immunoglobulin G of the species of origin of the primary antibodies and conjugated to Violet 421 ( 1:250 ) ; DyLight405 ( 1:250 ) ; Alexa 488 ( 1:1 , 000 ) ; cyanine 3 ( 1:400 ) ; Alexa 647 ( 1:250 ) ; cyanine 5 ( Cy5 , 1:250 ) . With the exception of donkey‐antimouse‐Alexa488 purchased from Invitrogen , all secondary antibodies were purchased from Stratech . For cell counting , image stacks ( 212 × 212 μm area; 512 × 512 pixels; z stack height on average 12 μm ) were acquired using LSM 710/AxioImager . Z1 ( Carl Zeiss ) laser scanning confocal microscope equipped with Plan-Apochromat 40×/1 . 3 Oil DIC M27 objective and controlled using ZEN ( 2008 v5 . 0 Black , Carl Zeiss ) . In a second set of sections , images were taken using Leitz DM RB ( Leica ) epifluorescence microscope equipped with PL Fluotar 40×/0 . 5 objective . Counting was performed either using ImageJ ( v1 . 50b , Cell Counter plugin ) on the confocal image stacks or OPENLAB software for the epifluorescence documentation . For the CCK counts , numbers were pooled from two separate reactions testing for a given combination of primary antibodies ( n = 3 mice each reaction , 2–3 sections each mouse ) and reported as average values ± standard deviation . For the testing of intensely nNOS-positive neurons , cells were selected using Leitz DM RB ( Leica ) epifluorescence microscope equipped with PL Fluotar 40×/0 . 5 objective . Cells were pooled from three separate reactions testing for a given combination of primary antibodies ( n = 3 mice each reaction , 2 sections each mouse ) and reported as pooled data . Image processing was performed using ZEN ( 2012 Blue , Carl Zeiss ) , ImageJ ( v1 . 51m , open source ) , Inkscape ( 0 . 92 , open source ) , and Photoshop ( CS5 , Adobe ) . Wild-type ( C57BL/6/CBA ) male and female adult ( p30 ) mice and Lhx6-CreTg transgenic mice were perfusion-fixed , as previously described ( Rubin et al . , 2010 ) , followed by immersion fixation overnight in 4% paraformaldehyde . Fixed samples were cryoprotected by overnight immersion in 20% sucrose , embedded in optimal cutting temperature ( OCT ) compound ( Tissue Tek , Raymond Lamb Ltd Medical Supplies , Eastbourne , UK ) , and frozen on dry ice . 30 μm cryosections were collected in DEPC-treated PBS and double in situ hybridization was carried out as described ( Rubin et al . , 2010 ) . Probes used included either a Cxcl14- ( digoxgenin ) DIG RNA probe in combination with Reln- ( fluorescein ) FITC; Npy-FITC , Sst-FITC , or Vip-FITC probes; or a Cxcl14-FITC probe with Nos1-DIG , Kit-DIG , Scl17a8-DIG , or Pvalb-DIG probes . DIG-labeled probes were detected with an anti-DIG-alkaline phosphatase ( AP ) -conjugated antibody followed by application of a Fast Red ( Sigma ) substrate . The first reaction was stopped by washing 3 × 10 min in PBS , and the sections were incubated with an anti-FITC-Peroxidase ( POD ) -conjugated antibody ( 1:1 , 500—Roche ) overnight . The POD signal was developed by incubating the sections with Tyramide-FITC:amplification buffer ( 1:100 , TSA-Plus , Perkin Elmer ) for 10 min at room temperature . For immunohistochemistry after in situ hybridization , the following antibodies were used: anti-Calbindin ( rabbit , 1:1 , 000 , Swant , Bellinzona , Switzerland ) ; anti-pro-CCK ( rabbit , 1:2 , 000 , Somogyi et al . , 2004 ) ; anti-GFP ( chicken , 1:500 , Aves Labs ) . All sections were counterstained with Hoechst 33258 dye ( Sigma , 1 , 000-fold dilution ) and mounted with Dako Fluorescence Mounting Medium ( DAKO ) . For cell counts , images ( at least two sections per mouse ) were acquired on an epifluorescence microscope ( Zeiss ) with a 10× objective . Several images spanning the entire hippocampal CA1 were stitched using Microsoft Image Composite Editor . Cells were counted manually in the CA1 area , including sr and slm , and in a subregion spanning 100 μm across the border between sr and slm , where most Cxcl14-positive cells are located . Confocal images ( z stack height on average 25 μm , 2 μm spacing ) were taken on a Leica confocal microscope under a 10× objective and processed for contrast and brightness enhancement with Photoshop ( CS5 , Adobe ) . A final composite was generated in Adobe Illustrator ( CS5 , Adobe ) . Code for cluster analysis and all other algorithms can be found at https://github . com/cortex-lab/Transcriptomics . To measure how well separated each cluster is from its neighbors , we define an isolation metric equal to the deletion loss ( described in the previous section ) , divided by Nklog ( 2 ) , where Nk is the number of cells assigned to cluster k . This has an information-theoretic interpretation , as the number of additional bits that would be required to communicate the gene expression pattern of a cell in cluster k , using a code defined by the probability model if cluster k were deleted . Each cluster produced by the EM algorithm is specified by a mean expression vector . To understand the relationship between these cluster means , we applied a clustering method to the clusters themselves . This was achieved using Ward’s method , with a distance matrix given by the K-L divergence between cluster means , weighted by the number of cells per cluster . To visualize the locations of the cells , we derived a variant of the tSNE algorithm [51] appropriate for data following a negative binomial distribution . Stochastic neighbor embedding algorithms such as tSNE start by converting Euclidean distances between pairs of high-dimensional vectors xi into conditional probabilities according to a Gaussian distribution: pj|i=N ( xj;xi , σi2 ) /∑k≠iN ( xk;xi , σi2 ) . The tSNE algorithm then adjusts the locations of low-dimensional representation yi in order to minimize the K-L divergence of a symmetrized pj|i , with a t-distribution on the yi . The Gaussian distribution , however , is not the most appropriate choice for transcriptomic data . We found that we obtained better results using the same negative binomial distribution as in the ProMMT algorithm: pj|i=NB ( xj;xi , r ) /∑k≠iNB ( xk;xi , r ) where NB ( xj;xi , r ) =exp[∑g∈Sxgjlog ( xgixgi+r ) +rlog ( rxgi+r ) ] excluding a binomial coefficient that cancels when computing pj|i . The sum runs over the set of genes g that were chosen by the ProMMT algorithm . In the original tSNE algorithm , variations in distance between the points xi are overcome by adjusting the variance σi2 for each point i to achieve constant perplexity of the symmetrized conditional distributions . We took the same approach , finding a scale factor λi for each cell i to ensure that the scaled symmetrized distribution pji=expλi ( log ( pj|i ) +log ( pi|j ) ) had a fixed perplexity of 15 . This computation and the implementation K-L minimization was achieved using Laurens van der Maaten’s drtoolbox ( https://lvdmaaten . github . io/drtoolbox/ ) . The algorithm was initialized by placing all points on a unit circle , with angular position determined by their parent cluster , linearly ordered by the hierarchical cluster clustering . For comparison , we ran four other methods of tSNE analysis ( S4 Fig ) using either all genes or the 150 genes found by ProMMT , and either a Euclidean metric or a Euclidean metric after log ( 1+x ) transformation . Perplexity of 15 was again used and initialization was the same as before . Using all genes gave results that were difficult to interpret , particularly for log-transformed data , which we ascribe the noise arising from the large number of weakly expressed genes in the database . Using the gene subset provided more interpretable results , and combining the gene subset with log ( 1+x ) transformation yielded results similar to nbtSNE , while a Euclidean metric yielded a less clear distinction of isolated classes such as Cck . Lypd1 and Sst . Nos1 . We conclude that the alignment of nbtSNE to the probability distribution of RNA counts allows the algorithm to take into account differences between weakly expressed genes , and that a log ( 1+x ) transformation approximates this probability distribution . We also conclude that gene subsetting prevents noise from the large number of genes that do not differ between classes from swamping the signal , and that this is particularly important with algorithms sensitive to changes in weakly expressed genes . We suggest that nbtSNE provides a principled probabilistic method for choosing the transformation and gene subset required for informative tSNE analysis . To investigate whether a pair of clusters were discretely separated or tiled a continuum we developed a method of cross-validated negative binomial discriminant analysis . This analysis assesses the separation of two clusters k1 and k2 by computing the log likelihood ratio for each cell to belong to the two clusters . It is simple to show that this ratio for a cell c is given by Δc=∑g∈Gxc , g ( logpg , k1-logpg , k2 ) +r ( log ( 1-pg , k1 ) -log ( 1-pg , k2 ) ) The sum runs over all genes g in the database , not just the set S found by the ProMMT algorithm . The degree to which clusters k1 and k2 are discrete is visible by the bimodality of the histogram of Δc , which can be quantified using a d’ statistic , μ1-μ2 ( σ12+σ22 ) /2 , where μi and σi represent the mean and standard deviation of Δc for cells arising in cluster i . In this analysis , it is essential that the ratios Δc are computed on a separate “test set” of cells to the “training set” used to estimate pg , k , otherwise even a random division of a single homogeneous cluster would give an apparently bimodal histogram because of overfitting . To model continuous variation between cells , we used a negative binomial latent factor model . The model is parametrized by two matrices , W and F of size Ngenes × Nfactors and Nfactors × Ncells . The distribution of each cell follows a negative binomial distribution with mean μgc = r exp ( ∑f Wgf Ffc ) : Pr ( xgc;W , F ) =NB ( xgc;rexp ( ∑fWgfFfc ) , r ) This corresponds to the natural parameterization of the negative binomial , p = 1 / ( 1 + exp ( ∑f Wgf Ffc ) ) . As usual , we take a fixed value of r = 2 . For the analysis described in this study , we use only a single latent factor , but add a second column to F of all ones to allow the mean expression level to vary between genes . Given a dataset xgc , we fit the matrices W and F by maximum likelihood . As the negative binomial distribution with fixed r belongs to the exponential family , we can use the simple alternating method of Collins and colleagues [83] . Note that we do not require a sparse algorithm because ( unlike in clustering ) , the number of parameters is fixed . However , to avoid instability , only genes that have reasonable expression levels in the database are kept ( genes are included if at least 10 cells express at least 5 copies of the RNA ) , and a quadratic regularization penalty −50[|W|2 + |F|2] is added to the log likelihood . To relate the correlations of each gene with the latent factor to their GO categories ( S2 Table ) , we used the MGI mouse GO database ( downloaded 2 April 2018 ) , accessed via MATLAB’s bioinformatics toolbox . An enrichment score was computed for each GO term by summing the Spearman rank correlations of gene expression with the latent factor , over all genes annotated with that term . | Single-cell RNA sequencing allows scientists to count the number of copies of each gene expressed in multiple individually isolated cells . Because different cell types express genes in different amounts , “clusters” of cells with similar expression patterns are likely to correspond to different cell types . As well as discrete classes , however , cells also show continuous variation in gene expression . To study the relationship between cell classes and continua in a well-understood brain system , we applied new analysis methods to a dataset of inhibitory interneurons from area CA1 of the mouse hippocampus . Thanks to decades of intensive work , at least 23 classes of CA1 interneurons have been previously defined . We were able to identify them all with our transcriptomic clusters but unexpectedly found three to be identical . Because the connectivity of these cells has already been established , we were also able to identify the primary mode of continuous variation in these cells , which related to their axon target location . This in-depth understanding of the relatively simple cortical circuit of CA1 not only clarifies the cellular composition of this important brain structure but also will form a solid foundation for understanding more complex structures , such as the isocortex . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"methods",
"and",
"resources",
"applied",
"mathematics",
"neuroscience",
"factor",
"analysis",
"simulation",
"and",
"modeling",
"algorithms",
"mathematics",
"clustering",
"algorithms",
"statistics",
"(mathematics)",
"genome",
"analysis",
"interneurons",
"neuronal",
"dendrites",
"research",
"and",
"analysis",
"methods",
"animal",
"cells",
"mathematical",
"and",
"statistical",
"techniques",
"gene",
"expression",
"statistical",
"methods",
"cellular",
"neuroscience",
"cell",
"biology",
"neurons",
"genetics",
"transcriptome",
"analysis",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"genomics",
"computational",
"biology"
] | 2018 | Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics |
Many cellular stress-responsive signaling systems exhibit highly dynamic behavior with oscillatory features mediated by delayed negative feedback loops . What remains unclear is whether oscillatory behavior is the basis for a signaling code based on frequency modulation ( FM ) or whether the negative feedback control modules have evolved to fulfill other functional requirements . Here , we use experimentally calibrated computational models to interrogate the negative feedback loops that regulate the dynamic activity of the transcription factor NF-B . Linear stability analysis of the model shows that oscillatory frequency is a hard-wired feature of the primary negative feedback loop and not a function of the stimulus , thus arguing against an FM signaling code . Instead , our modeling studies suggest that the two feedback loops may be tuned to provide for rapid activation and inactivation capabilities for transient input signals of a wide range of durations; by minimizing late phase oscillations response durations may be fine-tuned in a graded rather than quantized manner . Further , in the presence of molecular noise the dual delayed negative feedback system minimizes stochastic excursions of the output to produce a robust NF-B response .
Many important signal transduction pathways contain a negative feedback motif consisting of an activator that activates its own repressor . Activated repression is capable of generating oscillatory behavior [1] and has been observed to do so in biological systems such as the Hes1 regulatory protein which controls neuronal differentiation [2] , the p53-Mdm2 system that mediates the DNA damage response [3] , and the NF-B ( Q04207 ) signaling network that governs the immune response and inflammation [4] , [5] . The role of activated repression is well understood in the context of transient signaling as functioning to limit the duration of the induced activity . Indeed , misregulation of the negative feedback mechanisms that control NF-B and p53 has been shown to generate prolonged inflammatory or genotoxic stress responses , respectively , that lead to cell death or chronic disease [6] , [7] . Further , negative feedback can sensitize and speed-up responses to weak or transient input signals [8] when compared to constitutive attenuation mechanisms . In contrast , the physiological role of oscillatory signaling behavior remains poorly understood . Recent work has shown that , in the calcium stress pathway in yeast , the frequency of nuclear localization of a stress-response transcription factor can be modulated by the magnitude of the extracellular calcium concentration , and this frequency modulation results in a coordinated expression of target genes [9] . In the NF-B and p53 signaling systems , the function of oscillations is still unknown . Oscillations in p53 activity were proposed to represent a counting mechanism that quantizes the response , ensuring a robust but appropriate amount of activity for a specific degree of DNA damage [10] . An alternate view was proposed in which oscillations of the p53-controlling ATM kinase activity allow for periodic sampling of the damaged DNA to track its repair and , if necessary , drive further p53 signaling to sustain the repair programs [11] . Oscillations in NF-B activity were proposed to determine which genes would be transcriptionally induced , thereby representing a temporal code that conveys information about the stimulus to gene promoters [5] . However , it is not clear whether or not the frequency encodes information in this systems as no differences in NF-B target gene expression were observed between oscillating and non-oscillating genetic variants [12] . Recent work has demonstrated that oscillations in NF-B activity can be generated by pulsatile stimulation with TNF ( P06804 ) [13] . However , an analysis of the repeated activation of NF-B that is driven by an oscillating signal provides little information about the role of oscillations that naturally arise with persistent stimulation . Thus , the role ( s ) of oscillations in NF-B activity remains unclear and several questions are still unanswered: Do these oscillations convey information encoded in the frequency to downstream processes ? Do they function to generate a periodically recurring phase of sensitivity to stimuli or regulatory crosstalk representing a potential “counting” mechanism ? Do they “quantize” the output signal , thus specifying robust units of activity ? Or , are the oscillations caused by persistent signaling simply a non-functional by-product of the requirement for the negative feedback architecture to enable sensitive , fast responses to transient stimuli ? Mathematical models comprised of a small number of equations have led to a greater understanding of biological processes in terms of molecular interactions , diffusion , dose responses , gradient sensing , the role stochasticity in gene expression and in fate decisions [14]–[17] . Although several models of networks with autoregulation have been developed [18]–[20] , most of these networks do not incorporate delays . In signaling , however , such elegant models often do not faithfully reproduce the dynamic behavior of the signaling system because actual biological networks involve many molecular interactions that tend to slow overall signal processing . Larger models comprised of many molecular species and parameters have proven useful in exploring dynamic signaling behavior via computational simulations in conjunction with experimental studies , but they are analytically intractable and therefore do not provide the degree of conceptual insights that small models do . Here we pursue an alternative approach to modeling NF-B signaling . We construct a new model that replaces cascading reactions with a single but delayed compound reaction that enables both recapitulation of experimentally observed dynamics and the use of powerful analytical tools . With these tools , we explore the physiological function of the dynamic behavior of NF-B produced by the activated repression mechanism mediated by its inducible inhibitors , IB ( Q9Z1E3 ) and IB ( O54910 ) . The mathematical analysis results in predictions that are addressed experimentally and thus lead to fundamental insights about the function and origins of this signaling system .
The basic structure of the NF-B signaling module is shown in Figure 1 A [4] . In resting cells , NF-B is sequestered in the cytoplasm by IB proteins . Cellular stimulation leads to activation of the IB kinase ( IKK ) which phosphorylates IB proteins thus targeting them for degradation . Upon degradation of IB proteins , NF-B moves into the nucleus and activates hundreds of target genes including the predominant IB isoform , IB . Synthesized IB enters the nucleus , binds to NF-B , and the IB-NF-B complex is exported back to the cytoplasm . Thus , the core feature of the NF-B signaling module is a negative feedback loop mediated by IB . This can be abstracted to a simple motif in which ( NF-B ) activates ( IB ) , represses , and repression of by is relieved by ( active IKK ) ( Figure 1B ) . Using this motif as a guide , we formulated our model of the IB-mediated NF-B response as a set of 9 reactions and 6 variables ( Tables 1 , 2 ) . Specifically , the model assumes that the total number of the NF-B molecules ( ) is conserved , however they can exist either in free/nuclear form ( ) or sequestered outside of nucleus within the IB-NF-B complex ( ) . The model contains non-delayed reactions for the binding of free NF-B to the unbound IB promoter ( ) to form the bound IB promoter ( ) , binding of IB protein ( ) to free NF-B to form the IB-NF-B complex , constitutive degradation of IB , and induced degradation of free and bound IB proteins by the active IB kinase IKK ( ) producing free NF-B . In contrast , a compound delayed reaction describes the synthesis of IB protein . This reaction involves a time delay , which represents the time needed for transcription , translation , nuclear import and export , and protein-protein interactions . Using experimentally validated assumptions , we reduced the set of mass-action kinetics equations for the 9 reactions to a single delay-differential equation: ( 1 ) where is the total IB concentration ( the sum of free IB ( ) and IB bound to NF-B ) , , , are the probabilities for the IB promoter to be free or bound to NF-B , respectively , , , and the subscript denotes the variable taken at time ( see Methods for details of the derivation ) . The rates of individual reactions are defined in Table 2 . Mirroring the biological system , the non-dimensional time-dependent parameter , which characterizes the active IKK concentration , is used as the proxy input signal . The first term in the r . h . s . of Eq . 1 represents constitutive synthesis from the unbound IB promoter , the second term represents induced synthesis from the NF-B-bound IB promoter , the third term represents constitutive degradation of IB protein , and the fourth term represents IKK-induced degradation of IB . Values of correspond to the rate of IKK-induced degradation of NF-B-IB complex which is of the same magnitude as unbound IB . Nuclear NF-B level at any time can be determined directly from IB levels via . The time delay is incorporated in the synthesis terms: we assume that the rate of production of new proteins at time depends on the state of the system at time . Incorporating this time delay allows us to explore the behavior of the negative feedback loop without simulating the full set of reactions associated with it . We obtained values for the time delay and for the other model parameters by calibrating the behavior of the model with experimental results ( Table S1 ) . As a starting point , we used parameter values from biochemical measurements [21] . However , some modifications were necessary because these values represent the rates of single reaction steps and the model contains compound reactions . To validate the model , we compared it to experiments . In response to a persistent input signal ( starting at time ) , our simulations of the IB-mediated negative feedback system show pronounced oscillations in nuclear NF-B levels with an oscillation period of about 90 minutes ( Figure 1 C ) . Oscillations with a similar period were observed experimentally when mutant cells containing only the IB feedback loop were persistently stimulated with the inflammatory cytokine TNF ( Figure 1D ) . To address the dynamics of the wild-type NF-B system that feature both IB and IB feedback loops , we expanded the model to include an additional 9 reactions and 4 variables involving IB ( Tables 3 , 4 ) . Following the same reduction procedure ( see Methods for derivation ) , we derived a deterministic model consisting of two coupled delay-differential equations for the concentrations of the two IB isoforms , IB ( ) and IB ( ) , ( 2 ) ( 3 ) where , , , , , , and , . Parameter here is the scaling factor which characterizes the relative strength of the secondary feedback loop . In Eqs . 2 and 3 , represents total IB ( the sum of free IB ( ) and IB bound to NF-B ( ) , and represents total IB ( the sum of free IB ( ) and IB bound to NF-B ( ) ) . The terms in the r . h . s . of Eqs . 2 and 3 again represent constitutive synthesis from the identical unbound IB and IB promoters , induced synthesis from the NF-B-bound promoters , constitutive degradation of IB and IB proteins , and IKK-induced degradation of IB and IB . Nuclear NF-B levels are determined directly by IB and IB levels . Parameter values for the IB-mediated reactions were determined in the previous section . For the IB feedback reactions , we use the same parameter values except for the constitutive synthesis and the constitutive degradation rates , which were chosen based on experimental measurements [21] ( Table S1 ) . The advantage of our modeling approach is that it allows for analytical studies of the network dynamics . Here , we perform a linear stability analysis of the delay-differential equation ( 1 ) to identify the characteristic period and decay rate of NF-B oscillations produced when input signal is present ( ) . For sufficiently large , induced synthesis and degradation are much stronger than basal ones , so the latter can be neglected ( ) . Expressing via and substituting it into , yields a closed equation for in the form ( 4 ) where and the function has the form ( 5 ) The fixed point ( stationary solution ) of this equation is given by the algebraic equation ( 6 ) The stability of this solution is determined by the eigenvalue of the linearized equation ( 4 ) linearized near the fixed point ( see Methods for details ) . The corresponding eigenvalue can be found in terms of the Lambert function defined via , ( 7 ) The imaginary part of gives the oscillation frequency , and the ( negative ) real part of gives the decay rate of oscillations . Plotting the period ( ) ( Figure 2A ) and decay ( ) ( Figure 2B ) of the oscillations as a function of the delay reveals a strong dependence . In contrast , the signaling perturbation ( the active IKK kinase ) that acts as the input for the model determines the amplitude of the response but only negligibly affects the period or the oscillation decay ( Figure 2B ) . The mathematical reason for this asymmetry is that the imaginary part of the Lambert function for negative values of its argument changes very weakly for arguments below ( , ) and asymptotically approaches for very large negative values of the argument . This is why the period of dampened oscillations ( ) depends strongly on delay and only very weakly on . Meanwhile , the real part of the eigenvalue ( the decay rate ) is linearly proportional to because of the second term in Eq . ( 7 ) and also strongly depends on because of the first term . Thus , we find that the period is highly dependent on the delay but is rather insensitive to changes in the input level . This is confirmed by direct simulations of the full nonlinear equation ( 1 ) , where time series of are plotted for several different values of and ( Figure S1 ) . Since variations of stimulus do not lead to significant frequency modulation of NF-B activity , oscillations of NF-B are unlikely to encode information about the stimulus . The main qualitative difference between the one-loop system considered in the previous section , and the wild-type NF-B module is the presence of another IB isoform , IB , which also provides negative feedback regulation on NF-B activity ( Figure 3A , B ) , however with slower kinetics [21] . Experimental and computational work has shown that IB-mediated feedback can cause damping of IB -mediated oscillations [21] and ( Figure 3C ) . More recent computational work has predicted that IB-mediated feedback desynchronizes oscillations but does not dampen oscillations in single cells [13] . Thus , the mechanism by which IB-mediated feedback produces damped oscillations at the population level is not well established . Furthermore , it is unknown whether the damping function of the IB-mediated feedback loop has evolved to achieve a specific regulatory function or may simply be a secondary consequence of another function . We hypothesize that the primary role of the second feedback loop is to mitigate oscillatory behavior produced by the first feedback loop . To address our hypothesis that IB-mediated feedback specifically evolved to dampen IB-mediated oscillations , we performed a parameter optimization procedure on the wild-type model ( Eqs . 2 and 3 ) to determine the IB synthesis parameters that result in maximum damping . To characterize the degree of damping , we chose the maximum peak-trough difference after 6 hrs as a metric for the persistence of oscillations . According to the definition of this performance metric , “optimal damping” occurs when this metric is minimized . In our optimization procedure , we varied two important parameters , the time delay of the second feedback loop and the scaling factor which simultaneously varies the rates of constitutive and induced synthesis of IB . Choosing is equivalent to the complete removal of the IB-mediated negative feedback loop while represents the case in which the inducible synthesis rates for IB are the same as for IB . The two-dimensional optimization search is shown in a color map ( Figure 3D ) indicating that the performance metric is minimized at . Time course simulations with the optimized parameter set show a high degree of damping ( Figure 3G ) similar to what is observed experimentally ( Figure 3C ) . To determine whether these optimized parameter values correspond to observations , we measured relevant parameter values experimentally . The synthesis delays for IB and IB were determined by measuring IB and IB mRNA levels in a time course of TNF-treated murine embryonic fibroblasts ( MEFs ) in multiple independent experiments ( Figure 3E , S2A , B ) . The measured delay for IB was , and for IB , which agrees well with the model prediction for optimal damping . Since it is difficult to measure the promoter strength experimentally , we employed an implicit way of comparing experiment with the model . To relate the parameter value to experimental measurements , we set in the model and calculated the ratio of peak values for IB and IB proteins , which we found to be equal 3 . 9 . Then we measured the ratios of basal ( unstimulated ) to peak protein levels for IB and IB in experiment via quantitative Western blots of whole cell lysates generated during a TNF time course . These were compared to recombinant protein standards to derive absolute molecule number per cell . Peak IB protein levels were measured to be 379 , 800 molecules per cell , and IB 71 , 300 molecules per cell , with both values being subject to an estimated 25% error ( Figure 3F , S2C , D ) . These protein levels correspond to the experimental peak values ratio which is close to the model prediction . We next addressed why the NF-B signaling module may have evolved to produce oscillatory behavior if the oscillation frequency is not a function of the stimulus and does not constitute a signaling code . We first simulated persistent stimulation of a variant NF-B system without feedback ( we assume that IB is constitutively produced , so , in Eq . 1 ) and found that this system produces long term , non-oscillatory NF-B activity ( Figure 4A Top , blue line ) . As TNF is secreted in bursts and therefore perceived by surrounding cells as transient or pulse stimulation , we then performed stimulations of pulses 15 , 30 , and 45 min in duration . In the negative feedback-deficient NF-B system , the pulses resulted in transient responses that were attenuated very slowly . Faster attenuation can be achieved by increasing the constitutive synthesis rate , . Increasing by two orders of magnitude results in pulse NF-B responses to transient stimuli , but the responsiveness ( in amplitude ) is much reduced ( Figure S4 ) . We then performed similar simulations in a single negative feedback loop NF-B system and found that this network topology allows for a rapid shutdown of NF-B activity for transient inputs ( Figure 4A Middle ) . This suggests that the NF-B network may have evolved from a pathway without feedback to a pathway with a single negative feedback loop to allow for a more sensitive transient response . Although the negative feedback indeed allows for greater sensitivity , a secondary consequence is that pronounced oscillations arise when the input signal persists for a long time period ( Figure 4A Middle , blue line ) . The addition of a second negative feedback loop with a different time delay can help to dampen these oscillations , while preserving the responsiveness of the signaling module to transient stimuli ( Figure 4A Bottom ) . By plotting the duration of the response ( above a given threshold ) we investigated what may be called “temporal dose response curves” of the single and dual feedback systems ( Figure 4B ) . The dual feedback system has a response duration close to 60 min for short pulses ( min ) , and a duration proportional to the input duration for longer pulses . The single feedback system has the same behavior as the dual feedback system for short inputs , but for longer inputs the single feedback system produces a quantized response with the same output duration for several different input durations . Our analysis indicates that a dual feedback system is able to produce temporally graded responses , whereas a single feedback system that oscillates does not . Given that the duration of the second phase of the NF-B response to TNF is a critical determinant of gene expression programs [4] , we suggest that the NF-B system has evolved a dual feedback system that allows for NF-B activity whose duration is more closely related to the duration of the cytokine stimulus . This fine temporal control , achieved via dual negative feedback , may be critical for complex cytokine-mediated cell-to-cell interactions involved in the adaptive immune response present in vertebrates , but may not be necessary for innate patogen-induced immune responses . We hypothesized that , on an evolutionary timescale , the appearance of dual negative feedback loops that regulate NF-B activity may coincide with the transition from an innate to an adaptive immune system . To address this hypothesis , we used BLASTP with an E-value cutoff of 1e-25 to search for homologs of the mouse IB and IB protein sequences in other organisms ( see Methods ) . We found homologs for both IB and IB , not only in other mammals ( such as chimp , dog , platypus ) , but also in other vertebrate classes including fish , amphibians , and birds ( Figure 5 ) . Thus , dual negative feedback regulation of NF-B activity appears to be present in all organisms with adaptive immunity . In contrast , we did not find any invertebrate organisms with homologs for both IB and IB ( Figure 5 ) . Therefore , invertebrates , which lack adaptive immunity , also appear to lack the potential for dual negative feedback regulation of NF-B mediated by IB and IB suggesting that the temporal control achieved with this regulatory architecture is not necessary for innate immune responses . Thus far , we have examined the response of the network to transient stimulation in the absence of fluctuations . However , it is well known that noise in gene expression can cause significant variability in cellular responses [18] , [22]–[26] . Sometimes this variability can be beneficial [27] , but in most cases , noise has a detrimental effect on the robustness of cellular functions . Mechanisms have presumably evolved to mitigate the unwanted effects of noise , especially in signaling pathways . In this section we examine the variability in the response of the NF-B module that arises due to intrinsic and extrinsic noise , and we demonstrate that the dual-feedback loop architecture allows for a more robust response than the single feedback loop system . Further , we investigate how the relative contribution of intrinsic and extrinsic fluctuations depends on the size of the system . The concentration of signaling molecules such as NF-B can vary significantly between cells [28] . This variability in protein levels represents a source of extrinsic noise . We examined the variability in the response of the network to fluctuations in the total level of NF-B and fluctuations in the IKK input level by simulating the network behavior with total NF-B levels and active IKK levels distributed within a certain rage around their nominal values . The coefficient of variation ( CV ) in peak nuclear NF-B levels and the CV in late-phase nuclear NF-B levels is defined as where ( ) are the maximum ( minimum ) values of NF-B at the peak or during the late phase . NF-B late-phase response is defined as the nuclear NF-B level following the trough after the first peak response . In Text S1 we compare the extrinsic CV in the peak and the late phase for various values of IKK and NF-B ( see Figure S5 ) . Intrinsic noise arises from the stochastic nature of biochemical processes such as transcription and translation [24] . To examine the response of the NF-B signaling module in the presence of intrinsic genetic noise , we used the Gillespie algorithm [29] modified according to [30] to perform stochastic simulations of both regular and delayed biochemical reactions included in our delayed feedback model . These latter reactions are initiated at times dictated by their respective rates , but the numbers of molecules are only updated after the time delay since the reaction initiation . We ran stochastic simulations of both a single and dual feedback system and estimated the ensemble average of the number of NF-B molecules and the magnitude of fluctuations as characterized by the standard deviation and the coefficient of variation . To determine how the variability in the response varies with the magnitude of the input and the size of the system , we determined the CV in peak nuclear NF-B levels and the CV in late-phase nuclear NF-B levels for several values of IKK ( Figure 6A , C ) and for systems with up to 100 , 000 NF-B molecules ( Figure 6B , D ) . In Figure 6 , we also plot CV values for extrinsic variations ( ) in total NF-B at several values of IKK ( Figure 6A , C ) and CV values for extrinsic variations in IKK ( ) for several different system sizes ( Figure 6B , D ) . We find that , even with this relatively low level ( ) of extrinsic variability in IKK and NF-B protein levels [28] , variability in the response of the network is dominated by extrinsic noise for large systems ( NF-B molecules ) . The CV in late-phase nuclear NF-B levels is similar for extrinsic and intrinsic noise when the size of the system is reduced to 1000 NF-B molecules . Next , we investigated the behavior of the NF-B signaling module in this regime where intrinsic noise levels become significant by analyzing stochastic simulations produced with a system with total NF-B levels set to 1000 molecules . We ran stochastic simulations of all three systems studied deterministically above: no-feedback , single negative feedback , and dual negative feedback ( Figure 7 ) . Note that ensemble-averaged time series agree with the deterministic simulations very well ( Figure S6 ) . In the case of no feedback ( Figure 7A ) there is a strong robust response to the incoming persistent signal as characterized by the low values of the coefficient of variation . However , as we have seen above in Figure 4A , the major flaw of this system is its slow response to the pulse-like signals . Next , we simulated the 9 biochemical reactions included in the IB-mediated single negative feedback loop ( Figure 7B ) . In single runs the first peak in nuclear NF-B levels appears to be very robust , as illustrated by Figure 7B Top . The CV is lowest ( ) during the first peak in nuclear NF-B indicating that this portion of the response is very robust . Subsequent peaks in this undamped system lead to higher CV ( ) in the later portion of the response . Next , we performed stochastic simulations of the 18 biochemical reactions included in the dual delayed feedback model ( with both IB- and IB-mediated feedback ) ( Figure 7C ) . In the dual feedback model , as in the single IB-mediated feedback model , there is a very robust first peak . However , unlike the single IB-mediated feedback model , in the dual feedback system the noise levels remain at a low level ( ) following the first peak in nuclear NF-B ( Figure 7C Bottom ) . Thus , the dual feedback architecture allows for lower noise levels also in the later portion of the response . What is the underlying reason for the robustness of the initial response from this circuit ? The main source of intrinsic noise lies in the transcription and translation of IB isoforms , since they are transcribed from single genes . In contrast , fluctuations in protein degradation and transport processes are relatively small , because the copy numbers of the corresponding molecules are large . In the NF-B network , the peak in nuclear NF-B levels that occurs following stimulation is produced via the degradation of IB proteins that bind and sequester NF-B in the cytoplasm . Thus , we argue that robustness of the initial response of the NF-B circuit is explained by the fact that it uses the sequestering mechanism and does not rely on the protein production . To test this hypothesis , we simulated the behavior of an alternative network that relies on transcription of auto-repressor , rather than the degradation of inhibitor proteins , for signaling ( Figure 7D ) . This system can be modeled with two variables: , the number of repressor molecules , and , the binary state of the promoter ( corresponds to the unbound promoter and corresponds to bound promoter ) , and with four reactions ( binding and unbinding of the repressor to the promoter , degradation of the repressor , and delayed synthesis of the repressor with rate where is the external signal ( Tables S2 , S3 ) . The input signal activates the production of the auto-repressor which after a certain time delay binds to the promoter and terminates further synthesis . Deterministically , this circuit also provides a desired response to a persistent stimulation with a large well-defined first peak . However , stochastic simulations reveal significant differences in the noise performance of this design as compared with the NF-B circuit ( note that the agreement between deterministic and stochastic simulations is less accurate in this case because of the strong promoter fluctuations ( Figure S6D ) . Activation of the auto-repressor network is much less robust than the activation of the NF-B network ( cf . Figure 7D and Figures 7B , C ) . In fact , in the auto-repressor network , the coefficient of variation is highest ( ) during the initial peak ( Figure 7D Bottom ) . These results confirm our conjecture that the sequestering mechanism incorporated in the design of the NF-B network gives rise to a much more robust activation of NF-B than alternative networks that rely on transcription for activation and signaling . This finding is in accord with recent work [31] where the sequestering of Cdc20 protein was also implicated in the noise resistance of the spindle assembly checkpoint . As we mentioned previously , recent computational work has suggested that persistent oscillations are present in wild-type cells with both IB- and IB-mediated feedback but stochastic variability leads to desynchronization among individual cells and therefore produces damped oscillations at the population level [13] , [32] . Our computational results demonstrate that , although stochastic oscillations are still present in individual cells with both IB- and IB-mediated feedback ( Figure 7C ) , the oscillatory propensity can be greatly reduced by the second feedback loop in the wild-type NF-B signaling module . Further , stochastic simulations of the dual-feedback network reveal highly synchronized damped oscillations ( Figure S7C ) with cellular variations due to intrinsic noise becoming significant only when the system size is drastically reduced ( Figure 7C ) . To show that our results are not limited to the conceptual NF-B model introduced above , we simulated the more detailed stochastic NF-B model formulated in [32] , which explicitly incorporates IKKK/IKK signaling cascade and NF-B shuttling between the nucleus and the cytoplasm ( see Methods and Figure S8A ) . One of the key assumptions made in the model [32] is that the strong stochasticity of the NF-B response is caused by the slow and stochastic binding/dissociation of NF-B to the corresponding promoters of IB , IB , and A20 target genes . The slow rates chosen by the authors for these reactions lead to the high variability of oscillatory dynamics among cells ( Figure S8B ) . However , there is experimental evidence that the binding time of NF-B may be significantly shorter , at least in certain types of cells . According to Fluorescence Recovery After Photobleaching ( FRAP ) measurements in HeLa cells [33] , the typical time scale of NF-B binding to the target promoters is on the order of a second rather than minutes , suggesting more rapid equilibration between the NF-B-bound promoters and the pool of unbound nuclear NF-B molecules . We found that increasing the binding and dissociation rates by times profoundly changes the dynamics of the signaling system . NF-B trajectories become more regular , suggesting that the behavior of individual cells translates more directly into the behavior of the population ( Figure S8C ) . After adjusting the binding/dissociation rates along with a few other parameters ( Table S5 ) , the updated model recapitulated the population response to chronic TNF stimulation under various genetic conditions ( WT , , and ) ( Figure S9 ) in agreement with earlier experimental results [4] , [21] , [34] . To quantify the magnitude of the late oscillatory NF-B response to a chronic TNF stimulation , we chose as a metric the average maximum peak-trough difference 5 hrs after initial stimulation . This quantity can be computed in two different ways . The mean single-cell variability can be characterized by the magnitude found by computing the maximum peak-trough differences for individual trajectories , and then averaging them over all trajectories: ( 8 ) The population-level variability can be characterized by the magnitude which is found by first computing an average trajectory and then computing its maximum peak-trough difference: ( 9 ) If the stochasticity is small , these two measures are similar , however for strong stochasticity they may differ significantly . Using these metrics , we first confirmed that for the parameter values adopted by [32] , the model shows significant single-cell oscillations both in the and in the WT , independently of the time delay in the IB loop ( , Figure 8A ) , but the population-averaged response shows significant oscillation dampening for the time delay around 45 min ( , Figure 8C ) . However , for our re-parameterized model with fast binding/dissociation , the stochasticity of individual trajectories is small , and both metrics show similar trend: the amplitude of oscillations in the WT is strongly suppressed at the optimal time delay of 45 min both for the population average ( Figure 8B ) and the individual cells ( Figure 8D ) , which falls within the margin of error of our experimental results ( Figure 3D ) .
In this work we have developed a minimal model of the NF-B signaling pathway that uses a small number of reactions ( some of them compound ) thus making it amenable to mathematical analysis . Previously , another simplified model of NF-B signaling was developed in which a massive overshoot in IB resulted in an effective slowing of signaling dynamics [35] , and produced spiky oscillations that are not seen in physiological conditions . Our model , which utilizes an explicit time delay , recapitulates experimentally observed signaling behavior . It demonstrates that models with explicit time delays can be useful for investigating the mechanistic basis of the dynamic behavior of signaling pathways . Using this model , we explored the potential role of NF-B oscillations which are observed in a variant of the NF-B signaling module with the secondary negative feedback loop involving IB , disabled . In particular , we addressed the question of whether the frequency of these oscillations contains information , as in neurons which sometimes encode information in the frequency of action potentials [36] and in the activation of the transcription factor NF-AT which is responsive to the number of pulses [37] . By analyzing the oscillatory response of a system regulated solely by the IB-mediated negative feedback loop , we found that both the frequency and the decay rate of the oscillations produced by this system are highly dependent on the internal parameters of the circuit , but are not sensitive to changes in the input signal levels . This result suggests that the oscillatory frequency does not encode information about the stimulus . Hence , stimulus-specific gene expression is unlikely determined by stimulus-specific frequencies of NF-B oscillations . If there is a temporal code for stimulus-specific gene expression it is unlikely to involve frequency modulation , but may involve amplitude modulation over time . When a second feedback regulator , IB , is added to the model , the oscillations caused by a persistent stimulation are significantly dampened , in agreement with our earlier findings [21] . By performing an optimization procedure , we determined that the specific experimentally observed parameter values for the synthesis delay and peak protein abundance of both IB isoforms correspond to maximal efficiency of damping . These findings suggest that the second feedback ( IB ) has evolved to produce damping of the oscillatory behavior of the first feedback ( IB ) . Furthermore , we demonstrated that this finding is not limited to our simple model , but can be expanded to more complex models . For example , in a recent model by [32] with fast binding/unbinding rates of NF-B the secondary IB feedback leads to a reduction in NF-B oscillations in individual cells . However , cell-cell variability and extrinsic noise can further reduce NF-B oscillations on a population level . From the evolutionary perspective , we have a peculiar situation in which a signaling module apparently first developed a negative feedback loop that made it prone to oscillations , and then added a secondary loop which mitigated these oscillations . This brings the question , if oscillatory responses are not beneficial to the cell , why has the primary negative feedback appeared in the system in the first place ? By comparing transient response of several variants of signaling modules ( 0- , 1- and 2-feedback loop designs ) in the presence of stochastic fluctuations we showed that the primary negative feedback loop involving the release of sequestered NF-B proteins created a strong , rapid , and robust response to short pulses of active IKK signal . However , for longer signals a single-feedback-loop system exhibits a suboptimal “temporal dose response behavior” that leads to a quantized response to signals of different durations . In contrast , the dual feedback network generates response durations that are proportional to the stimulus input durations . Fine-tuning of the response duration may be reflective of a signaling code in which duration of NF-B activity may be a key determinant of stimulus-specific gene expression program . Cytokines such as TNF facilitate adaptive responses at the effector stages [38] . The evolution of cytokines is associated with the evolution of an adaptive immune system to allow for coordination of various cell types [39] . Unlike pathogen exposure , cytokines are produced during varying amounts of time thereby generating time-varying signals . Our analysis showed that the dual negative feedback module is more capable at distinguishing differences in the duration of incoming signals . This function is important for the transduction of cytokine signals , but not pathogen signals . Our BLASTP analysis indeed demonstrates that the evolution of the dual negative feedback system may correlate with the evolution of adaptive immunity .
Using mass action kinetics , the full set of reactions for the dual feedback loop NF-B system ( Tables 2 , 4 ) can be expressed by the following ODEs: ( 10 ) ( 11 ) ( 12 ) ( 13 ) ( 14 ) ( 15 ) ( 16 ) ( 17 ) ( 18 ) The total number of binding sites on each promoter is conserved: ( 19 ) ( 20 ) We assume that the total amount of NF-B in the cell is conserved ( 21 ) Since the number of binding sites available for NF-B protein is small , we can neglect the amount of NF-B bound to the IB and IB promoters , so ( 22 ) Solving Eq . 22 for yields: ( 23 ) DNA binding reactions are usually fast , so we can assume that they are at quasi-equilibrium at all times , ( 24 ) ( 25 ) Using Eqs . 19 and 20 , substituting into Eqs . 24 and 25 , and solving for , , , yields: ( 26 ) ( 27 ) ( 28 ) ( 29 ) where . We also assume quasi-equilibrium for IB NF-B binding reactions , ( 30 ) ( 31 ) Substituting and from Eqs . 30 and 31 into Eq . 23 yields: ( 32 ) Now we can solve Eq . 32 for ( 33 ) and substitute it in Eqs . 12 and 16 . These equations contain both fast and slow terms . However , it is easy to see that rate equations for variables and contain only slow terms: ( 34 ) ( 35 ) and can in turn be expressed via and by: ( 36 ) ( 37 ) where and . Equations 34–35 combined with definitions Eqs . 26–29 , 33 , 36 , and 37 represent a closed system of two delay-differential equations 2 , 3 for the dual-feedback NF-B module . Setting in these equations leaves us with a single delay-differential equation for the single feedback loop system Eq . 1 . The fixed point of Eq . ( 4 ) is given by the algebraic equation ( 6 ) . Unfortunately , Eq . ( 6 ) does not permit finding in explicit form . However , this calculation can be significantly simplified if the total number of NF-B proteins is large , so , then can be neglected as compared with total . Then , and , and expression ( 5 ) for simplifies: ( 38 ) Now the stationary level of can be obtained explicitly ( 39 ) The stability of this stationary solution is determined by the linearized equation ( 4 ) for a small perturbation near , ( 40 ) where , subscript again indicates the delayed value of taken at time , and . Using formula ( 38 ) we obtain ( 41 ) where is given by Eq . ( 39 ) . The eigenvalue of the linearized equation ( 40 ) is found by substituting , yielding the transcendental equation ( 42 ) whose solution is given by Eq . ( 7 ) . For the analysis of a full NF-B system , we adopted the basic structure of the NF-B model formulated in [32] which in turn was based on the population-level model first proposed in [4] . The structure of the model is shown in Figure S8 A . In resting cells , NF-B is sequestered in the cytoplasm by IB proteins . In response to TNF stimulation , IKKK protein becomes active , and activates IKK kinase . IKK phosphorylates IB proteins targeting them for degradation . Upon degradation of IB proteins , NF-B moves into the nucleus and activates hundreds of target genes . In the model , we focus on the dynamics of three genes associated with the negative feedback of the system . Following NF-B activation , synthesized A20 proteins attenuate TNF signal by repressing IKKK and IKK transitions into their active states . NF-B also binds IB and IB protein promoters , which following translation in the cytoplasm , translocate back into the nucleus and bind free NF-B sequestering it out of the nucleus . In addition , IB proteins are directly responsible for NF-B dissociation from the DNA . The biological processes in the model were interpreted through stochastic and deterministic representations similar to [32] . Nuclear transport , complex formation , synthesis , transcription , and translation were described through a set of ordinary differential equations ( Text S1 ) . Regulation of gene activity through NF-B binding and dissociation from DNA was modeled using stochastic representation . The time-evolution of the system was accomplished through a hybrid simulation algorithm that uses Gillespie algorithm [29] to evaluate the state of stochastic processes and an ODE solver to compute the state of deterministic processes . We performed two BLASTP searches ( using default parameters ) to search for IB and IB homologs . The mouse IB protein sequence ( gi28386026 ) was used as the query for the first search . The mouse IB protein sequence ( gi2739158 ) was used as the query for the second search . We used an E-value of 1e-25 as a cutoff for both searches . Homologs for IB were found in the organisms listed in Table S6 , and homologs for IB were found in the organisms listed in Table S7 . Note that we selected only unique homologs for both IB and IB in all vertebrates . We did not find unique IB homologue for several vertebrates . We expect that this is due to the fact that complete genomes are not currently available for these organisms . Table S8 lists the genome status ( as of 6/1/11 ) of all organisms for which IB or IB homologs were found ( http://www . ncbi . nlm . nih . gov/genomes/leuks . cgi ) . Immortalized murine embryonic fibroblasts [4] were chronically stimulated with 10 ng/mL TNF ( Roche ) and IB and IB mRNA and protein levels were monitored by RNase Protection Assay ( RPA ) and Western Blot , respectively , as previously described [21] . RPA results for each time course were quantitated using ImageQuant software ( GE Healthcare ) and used to determine the time of half-maximal inducibility between basal and peak mRNA levels for IB and IB ( Figure S2 A , B ) . Western Blot results were also quantitated with ImageQuant software and used to determine the time point of peak expression . The basal abundances of IB and IB protein were determined via comparison to a standard curve of recombinant IB protein ( R Tsu , JD Kearns , C Lynch , D Vu , K Ngo , S Basak , G Ghosh , A Hoffmann in preparation ) . The peak abundances of IB and IB were determined via multiplication of the basal value by the fold inducibility at the peak time point ( Figure S2 C , D ) . Experimental levels of nuclear NF-B in cells with only the IB-mediated negative feedback loop intact and in wild-type cells containing both IB- and IB-mediated negative feedback were determined by EMSAs in [4] . | Many signaling events are controlled by negative feedback circuits: as a result they are highly dynamic and in some cases show oscillations The presence of oscillations has led to the hypothesis that signaling pathways convey information about the stimulus via the frequency of oscillations and spikes of activity , analogous to frequency modulated ( FM ) radio signals . One such signaling protein is NF-kB which controls the inflammatory and immune response to cytokines and pathogens . We show here that the topology of the negative circuit does not allow for frequency modulation by the signaling input . Instead , we show that a second negative feedback circuit may be tuned to dampen the oscillations . In fact , the resulting dual negative feedback motif allows for better tracking of the duration of the incoming signal than the single negative feedback circuit , as well as better buffering of noise present in the incoming signal . Thus we propose that the negative feedback topology has evolved to provide complex dynamics of NF-kB in vertebrate animals and not for the purposes of oscillations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"biochemical",
"simulations",
"signal",
"transduction",
"signaling",
"in",
"cellular",
"processes",
"immunological",
"signaling",
"signaling",
"cascades",
"regulatory",
"networks",
"immunology",
"signaling",
"pathways",
"signaling",
"networks",
"biology",
"molecular",
"cell",
"biology",
"computational",
"biology",
"immune",
"response",
"signaling",
"in",
"selected",
"disciplines"
] | 2013 | Dual Delayed Feedback Provides Sensitivity and Robustness to the NF-κB Signaling Module |
Hepatitis C virus ( HCV ) p7 is a membrane-associated oligomeric protein harboring ion channel activity . It is essential for effective assembly and release of infectious HCV particles and an attractive target for antiviral intervention . Yet , the self-assembly and molecular mechanism of p7 ion channelling are currently only partially understood . Using molecular dynamics simulations ( aggregate time 1 . 2 µs ) , we show that p7 can form stable oligomers of four to seven subunits , with a bias towards six or seven subunits , and suggest that p7 self-assembles in a sequential manner , with tetrameric and pentameric complexes forming as intermediate states leading to the final hexameric or heptameric assembly . We describe a model of a hexameric p7 complex , which forms a transiently-open channel capable of conducting ions in simulation . We investigate the ability of the hexameric model to flexibly rearrange to adapt to the local lipid environment , and demonstrate how this model can be reconciled with low-resolution electron microscopy data . In the light of these results , a view of p7 oligomerization is proposed , wherein hexameric and heptameric complexes may coexist , forming minimalist , yet robust functional ion channels . In the absence of a high-resolution p7 structure , the models presented in this paper can prove valuable as a substitute structure in future studies of p7 function , or in the search for p7-inhibiting drugs .
Hepatitis C virus ( HCV ) infection is a global health problem affecting approximately 2% of the world's population [1] , [2] . HCV is a leading cause of chronic hepatitis , liver cirrhosis and hepatocellular carcinoma , and current therapies based on pegylated interferon and ribavirin are poorly tolerated by patients and ineffective in up to 50% of cases [3] . Among the difficulties in treating HCV is its high degree of genetic diversity — i . e . , there are seven distinct genotypes of HCV , each with many subtypes [4] , which respond differently to treatment [5]–[7] . This genetic diversity is due in part to the error-prone RNA polymerase of HCV , along with its rapid replication rate; the same features can lead to further viral diversification within individual patients , increasing resistance to treatment [8]–[10] . HCV drug design must , therefore , target characteristics that are well-conserved among all HCV genotypes , as well as prove robust against mutations that could confer viral resistance . The assembly process and ion-channel function of the p7 viroporin , now known to be essential for viral replication , constitute potential drug targets . For example , the small molecule BIT225 , which is thought to inhibit the ion-channel activity of p7 , has shown promising results in recent clinical trials [11] , though its mechanism of action is not yet well understood . Such cases highlight the biomedical relevance of developing functional models of the p7 channel . HCV , first identified in 1989 [12] , [13] , is a member of the family Flaviviridae , which is a class of small enveloped RNA viruses . The HCV genome consists of a 9 . 6-kb positive-stranded RNA molecule encoding a single polyprotein precursor , later processed by both host and viral proteases into ten separate proteins . These are: the Core protein , which composes the nucleocapsid; the viral envelope E1 and E2 glycoproteins; the p7 viroporin; NS2 , required for virus assembly; and the replication machinery consisting of NS3 , NS4A , NS4B , NS5A , and NS5B ( reviewed in [14] ) . The focus of the present work is on p7 , a small integral membrane protein of 63 amino acids , which oligomerizes [15]–[18] , forming cation-selective pores [15] , [16] , [19]–[22] . It has been demonstrated that , while dispensable for RNA replication , p7 is essential for efficient HCV infectivity in vivo [23] and the production of infectious virus particles [24] , [25] . Most recently , it has been shown that p7 ion channel activity , resulting in a global loss of organelle acidity in the host cell , is required for the effective assembly and release of nascent virions [26] . Furthermore , there is a growing body of evidence suggesting that p7 is critical for other functions in virus assembly and egress unrelated to its channel activity ( reviewed in [27] ) , and that it likely acts in concert with additional viral factors such as Core , E1 , E2 and NS2 [24] , [28]–[33] . The ability of p7 to control the permeability of the membrane to ions and to facilitate virus production qualifies it as a viroporin , alongside with , for instance , M2 from influenza A virus [34] , [35] , picornavirus 2B [35] , [36] and Vpu from HIV-1 [37] , [38] . The essential features of a viroporin are that it forms small membrane-spanning structural units , usually consisting of one or two helices that can oligomerize into a channel [35] , [38] , [39] . The resulting structures range in complexity from passive , non-selective pores , which allow ions to flow through in an uncontrolled fashion , to selective ion channels with a specific gating mechanism , as found , for example , in M2 . Weakly selective ion channels like Vpu fall somewhere in the middle , exhibiting “channel-pore dualism” [37] , [40] , [41] . The p7 protein of HCV forms two antiparallel transmembrane ( TM ) segments connected by a conserved , cytosolic , positively charged loop region , with both the N– and C–termini facing the lumen of the endoplasmic reticulum ( ER ) [42] . Combined NMR experiments and molecular dynamics ( MD ) simulations that we published recently [16] , [43] led to the identification of the secondary structure elements of p7 , and to the construction of a three-dimensional model of the monomer in a phospholipid bilayer . The first TM segment can be divided into an N–terminal helix ( 2–16 ) and the TM1 helix ( 19–33 ) , separated by a turn involving the highly conserved G18 residue [16] , [42] . TM1 is connected to the second TM segment , TM2 , by a long cytosolic loop containing two fully conserved basic residues at positions 33 and 35 . The TM2 helix is slightly bent due to the presence of residue P49 , and the seven-residue C–terminal segment is unfolded . The overall structural motif is in keeping with that deduced from the NMR experiments reported by Cook et al . [44]–[46] . p7 oligomers have been found in both heptameric [17] and hexameric [15] , [18] forms , and several hypothetical models of the p7 complex based on secondary-structure predictions have been reported [17] , [18] , [22] , [47] . Clarke et al . [17] observed heptameric p7 complexes in low-resolution TEM images , and suggested an arrangement in which the monomers are packed in such a way that TM1 from one monomer could form favorable hydrophobic contacts with the TM2 of the next monomer . A similar heptameric model was later constructed by StGelais et al . [22] . Patargias et al . [47] modeled a hexameric complex employing structure prediction and rigid-body docking in which adjacent monomers had minimal contact . Most recently , Luik et al . [18] observed p7 hexameric complexes in short-tail DHPC ( 1 , 2-diheptanol-sn-glycero-3-phosphocholine ) lipids by electron microscopy ( EM ) , constructing a low-resolution three-dimensional density map at 16 Å resolution , which revealed a highly tilted , flower-like arrangement . While the basic structural features of p7 are becoming gradually better understood , the conditions that lead to the assembly of a functional channel and the mechanism of ion channelling in terms of gating and selectivity remain in large measure unknown . In the work presented here , we used the monomeric p7 structure from Montserret et al . [16] to construct oligomeric models with four to seven subunits , which were evaluated via MD simulations in a hydrated POPC ( 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ) bilayer , the thickness of which resembles that of the ER membrane . In addition , we fitted our most favorable hexameric model into the flower-like EM density kindly provided by N . Zitzmann [18] and immersed the resulting structure in POPC ( C16:C18 ) and DHPC ( C7 ) bilayers in an effort to understand the connection between the highly-bent structure found in DHPC lipids and our more upright channel models , which would seem better suited to the ER membrane . Our work can by no means be considered an exhaustive combinatorial search of all possible p7 oligomer structures; instead , we focused on constructing and evaluating structures representative of the types of putative p7 models already suggested in the literature [17] , [18] , [22] , [47] . In the absence of high-resolution structural data for p7 , these simulations allowed us to determine likely characteristics of a functional p7 channel , including the optimal number of monomers , the possible role of certain pore-lining residues in channel gating , and the adaptability of the structure to different lipid environments . Our results reveal that p7 appears to form structurally plastic , minimalist ion channels , compatible with the coexistence of multiple oligomeric states .
Figure 1 illustrates the different p7 oligomeric models that have been constructed . The Hexamer B and Heptamer B models ( Figure 1B ) remained robust throughout the simulation , as demonstrated by their overall retention of structure and symmetry , as well as by the quickly plateauing distance root-mean square deviation ( RMSD ) with respect to the starting arrangement ( see Figure 2 ) . Noteworthily , the conformation of the different p7 subunits is similar to that of the isolated monomer when immersed in a hydrated POPC bilayer , [16] , [43] but at variance with that inferred from EM densities in a thinner DHPC environment [18] ( see below ) . Unlike Hexamer B , the Hexamer A model ( Figure 1A ) quickly collapsed during equilibration , as evidenced by loss of tertiary structure and partial loss of secondary structure . The Heptamer A model ( Figure 1A ) did not collapse , but rather began to re-arrange itself to make more inter-subunit contacts , forming a seemingly stable complex . The Hexamer C model ( Figure 1C ) also remained stable , but with a slightly larger RMSD compared with Hexamer B . The tetrameric and pentameric motifs also appear to be robust throughout the simulations . Although p7 has never been reported hitherto as a tetramer or a pentamer , such structures may be viable , and the possibility of their presence in the cell membrane cannot be ruled out , perhaps as intermediate stages in p7 self-assembly prior to the formation of the final hexameric or heptameric complexes . The coexistence of multiple oligomeric forms , including those with even and odd numbers of subunits , would not be unique to p7 . For example , the Vpu viroporin of HIV-1 exhibits such polymorphic behavior [48] , [49] , as does the antimicrobial peptide alamethicin [50] . Because the p7 monomers are not covalently bound , but instead interact primarily via van der Waals and hydrogen-bonding contacts ( see Table 1 and Figure S1 ) , there could easily be more than one oligomeric arrangement for which inter-subunit contact is sufficient to support a stable structure . Inter-subunit interactions were evaluated for each of the oligomeric models . The Hexamer and Heptamer B models appear to have the best inter-subunit contact , as illustrated from the solvent-accessible surface area ( SASA ) per subunit , which is seen to be minimized in the Hexamer B and Heptamer B models ( Figure 2B ) . The pentamer , tetramer , Heptamer A , and Hexamer C had similar SASAs . Analysis of inter-subunit hydrogen bonding showed that the Hexamer B model featured more hydrogen-bonding between subunits than any of the other models , even though presumably the monomers are more closely packed in the Heptamer B model ( see Table 1 ) . Hexamer C formed fewer hydrogen bonds than Hexamer B , albeit this is consistent with its larger SASA . The pentameric and tetrameric , as well as the Hexamer A and Heptamer A models involved very little hydrogen bonding . In the isolated , monomeric protein , TM1 and TM2 are , at least in part , held together by virtue of a double – interaction resulting from the stacked F26 , Y45 and W30 residues [16] . These interactions appear to be roughly preserved in the p7 oligomers ( see Figure S5 ) . Additionally , the inter-subunit interaction energies were measured for each oligomer model , and were observed to be lowest for the Hexamer B and Heptamer B models ( see Figure S1 ) . These values ought to be regarded at a qualitative level , as they ignore the entropic contribution to the self-assembly of the monomers into oligomers . Ideally , it would be desirable to determine rigorously the binding free energy associated to the formation of the latter , but this enterprise , arguably not amenable to the current capacities of MD simulations , falls beyond the scope of the present investigation . The Hexamer B , Heptamer B , pentamer and tetramer models ( which are right-handed as viewed from the terminal side ) were thought to be promising in terms of the residues that point towards the center of the pore , in particular , H17 and F25 , which had already been proposed to face the pore , but also the hydrophilic S21 ( see Montserret et al . [16] and references therein ) . Hexamer C , which has the opposite handedness as Hexamer B , in contrast leaves mostly hydrophobic residues facing the interior of the pore , which seems unfavorable . The initial structure of Hexamer C also leaves H17 pointing towards the body of the protein , rather than towards the central pore , though over the course of the simulation , three of the subunits rotated slightly to allow H17 minimal access to the central pore . The pore lining residues of Heptamer A are similar to those of Hexamer C . The Heptamer A model , in which the subunits initially pointed away from the center , eventually settled into a conformation with more contacts and a slight bias towards left-handedness , similar to Hexamer C . A list of pore-lining residues for each model is given in Table 2 . Our simulations thus cannot rule out the possibility of left-handed p7 oligomers; however , the set of pore-lining residues seems inconsistent with existing data . With the exceptions of Hexamer B and Hexamer C , all channel models formed a continuously open solvent-accessible pore , as illustrated by Heptamer B in Figure 3B . In the case of Hexamer B ( see Figure 3A ) , the pore was initially sealed , but later opened , allowing water molecules to flow through for the remainder of the simulation . When sealed , the pore of Hexamer B was blocked in two places by rings of adjacent hydrophobic residue side chains , thereby forming an energetic barrier to water permeation . This constriction is manifested also in a discontinuity in the electrostatic potential within the channel ( see Figure S2 ) . One seal was found at the level of residue F25 , already hypothesized to play such a role [16] , with a second seal at the level of I32 , which was not yet identified to be a putative pore lining residue . The F25 barrier is revealed in the pore-radius profile of Hexamer B ( see Figure 3A ) , which depicts the width of the pore along the longitudinal axis of the complex . In the first 65 ns of the simulation , the protrusion of the F25 ring constricts the pore to a radius smaller than that of a water molecule ( solid line and red line , respectively , Figure 3A ) . The barrier formed by the F25 side chains recedes when the pore opens , allowing water permeation ( dotted line , Figure 3A ) . In contrast , the pore-radius profiles characterizing the other models reveal no constrictions at the level of F25 narrow enough to preclude water diffusion ( Figure 3B and Figure S3 ) . The I32 side chains are not bulky enough to block the channel; possibly they prevent the passage of water by withholding hydrogen-bonding partners . A similar behavior was noted in MD simulations of M2 due to a ring of valine residues [51] . In the present case , the opening of the channel was precipitated by the interaction of water molecules with the F25 side chains . Random fluctuations of the F25 side chains eventually allowed a water molecule to slip through , after which the pore opened quickly ( see Video S2 ) . The Hexamer C model was also initially sealed , this time at the level of Y31 and L20 . We did not observe spontaneous opening of the central pore during the simulation . In order to also sample the open conformation of the pore , we briefly applied an electric field in the simulation , which resulted in rearrangement of the side chains of the pore-lining residues sufficient to open the pore . Hexamer C was then allowed to equilibrate in the open conformation . Conductance calculations were performed for the Heptamer A , Heptamer B , and Hexamer B models by applying a constant electric field to the MD simulation and analyzing the subsequent flux of ions through the channel , as described in Materials and Methods , using 250 mM KCl . The results of these calculations are summarized in Table 3 . Montserret et al . [16] reported conductance measurements of 22 pS at 60 mV and 41 pS at 140 mV . Ion translocation failed to occur in simulations with potential differences of 60 mV and 140 mV; it was therefore necessary to apply higher voltages and interpolate the resulting measurements under the assumption that conductance scales linearly with voltage . Simulation of Hexamer B at 1500 mV yielded a conductance measurement of 236 . 2 pS , which , interpolated to 60 mV and 140 mV , would give 9 . 4 pS and 22 pS , respectively . The Heptamer B model gave a conductance value of 130 . 4 pS at 1500 mV , which would give 5 . 2 pS and 12 . 2 pS at 60 mV and 140 mV , respectively , and Heptamer A yielded 183 . 5 pS at 1500 mV , giving 7 . 3 pS and 17 . 1 pS at 60 mV and 140 mV . That the simulations give conductance values with the correct order of magnitude is encouraging . We do not , however , expect the simulated conductance values to match the specific values obtained experimentally . In experimental conductance measurements , it is impossible to know the proportion of heptamers to hexamers in the sample , and the resulting measurement represents an ensemble average of the action of all the oligomers in the sample . By contrast , MD simulations can only report the behavior of one specific structure . Additionally , the treatment of ions in MD with the standard , non-polarizable CHARMM force field is imperfect , and yields behaviors and diffusion constants which differ slightly from experimental values [52] , [53] . One intriguing feature of the present set of conductance calculations is the difficulty of reproducing the expected ion selectivity , which is somewhat higher for cations according to experiment [16] , [43] . Discrepant ion selectivities between simulations and expriment are not novel [54] , [55] . In the present case , it is believed to stem from clogging of the pore entrance by chloride ions interacting with the titratable K33 and R35 residues of the loop regions . Over the time scale of the simulations , unbinding events of the anions are too scarce to permit diffusion of potassium ions through the pore . Yet , if the positively charged residues at positions 33 and 35 are replaced by glutamine , the experimentally observed ion selectivity is recovered ( see Video S4 ) , hence suggesting that either the present trajectories are too short , or that ion parametrization is suboptimal [53] . In contrast with the flower-like EM model of the p7 hexamer reported by Luik et al . [18] , the Hexamer B model that we propose to represent a functional p7 channel appears to be a cylindrical , upright complex in the membrane . This discrepancy could be due to the size difference of the phospholipid hydrophobic chain , which is very short in the DHPC environment used in EM studies ( C7 ) , compared to that of POPC used in MD simulations ( C16:C18 ) . To test this hypothesis , we drove the p7 Hexamer B model into the EM envelope of the p7 hexamer using the molecular dynamics flexible fitting ( MDFF ) algorithm ( see Video S1 ) . In recent years , MDFF has been used successfully for structure determination with higher-resolution maps [56]–[59] . Yet , structures obtained using lower-resolution data , such as the 16-Å p7 map , ought to be viewed as suggestive rather than as representing an accurate native structure . In fact , there is some variation in the final structure based on the original orientation of the structure and the force constants utilized to fit the Hexamer B into the EM map ( see Figure S4 ) . Once driven into the EM envelope ( Figure 4A ) and equilibrated via MD in the thin DHPC environment ( Figure 4B ) , the simulated structure largely retains its original bent conformation , in agreement with the experimental observations of Luik et al . [18] . Conversely , if placed in a POPC environment , which would more closely resemble the ER membrane , the helices begin to straighten up as the structure progressively evolves towards a more upright conformation , akin to that of Hexamer B described above ( see Video S3 ) . The tilt of the inner and outer helices of the structure is displayed in Figure 4C . Put together , these results illuminate the structural plasticity of the p7 monomer in an oligomeric context , and , hence , its adaptability to the membrane bilayer thickness .
In this article , we have described the construction of several model p7 oligomers with four to seven subunits . We find that all oligomerization states investigated form structurally stable ion channels , provided that neighboring subunits come into close contact . Oligomers with six or seven subunits display a slight advantage over oligomers with four or five subunits in terms of inter-subunit interactions , consistent with the prevalence of hexameric and heptameric complexes observed in analytical centrifugation experiments [16] and reported by others [15] , [17] , [18] . Oligomeric models constructed for alternate genotypes of p7 were also found to be stable in preliminary simulations ( data not shown ) , thereby further reinforcing the view of robust channels . In our simulations , we observed that the heptameric pore was constantly accessible to the solvent , whereas the hexameric pore was blocked for part of the trajectory . This behavior is suggestive of a picture of the cell membrane in which transiently open p7 hexamers might be accompanied by always-open p7 heptamers . Each of the models were constructed from the p7 monomer described earlier [16] , resulting in cylindrical oligomers , in contrast to the corolla-shaped complex observed by Luik et al . [18] . The cylindrical hexamer can , however , be fitted into the flower-like EM envelope . p7 thus appears to be sufficiently flexible to adapt to varying lipid environments , like DHPC and POPC , naturally resulting in a highly tilted structure in short-tail lipids , and a more upright conformation in long-tail lipids , the latter being compatible with the thickness of the ER membrane . We note that this structural flexibility is shared by other viroporins such as Vpu of HIV , which has been reported to adapt to the lipid environment via kinking rather than forcing the lipid environment to adjust [60] . Very recently , it has been demonstrated that changes in lipid composition modify the ion-channel activity of p7 , suggesting that p7 can indeed rearrange in response to changes in the lipid environment [61] . Altogether , our findings allow us to conclude that p7 forms flexible and minimalist , yet robust , functional ion channels . Efforts to resolve the complete structure of native viroporins with an accuracy adequate to determine their ion-channel mechanisms face particular challenges . Aside from the intrinsic difficulties in preparing large amounts of purified membrane protein , the structural flexibility and polymorphism of viroporins deeply complicate experimental investigations , as exemplified in the case of Vpu for which the numerous studies published over the last 15 years have not yielded a consensus on the mechanisms underlying its function . These challenges explain in large measure why efforts to disentangle the mechanisms that underlie the formation and the function of viral ion channels have remained hitherto rather scarce . Computational studies of viroporins are burdened with skepticism that useful information can be inferred from simulations relying on a modeled structure . However , in the context of the HCV p7 viroporin as an attractive therapeutic target , MD simulations based on three-dimensional constructs offer a way to yield novel insights in a field where high-resolution data is lacking . The authors admit that even over ample time scales , successful MD simulations are not enough to prove the accuracy of a model; however , they remain a useful tool to probe the viability of a model , detect flaws in its construction and confront hypotheses put forth by experiments . In the instance of p7 oligomeric channels , the wealth of structural information accrued in recent years , particularly on the monomeric state of the protein , constitutes a solid base for the design and refinement of the p7 complexes described herein . The observation that at least one oligomeric construct of each class examined here was structurally stable in simulation suggests that complexes with four to seven subunits could coexist in the cell membrane . Reported data [15]–[18] , however , reveal that p7 is largely observed as hexa- and heptameric complexes . It is possible that tetra- and pentameric complexes can form , but are in some way unfavorable or less functional as ion channels compared to hexa- or heptameric complexes . In addition , hexa- and heptameric complexes may be more favorable energetically , such that tetra- or pentameric complexes would organize only transiently , as intermediates in the formation of hexa- or heptameric complexes . This is in agreement with the notion that , due to their small size , viroporins most likely self-assemble to form a pore in the membrane [37] . In the case of p7 , this notion is supported by the spontaneous assembly of p7 oligomers exhibiting active and appreciably selective cation channeling when reconstituted with lipids [15][16[19]–[22] . Although concerted organization in an all-or-nothing step cannot be ruled out , our results suggest that p7 monomers assemble sequentially . An important consequence of such a mechanism relates to the regulation of p7 release in the HCV life cycle . It is unlikely that p7 monomers are released during the processing of the HCV polyprotein at the ER membrane since the spontaneous formation of active p7 ion channels could stress the ER membrane , ultimately leading to cell apoptosis [62] . Given that no feedback mechanism regulating p7 activity has yet been identified , it is reasonable to suggest that the release of free p7 must be regulated upstream . p7 has been reported to interact with various HCV proteins including core , E1 , E2 , and NS2 [24] , [28]–[31] , [33] . It is possible that p7 is retained in an inactive state via its interactions with other viral factors , and is later released when it is needed for HCV particle assembly and/or egress . A similar hypothesis has been put forth in the case of the HIV viroporin , Vpu , which can engage in protein-protein interactions , but can also self-assemble to form channels or pores [37] . Because of its central organizing role in HCV assembly , it is tempting to speculate that NS2 is involved in the orchestration of p7 release and assembly [28]–[31] . Following this hypothesis , one can assume the existence of a specific molecular mechanism allowing the putative p7-NS2 complex to dissociate at the critical stage of viral-particle formation and egress . Further , one cannot exclude that variation in the thickness between the ER and the export-vesicle membranes could play a role in the regulation of p7 oligomerization , as well as in ion-channel activity , just as the composition of the lipid membrane appears to do [61] . Compared with selective ion channels of eukaryotic cells like , for instance , voltage-gated potassium channels , which exhibit specific pore-lining motifs [63] , the p7 channel appears to have a minimalist channel architecture , as generally observed in viroporins [37] . It is solely held together through non-covalent , inter-monomer interactions , and tends to form a stable pore open to the solvent . The transient nature of the pore in the Hexamer B model suggests the possibility of gating via minimal conformational change . Noteworthily , none of the p7 hydrophilic pore-lining residues studied by mutagenesis , which display substantial intra- and inter-genotype variability , appears to be really essential for p7 ion channeling in vitro ( discussed in [16] ) . Our study indicates a role for hydrophobic residues at or near positions 25 and 32 in gating the p7 channel . The involvement of F25 was already suggested by the hyperactivity of channels in which F25 and neighboring F22 and F26 were replaced by alanines [22] , as well as its role in conferring resistance to alkylated imino-sugars [10] , but the significance of I32 has yet to be explored . While there is some variation in sequence at these positions across the multiple genotypes of p7 , there seem always to be bulky hydrophobic residues at positions 32 and 24 or 25 . The presence of these hydrophobic barriers prompts a scenario in which gating could be promoted by small movements of the -helical segments ( see the earlier discussion in Montserret et al . [16] ) . Together with the fact that p7 is only weakly ion selective , it is possible that hydrophilic pore-lining residues would be all that is required to attract and conduct ions across the channel . All these features , shared among most viroporins , are consistent with the notion that the mechanism of gating of viroporins in general [37] , and of p7 in particular , is rather minimalist . These findings have important implications for the development of drugs aimed at blocking p7 activity . Targeting of pore lining-residues could , indeed , be vain and/or rapidly induce viral resistance , as suggested by the large natural amino acid variability . In contrast , compounds targeting strictly conserved residues essential for p7 assembly could inhibit oligomer formation and are expected to be more promising .
The p7 oligomeric models ( genotype 1b , strain HCV-J , accession number D90208 ) were constructed by applying the appropriate symmetry operations to the model of the p7 monomer determined by NMR and MD , as described by Montserret et al . [16] . The homogeneity of the NMR signals , measured for p7 reconstituted with phospholipids [44] , mirrors that of the structure of the individual p7 subunits , and further supports the notion of symmetrically-arranged p7 oligomers . Insofar as the hexamer and the heptamer are concerned , two types of oligomeric states were constructed — one in which the monomers extend radially away from the center and have minimal contact with one another ( referred to as Hexamer A and Heptamer A , Figure 1A ) , consistent with the model put forth by Patargias et al . [47] , and one in which the monomers are rotated such that adjacent subunits make better contact ( referred to as Hexamer B and Heptamer B , Figure 1B ) , in line with the models described in [17] , [22] . The tetrameric and pentameric constructs followed the pattern of Hexamer B and Heptamer B . Finally , an additional hexameric model ( referred to as Hexamer C ) was constructed , which was similar in arrangement to Hexamer B , but with opposite handedness . All models are depicted in Figure 1 . The Hexamer B p7 model was fitted into the EM map from [18] employing the molecular-dynamics flexible-fitting ( MDFF ) algorithm [64] . The MDFF method utilizes the EM map to create an external potential , which drives the atoms towards regions of higher density . During the fitting process , geometrical restraints are enforced on the dihedral angles to preserve the secondary structure of the protein . In fitting p7 into the EM map , additional forces were applied to maintain the six-fold symmetry of the model . In each of the p7-membrane systems , an all-atom representation was used for protein , water and ions , whereas the united atom lipid model described in [65] was used for the lipids . All simulations were performed using the MD program NAMD [66] and the CHARMM27 force field with CMAP corrections [67] , [68] . The equations of motion were integrated with a multiple time-stepping algorithm [69] , [70] in which bonded interactions were evaluated every 2 fs , short-range non-bonded interactions every 2 fs , and long-range electrostatics interactions every 4 fs . Short-range non-bonded interactions were truncated smoothly with a spherical cutoff radius of 12 Å , and a switching distance of 10 Å . Periodic boundary conditions were assumed . Long-range electrostatic interactions were computed employing the particle mesh Ewald ( PME ) method [71] , with a grid point density of approximately 1/Å3 . Temperature and pressure were maintained at 300 K and 1 bar , respectively , using Langevin dynamics with a friction coefficient of 1 ps−1 and the Langevin piston method [72] . Table 4 lists all simulations performed . In the tetramer , pentamer , hexamer and heptamer simulations , the p7 model was placed in a fully hydrated , thermalized POPC membrane , and restrained for the first few nanoseconds to enhance lipid packing around the protein . The restraints were then released and the protein was allowed to equilibrate in the isobaric-isothermal ensemble for the times listed in Table 4 . The upright Hexamer B model was fitted into the EM map , and the resulting structure was then placed in POPC and DHPC membranes . Again , motion of the protein was initially restricted to improve lipid packing; after this stage , additional restraints were applied to preserve the secondary structure and symmetry of the complex . These restraints facilitated a gentle release from the EM-fitted structure . Ultimately , all restraints were removed and the protein was allowed to equilibrate in the isobaric-isothermal ensemble . Conductance measurements can be obtained from MD simulations by applying a constant electric field to the system in the direction . The ionic current is given bywhere is the dimension of the periodic box , is the chosen time interval ( 1 ps in our calculations ) , and are the charge and coordinate of atom . The sum runs over all of the atoms enclosed in a defined region of interest ( usually the interior of the pore ) at time . An average current is calculated from the instantaneous currents measured over the course of the conductance simulation . The conductance is then given by . The uncertainty in the conductance is approximated , assuming Poisson statistics of independent events , as , where is the ion charge , is the applied voltage , and is the total number of ion translocation events observed in simulation time . An ion concentration of 250 mM KCl was used . | Hepatitis C remains a serious global health problem affecting more than 2% of the world's population , and current therapies are effective in only a subset of patients , necessitating an ongoing search for new treatments . The p7 viroporin is considered to be an attractive possible drug target , but rational drug design is hampered by the absence of a high-resolution p7 structure . In this paper , we explore possible structures of oligomeric p7 channels , and discuss the strengths and shortcomings of these models with respect to experimentally determined properties , such as pore-lining residues , ion conductance , and compatibility with low-resolution electron microscopy images . Our results present an image of p7 as a rudimentary , minimalistic ion channel , capable of existing in multiple oligomeric states but exhibiting a bias towards hexamers and heptamers . We believe that the work presented here will be valuable for future research by providing plausible 3-dimensional atomic-resolution models for the visualization of the p7 viroporin and serve as a basis for future computational studies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"computational",
"chemistry",
"chemistry",
"biology",
"computational",
"biology",
"biophysics"
] | 2012 | The p7 Protein of Hepatitis C Virus Forms Structurally Plastic, Minimalist Ion Channels |
The ultimate stage of the transmission of Dengue Virus ( DENV ) to man is strongly dependent on crosstalk between the virus and the immune system of its vector Aedes aegypti ( Ae . aegypti ) . Infection of the mosquito's salivary glands by DENV is the final step prior to viral transmission . Therefore , in the present study , we have determined the modulatory effects of DENV infection on the immune response in this organ by carrying out a functional genomic analysis of uninfected salivary glands and salivary glands of female Ae . aegypti mosquitoes infected with DENV . We have shown that DENV infection of salivary glands strongly up-regulates the expression of genes that encode proteins involved in the vector's innate immune response , including the immune deficiency ( IMD ) and Toll signalling pathways , and that it induces the expression of the gene encoding a putative anti-bacterial , cecropin-like , peptide ( AAEL000598 ) . Both the chemically synthesized non-cleaved , signal peptide-containing gene product of AAEL000598 , and the cleaved , mature form , were found to exert , in addition to antibacterial activity , anti-DENV and anti-Chikungunya viral activity . However , in contrast to the mature form , the immature cecropin peptide was far more effective against Chikungunya virus ( CHIKV ) and , furthermore , had strong anti-parasite activity as shown by its ability to kill Leishmania spp . Results from circular dichroism analysis showed that the immature form more readily adopts a helical conformation which would help it to cause membrane permeabilization , thus permitting its transfer across hydrophobic cell surfaces , which may explain the difference in the anti-pathogenic activity between the two forms . The present study underscores not only the importance of DENV-induced cecropin in the innate immune response of Ae . aegypti , but also emphasizes the broad-spectrum anti-pathogenic activity of the immature , signal peptide-containing form of this peptide .
The recent emergence of dengue constitutes a serious health threat and today , the disease is considered one of the most serious arthropod-borne human viral diseases in terms of both morbidity and mortality [1] , with approximately 50–100 million new infections per annum , including 200 , 000–500 , 000 cases of potentially life-threatening dengue hemorrhagic fever or dengue shock syndrome [2] , [3] which is the leading cause of infant mortality in several Asian countries . The National Institute of Allergy and Infectious Diseases has classified DENV as a category A biothreat pathogen . There is currently no licensed vaccine or drug treatment against this pathogen and , at present , the only method of preventing transmission is by controlling its vector , the Ae . aegypti and Aedes albopictus ( Ae . albopictus ) mosquitoes . Dengue viruses circulate in nature as four distinct serological types ( DENV-1 to -4 ) which are ingested by the vector when it feeds on an infected host . The virus then crosses the gut epithelium into the hemolymph to reach the salivary glands via various routes . Once in the insect's saliva , it is inoculated into a vertebrate host to recommence its replicative cycle [4] . An important consequence of this transmission process is the interference of DENV with both the invertebrate and human immune systems . While infection of vertebrates causes severe disease , the presence of DENV in mosquitoes is non-pathogenic and results in life-long , persistent infection . This difference may reflect the capacity of insects to mount a highly effective innate immune response of both cellular and humoral nature to control invading microbes [5] , [6] . The immune pathways and effectors activated in response to pathogens in Ae . aegypti still remain elusive . However , recent studies have shown that oral infection with DENV elicits dsRNA and production of DENV-specific siRNAs in the midgut [7] , supporting a major role for the RNAi pathway in the control of DENV replication in Ae . aegypti . In addition , the Toll and the Janus kinase/signal transduction and activators of transcription ( JAK-STAT ) pathways have also been reported to play an important role in controlling DENV replication in the midgut [8] , [9] . Finally , the recent sequencing and annotation of the Ae . aegypti genome is of importance to identify new effectors in the immune system of this vector [10] . However , although the salivary gland of Ae . aegypti is the final organ required to be infected prior to the transmission of DENV to humans , the complex relationship between this organ and the virus remains unknown . Antimicrobial peptides ( AMP ) , lysozyme , and pathogen pattern recognition receptors are commonly found in the salivary gland of many hematophagous arthropods , pointing to the importance of this organ in the vector's immune defense mechanism [11] , [12] . For example , a recently discovered tick protective antigen , subolesin , reportedly is active against pathogens in the salivary gland via NF-kB-dependent and independent signal transduction pathways that regulate innate immune responses [13] . Moreover , several defense response genes have been found in the salivary gland EST clusters of biting midge Culicoides sonorensi [14] . It is of note that a comparative transcriptome analysis of plasmodium-infected Anopheles gambiae salivary glands also revealed the induction of several innate immune response genes , encoding proteins such as prophenoloxidases and AMP [15] . In order to decipher the complex relationship between DENV and the innate immune system of Ae . aegypti , we have investigated whether the expression of some acute-phase proteins is modulated in salivary glands following infection of the vector by DENV . To this aim , using the Digital Gene Expression ( DGE ) analysis combined with a deep sequencing approach , we have carried out a comparative genomic analysis of uninfected salivary glands and salivary glands of female Ae . aegypti mosquitoes infected with DENV ( referred to in the present study as uninfected and DENV-infected salivary glands ) . Among the up-regulated genes , we have identified a putative antibacterial peptide , belonging to the cecropin family , indicating that the IMD pathway is involved in the mosquito's defense against DENV in salivary glands . We further demonstrate that the immature , signal peptide-containing form of this peptide displays wide anti-infectious properties and is active against DENV , Chikungunya virus , as well as the protozoan parasite Leishmania .
Venous blood from anonymous healthy human volunteers was obtained from the blood bank ( Etablissement Français du Sang ) in accordance with its guidelines , published in the French Journal Officiel , with informed written consent from each volunteer . The study was conducted according to the guidelines of the Institutional Review Board of the Institut de Recherche pour le Développement and was approved by the Institutional Review Board of the Institut de Recherche pour le Développement . Ae . albopictus C6/36 cells , used for the propagation of the four dengue serotypes DENV-1 ( Hawaii strain [16] ) , DENV-2 ( 16681 strain [16] ) , DENV-3 ( H87 strain [16] ) and DENV-4 ( 814669 strain [17] ) were grown in M199 medium ( Invitrogen , France ) , supplemented with 10% fetal calf serum ( FCS , Lonza , Switzerland ) at 28°C . All DENV serotypes were passed three times in C6/36 cells . HEK-293T ( Human Embryonic Kidney 293 ) and LLC-MK2 ( Monkey kidney epithelial ) cells were grown in DMEM , 10% FCS , at 37°C . The Ae . aegypti mosquito strain used in our study was a Liverpool strain originating from West Africa which has been maintained at the Liverpool School of Tropical Medicine since 1936 [18] . Mosquitoes were reared and maintained at 26°C±0 . 5°C with 75–80% relative humidity and a 12∶12 h ( light∶dark ) photoperiod . Infectious blood meals were offered 3 days post-emergence to adult , female , Ae . aegypti mosquitoes using a silicone membrane feeder system [19] . Human blood was combined ( 1∶1 ) with the culture supernatant of C6/36 cells infected with DENV-2 16681 to provide a titrated blood meal of 5 . 106 plaque forming units ( PFU ) /ml . For the control group of mosquitoes , human blood was mixed in the same proportion with culture supernatant of uninfected C6/36 cells . Mosquitoes that failed to feed were discarded . At different time-points after the blood meal , salivary glands were carefully dissected in Phosphate Buffered Saline ( PBS ) and washed 3 times with PBS to prevent contamination with the fat body [11] . Each pair of salivary gland was frozen separately at −80°C in 20 µL acid guanidium thiocyanate-phenol-chloroform solution ( RNable Eurobio , France ) . Before use of salivary glands for further experiments , the RNA of carcasses corresponding to the group of mosquitoes fed with infectious blood meal was extracted individually and tested for the presence of DENV by semi-quantitative PCR . Glands of mosquitoes containing DENV were then pooled ( 40 pairs of salivary for each time point ) and lysed with 500 µL of RNable . RNA was precipitated following the addition of isopropanol , collected by centrifugation at 12000×g for 15 minutes and washed once with 75% ethanol . After a brief air-drying , the pellet was dissolved in RNase/DNase free water . RT-PCR was carried out on 2 µg of salivary gland RNA using the QIAGEN OneStep RT-PCR kit and 15 pmol of specific primers: DF: 5′ TCA-ATA-TGC-TGA-AAC-GCG-CGA-GAA-GAA-ACC-G3′; DR: TTG-CAC-CAA-CAG-TCA-ATG-TCT-TCA-GGT-TC3′ . The PCR steps were: 50°C for 30 min , 95°C for 15 min , 39 cycles ( 94°C for 1 min , 55°C for 30 sec , 72°C for 1 min ) , 72°C for 10 min . The quality of the total RNA was checked by capillary electrophoresis analysis using an Agilent BioAnalyser 2100 ( Agilent , Palo Alto , CA , USA ) and quantities were measured using a NanoDrop ND-1000 spectrophotometer ( Thermo Scientific , Les Ulis , France ) . Sequence tags were prepared with the Illumina Digital Gene Expression Tag Profiling Kit following the manufacturer's protocol ( version 2 . 1B ) . One microgram of total RNA was incubated with oligo-dT beads to capture the polyadenlyated RNA fraction . First- and second-strand cDNA synthesis were performed while the RNA was bound to the beads . Still on the beads , samples were digested with NlaIII to retain a cDNA fragment from the most 3′ CATG to the poly ( A ) -tail . Subsequently , the GEX adapter 1 was ligated to the free 5′ end of the RNA , and the sample was digested with MmeI which cuts 17 bp downstream of the CATG site . At this point , the fragments detach from the beads . After dephosphorylation and phenol extraction , the GEX adapter 2 was ligated to the 3′ end of the tag . To enrich for the desired fragments , 15 cycles of PCR amplification with the Phusion polymerase ( Finnzymes Espoo , Finland ) were performed with primers complementary to the adapter sequences . The resulting 85 bp fragments were purified by excision from a 6% polyacrylamide TBE gel . The DNA was eluted with 1× NEBuffer 2 by gentle rotation for 2 h at room temperature . Gel debris was removed using a Spin-X Cellulose Acetate Filter ( 2 ml , 0 . 45 µm ) and the DNA was precipitated by adding 10 µl of 3 M sodium acetate ( pH 5 . 2 ) and 325 µl of ethanol ( −20°C ) , followed by centrifugation at 14 , 000 rpm for 20 min . After washing of the pellet with 70% ethanol , the DNA was resuspended in 10 µl of 10 mM Tris–HCl , pH 8 . 5 and quantified using a Nanodrop 1000 spectrophotometer . Cluster generation was performed after applying 4 pM of each sample to individual lanes of an Illumina 1G flowcell . After hybridization of the sequencing primer to the single-stranded products , 18 cycles of base incorporation were carried out on the 1G analyzer according to the manufacturer's instructions . Image analysis and base calling were performed using the Illumina Pipeline and sequence tags were obtained after purity filtering . This was followed by sorting and counting of the unique tags . DGE libraries were registered in Gene Expression Omnibus ( GEO , NCBI ) under account number GSM537747 for uninfected salivary glands and GSM537746 for DENV-infected salivary glands . Generation of expression matrices , data annotation , filtering and processing were performed using BIOTAG software ( Skuld-Tech , France ) [20] . First , flat files were downloaded from the GenBank ( UniGene Build #12 ) and Ensembl ( AaegL1 , Jun 2009 ) databases . A table was constructed by extracting virtual tags from the representative sequences associated with each UniGene and Ensembl cluster file . Then BIOTAG functions were used for tag-to-gene assignment and subsequent data management . A query using tag sequence as the primary key allowed us to match experimentally obtained DGE sequences and virtual sequences with pre-selected annotations . Results are displayed in a table that provides the sequence of each DGE tag , its number of occurrences with the matching cluster number , its location in the sequence and other data extracted from the source file , including GenBank/Ensembl accession numbers . The statistical value of DGE data comparisons , as a function of tag counts , was calculated by assuming that each tag has an equal chance of being detected . For several highly expressed transcripts , we checked that tag frequencies in successive sequence batches were distributed in agreement with a binomial law [20] . Selected genes were chosen based on a comparison between the two libraries , combined with the significance threshold of the observed variations ( p-value<0 . 01 ) . Total RNA was extracted from salivary glands using RNable ( Eurobio , France ) following the manufacturer's protocol . The RNA was resuspended in 30 µL of RNAse-free distilled water and stored at −80°C . Subsequently , 0 . 6 µg of each RNA was reverse-transcribed using the SuperSript VILO cDNA Synthesis Kit ( Invitrogen , Cergy Pontoise , France ) following the manufacturer's instructions . TaqMan universal PCR master mix ( Applied Biosystems , Courtaboeuf , France ) was used in all qPCR procedures . Each reaction of 50 µl contained 300 nM of forward primer Denv_F 5′AGGACYAGAGGTTAGAGGAGA3′ ) , 300 nM of reverse primer ( Denv_R 5′CGYTCTGTGCCTGGAWTGAT3′ ) , 150 nM of specific probe ( Denv_P 6FAM_5′ACAGCATATTGACGCTGGGARAGACC3′_TAMRA ) and 1× TaqMan universal PCR master mix . Primers and probe sequences targeted all dengue serotypes [21] . Amplification in an Applied Biosystem 7300 real-time PCR system involved activation at 95°C for 10min followed by 40 amplification cycles of 95°C for 15 sec , 60°C for 15 sec and 72°C for 30 sec . Real-time data were analyzed using SDS software from Applied Biosystems . Viral RNA was quantified by comparing the sample's threshold cycle ( Ct ) values with a the Dengue virus RNA standard curve which was obtained as follows: firstly , total viral RNA from the cell culture was purified using QIAamp Viral RNA kit ( Qiagen , Courtaboeuf , France ) following the manufacturer's protocol . Then , standard RT-PCR was carried out by using a primer containing the T7 promoter sequence ( T7_Denv_F 5′TAATACGACTCACTATAGGAGGACYAGAGGTTAGAGGAGA3′ , Denv_R 5′CGYTCTGTGCCTGGAWTGAT3′ ) . The PCR product , containing the T7 promoter sequence was used to generate Dengue RNA fragments by in vitro transcription using the MAXIscript kit ( Ambion , Austin Texas , USA ) . Then , RNA was purified by precipitation in sodium acetate and absolute ethanol . The amount of RNA generated was determined by spectrophotometry and converted to molecular copies using the following formula:RNA standards containing 1010 to 102 RNA copies were used to construct a standard curve . At various days following infection of female Ae . aegypti mosquitoes , DENV-infected and uninfected salivary glands were dissected in PBS and fixed in 100% acetone at −20°C for 1h . The tissue was incubated for 30 min in 10% goat serum 0 . 3% Triton X-100 to prevent non-specific staining and incubated overnight with the monoclonal 3H5 antibody which is directed against the DENV-2 envelope protein . Cells were washed with PBS and incubated overnight with phalloidin-Tetramethyl Rhodamine Iso-Thiocyanate ( TRITC ) at 4°C . Hoechst dye was used to stain the nucleus and preparations were examined with a confocal microscope Zeiss 5 Live Duo , as previously described [22] . 5 days after the infection of female Ae . aegypti mosquitoes , DENV-infected and uninfected salivary glands and fat body were dissected in PBS and fixed with 4% paraformaldehyd at −20°C for 1h . The tissue was incubated for 30 min in 10% goat serum 0 . 3% Triton X-100 to prevent non-specific staining and incubated overnight with the AAEL000598-specific polyclonal antibody . Cells were washed with PBS and incubated overnight with Hoechst dye at 4°C to stain the nucleus . Preparations were examined as described above . cDNA was synthesized using 700ng salivary gland RNA ( ten pooled salivary glands for each time point ) or carcass and the MMLV reverse transcription Kit , following the manufacturer's protocol ( Invitrogen , France ) . PCR reactions were run following the Roche Light Cycler LC480 protocol . PCR was performed using 1 µl of first cDNA , 3 . 33 µM of each primer and 2 . 5 µl of SYBR Green ( Roche , France ) in a 4 µl reaction volume . The cycling conditions were 40 cycles of 95°C for 10s , 57°C for 20s , and 72°C for 25s . RNA was quantified by calculating 2−ΔΔCT . Normalization was performed using a set of two internal control genes , the 40S ribosomal protein S17 and the ribosomal protein L28 . The following specific primers were used: AAEL000598: forward 5′-GCTGTTCGCAATTGTGCTGTT-3′ , reverse 5′-CAATTTCTTTCCCAGCTTCTTCA-3′; 40S ribosomal protein S17: forward 5′-CGCTGGTTTCGTGACACATC-3′ , reverse 5′-TCTCTGCGCTCACGTTCCT-3′ and ribosomal protein L28: forward 5′-CCACGGTTAAGGTTACGCTGAA-3′ , reverse 5′-CGACGGTAACGGTTCTTGTTG-3′ . Surface-enhanced laser desorption ionisation time of flight mass spectrophotometry ( SELDI-TOF-MS ) Protein-Chip arrays ( Biorad , France ) were used as previously described [23] . Briefly , salivary gland extracts ( from 4 groups of infected and uninfected mosquitoes ) were diluted in a binding buffer ( 100 mM ammonium acetate , pH 4 ) and applied to the cation exchanger ( CM10 ) chip . After 24h incubation at 4°C , unbound proteins were removed by three successive 5-minute washes with a buffer containing 100 mM ammonium acetate , Triton X-100 and 5 mM Hepes ( pH 7 ) . Chip-captured proteins were air-dried and covered with a matrix ( 3 , 5-dimethoxy-4-hydroxycinnapynic acid ( SPA ) in 99 . 9% acetonitrile and 0 . 1% trifluoroacetic acid ) , used to absorb laser energy . The ionized and desorbed proteins were detected and their molecular masses displayed on the proteogram . Peaks were determined using SELDI-TOF-MS analysis with Protein-Chip Biology System 3 . 5 software . Depletion of the 3 . 8 kDa MW peptide from infected salivary glands was achieved by adding magnetic Bio-Adembeads coupled with protein G ( Ademtech , France ) , pre-incubated for 2 h at 4°C with an anti-GK pAb After overnight incubation at 4°C followed by magnetic bead removal , the supernatant was analyzed by SELDI-TOF-MS . The GK-specific polyclonal antibody was raised in rabbits following the immunization with the GK peptide and purified from immune serum by a GK-Sepharose affinity chromatography . This antibody binds to both uncleaved and cleaved AMP ( data not shown ) . Ae . aegypti peptides were chemically synthesized by Proteogenix ( France ) and checked by mass spectrometry ( purity over 95% ) . The sequences of GK and MK peptide are: GK: GGLKKLGKKLEGAGKRVFKASEKALPVVVGIKAIGK; MK: MNMNFTKLFAIVLLAA LVLLGQTEAGGLKKLGKKLEGAGKRVFKASEKALPVVVGIKAIGK . The Ae . aegypti peptide ( MT peptide ) that has an irrelevant sequence , but a MW ( 6624 . 73 g/mol ) similar to that of the MK peptide has been used as negative control . The sequence of the MT ( AAEL000160 ) peptide is: MTLERIQETPALKGAPLSPLLRSLSGTLCMISQQRSVSHRT SKYSSNRHRKLQPFRET . In addition , a peptide ( SC ) of identical amino acid composition as the GK peptide , but with a scrambled sequence , has been used in the antibacterial assay . The sequence of the SC peptide is: VAKGLIKGVKAKGELPAKGVFKGLKESIGKRAVLKG . Escherichia coli bacteria were cultured in LB medium with or without peptide . After overnight incubation at 37°C , bacterial growth was estimated by measuring the change in OD600nm on a microplate spectrophotometer . The synthetic peptides' minimum inhibitory concentrations ( MICs ) of each of the synthetic peptides were expressed as the lowest concentration of the peptide that completely inhibited bacterial growth . The effect of peptides on viral production was assessed in permissive Ae . albopictus C6/36 cells infected with Dengue virus serotype 1–4 at a multiplicity of infection ( MOI ) of 1 . Briefly , cells were exposed to DENV for 1 h at 4°C . Unbound virus was removed by washing cells 3 times with cold medium . Cells were then incubated for 24 h with different concentrations of GK or MK peptides ( 5 µg/ml and 10 µg/ml ) . The MT peptide at 10 µg/ml was used as a control in this experiment . Level of infection was determined by quantification of viral RNA by real-time PCR as described above . The consequence of peptide treatment on productive infection of mosquito cells was assayed as follows . Supernatants from DENV-infected C6/36 cells maintained in the presence of 10 µg/ml of peptides ( GK , MK and MT ) were collected at 24 h post infection ( hpi ) . The production of viral particles in supernatants was determined by plaque titration assay on adherent permissive LLC-MK2 cells , which are permissive to all DENV serotypes . Briefly , LLC-MK2 cells were seeded in 6-well plates for 24 h at 37°C . After cell propagation , growth medium was removed and serial dilutions of Viral supernatants in DMEM 10% FCS were added to the cells . The inoculated cells were further incubated for 1 h at 37°C . Fresh 2× nutrient medium ( Earle's balance salts supplemented with 0 . 07% ( w/v ) yeast extract , 0 . 33% ( w/v ) lactalbumin hydrolysate , 4% FBS , 7 . 5% NaHCO3 , 2% essential amino acid ) containing 1 . 5% agarose ( Seakem ) was added into each well volume by volume . The plates were incubated at 37°C , and plaques were visualized 5 to 7 days later , after addition of a second overlay of the above agarose solution supplemented with neutral red ( 0 . 1 mg/ml ) . The virus titre is expressed as Plaque Forming Unit ( PFU ) per millilitre . Permissive HEK-293T cells were incubated with CHIKV ( Strain 147-2-GFP ) at a MOI of 1 and increasing concentrations of GK , MK or MT peptides . After 2 h at 37°C , viral input was removed and replaced by fresh medium supplemented or not with the peptides . After an additional 48 h in culture , the percentage of infected cells was directly related to the number of GFP-labelled cells counted by flow cytometry . A minimum of 10 , 000 cells were analyzed for each data point . Cell viability was determined using a 3- ( 4 , 5-Dimethylthiazol-2-yl ) -2 , 5-diphenyl tetrazolium bromide ( MTT ) -based assay ( Sigma , France ) [24] . Cells were set up and incubated with peptides under conditions that were identical to those used for antiviral assays . The cellular viability was determined measuring the absorbance values at 570 nm that were plotted against peptide concentrations to determine the IC50 values . Leishmania infantum ( strain MHOM/MA/67/ITMAP-263 ) and Leishmania braziliensis ( strain MHOM/BR/75M2904 ) promastigotes expressing the luciferase gene were used to test the anti-leishmanial activities of the AMP as previously described [25] , [26] . Briefly , 105 promastigotes were inoculated into aliquots of 100 µl of medium in 96 well plates . After 4 h , the GK , MK or MT peptides were added and the plate was incubated for another 72 h before luciferase activity was assayed . Results are expressed as the relative light units ( RLU ) index ( = [RLU in treated wells/RLU in untreated wells]×100 ) , whereas 100% represents parasite growth in the absence of peptides . CD was performed to determine the peptides' secondary structures . CD spectra of peptide solutions in PBS or in different concentrations of trifluoroethanol ( 10% , 20% and 40% ) were obtained using a Chirascan Circular Dichroism Spectrometer ( Applied Photophysics , United Kingdom ) at 20°C , with a water-jacketed quartz cell of 1 mm path length . Wavelengths from 188 to 260 nm were measured with a step resolution of 0 . 5 nm and a bandwidth of 5 nm . CD spectra were generated from an average of 2 scans of each sample . Secondary structures were determined using Chirascan software . The mean residue ellipticity [θ] ( in degrees . cm2 . dmol−1 ) was calculated using [θ] = [θ]obs ( MRW/10 . l . c ) , where[θ]obs is the ellipticity measured in millidegrees , MRW is the peptide's mean residue MW , c is the concentration of sample in mg mL−1 , and l is the optical path length in cm . Spectra were plotted as molar ellipticity [θ] versus wavelength . The Ensembl or Entrez gene ID for the genes and proteins cited in the text is: AAEL000598 ( putative antibacterial peptide , cecropin ) ; AAEL000160 ( hypothetical protein ) ; AAEL014640 ( peptidoglycan recognition protein-lc isoform ) ; AAEL007064 ( gram-negative bacteria binding protein ) ; AAEL007619 ( Toll ) ; AAEL007768 ( MYD88 ) ; AAEL003849 ( hypothetical protein , defensin ) ; AAEL003857 ( hypothetical protein , defensin ) ; AAEL009670 ( lysozyme P ) ; AAEL015639 ( transferrin ) ; 5565542 ( diptericin ) .
Salivary glands from Ae . aegypti females were collected at various time points following oral infection with DENV-2 , and the kinetics of infection , as detected by the presence of viral transcripts , were monitored by real-time PCR . Viral transcripts could already be detected in the salivary glands 24 h after infection , albeit at very low levels ( 1 . 5 log10 RNA copies per pair of salivary glands ) . The expression of DENV transcripts increased over time until 14 days after infection ( Figure 1 ) . Because of the likelihood that salivary gland genes targeted by DENV are modulated with differential kinetics , we compared the transcriptomes of DENV-infected and uninfected salivary glands at a series of time points between 1 and 14 days after the blood meal , with the aim to follow the changes in the overall gene expression profile throughout DENV infection . From this comprehensive analysis of gene expression profiles , more than 11 million DGE tags were sequenced . Among them , 39 , 912 unique tags with an occurrence of over 10 were detected . Thirty nine percent of these matched annotated genes in the GenBank and Ensembl databases ( Supporting Information , Table S1 ) . Based on criteria of probability ( p<0 . 01 ) and the strength of induction ( >2-fold ) , 1 , 111 transcripts were selected the expression of which was up-regulated in uninfected salivary glands , and 649 that were up-regulated in DENV-infected salivary glands ( Supporting Information , Table S1 ) . Among the most strongly up-regulated genes in DENV-infected Ae . Aegypti salivary glands , we identified a gene belonging to the cecropin family ( AAEL000598 ) ( Table 1 ) , a family of small cationic antimicrobial peptide that are induced in insects in response to hypodermic injury or bacterial infection [5] . Real-time quantitative PCR analysis was carried out to confirm the result obtained by DGE analysis and to investigate the chronology of the expression of this AMP in salivary glands following DENV infection . When compared with the expression profile observed in uninfected samples , a mRNA peak expression was detected 5 days post infection ( dpi ) in infected salivary glands ( Figure 2A ) . Salivary glands were harvested from infected and uninfected mosquitoes at different time points to corroborate the over-expression of the AAEL000598 AMP , using the SELDI-TOF-MS technique . Results from this analysis revealed the presence of a peptide with a MW of 3 . 68 kDa that was more highly expressed in infected than in uninfected organs at 5 , 9 and 14 dpi ( Figure 2B ) . Incubation of 10 pooled salivary glands , collected at 5 dpi , with a polyclonal antibody raised against a synthetic peptide derived from the AEEL000598 sequence , immobilized onto protein G beads , followed by depletion of the Ab-peptide complex , resulted in the complete removal of the 3 . 68 kDa peak from the mass spectrometry peptide profile . This result confirms that the identified AMP in DENV-infected salivary glands corresponds to the cecropin AAEL000598 gene product ( Figure 2B ) . Expression of AAEL000598 in salivary glands at 5 dpi was confirmed using immunofluorescence analysis ( Supporting Information , Figure S1 ) . In contrast , the peptide was not detected in the fat body at the same time point following DENV infection . Then we investigated whether the expression profile for AAEL000598 matched the kinetics of virus replication . Salivary glands isolated from mosquitoes at different time points post infection were prepared for confocal microscopy and labelled with a mouse monoclonal antibody specific for the DENV-2 envelope . Interestingly , the viral envelope protein was detected starting at 5 dpi ( Figure 3 ) . Our results suggest that the immune response against DENV in the salivary gland is activated early through intracellular signalling pathways that induce expression of this AMP . In order to corroborate the supposed antibacterial activity of the identified AMP , its immature , signal sequence-containing , form ( called MK ) and its mature form ( called GK ) ( Supporting Information , Figure S2 ) were chemically synthesized and their potency was evaluated in antibacterial tests . Both peptides were tested on the Gram-negative strain E . coli ( Figure 4 ) . Both the GK and the MK peptides killed E . coli in a dose-dependent manner with minimum inhibitory concentrations of 0 . 625 µM ( GK ) and 5 µM ( MK ) , ( Figure 4 ) , respectively , which is consistent with a previous report showing that cecropins preferentially target gram-negative bacteria [27] . The Ae . aegypti MT peptide ( AAEL000160 ) which has been reported in the Ensembl data base as a hypothetical protein with a MW similar that of MK peptide , used as a negative control in these experiments had no effect on bacterial growth . Similar results were also obtained with a peptide of identical amino acid composition to the GK peptide but with a scrambled sequence ( Figure 4 ) . Next , antiviral activity of GK and MK peptides against DENV replication was tested in permissive C6/36 cells infected with each of the four DENV serotypes and treated with various concentrations of the GK , MK or with the MT control peptide . Twenty-four h post-infection , quantitative real-time PCR was performed to measure viral RNA accumulation in C6/36 cells ( Figure 5 ) . The results show that intracellular viral RNA levels in DENV-infected C6/36 cells incubated in the presence of GK or MK peptides were reduced , in a dose dependent manner , when compared with levels detected from control cells infected with DENV and maintained in the presence of medium alone or cultured with the MT peptide . Under these experimental conditions , the MK peptide was approximately 10-fold more potent than the GK peptide , used at a similar concentration , and reduced the viral RNA by more than 1 . 5 log compared to the positive control . Similar results were obtained with all four DENV serotypes ( Figure 5 ) . From the two concentrations used , a more efficient inhibition was observed at a concentration of 10 µg/ml of either peptide . Neither peptide , at any of the concentrations used , generated cytotoxic effects as demonstrated by the IC50 values that were >290 µg/ml and 250 µg/ml for the GK and MK peptides , respectively . The plaque reduction infectivity assay was also performed in virus-containing supernatants to determine virus count reduction as a result of treatment of infected C6/36 cells with the three peptides ( Table 2 ) . The amount of virus plaques was markedly decreased by about a hundred fold in the presence of the MK peptide and ten fold with GK peptide , as compared to infected cells , either untreated or treated with MT peptide . Furthermore , mean infectious titer correlated with RNA copy number , as determined by quantitative RT-PCR ( Figure 5 ) . To further investigate the antimicrobial properties of the cecropin-like peptides , we evaluated their inhibitory effects in a CHIKV model of infection . To this end , we used a model of HEK293T cells . This human epithelial cell line , was reported to be susceptible to CHIKV infection [28] , [29] and was thus used as an indicator cell line for viral infectivity . As shown in Figure 6 , both GK and MK peptides inhibited the infection of HEK-293T cells by the CHIKV 147-2 strain in a dose dependent way . The MK peptide was more potent with inhibition levels of about 50% at 10 µg/ml and 80% at 80 µg/ml , respectively . Under these experimental conditions , the control MT peptide induced no reduction of cell infection at any of the concentrations used . It is important to note that the GK IC50 was >290 µg/ml and that of the MK peptide was 200 µg/ml , indicating that none of the peptides had cytotoxic effects used at these concentrations . Finally , in order to determine the spectrum of anti-pathogenic activity of the GK and MK peptides , their capacity to kill the human protozoan parasites L . infantum and L . braziliensis promastigotes , was evaluated as well ( Figure 7 ) . Of note , whereas the MK peptide mediated potent activity against both parasites with respective IC50 values of ∼15 µM ( Figure 7A ) and ∼10 µM ( Figure 7B ) , the GK and the MT control peptides were inactive . The difference between the GK and MK peptides in their ability to display antimicrobial activity might be due to differences in their molecular conformation ( Supporting Information , Figure S2 ) . Therefore , circular dichroism ( CD ) spectra were used to study the content of secondary structures of both peptides . Figure 8A represents the CD spectra of the GK and MK peptides at 25°C in PBS and shows that the MK peptide contains α-helical structures , as indicated by a negative ellipticity at wavelengths of 208 and 222 nm , as well as a CD peak at 195 nm . In contrast , the CD spectrum of the GK peptide shows a negative band around 200 nm which is typical for the back bond of a peptide in random-coil conformation . In order to test whether the GK peptide was able to adopt a helical structure in a hydrophobic environment , it was diluted in PBS containing various proportions of TFE ( 10–40% ) ( Figure 8B ) . With an increasing concentration of TFE , the conformation of the GK peptide gradually changed from a random-coil ( 10% TFE/PBS ) to an α-helix structure in a relatively hydrophobic environment ( 40% TFE/PBS ) . This property is shared by many membrane-binding peptides [30] . In contrast , the MK peptide maintained its helical conformation independently of the concentration of TFE ( data not shown ) . Another strongly up-regulated gene , with a 9-fold over-expression in DENV-infected salivary glands , was identified as the membrane-spanning peptidoglycan recognition protein ( PGRP-LC , AAEL014640 ) ( Table 1 ) . The PGRP-LC protein is known to be important in innate immune responses in Drosophila [31] . A member of the Gram-negative bacteria-binding protein ( GNBP ) family ( AAEL007064 ) , the Toll-like receptor Toll5A ( AAEL007619 ) and the MYD88 factor ( AAEL007768 ) , were also up-regulated in infected salivary glands ( Table 1 ) , suggesting that the Toll pathway is activated in Ae . aegypti salivary gland anti-dengue defense mechanisms . Interestingly , the most significantly down-regulated gene detected encodes a conserved hypothetical protein ( AAEL003849 ) that is also an AMP belonging to the defensin family ( Table 1 ) . Moreover , the expression of another defensin gene ( AAEL003857 ) was also found to be down-regulated in infected salivary glands . These results underscore the ability of the virus to survive the rigor of the mosquito's immune system . Lysozyme P ( AAEL009670 ) is another immune-related mosquito gene [32] with enhanced expression in DENV-infected salivary glands ( Table 1 ) . Lysozyme C which has been linked to melanization reactions has been shown to be up-regulated in the midgut following DENV infection of Ae . aegypti [8] , thereby pointing to the importance of lysozyme family in the host defense mechanisms of this vector . In our study , transferrin ( AAEL015639 ) was induced by DENV infection of the salivary gland ( Table 1 ) . It has been shown that transferrin synthesis and secretion are increased when Aedes mosquito cells are exposed to bacteria , suggesting that mosquito transferrin may act as an acute-phase protein [33] .
DENV is not simply passively transported to humans by its vector Ae . aegypti; rather , it intimately interacts with the mosquito , allowing viral multiplication that induces significant biochemical and molecular changes in the host . Following the ingestion of an infectious blood meal , replication of DENV occurs in different mosquito tissues , including the salivary glands . Viral replication in this compartment is a prerequisite for subsequent injection of infectious saliva into the human host and continuation of the DENV transmission cycle . Accordingly , it is of crucial importance to elucidate the immune response mounted against DENV in this particular compartment for future elaboration of antiviral strategies . The comprehensive transcriptome analysis of the Ae . aegypti salivary gland in response to DENV infection performed in this study has allowed us to identify unique sequences of genes the products of which are modulated following viral infection . It is of note that , despite the availability of the completely sequenced genome of Ae . Aegypti [10] , a significant number of tags unmatched with annotated databases was observed . Unmatched tags might correspond to novel transcripts not yet identified in the Aedes genome , including alternatively spliced transcripts from known genes , as well as transcripts from novel genes . The latter possibility is underscored by the recent demonstration that the use of novel DGE tags as probes has permitted the identification of transcripts and genes in the human genome that were difficult to identify by conventional methods [34] . Among the modulated tags , the AAEL000598 sequence was the most strongly up-regulated gene in DENV-infected salivary glands . This sequence encodes a small cationic AMP belonging to the cecropin family [5] . The DENV-induced up-regulation of the AAEL000598 gene resulted in the production of a cecropin-like peptide at 5 dpi , the identity of which was confirmed using an antibody specific for the AEEL000598 gene product . Both the RNA and protein expression patterns concur with the detection of viral antigen early after the blood meal ( 5 dpi ) . This result corroborates a recent study suggesting that the extrinsic incubation period ( EIP ) for DENV may be shorter than previously reported and that it depends on the nature of the viral strain , as well as the genetic background of the vector [4] . As an example , short EIPs have been observed for other vector-arbovirus interactions: EIPs are only 2 days for CHIKV in Ae . Aegypti [35] , for Rift Valley fever virus in Culex pipiens [36] and for Venuezuelan Equine Encephalitis virus in Ae . aegypti [37] . With respect to our data , the presence of DENV RNA , associated with an absence of envelope expression in salivary glands at 24 hpi , suggests either that the viral particles present in this tissue at this time point are incomplete , or that there is not yet enough virus present to allow detection of the viral envelope by confocal microscopy . Investigating the assembly of entire particles , for example by electron microscopy imaging , would help to discriminate between these hypotheses . Fat body tissue has been reported to express cecropin as well and it is therefore important to rule out potential contamination of the salivary glands , used for the DGE analysis , by the surrounding fat body . The induction of cecropin in fat body tissue by DENV is however rapid and transient , as , following its expression 24 hpi , it is no longer detected at 3 dpi [38] . Therefore , the absence of cecropin expression in fat body tissue at 5 dpi , as demonstrated in the present study , argues for a lack of contamination during the dissection procedure . To further rule out contamination of the salivary gland samples by fat body tissue , we also focused on diptericin expression in both tissues . Tag-DGE analysis of the salivary glands RNA libraries revealed a maximum of 3 diptericin tag-DGE counts for 6 million tag-DGEs sequenced per library . This result is very close to background . Given that diptericin is highly expressed in fat body tissue [38] , these results confirm the absence of contamination of samples used in the present study . The functional properties of the AEEL00598-encoded cecropin-like peptide were determined using two synthesized peptides MK and GK , corresponding to the pre-protein and mature product , respectively . Both peptides were found to display broad anti-infectious properties and were active against DENV , CHIKV , as well as against E . coli bacterial strain . However , in some experiments the immature peptide was significantly more potent than the mature peptide used at a similar concentration: although CHIKV infection was less sensitive to the antiviral properties of the immature peptide , both peptides interfere with Flaviviruses and Alphaviruses , viral pathogens that belong to two different families of RNA viruses . Moreover , the immature peptide was efficacious against Leishmania while the mature was not , thus confirming that the immature form of the cecropin peptide has an even broader spectrum of activity , being able to inhibit pathogens vectored by Aedes mosquitoes , as well as a parasite like Leishmania that is not transmitted by mosquitoes . Based on the results from CD spectra analysis , these differential effects may be due to conformational differences , which are associated with the presence of the signal sequence in the immature form of the peptide . However , from a mechanistic point of view , the mode of action of AMPs , their selectivity for certain pathogens , as well as their lack of activity against normal eukaryotic cells , remain yet to be determined . This selectivity appears to depend on the lipid composition of the target membrane which determines its fluidity and charge the microbial membrane showing a highly negative electrical potential ) and whether or not cholesterol is present . Indeed , the plasma membrane of the Leishmania promastigote shows significant differences from those of other eukaryotic cells [39] , [40] because it has a high negative charge due to high levels of lipophosphoglycan and contains a higher proportion of anionic phospholipids . In this context , as evidenced for bacteria , AMPs are able to alter the structure of biological membranes as a result of the binding of positively charged regions of their α-helical peptides to negatively charged lipids in the membrane [41] . It has been suggested that AMPs above and beyond their ability to permeabilize and disrupt membranes may affect microbial viability by interacting with intracellular targets or disrupting key intracellular processes via the alteration of cytoplasmic membrane septum formation , the inhibition of cell-wall , nucleic-acid and protein synthesis , as well as the inhibition of enzymatic activity , following their translocation across the plasma membrane ( review in [42] . Such properties could explain virucidal activity of α-helical peptides . In addition , it is possible that such peptides could inhibit viral replication by interfering with membranes of the endoplasmic reticulum system , as DV replication is known to depend on the integrity of these membranes [43] . Results from structural characterization by Nuclear Magnetic Resonance analysis and the determination of the crystal structure of the peptides will provide more insight into their mode of action . Cecropin family peptides do have anti-pathogenic activities . A recent study showed that co-overexpression of two AMPs , cecropin A and defensin A , in transgenic Ae . aegypti mosquitoes results in a cooperative antibacterial and anti-plasmodium action [44] . Moreover , peptides of the cecropin family reportedly possess anti-HIV activity [45] . Finally , scorpine , a scorpion AMP that resembles a hybrid between a defensin and cecropin was shown to have antibacterial and antiplasmodial activity , as well as antiviral activity against DENV [46] . Such properties encourage the design of analogs consisting of sequences containing single amino acid replacements or hybrid peptide sequences derived from cecropins or cecropins with other AMPs , such as thanatin , melittin , magainin and temporin . In light of the antipathogenic properties observed for the MK and GK peptides , it is to be expected that in the near future a rational design approach based on these peptides may yield derived products that are more effective at lower concentrations . For example , the C-terminal amidation of these peptides might enhance their stability and cationicity , as has been demonstrated for the C-terminally amidated cecropin A of the giant silk moth Hyalophora cecropia which is more active than cecropin A itself [47] . Otherwise , as the antimicrobial activity of several of these analogs is more potent than that of their parent molecules [48] , hybrid molecules based on the structure of MK and GK peptides could be powerful effectors . Insect AMPs have been selected throughout evolution for their low toxicity to eukaryotic cells . Over the past decade , strains of many common microbes have continued to develop resistance to drugs [49] and , because of the urgent need for novel treatment modalities , insect AMPs could serve as a template for the design of novel therapeutic compounds . Besides the AAEL000598 gene , several other genes were identified the expression of which was up-regulated following DENV infection of Ae . Aegypti salivary glands . Among these , the PGRP-LC protein is known to be important in innate immune responses in Drosophila [31] , being involved in the signal transduction events that lead to the induction of AMP gene expression . PGRP-LC recognizes molecular patterns in exogenous microbes and activates the IMD pathway in response to pathogen infection [50] , [51] . This pattern recognition receptor could therefore be involved in the up-regulation of expression of the AAEL000598 gene . Up-regulation of GNBP ( AAEL007064 ) , Toll5A ( AAEL007619 ) and MYD88 ( AAEL007768 ) gene expression in infected salivary glands supports the notion that the Toll pathway is activated following DENV infection of Ae . Aegypti . This result furthermore corroborates a recent study showing that silencing of MYD88 , a key component of the Toll pathway , results in a small but significant increase in DENV load in the midgut of infected mosquitoes [8] . Accordingly , following infection the Toll pathway regulates expression of antiviral molecules . Interestingly , a mosquito RNAi-mediated silencing of Cactus , a negative regulator of the Toll immune pathway , has been shown to induce the expression of innate immune response-related genes including AAEL000598 [8] . Genetic analysis of the systemic immune response of Drosophila has indicated that activation of the Toll pathway accounts primarily for the response of this invertebrate to infections by fungi and Gram-positive bacteria [52] , whereas the IMD pathway is mainly induced in response to Gram-negative bacterial infection . For example , in Drosophila , cecropin A is regulated preferentially , but not exclusively , by the IMD pathway [53] . Although both pathways can be activated independently , they usually function synergistically , as has demonstrated in flies infected with the Drosophila C virus [54] , [55] . The interaction between both pathways during the infection of mosquitoes by DENV remains however to be established . Like the midgut , the Ae . aegypti mosquito salivary gland compartment harbours a potent cellular response against DENV . The results from the analysis of the Ae . Aegypti salivary gland transcriptome reported in the present study show that many of the genes that are up-regulated following infection with DENV , encode proteins that are involved in the innate immune response and that participate in the IMD signalling pathway . Recent studies have provided important insights into Ae . aegypti immune responses to DENV-2 infection in the midgut [8] , [9] , [38] showing that the Toll and the JAK-STAT pathways , as well as the RNAi machinery are important for the mosquito's defense against DENV infection . No involvement of the IMD pathway in the control of DENV-2 was reported in these studies , but this is to be expected since the analyses were carried out 10 dpi , whereas elicitation of the IMD pathway represents an acute response [56] . Together with these data , the results presented here demonstrate that both the IMD and Toll-like signaling pathways are modulated following infection of the salivary gland by DENV-2 . This is quite consistent with the very recent data in the literature , reporting up-regulation of cecropin in the midgut and fat body 24 h after oral infection of the mosquito with DENV-2 [38] . Studies on the survival of DENV-infected mosquitoes in which one of these signaling pathways is defective could shed light on the molecular mechanisms that underlie the interplay of these pathways , as well as on the regulation of their respective activity . The effectiveness of defense of the vector is likely to rely on the arsenal of strategies present in the midgut , fat body and salivary glands , which help the insect survive in a hostile environment that is harboring a wide variety of potential pathogens . It is nevertheless important to stress that , in spite of these immune response mechanisms , DENV accumulates viral genome RNA and infectious virus in the midgut and salivary gland , which is subsequently transmitted to humans . This underscores the complexity of the interaction between the virus and its vector , as well as the notion that the escape mechanism of the virus within the vector remains poorly explored . From an evolutionary point of view , the mosquito immune response has evolved to fight the infection until its detrimental fitness consequences are eliminated , albeit not to fully eradicate the virus . In this context , it is not paradoxical that mosquitoes can both mount an apparently robust response to virus infection and remain competent vectors . Further studies would be necessary to test this hypothesis . It would also be interesting to carry out RNAi-mediated knockdown studies of this peptide to understand his contribution in the antiviral signalling pathways . Finally , the results from this transcriptome analysis will facilitate the understanding of the molecular mechanisms of the insect-virus relationship and could help to develop novel strategies to reduce the transmission of viral diseases , including the use of transgenic insects or the identification of novel sources of pharmacological compounds . | Dengue viruses ( DENV ) are generally maintained in a cycle which requires horizontal transmission via their arthropod vector , Ae . aegypti , to the vertebrate host . One important consequence of this process is the interference of the virus with the immune systems of both the mosquito and its host . While infection of humans causes disease , the presence of DENV in mosquitoes gives rise to life-long and persistent infection with active viral replication in the salivary glands . In the present study , we have evaluated the mosquito's immune response following DENV infection by analyzing the gene expression profile of infected and uninfected salivary glands . The results show that DENV infection activates signaling pathways and induces the expression of gene products that are involved in the innate immune response to DENV infection , and in particular a putative antibacterial cecropin-like peptide . The immature and mature forms of this peptide were found to be active against a variety of pathogens including DENV and Chikungunya viruses , as well as the Leishmania parasite . This study is the first to establish a comparative analysis of uninfected salivary glands and salivary glands of female Ae . aegypti mosquitoes infected with DENV . We demonstrate that certain DENV-induced peptides possess broad-spectrum anti-pathogenic activity and may have therapeutic potential in the treatment of human infectious disease . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"virology/emerging",
"viral",
"diseases",
"infectious",
"diseases",
"virology"
] | 2011 | Induction of a Peptide with Activity against a Broad Spectrum of Pathogens in the Aedes aegypti Salivary Gland, following Infection with Dengue Virus |
The gene expression pattern specified by an animal regulatory sequence is generally viewed as arising from the particular arrangement of transcription factor binding sites it contains . However , we demonstrate here that regulatory sequences whose binding sites have been almost completely rearranged can still produce identical outputs . We sequenced the even-skipped locus from six species of scavenger flies ( Sepsidae ) that are highly diverged from the model species Drosophila melanogaster , but share its basic patterns of developmental gene expression . Although there is little sequence similarity between the sepsid eve enhancers and their well-characterized D . melanogaster counterparts , the sepsid and Drosophila enhancers drive nearly identical expression patterns in transgenic D . melanogaster embryos . We conclude that the molecular machinery that connects regulatory sequences to the transcription apparatus is more flexible than previously appreciated . In exploring this diverse collection of sequences to identify the shared features that account for their similar functions , we found a small number of short ( 20–30 bp ) sequences nearly perfectly conserved among the species . These highly conserved sequences are strongly enriched for pairs of overlapping or adjacent binding sites . Together , these observations suggest that the local arrangement of binding sites relative to each other is more important than their overall arrangement into larger units of cis-regulatory function .
Recent studies revealing how the gain , loss and repositioning of transcription factor binding sites within regulatory sequences can alter gene expression with observable phenotypic consequences [1] have focused efforts to understand the molecular basis for organismal diversity on the evolution of regulatory DNA . However , a growing body of work has demonstrated that alterations of binding-site composition and organization often leave regulatory sequence function unchanged [2]–[9] . The potential for significant changes in regulatory sequences to have no functional consequences complicates efforts to identify sequence changes that are likely to affect gene expression and phenotype . But precisely because many of these changes do not affect regulatory output , they provide a powerful opportunity to understand how the arrangement of transcription factor binding sites in a regulatory sequence determines its output . We believe that identifying divergent enhancers that drive similar patterns of expression , and distilling the common principles that unite them , will allow us to decipher the molecular logic of gene regulation . We began to explore the effectiveness of this approach with the extensively studied regulatory systems of the early D . melanogaster embryo [10] , using the recently sequenced genomes of 12 Drosophila species to document the evolutionary fate of transcription factor binding sites in early embryonic enhancers ( Peterson , Hare , Iyer , Eisen , unpublished ) . A consistent pattern emerged: while binding site turnover is common , a large fraction of the binding sites in most enhancers are conserved across the genus ( see Figure 1 ) . The extent to which variation in enhancers from sequenced Drosophila species represented all of the possible variation in these sequences was unclear . Perhaps the conserved sites were an imperturbable core essential for each enhancer's function . Or , perhaps , there had simply not been enough time since the divergence of the genus for mutation to have generated alternative configurations that would produce identical expression patterns . To resolve this ambiguity it was necessary to reconstruct binding site turnover events that occurred over longer evolutionary timescales by comparing Drosophila enhancers to their counterparts in species from outside the genus . The appropriate species for such comparisons would share basic patterning mechanisms with Drosophila species , but be sufficiently diverged from Drosophila to provide significant additional data on the constraints on binding site turnover . Ideally , these species would be amenable to experimental analysis and have fully sequenced genomes . Unfortunately , the closest available genome sequences were from several very distantly related mosquito species [11] , whose most recent common ancestor with Drosophila lived approximately 220 million years ago . These sequences were unlikely to be informative because of several important differences between early-embryonic patterning in Drosophila and mosquitoes . Mosquitoes , for example , lack the primary anterior morphogen in Drosophila , the modified Hox gene Bicoid , which is found only in higher cyclorrhaphan Diptera ( the “true flies” ) [12] . With essentially no information on non-coding sequences and regulatory networks from flies outside the Drosophilidae , we reasoned that other groups within the Acalyptratae , the speciose 100 million year-old division of Diptera that includes Drosophila , represented the best compromise between our aims to maximize sequence divergence and minimize regulatory network divergence . We selected three families , Sepsidae , Diopsidae and Tephritidae , that span acalyptrate diversity , have well-characterized phylogenies , and contain multiple species whose specimens could be readily obtained . In this paper we present results on gene regulation in sepsids , which , due to their small genomes , were the most amenable to genome analysis . Specifically , we report the sequence and experimental characterization of the even-skipped locus from six sepsid species . The particular species were selected to include the major sepsid lineages , and , in several cases , because of the amenability of the species for embryological study . We chose to characterize multiple sepsid species to facilitate the identification of sepsid enhancers by intra-family comparisons [13] , [14] and to enable comparisons of enhancer evolution between sepsids and drosophilids .
The six sepsid species we selected for this study , Sepsis punctum , Sepsis cynipsea , Dicranosepsis sp . , Themira superba , Themira putris and Themira minor , have genome sizes that range from 134 Mb to 285 Mb ( Table 1 ) . We generated a whole-genome fosmid library for each species , identified eve-containing clones by hybridization with a species-specific eve probe generated by degenerate PCR , and shotgun sequenced the clones to an average 13× coverage ( Table S1 ) . We annotated the assembled sequences ( Table S2 ) to identify all protein-coding genes with homologs in D . melanogaster ( Figure S1 ) . All of the sequenced clones contained clear eve orthologs , and the organization of the eve locus is very similar in sepsids and drosophilids ( Figure 2 ) . The sepsid loci are slightly larger ( Table 1 ) , consistent with their overall larger genome sizes . The genes flanking eve , however , are different between the families . A maximum likelihood tree calculated using seven protein-coding gene sequences in all six sepsids and a subset of Drosophila species demonstrates that the sepsid species are about twice as diverged from D . melanogaster than D . melanogaster is from the most distantly related Drosophila species ( Figure 3A ) . Examination of the eve locus from sequenced Drosophila species shows that there is readily detectable non-coding sequence conservation spanning the entire locus , even between the most distantly related species ( Figure 2 ) . The average pairwise noncoding match score ( a BLASTZ [15] based measure of sequence similarity; see Materials and Methods ) between D . melanogaster and members of the virilis-repleta clade is 20% ( Table S3 ) . We observe a similar pattern in the sepsid eve loci . The average pairwise noncoding match score between S . cynipsea and Themira species is 17% ( Table S3 ) . However , there is minimal non-coding sequence conservation between families outside of a few small ( approximately 20–30 bp ) blocks of extremely high conservation scattered across the locus ( Figure 2 ) . The average pairwise noncoding match score between D . melanogaster and the sepsids is 4% ( Table S3 ) . Maximum likelihood non-coding trees from the eve locus in sepsids and Drosophila reveal that the two families span roughly the same amount of non-coding divergence ( Figure 3B ) . We established a colony of the T . minor from adults captured in Sacramento , CA , and developed protocols to recover and fix T . minor embryos . The overall morphology and pattern of embryonic development is very similar in sepsids and Drosophila ( Figure S2 ) . As expected from studies of other dipterans , T . minor eve is expressed in a characteristic set of seven stripes in blastoderm embryos ( Figure 4D , H ) . We were additionally interested in comparing the trans-regulatory network of this sepsid to that of drosophilids . In D . melanogaster , eve expression in the blastoderm is regulated by the transcription factors Bicoid ( BCD ) , Caudal ( CAD ) , Hunchback ( HB ) , Giant ( GT ) , Krüppel ( KR ) Knirps ( KNI ) and Sloppy-paired 1 ( SLP1 ) . hb , gt and Kr are expressed in T . minor in patterns that mimic those of their orthologs in D . melanogaster embryos ( Figure 4A–C , E–G ) . This is in contrast to AP patterning factors in the mosquito , in which there have been shifts in expression domains , and presumably changes in regulation of the downstream genes [16] . We were unable to clone the kni , slp1 and cad genes from T . minor . In D . melanogaster , bcd RNAs are tethered to the anterior pole of the embryo , with BCD protein diffusing away from the pole to create a strong anterior to posterior gradient . BCD antibodies were not cross-reactive in T . minor , and we were unable to characterize the T . minor BCD gradient . Key elements of the heart regulatory network are conserved between flies and vertebrates [17] . As we therefore expect this network to be conserved between the sepsid and Drosophila species , and our supply of T . minor embryos was limited , we did not examine the expression of heart regulators . Since the sepsid and Drosophila trans-regulatory networks regulating eve expression appear to be similar , we reasoned that sepsid enhancers would contain similar collections of transcription factor binding sites as their Drosophila counterparts . In D . melanogaster , clusters of HB , CAD , KNI , KR , and BCD binding sites in the eve locus have been shown to correspond to known stripe enhancers [18] . We therefore examined the density of predicted HB , CAD , KNI , KR , GT and BCD binding sites across each fosmid sequence ( Figure S3 ) and identified 18 candidate sepsid stripe enhancers ( Table S4 ) ( We recently generated GT in vitro binding data which was not available when the initial D . melanogaster work was carried out ) . Each of these predicted enhancers contained a small number of short ( 20–30 bp ) sequences conserved between sepsids and drosophilids , which established presumptive orthology with specific regions of the D . melanogaster genome . In essence , the binding site plots showed us where sepsid enhancers could be found , and the small islands of sequence conservation suggested their likely function . We also identified putative eve muscle-heart enhancers ( MHE ) ( Table S4 ) in the sepsid species by looking for short blocks ( 20–30 bp ) of high similarity ( >90% ) that overlap functionally verified transcription binding sites from the D . melanogaster MHE in pairwise alignments between the D . melanogaster MHE and each of the sepsid intergenic regions . We chose to test whether candidate enhancers from one species in each of the two sepsid clades were capable of driving expression in D . melanogaster embryos . Enhancer-reporter cassettes for each of these 8 constructs were introduced into the D . melanogaster genome via Phi-C31 phage-mediated targeted integration [19] , [20] . Remarkably , despite their extensive sequence differences , all of the tested sepsid sequences drive very similar expression patterns to those driven by their orthologous D . melanogaster enhancers ( Figure 5 ) , although there are some small and intriguing differences . This confirms that these sepsid sequences are functional eve enhancers that , with their high degree of sequence divergence , represent markedly different examples of how to construct an eve enhancer . The D . melanogaster minimal stripe 2 element drives expression in a single stripe in the stage 5 blastoderm from 63–57% egg-length through activation by broad anterior gradients of BCD and HB and localized repression by GT and SLP1 in the anterior and KR in the posterior [21] ( Figure 5A; Table 2 ) . The sepsid stripe 2 enhancers in the transgenics similarly drive expression from 62–55% egg-length ( Figure 5B , C; Table 2 ) . In 78% of embryos containing the S . cynipsea enhancer and 55% of embryos containing the T . putris enhancer , we observe expression in stripe 7 from the sepsid stripe 2 enhancers; similar behavior has also been observed for D . melanogaster stripe 2 constructs [21] . The D . melanogaster stripe 3+7 enhancer ( Figure 5D ) is broadly activated by dStat and Tailless ( TLL ) ( stripe 7 only ) , and the two stripes of expression at 53–47% and 21–12% egg-length ( Table 2 ) are carved out by domains of HB , KNI , and SLP1 repression [22] . Stripe 3 expression in the transgenics containing sepsid stripe 3+7 enhancers agrees well with D . melanogaster ( Figure 5E , F; Table 2 ) . The anterior border of stripe 7 corresponds to that in D . melanogaster , but in embryos containing either the S . cynipsea or T . putris stripe 3+7 element , stripe 7 expression extends posteriorly . Significantly , the stripe 3+7 enhancer has been inverted in the Sepsis species relative to the other sepsids and Drosophila . This strongly suggests that these enhancers are orientation-independent in their native genomic context . The D . melanogaster stripe 4+6 enhancer drives expression in 2 stripes from 47–40% and 30–22% ( Figure 5G ) . There is some evidence that stripe 4+6 expression is activated broadly by Dichaete and restricted to 2 stripes by HB and KNI repression , but the precise details of its regulation are less well understood [23] , [24] . This pattern is reproduced in our transgenics , with expression from 46–40% and 31–25% egg-length ( Figure 5H , I; Table 2 ) . In stage 11 D . melanogaster embryos , eve is expressed in laterally-symmetric , metameric pairs of pericardial cells in the dorsal mesoderm ( Figure 5J ) [25] . The eve MHE integrates activation and repression from multiple signaling pathways , including DPP and WG from the dorsal ectoderm and RAS in the dorsal mesoderm [26] . In addition , broad domains of TIN and TWI in the dorsal mesoderm activate expression . This metameric pattern is faithfully reproduced by the sepsid MHE enhancers ( Figure 5K , L ) . That enhancers with minimal sequence conservation have conserved function suggests that they share some common features beyond primary sequence . In order to examine what these shared properties might be , we examined and compared the composition and organization of predicted transcription factor binding sites in all of the characterized eve enhancers . We restricted our analysis of each enhancer to those factors known to be involved in the activity of the particular enhancer . We aligned enhancer sequences from within each family , and plotted predicted transcription factor binding sites on these alignments ( Figure 6 ) . 92% of D . melanogaster binding sites are found in the same location in enhancers from other species within the closely related melanogaster subgroup ( Table S3 ) , 29% of sites are similarly conserved between D . melanogaster and the species of the virilis-repleta clade ( Table S3 ) . An average of 22% of sites are conserved between S . cynipsea and Themira species . The non-coding divergence between these two sepsid clades is similar to that between D . melanogaster and the virilis-repleta clade ( Figure 3 ) . This is likely an underestimate of the conserved sites within the sepsids as these are not minimal enhancers and thus should contain a larger portion of non-conserved background sites . The lack of sequence similarity between families made nucleotide level alignment of sepsid enhancers to their Drosophila orthologs impossible . However , the previously described small blocks of high sequence conservation allowed us to orient and crudely align the sepsid and drosophilid enhancers to each other . In examining plots like this for all four enhancers , it was clear that few of the binding sites conserved within each family were conserved between families ( Figure 6; Figure S4 ) . Only 5% of D . melanogaster binding sites are conserved in pairwise comparisons with sepsid species , representing an additional 84% reduction in conserved sites compared to the virilis-repleta clade ( Table S3 ) . However , we note that all of the highly conserved blocks contained at least one , and often several , highly conserved binding sites , and that most of these sites correspond to known in vitro footprints for the corresponding factor in D . melanogaster [27] ( Figure S4 ) . Most early embryonic enhancers in D . melanogaster contain unusually large numbers – compared to random non-coding sequence – of predicted binding sites for the factors involved in their regulation [18] , [28] , although the exact relationship between binding site density and function remains to be elucidated . Binding site density is conserved between enhancers in D . melanogaster and D . pseudoobscura [13] , [14] , but it is not clear how much of this conservation is due to selection to maintain binding sites , and how much is due to the overall high level of sequence conservation between D . melanogaster and D . pseudoobscura . Given the overall lack of sequence and binding site conservation between sepsid and Drosophila enhancers , we were particularly interested in the characteristics of the small sequence blocks that are conserved between the families . We noticed that all of these blocks contained overlapping or tightly spaced binding sites . To analyze this more rigorously , we classified predicted D . melanogaster binding sites for footprinted factors in the eve MHE , stripe 2 and stripe 3+7 enhancers into four categories ranging from non-conserved ( present only in D . melanogaster and its immediate sister taxa ) to extremely highly conserved ( present in Drosophila and sepsids ) . We then classified sites based on their proximity to other predicted binding sites: overlapping sites that share one or more bases with another binding site , neighboring sites that are within 10 bases of another site but do not overlap , and isolated sites . Overlapping sites are more often extremely conserved , close sites are more often highly conserved and isolated sites are more often minimally or non-conserved than expected by chance ( Figure 7; p<0 . 007 , p<0 . 01 , p< . 049 , Chi-squared test ) . However , the number of sites is too small to detect relationships between conservation and the spacing of pairs of sites for specific factors .
Our work extends in both the extent of divergence and number of enhancers examined the pioneering work on binding site turnover of Ludwig and Kreitman , who showed in a series of papers that the eve stripe 2 enhancer from other Drosophila species drives a stripe 2 pattern in transgenic D . melanogaster embryos despite the imperfect conservation of functional binding sites [5] , [6] , [8] . Although several examples of Drosophila regulatory sequence conservation over long evolutionary distances had been reported prior to Ludwig and Kreitman's work on eve stripe 2 [29] , [30] , eve regulation has become the preeminent model for the study of binding site turnover . It remains one of the few cases where observations of expression pattern conservation have been followed up with studies of functional complementation [7] . We have nearly doubled the evolutionary distance analyzed by Ludwig and Kreitman . Furthermore , in their comparisons the majority of binding sites were conserved , while our species sample has very few conserved binding sites . We have also generalized their observation to include additional enhancers responding to a different suite of transcription factors , including one ( the MHE ) active following gastrulation . Previous reports of the functional equivalence of divergent enhancers in Drosophila have involved blastoderm enhancers , leaving open the possibility that the observed binding site turnover was a byproduct of the syncitial nature of the early Drosophila embryo . Our data on the MHE demonstrates that extreme binding site turnover with functional conservation occurs in enhancers active in a cellular context . A handful of isolated case studies support our findings . For example , the tailless enhancer from the house fly Musca domestica [31] and the single-minded enhancer from the mosquito Anopheles gambiae [32] drive similar patterns as their endogenous orthologs in D . melanogaster embryos despite having different organization of binding sites , and non-coding sequences from the human RET locus drive ret-specific expression in zebrafish despite the absence of detectable sequence similarity between human and zebrafish RET non-coding DNA [33] . Nonetheless , in each of these cases simple transcription factor “grammars” were conserved , offering a ready molecular explanation for the conserved function . No such grammar is as of yet apparent in the eve enhancers . Such remarkable flexibility in the organization of enhancers suggests that the protein-protein and protein-DNA interactions that mediate the activity of developmental enhancers are not highly structured as , for example , is seen in enhanceosomes [34] . If they were , it is hard to imagine how such wildly different sequences could produce identical expression patterns in the same trans-regulatory context . The extent of binding site turnover is consistent instead with the recently proposed “billboard” model of enhancer activity in which enhancers contain multiple sub-elements that independently interact with cofactors and the basal machinery to dictate transcriptional output [35]–[37] . In proposing the billboard model , Kulkarni and Arnosti proposed that billboard enhancers would be more evolutionarily pliable than enhanceosomes , and suggested that the eve stripe 2 results from Ludwig and Kreitman were understandable if eve stripe 2 were a billboard enhancer [35] . Their model does not , however , predict how evolutionarily flexible billboard enhancers should be . Our discovery of extreme sequence and binding site divergence between functionally equivalent sepsid and Drosophila enhancers shows that they are extremely flexible , a fact that must be accounted for in future models of enhancer activity . However even billboard enhancers are not infinitely flexible . One remarkable aspect of enhancer evolution is that despite the clearly frequent repositioning or replacement of transcription factor binding sites within enhancers , the enhancers themselves remain fairly compact . There must , therefore , be selection to keep the different sub-elements that contribute to an enhancer's output within the one to two kilobase span of a typical enhancer . This spatial constraint implies some functional interaction between enhancer sub-elements not currently captured by the billboard model . Given the extent of non-coding divergence between Drosophila and sepsids across most non-coding DNA , we were surprised to observe small islands of very strong sequence conservation . Our finding that there is a significant enrichment of overlapping or adjacent binding sites within conserved blocks lends evolutionary support to long-standing suggestions of the importance of direct competitive and cooperative interactions between bound transcription factors . Numerous studies have demonstrated that appropriate regulation of the eve stripe enhancers ( and other enhancers ) relies on the close proximity of multiple binding sites for both activators and repressors [21] , [36] , [38]–[41] . Of the 12 footprinted BCD , HB , KR , and GT sites in the minimal stripe 2 element , 8 fall into 2 clusters of about 50 base pairs each containing overlapping activator ( HB or BCD ) and repressor ( KR or GT ) sites . In transient transfection experiments using these binding site clusters , BCD and HB dependent activation was repressed by DNA binding of GT or KR , consistent with the short-range repression mechanisms of quenching or competition [40] . Knirps also mediates short-range repression in a range of 50–100 bp through quenching or direct repression of the transcriptional machinery when bound near a promoter [42] . Similarly , HB and BCD co-expression in transient transfection experiments results in multiplicative activation of a reporter construct containing a subset of the eve minimal stripe 2 element [40] . Mutation of single activator sites in the minimal stripe 2 element results in a significant reduction in expression , again suggesting that HB and BCD bind cooperatively to this enhancer [21] . The local quenching and cooperativity models predict that binding sites in close proximity to each other should be under strong purifying selection to remain close to each other . Under the generally accepted model of binding site turnover , sites are lost in one region of an enhancer when new mutations create a complementary site elsewhere in the same enhancer . The appearance of new sites is the rate-limiting step as there are more mutational steps required to create a new site from random sequence than to destroy an existing site . Since random mutations are far less likely to produce pairs of adjacent sites than single sites , we expect functionally linked pairs of sites to be subject to far lower rates of binding site turnover . In contrast , if binding site turnover is driven by base substitutions , we expect functionally independent sites that are adjacent or even partially overlapping to have essentially the same rates of binding site turnover as isolated sites . The conserved blocks we observed between sepsids and Drosophila were generally larger than individual sites , as has been previously reported within Drosophila [43] , consistent with the former model . Our observation that proximal sites are preferentially conserved additionally supports their direct functional linkage . However , we note that insertions and deletions are a major source of sequence variation in Drosophila , with D . melanogaster having a strong deletion bias [44] and deletion is thought to contribute significantly to binding site turnover [45] . Taking this into account , we expect to observe reduced turnover in even functionally independent binding sites if they are overlapping or adjacent , as some fraction of the deletions that would remove a binding site with a complementary site elsewhere would also affect adjacent , and presumably uncompensated sites . These deletions would be subject to purifying selection , and the rate of turnover for the proximal sites would be reduced . Assessing whether such an effect could explain our observation requires more data on relative rates of nucleotide substitution and insertion and deletions of different sizes in sepsids , which will be accomplished with the sequencing of sepsid genomes . We can , however , test the significance of our observation directly . The linked function model predicts that the paired binding sites we observe to be conserved between families should be more sensitive to manipulations that alter the spacing between the sites than paired binding sites that are not conserved . Though expression of the sepsid eve enhancers in D . melanogaster embryos is qualitatively very similar to the patterns driven by the D . melanogaster enhancers , there are subtle and interesting differences . Expression of stripe 7 exhibits the most variability across all enhancers in transgenics , including those enhancers from D . melanogaster . It was previously observed that stripe 7 is weakly expressed in D . melanogaster stripe 2 transgenics , and stripe 7 expression is weaker than the endogenous stripe in stripe 3+7 transgenics [21] , [22] , [40] . We frequently observed stripe 7 expression in all our non-Drosophila stripe 2 transgenics , and stripe 7 expression did not perfectly recapitulate endogenous expression , suggesting that regulatory information specifying this stripe is distributed across the upstream region , thus challenging the model of enhancer modularity in agreement with [46] . Information may be more diffusely spread across the locus in sepsids , resulting in missing information in our discrete cloned enhancers , in which case the native D . melanogaster pattern should be more accurately reproduced by cloning a larger regulatory region . Alternately , there could be changes within the non-Drosophila enhancers which result in expression differences in D . melanogaster despite conserved native eve expression , suggesting co-adaptation of each enhancer and its native trans environment . We began this study seeking taxa that were significantly more diverged from D . melanogaster than any Drosophila species , but which had sufficiently conserved cis-regulatory networks that their enhancers would have similar function to their D . melanogaster counterparts . Our choice of sepsids was guided by their relatively close – but not too close – position to Drosophila on published trees of Diptera [47] , by their relatively similar morphology suggestive of similar developmental mechanisms , and by practical considerations such as genome size and availability . We have now shown that the extensive sequence divergence between sepsids and Drosophila was not accompanied by extensive differentiation of early embryonic patterning mechanisms . Thus sepsids provide a valuable model for comparative analysis of Drosophila embryology and developmental cis-regulation . We were also able to establish a colony of sepsids ( T . minor ) in the lab from flies caught locally , and collect embryos for the developmental gene expression and morphology data presented here . Based on our experience , we believe that more extensive embryological and molecular work with sepsids is very feasible , although some may find the need to provide the colonies with fresh cow dung objectionable . The additional sequence divergence has enabled us to reach two important conclusions that could not be obtained in analyses of the 12 sequenced Drosophila genomes . Previous analyses of binding site turnover in Drosophila revealed substantial numbers of conserved binding sites within the genus , leaving open the question of whether these sites represented an imperturbable core necessary for enhancer function , or if there had simply not been sufficient divergence time for mutation to generate alternative configurations . We have now largely answered this question , at least for the eve enhancers – there does not appear to be an imperturbable core of sites at the level of overall enhancer organization . Although binding site conservation in Drosophila has been extensively studied , our observations about the relationship between conservation and binding site proximity were never described because this pattern was simply not evident in examinations of the multitude of conserved binding sites across the Drosophila genome . This relationship only became apparent when we observed just how striking the conservation of a small subset of sites was . More generally , this study highlights the value of the infrequently studied ( at least by molecular biologists ) Dipteran species outside of the genus Drosophila . It also points to a general strategy for dissecting the still elusive molecular mechanisms of enhancer function in which genome sequencing and functional studies are combined to catalog the diverse ways in which regulatory sequences with common function can be generated . Our initial foray into this domain has yielded exciting and unanticipated results . With the cost of genome sequencing plummeting , and with great improvements in Drosophila transgenesis , we expect this approach to be even more productive in the years to come .
Sepsis punctum , Sepsis cynipsea , Themira superba , Themira putris and Dicranosepsis sp . stocks were maintained in the Evolutionary Biology Laboratory at the National University of Singapore . Themira minor cultures were established at LBNL from specimens collected at McKinley Park in Sacramento , CA . Samples for genome sizing and genomic DNA isolation were flash-frozen adult flies . Genome sizing methods were adapted from [48] . Five adult heads for each species were dissected into 1 . 5 mL of Galbraith buffer on ice , homogenized with 15 strokes of an A pestle in a 15 mL Kontes Dounce tissue homogenizer , and filtered through 30 um nylon mesh . T . superba heads were combined with 5 D . virilis heads before homogenization . 7 uL of 1∶10 chicken red blood cells ( diluted in PBS ) and 50 uL of 1 mg/mL propidium iodide were added and samples were stained for 4 hours rocking at 4 degrees in the dark . Mean fluorescence of co-stained nuclei was quantified on a Beckman-Coulter EPICS XL-MCL flow cytometer with an argon laser ( emission at 488 nm/15 mW power ) . The propidium iodide fluorescence and genome size of Gallus domesticus ( red blood cell standard , 1 , 225 Mb ) were used to calculate the unknown genome sizes . For T . superba , D . virilis at 328 Mb , was used as a second internal standard . High molecular weight genomic DNA was obtained from approximately 500 mg of frozen adult flies using the Qiagen 500/G Genomic-tip protocol for isolation of genomic DNA from flies ( Qiagen Cat . No . 10262 ) . Fosmid libraries were generated according to the Fosmid ( 40 kb ) Library Creation Protocol developed at the DOE Joint Genome Institute ( http://www . jgi . doe . gov/sequencing/protocols/prots_production . html ) with the following modifications . DNA was end-repaired without hydro shearing , phenol-extracted , and precipitated a second time after gel-purification to increase cloning efficiency . Ligation reactions were incubated overnight at 16°C with T4 DNA ligase then packaged according to the JGI protocol . All libraries are at approximately 5× coverage with an average insert size of 39 . 5 kb . Species specific sequence for target genes was obtained by degenerate PCR with primers designed based on Drosophila protein sequences , with additional fly sequences used where available . 40 bp overlapping oligonucleotide probes were synthesized by Klenow extension of 24 bp oligos overlapping by 8 bp with radiolabeled dATP/dCTP . Oligos were designed against target gene regions with 50–55% GC and no matches to known PFAM domains . Overgo probes were hybridized in pools of 6–10 probes to high density colony array filters at 60 degrees C overnight as described in [49] and visualized on a Molecular Dynamics Storm 860 phosphorimager . Positive clones were isolated and fosmid DNA was extracted and printed in 12×8 arrays on nylon membranes for hybridization with single overgo probes , protocol as above . 1–3 fosmid clones for each gene in each species were selected by EcoRI and BglII restriction mapping from final dot blot positives and were shotgun sequenced . Selected fosmids were subcloned and sequenced at the Joint Genome Center; protocols are available at http://www . jgi . doe . gov/sequencing/protocols/prots_production . html . Chromatograms were reanalyzed using PHRED v0 . 020425 . c [48] , [50] , [51] using the phredPhrap Perl script supplied with the CONSED distribution to call bases and assign quality scores . The ARACHNE assembler [52] , [53] was then used to build scaffolds ( Table S2 ) . After assembly , contigs from fosmids tiling across a given locus for a particular species were further merged by alignment using BLAT [54] ( version 25; run with default parameters ) . Where matches exceeded 98% identity and extended to within 100 basepairs of either: a ) both ends of a single contig , or b ) one end of both contigs , one of the two sequences for the match region was chosen at random to construct a single representative sequence for the entire region , despite heterozygosity in fosmid libraries . Fosmid sequences and combined locus sequences are available as Dataset S1 . Protein-coding gene annotation of the fosmids was performed with reference to the Flybase D . melanogaster 4 . 3 annotations . D . melanogaster translations were compared to the fosmid sequences translated in six frames using BLASTX . GeneWise [55] was used to construct gene models on scaffolds having hits with e-value ≤1e−10 , with the query translation as template . Gene models were then filtered by requiring that the model translation find the original D . melanogaster query translation among the top hits in a reciprocal BLASTP search against the D . melanogaster translation set ( e-value threshold 1e–10 ) . We obtained established phylogenies of Drosophila [56] and Sepsidae [57] . Branch lengths for coding regions were determined using nucleotide sequence aligned in amino acid space with T-Coffee [58] . Codeml from the PAML package ( version 3 . 13d , codon frequencies estimated from base frequencies [F3×4] , no clock , single dN/dS across all branches estimated with a starting value of 0 . 4 , transition/transversion ratio estimated starting at 2 ) was used to estimate branch lengths over 10 sequenced Drosophila species ( not including D . simulans or D . sechellia ) and the 6 sepsid species reported here for orthologs of seven genes ( bcd , CG8386 , CG9119 , eve , odd , stumps , zen ) independently , as well as for the concatenation of all seven , and for the seven arrangements of all but one gene . The 15 resulting trees were compared both by visual inspection , and RMSD of branch lengths . Single gene trees constructed from alignments with 115 or fewer informative positions ( eve , CG9119 ) estimated many zero-length internal branches and higher RMSD from the full concatenation ( up to 390% average branch length ) , however no leave-one-out tree deviated from the seven gene concatenation by more than 15% of average branch length , suggesting that no single gene dramatically distorts the overall estimate of branch lengths in the full set concatenation . We therefore report final results for per-codon rate estimates of the full concatenation of 1566 informative positions . Phylogenies of noncoding regions surrounding the eve gene were estimated in each family separately using baseml from then the PAML package [59] ( Model: HKY , transition/transversion ratio estimated as above , alpha estimated starting at 0 . 5 ) . A total of 966 sites in Drosophila and 958 sites in Sepsid alignments proved informative in upstream and downstream regions combined . Final estimates from these upstream + downstream concatenations in each family are reported as per-base rates . Fresh cow dung was obtained from free-ranging , grass fed , and antibiotic-free Milking Shorthorn cows ( Bos taurus ) in the Tilden Regional Park in Berkeley , CA . Resting cows were approached with caution and startled by loud shouting , whereupon the cows rapidly stood up , defecated , and moved away from the source of the annoyance . Dung was collected in ZipLoc bags ( 1 gallon ) , snap-frozen and stored at −80 C . Dung aliquots were thawed at 4 C and moistened slightly before use . T . minor embryos were collected at room temperature in a 100 mm petri dish of fresh cow dung . Embryos were removed from the top layer of dung under a dissecting microscope then filtered through course mesh to remove grass and debris . Fixation was as previously described for D . melanogaster in 50% fixation buffer ( 1 . 3× PBS , 66 mM EGTA pH 8 . 0 ) containing 9 . 25% formaldehyde [21] . 500–1000 bp of coding sequence for each gene were amplified from genomic DNA by degenerate PCR and cloned into the pGEM-T-Easy vector , amplified with M13 forward and reverse primers , and gel-purified with Qia-quick PCR columns . 4 uL of product were used in 20 uL transcription reactions with digoxigenin-11-UTP as described by the manufacturer ( Roche DIG RNA Labeling Kit , Cat . No . 11 175 025 910 ) . Probes were then incubated in 100 uL of 1× carbonate buffer ( 120 mM Na2CO3 , pH 10 . 2 ) for 20 minutes , and reactions were stopped by addition of 100 uL stop solution ( 0 . 2 M NaOAc , pH 6 . 0 ) . Probes were precipitated with 8 uL of 4 M LiCl and 600 uL EtOH then resuspended in 1 mL hybridization buffer . Hybridizations were performed as described previously with 18–20 hour hybridizations [60] . Embryos were imaged on a Nikon Eclipse 80i scope equipped with a Nikon Digital Sight DS-U1 camera . We picked regions of the fosmid to test for enhancer activity based on manual inspection of two types of data: ( 1 ) D . melanogaster enhancers mapped to each fosmid sequence via pairwise alignments , and ( 2 ) conservation of putative binding sites for BCD , CAD , KR , KNI , GT , HB . We computed pairwise LAGAN ( Brudno et al . 2003 ) alignments of each sepsid fosmid to all of the other sepsid fosmids and the eve locus ( defined as 10 kb upstream and downstream of the annotated eve protein-coding gene ) . In all cases , short blocks of high sequence similarity between D . melanogaster enhancers and the sepsid fosmids allowed us to determine the rough location of the likely sepsid enhancer . We used PATSER [61] and position weight matrixes for BCD , CAD , KR , KNI and HB from [18] and GT from data in [27] to predict sites for each factor across each fosmid using a ln ( p-value ) cutoff of −6 . We assigned a conservation score to each site equal to the number of species in which a site for the same factor was predicted at an overlapping position in the pairwise alignment of the species . We examined the mean conservation score in 100 bp windows surrounding each mapped enhancer and selected boundaries for tested fragments to include regions of high single genome and conserved site density surrounding the region mapped from D . melanogaster . In the Themira clade , we tested stripe enhancer predictions from T . putris and the MHE prediction from T . superba as initially there was insufficient flanking sequence to recover all enhancers from the same species . Enhancers were cloned into either the NotI or BglII site in pBΦY-ayeCFP vector ( modified from pBDP-Gt81 , kindly provided by Barret Pfeiffer ) . Reporter constructs were injected into the D . mel attP2 landing pad strain [20] by Genetic Services , Inc . Injection survivors were pooled and red-eyed progeny were screened from the F1 generation . Integration was confirmed by two PCR reactions , one which amplifies across the cassette integration site ( fw: CGGCGGCAACCCTCAGCGGATG; rv: GCGAAGAGATAGCGGACGCAGCGG ) and one which amplifies the enhancer within pBΦY ( fw: AAATAGGGGTTCCGCGCACAT; rv: CCCCGCGCCCTTTTATACCG ) . S . cynipsea stripe 2 was confirmed with the cassette integration primers and primers that amplify within the enhancer ( fw: TGGCACAAGAGCGCCTCGAA; rv: GCGAGCCCCTTTTCCGTTGG ) . D . melanogaster eve stripe 2 and stripe 3+7 transgenic lines were kindly provided by Steven Small [21] , [22] and D . melanogaster stripe 4+6 from Miki Fujioka [23] . For the stripe enhancers , transgenic embryos were collected for 2 hours then aged for 2 hours at room temperature . For the MHE , embryos were collected for 6 hours and aged 4 hours . Fixation , CFP and lacZ probe synthesis , hybridization conditions and microscopy were as described above . Pairwise BLASTZ Score: BLASTZ [15] was run with default parameters except for MSP cutoff ( K ) of 1800 , outputting but not extending chains ( C = 1 ) . BLASTZ hits can occur on either strand , and to any position in the other sequence , thus opposite strand matches ( outside of the described inversion ) and “non-local” matches ( i . e . those to sequences far from the orthologous region defined by the synteny of matches overall ) are a straightforward internal description of the precision of this method , and the cutoff selected . The chosen cutoff was selected in order to preserve hits that overlapped the “conserved cores” found by MLAGAN in each enhancer , while minimizing the number of minus strand and non-syntenic hits . BLASTZ alignments were generated for D . melanogaster enhancers in the eve locus against the nine Drosophila species and six Sepsid species shown in Table S4 and Table S5 . The pairwise BLASTZ score is defined as the product of percent identity and the total length of HSP chains as reported by BLASTZ . D . melanogaster binding sites in BLASTZ hits: Using BLASTZ alignments calculated above , we tabulated the number of binding sites predicted by PATSER in D . melanogaster enhancer sequences that occurred within BLASTZ aligned regions . Numbers reported reflect the total number of binding sites for factors in each enhancer as follows: HB , KNI , DSTAT in the stripe 3+7 enhancer; BCD , HB , GT , KR , SLP1 in the stripe 2 enhancer; TWI , PNT , PAN , MED , MAD , TIN in the muscle-heart enhancer; HB , KNI in the stripe 4+6 enhancer . A p-value cutoff of ln ( p-value ) <−6 was imposed on PATSER output . Conserved binding sites in BLASTZ hits: We further tabulated the number of binding sites in a given D . melanogaster enhancer that fall within a BLASTZ HSP , in which the aligned sequence from the comparison species also contained an above-cutoff site prediction for the same transcription factor . In order for a site to be called “conserved” in a species pair , the comparison species binding site must overlap the D . melanogaster site by at least 1 bp . Conserved binding sites in multiple alignment: MLAGAN was used to compute multiple alignments of the four enhancers listed above ( stripe 2 , stripe 3+7 , stripe 4+6 and MHE ) with default alignment parameters . Pairwise comparisons between D . melanogaster and each other species in an alignment were conducted as follows: each binding site prediction in D . melanogaster calculated as above was categorized as conserved in that species if a binding site better than ln ( p-value ) <−6 was present aligned within 5 bp of the boundaries of the site prediction in D . melanogaster ( see alignment error correction , binding site dynamics in paper methods ) . Binding sites were predicted in each species for each experimentally determined enhancer with published DNaseI footprints ( stripe 2 , stripe 3+7 and the Muscle Heart Enhancer ) using PATSER [61] ( version 3e ) with a range of ln ( p-value ) cutoffs from −5 to −7 . Position Weight Matrices for factors known to regulate expression driven from each enhancer were drawn from [61] , except for GT ( N . Ogawa and M . D . Biggin , unpublished ) DSTAT [62] , PAN/dTCF , PNT , and TIN [63] and TWI ( D . Pollard , unpublished ) . PATSER was run with a GC content for each enhancer calculated from the entire eve locus in that species . D . melanogaster site predictions scoring above the cutoff were categorized into one of the following categories: “overlapping” if they overlap an above-cutoff site for a different factor by at least one basepair , “close” if the nearest base of another site ( for a different factor ) is within 10 bp , and “isolated” if neither condition is met Analyses described here use ln ( p-value ) <−6; results were robust to p-value cutoffs over the range described above ( data not shown ) . Next , binding site predictions for each species were mapped onto a multiple alignment of all 18 species ( 12 Drosophila and 6 Sepsids ) generated using MLAGAN ( Brudno et al . 2003 ) ( version 2 . 0 , default run parameters ) . Finally , for each D . melanogaster site , the nearest aligned bases in each other species ( plus/minus 5 bp for alignment error ) were evaluated for presence of a binding site for the same factor . Three clades of increasing evolutionary distance were considered: “minimal” ( melanogaster subgroup ) , “high” ( all sequenced Drosophila ) , or “extreme” ( 12 Drosophila and 6 Sepsids ) . A given site in D . melanogaster was categorized by the largest clade in which it is conserved , where conservation is defined as presence in at least 80% of the species in that group ( 4/5 in subgroup , 10/12 in Drosophila , 15/18 in all species considered here ) . Thus , each binding site in D . melanogaster is categorized both by proximity to other binding sites in D . melanogaster , and by evolutionary stability across increasingly divergent species groups . Tabulations of these two properties were examined for relatedness by G-test of independence with Williams' correction for small sample size [64] . Enhancers were chosen for this analysis and those described above based on previously available data , specifically regarding the location of binding sites for important regulators . We analyzed eve enhancers for which DNase I footprinted in-vitro binding sites had been determined , and each such enhancer examined the minimal sequence interval that mediated the complete expression pattern , extended to include all footprinted binding sites for relevant regulators . Binding site predictions for each enhancer , as calculated above , were plotted in alignment position coordinates for each enhancer described here . Alignment position coordinates for binding site matches that overlapped a gap ( in the species in which the site was predicted ) were plotted at the midpoint of the gap . P-value cutoffs above which to plot glyphs for each factor were chosen independently from among predictions over the range described above ( ln ( p-value ) between −5 and −7 in steps of 0 . 5 ) in order to maximize concordance with known DNase I footprinted binding sites in the D . melanogaster sequence for each enhancer . The height of each glyph is proportional to the score of that site prediction , and heights for the top scoring site match for each factor are normalized across all factors plotted in that enhancer . | The transformation of a fertilized egg into a complex , multicellular organism is a carefully choreographed process in which thousands of genes are turned on and off in specific spatial and temporal patterns that confer distinct physical properties and behaviors on emerging cells and tissues . To understand how an organism's genome specifies its form and function , it is therefore necessary to understand how patterns of gene expression are encoded in DNA . Decades of analysis of the fruit fly Drosophila melanogaster have identified numerous regulatory sequences , but have not fully illuminated how they work . Here we harness the record of natural selection to probe the function of these sequences . We identified regulatory sequences from scavenger fly species that diverged from Drosophila over 100 million years ago . While these regulatory sequences are almost completely different from their Drosophila counterparts , they drive identical expression patterns in Drosophila embryos , demonstrating extreme flexibility in the molecular machines that interpret regulatory DNA . Yet , the identical outputs produced by these sequences mean they must have something in common , and we describe one shared feature of regulatory sequence organization and function that has emerged from these comparisons . Our approach can be generalized to any regulatory system and species , and we believe that a growing collection of regulatory sequences with dissimilar sequences but similar outputs will reveal the molecular logic of gene regulation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"developmental",
"biology/embryology",
"genetics",
"and",
"genomics/comparative",
"genomics",
"computational",
"biology/transcriptional",
"regulation",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"computational",
"biology/comparative",
"sequence",
"analysis",
"developmental",
"biology/developmental",
"evolution",
"evolutionary",
"biology/genomics",
"evolutionary",
"biology/pattern",
"formation",
"developmental",
"biology/molecular",
"development",
"evolutionary",
"biology/developmental",
"evolution"
] | 2008 | Sepsid even-skipped Enhancers Are Functionally Conserved in Drosophila Despite Lack of Sequence Conservation |
Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders . In particular , the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity , explaining the varying effects of localized white matter pathology on cognition and behavior . Here , we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity . We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations , but show significant , interpretable deviations in improperly developed brains . More specifically , we investigated the effect of agenesis of the corpus callosum ( AgCC ) , one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself . These findings , including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging , show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome .
The brain’s anatomic connectivity network or the “connectome” is the substrate upon which most of the brain’s complex phenomena are enacted , and through which various brain disorders ramify . It has recently emerged that the brain network can be decomposed into its constituent “eigenmodes” , which play a specific and important role in both healthy brain function and pathophysiology of disease [1–8] . The dynamics of any physical linear system can be described by a few constituent eigenmodes . The celebrated Fourier basis is a well-known example , where sinusoids of varying wavelengths are mathematically described as the eigenfunctions of any bounded-energy linear time-invariant filter [9] . Eigenfunctions are key features of classical mechanics , where for example , standing waves in continuous media are eigenfunctions . In quantum mechanics , the “probability cloud” of the electron’s orbit around the nucleus is described via eigenfunctions of the Schrodinger wave equation [10] . In structural biology , the so-called “normal modes” that describe the degrees of freedom of large molecules are the eigenfunctions of the equations that capture the relationship between the atoms of the molecule [11] . Similarly , many phenomena related to graphs or networks can be described in terms of the constituent graph eigenmodes , a field known as spectral graph theory [12] . In this paper we use spectral graph theory to obtain and characterize the brain’s organization through its eigenmodes . The brain graph consists of nodes that represent anatomically defined gray matter regions , and edges whose weights are given by white matter fiber connections deduced from fiber tractography . Here we show that the eigenmodes are predictors of brain phenomena by serving as mediators of networked spread processes within the brain . We had previously noted this role of the brain graph’s Laplacian matrix [13] eigenmodes in the context of brain activity propagation [2] , and neurodegenerative pathology ramification [1] . Using network diffusion as the underlying model of spread , resting state BOLD functional connectivity ( FC ) was predicted in terms of structural network Laplacian eigenmodes [2] . A small number of Laplacian eigenmodes reproduced FC , and the eigenspectra of Laplacian and of FC are intimately related [14] . Other subsequent studies have explored the utility of the eigenmodes of the connectivity or adjacency matrix in capturing resting state functional networks [7 , 8] , in particular that a small number of such eigenmodes are sufficient to capture many elements of functional correlations in the brain [8] . The current study attempts to quantitatively characterize anatomic graph Laplacian eigenmodes of the human brain in order to cement the emerging understanding that Laplacian eigenmodes play a fundamental role in governing the spatiotemporal patterning of brain phenomena . We investigate to what extent a few low eigenmodes overlap between healthy subjects and between different scans of the same subject . We determine whether these eigenmodes are consistent across different atlas parcellations schemes . These results on consistency and conservation are necessary to support the role of Laplacian eigenmodes as substrates of information and pathology transmission within the brain . We next determine the influence specific white matter tracts exert on these eigenmodes , and show that these “importance maps” largely conform to previous maps [15 , 16] . Finally , we explore eigenmodes in neurological disease where we show that agenesis of the corpus callosum results in eigenmodes that largely resemble intact brains , but with lower eigenvalues . In particular , we expect that the second eigenmode would capture information diffusion between the hemispheres , and would therefore be especially affected by callosal dysconnectivity . Taken together , the present study serves to formally characterize the brain’s eigenmodes in health and their behavior in disease .
All study procedures were approved by the institutional review board at the University of California at San Francisco ( UCSF ) and are in accordance with the ethics standards of the Helsinki Declaration of 1975 , as revised in 2008 . We analyze the brain connectome , determined via diffusion-tensor imaging and probabilistic tractography , through analyzing the eigenmodes of the connectome’s network Laplacian . In summary , we investigate the neuroanatomical embedding of connections important to the Laplacian and how these constituent eigenmodes change in the case of agenesis of the corpus callosum .
Fig 7 ( top ) illustrates the first three eigenmodes of the control group and their corresponding eigenmodes in agenesis of corpus callosum ( AgCC ) and virtual callosotomy subjects . In all three cases , the second eigenmode represents diffusion between both hemispheres , albeit more polarized in cases of AgCC and virtual callosotomies , likely due to the lack of an intact corpus callosum . Instead of the gradual diffusion we see in healthy brains from one lateral side to medial areas of the brain and finally to the opposite lateral side , the bottleneck in left-right diffusion occurs solely at the corpus callosum in AgCC and virtual callosotomies . For higher eigenmodes , we see that each eigenmode in the control group is split into two eigenmodes in the AgCC or virtual callosotomy cases , each eigenmode representing the original control eigenmode across only a single hemisphere . Fig 7 ( bottom ) shows the eigenvalue associated with the second eigenmode , left-right diffusion , normalized by the mean eigenvalue . We find that in cases of AgCC , the normalized second eigenvalue is statistically significantly ( p < 0 . 05 ) higher in cases of AgCC than it is in virtual callosotomies , indicating a faster rate of diffusion between hemispheres . We also find that both AgCC and virtual callosotomies show lower normalized second eigenvalues than the control group .
First we showed that the network Laplacian possesses largely conserved eigenspectra ( Fig 4 ) between subjects . The eigenvalues associated with the eigenmodes , i . e . the eigenspectrum , provide a natural ordering of the eigenvectors . As argued previously , the reciprocal eigenvalues 1 λ i represent characteristic time constants of diffusive processes within each eigenmode i . Hence the long-term patterns of any linear process of spread , whether of activity or pathology , will essentially settle into the eigenmodes with the lowest eigenvalues . We found that these first few eigenmodes of a subject are not overly sensitive to atlas parcellation and connectome extraction techniques ( Fig 1 ) . Further , the lowest eigenmodes are reasonably stable , reproducible and consistent within and amongst subjects ( Figs 4 and 5 ) . These results serve to confirm that spectral graph theory of brain processes will likewise be generalizable within , between and across healthy subjects , suggesting that low eigenmodes might represent some invariant properties of brain networks . Why should graph eigenmodes in one person’s network be similar to another’s at all ? First , healthy subjects show canonical connectivity patterns at the macroscopic level despite significant variability in specific tracts and regions . Perhaps as a consequence , they are able to sustain highly repeatable functional connectivity patterns , e . g . the default mode network [49] . While we were able to rule out the effect of spatial cost-wiring rules through the use of geometrically-null random networks , there are still many conserved properties of brain networks , such as small-world and rich club properties , that can contribute to this consistency . Second , low eigenmodes may be conserved between individual brain networks , and only higher eigenmodes might be responsible for inter-subject variability . This is not unlike the low frequency harmonics displayed by many simple physical systems of diverse origin . It would be interesting to explore in more detail the topological factors that are driving the invariance in the brain’s low eigenmodes , and to test whether certain wiring rules also produce these invariants . This is a subject of future work . Previous studies on the the effect of lesions on the brain connectome largely focus on the node level , i . e . gray matter regions . In particular , rich club nodes have been implicated in neuropsychiatric disorders like schizophrenia , and Alzheimer’s disease [28 , 50] . Here we used eigenmodes to explore edge-level effects of lesions . While other approaches have identified white matter areas of high importance by examining the effects of simulated lesions on network performance measures such as efficiency or characteristic path length [51] , these maps often denote lateralized focal peripheries as the areas of highest importance and show little overlap with the rich club connections from [28] and [15 , 16] . We believe that eigenmodes provide an alternative approach . By separating overall transmission within the brain into its slowest , individual rate limiting eigenvectors , we can assess which white matter voxels and edges are most important to them . Interestingly , we find that importance maps for the second and fourth eigenmodes showed similarities to edge density maps of rich club and feeder connections , e . g . [28] , while the third eigenmode is similar to local connections ( those between non-rich club nodes ) . These similarities are robust under changes to how the lesion is defined , indicating they are latent properties of the network . In all cases , we find heavy weighting within posterior periventricular white matter , mirroring the observations in [15 , 16] . These similarities are interesting because “rich club” and Laplacian eigenmodes were historically defined in completely different contexts . While rich club nodes are associated with high degree by definition , it is not immediately apparent that the slowest diffusion processes would rely on this subnetwork disproportionately when compared to the third , faster , eigenmode . Possibly , faster diffusion processes rely on a broader range of connections , both rich and non-rich , when compared to the second eigenmode , and these redundancies de-emphasize rich club edges . Regardless , the convergence of the eigenmode- and edge density-derived importance maps from [15 , 16] is a promising indicator of the relationship between white matter anatomy and the structural connectome . Given that healthy brain eigenmodes appear largely conserved between and within subjects , next we explored how they might change in impaired brains . An intuitive way to investigate the influence of the corpus callosum , the largest inter-hemispheric fiber bundle , is through the study of individuals who are born without it , a condition known as agenesis of the corpus callosum ( AgCC ) [52] . Interestingly , these individuals often outperform patients with a corpus callosotomy on various motor control tasks that required inter-hemispheric communication [53] . Past studies on AgCC have focused largely on analysis of resting state fMRI networks [54 , 55] . Many studies note that AgCC subjects tend to perform closer to healthy subjects compared to patients who underwent corpus callosotomies and ascribe this difference on “replacement” tracts that are only present in AgCC cases , presumbably via adaptation [30] . In this paper , we were able to provide a quantitative intuition to the effect these structural adaptations have on inter-hemispheric communication through the network Laplacian . Specifically , we found statistically significant differences between the eigenspectra of AgCC and virtual callosotomy brains . Additionally , we validated the intuition that the low healthy eigenmodes might “split” into separate eigenmodes in cases of virtual callosotomies and AgCC , due to lack of inter-hemispheric connections . Many theoretical results exist on the eigenspectra of disjoint graphs [13] , which predict exactly this behavior . These results taken together serve to quantitatively characterize the anatomic graph Laplacian eigenmodes of the human brain in order to cement the emerging understanding that Laplacian eigenmodes play a fundamental role in governing the spatiotemporal patterning of brain phenomena . We find that these eigenmodes are consistent and robust across different atlas parcellations schemes , and also determine the influence specific white matter tracts exert on these eigenmodes . Finally , we explore eigenmodes in neurological disease where we show that agenesis of the corpus callosum results in eigenmodes that largely resemble intact brains , but with lower eigenvalues . In particular , we expect that the second eigenmode would capture information diffusion between the hemispheres , and would therefore be especially affected by callosal dysconnectivity . Together , the present study serves to formally characterize the brain’s eigenmodes in health and their behavior in disease . | While the structural connectome of the brain has emerged as a powerful tool towards understanding the progression of neurologic and psychiatric disorders , links between the anatomy of connections within the brain and the effects of localized white matter pathology on cognition are still an active area of investigation . Here , we propose the use of the diffusion process towards understanding perturbations of brain connectivity . We find that while the dynamics of this process are strongly conserved in healthy subjects , they display significant , interpretable deviations in agenesis of the corpus callosum , one of the most common brain malformations . These findings , including the strong similarity between regions identified to be crucial towards diffusion and nexus regions of white matter from edge density imaging , show converging evidence towards understanding the relationship between white matter anatomy and the structural connectome . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"diagnostic",
"radiology",
"neural",
"networks",
"nervous",
"system",
"brain",
"neuroscience",
"magnetic",
"resonance",
"imaging",
"mathematics",
"algebra",
"brain",
"mapping",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"imaging",
"techniques",
"corpus",
"callosum",
"connectomics",
"radiology",
"and",
"imaging",
"eigenvectors",
"neuroanatomy",
"anatomy",
"central",
"nervous",
"system",
"linear",
"algebra",
"diagnostic",
"medicine",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"eigenvalues"
] | 2017 | Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease |
SSG&PM over 17 days is recommended as first line treatment for visceral leishmaniasis in eastern Africa , but is painful and requires hospitalization . Combination regimens including AmBisome and miltefosine are safe and effective in India , but there are no published data from trials of combination therapies including these drugs from Africa . A phase II open-label , non-comparative randomized trial was conducted in Sudan and Kenya to evaluate the efficacy and safety of three treatment regimens: 10 mg/kg single dose AmBisome plus 10 days of SSG ( 20 mg/kg/day ) , 10 mg/kg single dose AmBisome plus 10 days of miltefosine ( 2 . 5mg/kg/day ) and miltefosine alone ( 2 . 5 mg/kg/day for 28 days ) . The primary endpoint was initial parasitological cure at Day 28 , and secondary endpoints included definitive cure at Day 210 , and pharmacokinetic ( miltefosine ) and pharmacodynamic assessments . In sequential analyses with 49–51 patients per arm , initial cure was 85% ( 95% CI: 73–92 ) in all arms . At D210 , definitive cure was 87% ( 95% CI: 77–97 ) for AmBisome + SSG , 77% ( 95% CI 64–90 ) for AmBisome + miltefosine and 72% ( 95% CI 60–85 ) for miltefosine alone , with lower efficacy in younger patients , who weigh less . Miltefosine pharmacokinetic data indicated under-exposure in children compared to adults . No major safety concerns were identified , but point estimates of definitive cure were less than 90% for each regimen so none will be evaluated in Phase III trials in their current form . Allometric dosing of miltefosine in children needs to be evaluated . The study was registered with ClinicalTrials . gov , number NCT01067443
Visceral leishmaniasis ( VL ) , caused by L . donovani , is a neglected disease in eastern Africa , where it affects mostly the very poor . The disease burden is high , with an estimated 29 , 400 to 56 , 600 cases annually[1] . VL is fatal if left untreated , but with access to early diagnosis and treatment the case fatality rate is low[2] . For decades , treatment with pentavalent antimonials ( sodium stibogluconate-SSG ) was the first-line regimen in Africa despite a risk of cardiotoxicity , liver and pancreatic toxicity and 4 weeks of hospitalization [3] . A 17-day regimen of SSG in combination with paromomycin ( PM ) demonstrated high efficacy six months post end of treatment in a Phase III trial ( 91% ) and is recommended as first line treatment in the eastern African countries , Sudan , South Sudan , Kenya , Uganda , Ethiopia , and Somalia [4 , 5] . Still , this short-course treatment is considered suboptimal in terms of route of administration , duration of treatment and potential adverse drug reactions , and there is a need for new treatments . An oral , safe , inexpensive , highly effective , short course treatment suitable for use in pregnancy is also urgently needed to control VL in the region [6] , and research is currently underway to identify and evaluate potential new candidates . In the meantime , as a short-term strategy , it was considered relevant to assess the efficacy of combination therapy with currently available treatment options . Combination regimens are generally recommended on the basis of potential additive or synergistic activity , increasing efficacy and allowing for shorter course durations , in turn leading to increased compliance , shorter hospitalization and reduced drug costs [6] . The rationale on the choice of drugs to combine is described below . A short course of AmBisome ( 5mg/kg on day 1 ) plus miltefosine ( 2 . 5 mg/kg on days 2–8 ) in India achieved six months cure in 98% ( 93–99% ) of patients aged 6–58 years [7] . At the time of this trial’s design , single and multiple doses of AmBisome were under evaluation in eastern Africa . The recommended regimen of AmBisome monotherapy in Asia is 3-5mg/kg over 3–5 days ( total dose of 15mg/kg ) or 10mg/kg as single dose administration , whereas in eastern Africa a total dose of 30mg/kg is needed to achieve satisfactory efficacy . On the other hand , to make this combination treatment more field-adapted , one single administration of AmBisome was proposed . A high dose and longer duration of AmBisome and miltefosine respectively were needed in the region , however 10mg/kg AmBisome was considered to be the maximum dose to be used as single administration due to safety concerns . Repeated doses of AmBisome and longer duration of miltefosine would diminish treatment practicality , potentially impact compliance and increase the cost of the regimen . AmBisome 10mg/kg was therefore considered the appropriate option to be administered as a single dose in eastern Africa , in combination with either miltefosine or SSG . Miltefosine administered as a 28 day course of 2 . 5mg/kg/day achieved 94% ( 95% CI: 91–97% ) cure at six months post end of treatment in patients 12 years and above in India [9] . In a subsequent study in Indian children aged 2–11 years , cure at six months was 94% ( 86–98% ) [10] . In HIV negative adult males ( 15 years and above ) in Ethiopia , this regimen achieved lower cure at six months in 89% ( 81–94% ) of patients [11] . Most detailed pharmacokinetic data for miltefosine came from a relatively healthy adult European patient group with cutaneous leishmaniasis [12] . While little is known about miltefosine pharmacokinetics in adult or paediatric patients in general , no such data are available in African VL patients . Regional pharmacokinetic data are needed to guide further dose optimization of miltefosine ( combination ) therapy for VL in eastern Africa . SSG 10-day regimen was defined based on its well characterized efficacy in the region [4] , to reduce hospitalization and improve the safety profile of the treatment . We report the results of a phase II randomized trial , conducted in Africa , to assess the safety , efficacy and pharmacokinetic properties of two 11-day treatment regimens combining AmBisome with SSG and AmBisome with miltefosine , and a 28-day miltefosine monotherapy [13] . The hope was that at least one regimen would reach 90% efficacy at initial and definitive cure .
The trial used a sequential design with a triangular continuation region [14] . The null hypothesis was that the proportion cured at day 28 ( p ) is less than or equal to a value p0 which we set to 75% . The alternative hypothesis is that p>p0 . If the upper boundary is crossed during an interim analysis , then the null hypothesis is rejected and we conclude p>75% . Crossing the lower boundary at the time of an interim analysis implies that null hypothesis ( proportion cured ≤75% ) is not rejected and there is specified power to exclude a proportion cured of pa , for which we chose a value of 90% . The type I error rate and power of the study were pre-specified as 5% and 95% , respectively ( α = β = 0 . 05 ) . Interim analyses were specified after every 15 patients in each arm . The maximum sample size per arm was 63 . Patients were recruited from Kimalel Health Centre in Kenya ( Baringo district ) , and Dooka and Kassab hospital ( Gedaref State ) in Sudan . These study sites are located in areas of stable endemicity . Eligible patients were HIV negative , and aged between 7 and 60 years with parasitologically confirmed VL who signed an informed consent ( if aged 18y and over ) or whom the parent or legal guardian consented to participate in the study ( if under 18y ) . The target population was primary cases , so known relapse cases , or receipt of any anti-leishmanial drugs in the previous 6 months , was an exclusion criterion . Other exclusion criteria were: severe protein and/or caloric malnutrition defined as kwashiorkor or marasmus in children and BMI <15 in adults; previous history of hypersensitivity reaction to SSG or amphotericin B; concomitant severe infection such as TB or other serious underlying disease which would preclude evaluation of patients response to the study medication; other conditions associated with splenomegaly such as schistosomiasis; previous history of cardiac arrhythmia or an abnormal ECG; Hb<5 g/dL; WBC <103/mm3; platelets <40 , 000/mm3 , abnormal liver function tests ( ALT and AST ) of more than three times the upper limit of the normal range , serum creatinine outside the normal range for age and gender , and major surgical intervention within two weeks prior to enrolment . Due to the potential teratogenicity of miltefosine , females of child bearing age were also excluded . The three treatment regimens were as follows: AmBisome ( liposomal amphotericin B , 50 lyophilized powder in vials , Gilead Pharmaceuticals , USA ) was given as a single dose on day 1 at a dose of 10 mg/kg body weight , infused in 5% dextrose over 1–2 hours . Miltefosine ( Impavido , Zentaris ) was provided as foil-wrapped blister packs . The dose was calculated on a basis of 2 . 5 mg/kg body weight daily , up to a maximum of 150 mg . However , since 10 and 50 mg capsules were available , the actual doses given were: 30mg for 10-<14kg; 40mg for 14-<18kg; 50mg for 18-<22kg; 60mg for 22-<26kg; 70mg for 26-<30kg; 100mg for 30-<50kg and 150mg for ≥ 50kg . This resulted in a dose range of 2 . 0–3 . 33 mg/kg/day of miltefosine . SSG ( 30 ml vials , each containing 100 mg/ml SSG , produced by Albert David , India ) was given as an intramuscular ( IM ) injection once daily , in a dose of 20 mg/kg body weight . Rescue treatment was AmBisome 30 mg/kg Intravenously ( IV ) split into multiple doses ( according to country protocol ) or SSG 20 mg/kg IM for 30–60+ days for patients not responding to initial rescue treatment or for patients requiring treatment for severe PKDL . Patients who did not meet inclusion criteria were offered free treatment outside the trial , according to national treatment protocols [15] . All patients were offered counseling and screening for HIV . Patients who tested positive were referred for appropriate treatment to the national HIV control programme and treated according to national treatment guidelines , surveillance and follow up according to the national protocol for HIV positive patients . Two efficacy endpoints were defined . The primary endpoint was parasitological cure at Day 28 ( initial cure ) , determined as absence of parasites on microscopy . This was used for interim analysis decisions . Patients who died or required rescue before study treatment could be completed were considered initial treatment failures . The secondary endpoint was assessed at Day 210 ( six months post end of treatment ) , representing final ( or definitive ) cure status . This was defined as lack of VL signs and symptoms , and no requirement for rescue treatment during the trial . Any patients with signs or symptoms of VL at any time during participation in the trial underwent confirmatory parasitological testing . Slow responders were defined as patients who had not cleared parasites at Day 28 ( D28 ) , but who were clinically well , did not require rescue treatment at D28 and remained clinically well throughout follow-up . These patients had a subsequent parasitological assessment at D56 . If microscopy was positive , the patient received rescue medication regardless of clinical presentation . The treatment outcome was classified as failure from the time point rescue was received . Parasitological assessment by microscopy was done on lymph node aspirates ( Dooka , Kassab ) , spleen aspirates ( Kimalel ) or bone marrow samples ( all sites ) . In Kimalel , 49 patients who had unpalpable spleen on D28 had bone marrow aspirate while 27 patients had splenic aspirate . Aspirates were smeared on two slides per sample , stained and graded according to the standard logarithmic criteria . Safety outcomes were the number ( % ) of patient experiencing a serious adverse event at any time , frequency of adverse event within 60 days of treatment onset and an adverse drug reaction ( ADR ) within 60 days . The study was designed and analyzed according to sequential methods , which have been developed to allow for discrete data analysis after a pre-specified number of patients are recruited . The triangular test is one such method and uses straight line stopping boundaries [13] . The continuation region is closed , which ensures a maximum sample size . A minimum sample size of 30 per arm was imposed to allow for adequate PK assessment . The trial was non-comparative and the sequential analysis was applied to each arm independently , allowing them to potentially stop at different times . Subjects were randomly allocated using block randomization , stratified by site ( Dooka , Kassab and Kimalel ) . Site investigators were blinded to block size and codes were concealed in sealed sequentially numbered , opaque envelopes under the control of the site investigator . The treating physician and patients were aware of the treatment given; miltefosine is oral medication and AmBisome and SSG are administered intravenously ( IV ) and IV or intramuscularly ( IM ) respectively . The laboratory technologists reading the slides were blind to treatment allocation . The pharmacokinetics of miltefosine were assessed in the two arms receiving miltefosine . In the AmBisome + miltefosine arm human sodium heparin samples were nominally collected on day 2 , 4 , 7 , 11 , 60 , 210 ( adults ) or day 2 , 7 , 11 , 60 , 210 ( children ) ; and in the miltefosine monotherapy arm on day 1 , 3 , 7 , 14 , 21 , 28 , 60 , 210 ( adults ) or day 1 , 7 , 14 , 28 , 60 , 210 ( children ) . On the first day of miltefosine treatment , blood samples were drawn prior to the first dose , 4 hours and 8 hours post first dose; whereas for all other time points blood samples were drawn prior to the first miltefosine administration of that day . Samples were stored and transported frozen at minimally -20°C until analysis . Sample preparation and miltefosine quantification were performed using a validated liquid chromatography tandem mass spectrometry assay with a lower limit of quantitation of 4 ng/mL ( LC-MS/MS ) [16] . End-of-treatment concentrations of miltefosine were compared with a Welch two sample t-test . Repeated measurements of the Leishmania parasite load in whole blood were performed using a qRT-PCR method targeting Leishmania kDNA . These pharmacodynamic samples were collected from all participants in all three treatment arms prior to treatment and on Day 3 , 7 , 14 , 28 , 60 , 210 . DNA/RNA isolation was performed partially on site using a modified Boom method [17] , where silica samples were stored and transported at minimally -20°C until the moment of further extraction and analysis . The qRT-PCR analysis was performed using a Bio-Rad CFX-96 real-time machine ( Bio-Rad , Veenendaal , the Netherlands ) . Parasite clearance rates were calculated as relative decreases from baseline of all patients who had a detectable parasite load at baseline . A linear mixed-effects model was fitted in R using the maximum likelihood method , with treatment day and arm as fixed effects and subject as a random effect . Data analysis was performed using STATA , version 13 [18] . The primary analysis was by intention-to-treat ( ITT ) . Interim analyses were conducted once every 15 patients had reached D28 primary endpoint assessment , using all available data and were based on the ITT analysis population . During each interim analysis , triangular region tests were performed [14] and a decision was taken whether to continue or stop recruitment in each arm based on the position of the test statistic being within or outside the triangular region . Once a decision was reached for each arm to stop , a point and interval estimate for the overall proportion cured at Day 28 ( p ) was obtained following Whitehead [19] based on all patient data using the ITT analysis population . The proportion of patients cured at day 210 is subject to sequential stopping as far as day 28 , but not thereafter . To take account of this , the probability of treatment success at D210 was estimated by a probability tree argument , using the delta method for its standard error [20] . This method takes account of the occurrence of slow response to treatment and relapse captured between D28 and D210 . Relapse patients are those without detectable parasites at D28 , but who develop signs and symptoms of VL during the follow-up period and have a confirmatory parasitological diagnosis anytime between D28 and D210 . This analysis was based on the ITT analysis population . A pre-specified subgroup analysis was to compare cure by sites using a χ2 or Fisher’s exact test at D210 , based on the ITT analysis population . Due to the relative geographical proximity and small numbers , data from the Sudanese sites were combined , post hoc , to conduct the subgroup analysis by country . Also , efficacy at Day 210 is compared by age groups split at 12 years , rather than the pre-specified 18 years . All adverse events were coded to have lower level preferred term , higher level term and system organ class classifications according to MedDRA , Version 12 . 0 . Safety outcomes were calculated as risk measures ( number and percent of patients out of those randomized ) where patients with multiple AEs or ADRs were only counted once . The incidence of ADRs was calculated as the number and percentage of patients experiencing each type of AE . Ethical approval was obtained from national and local Ethics Committees in Kenya and Sudan prior to the start of the trial in each country . Ethical approval was also granted by LSHTM’s Ethics Committee ( #5543 ) , and the Academic Medical Center Medical Ethics Committee issued a 'declaration of no objection' . Study participants or their parents/guardians ( for children ) gave a written signed informed consent before enrollment into the study . The study was registered with ClinicalTrials . gov , number NCT01067443 .
A total of 151 patients were enrolled in the study ( Fig 1 ) . The most common reasons for exclusion amongst patients with detectable parasites ( n = 531 ) were age less than seven years ( 20 . 5% ) , abnormal biological parameters ( 19 . 2% ) , being female of child bearing age ( 8 . 9% ) and refusal of consent ( 7 . 5% ) . Patients were recruited from May 2010 to Feb 2012 . All patients remained hospitalized from screening until completion of D28 assessment . Follow up was completed in Oct 2012 . The three arms appeared balanced with respect to baseline characteristics ( Tables 1–4 ) . Data from all 151 enrolled patients were available for the intention-to-treat analysis: 51 for the AmBisome + SSG arm , 49 for the AmBisome + miltefosine arm and 51 for the miltefosine monotherapy arm . There were four major protocol deviations . In three patients ( one from each treatment arm ) , body mass index at baseline was recorded as 15kg/m2 . One patient in the miltefosine monotherapy arm was discovered to have epilepsy and should also have been excluded during screening due to severe concomitant condition . Therefore , 147 patients were included in the per-protocol ( PP ) population , 50 for the AmBisome + SSG arm , 48 for the AmBisome + miltefosine arm and 49 for the miltefosine monotherapy arm . The first and second interim analyses indicated that all arms should continue ( Fig 2; Table 5 ) . The decision to stop recruitment was made after the third sequential analysis for promising D28 efficacy in all three arms based on crossing the upper triangular boundary . When accounting for the sequential trial design , cumulative efficacy at D28 was 85% ( 95% CI: 73%–92% ) in all three arms ( see Table 5 ) . Results from the per protocol analysis were similar to the ITT population . No patients were lost to follow up . One slow responder occurred in each treatment arm . Relapse occurred in one , seven and eight patients in the AmBisome + SSG , AmBisome + Miltefosine and Miltefosine arms respectively . When accounting for the sequential trial design and change of status between D28 and D210 , the D210 efficacy was 87% ( 95% CI: 77–97% ) for the AmBisome + SSG arm , 77% ( 95% CI: 64–90% ) for the AmBisome + miltefosine arm and 72% ( 95% CI: 60–85% ) for the miltefosine arm ( see Table 5 ) . D210 cure was consistently lower in Sudan ( Table 6 ) than in Kenya for each treatment , especially for the AmBisome + miltefosine arm ( p = 0 . 074 ) . The age distribution differed by country with more younger patients recruited in Sudan; 49 ( 65% ) Sudanese patients were aged less than 12 years compared to 25 ( 33% ) Kenyan patients ( chi-squared test p<0 . 0001 ) . Age , as a possible proxy for weight , could therefore be a realistic biological explanation for a difference in treatment response by country , although additional differences in parasite susceptibility and host factors cannot be excluded . In the present study population , age <12y was correlated with weight <30kg . Therefore post-hoc stratified analyses were conducted by age group ( <12 years , ≥12 years ) and country . An age cut-off of 12 years was chosen based on the observed differences in drug exposure between those two groups . Cure rates were consistently lower in patients aged less than 12 years in each arm in both countries ( Table 6 ) . There were four SAEs , occurring in two patients in each of the AmBisome containing arms ( 4% of patients randomised to each short-course arm; Table 7 ) . Three patients discontinued treatment . One SAE in each AmBisome arm was a serious adverse drug reaction ( SADR ) ; in the AmBisome + SSG arm , severe anaemia resulted in death at day 20 , and in the AmBisome + miltefosine arm , renal failure at day 3 which was resolved . The two unrelated SAE were upper respiratory tract infection and pneumonia . There were two deaths , one in each combination arm . These were severe pneumonia in the AmBisome + Miltefosine arm which was considered not related to study drug and severe anemia in the AmBisome + SSG arm which was considered possibly related . The proportion of patients with at least one treatment emergent adverse event ( TEAE ) was between 80 and 90% in each arm . The proportion of patients with at least one ADRs was between 73 and 78% in each arm , while the proportion of patients with at least one AE not related to study drug was between 33 and 45% . Treatment was stopped in 1 patient due to a non-serious , moderate increase in serum creatinine and elevation of blood urea after 14 days of miltefosine monotherapy treatment . Two patients in the AmBisome + miltefosine arm stopped treatment early ( due to serious renal insufficiency after 3 days of treatment and moderate non-serious increased blood creatinine after 7 days ) . The median number of adverse effects experienced was two per patient for each arm . Of all non-serious drug related events , three were severe and occurred in the miltefosine monotherapy arm: two cases of anaemia and one case of gastro-intestinal pain . In the AmBisome + SSG and in the AmBisome + miltefosine arms , all non-serious drug-related events were categorized as mild to moderate . Approximately 20% of patients in each arm treated with AmBisome experienced adverse events during infusion , and approximately 20% of patients in each arm treated with miltefosine vomited at least one dose . The overall occurrence of vomiting was 21% in all patients treated with miltefosine . The occurrence of repeated vomiting of the same dose was relatively low ( 6% of patients overall in both miltefosine arms ) . All treatment arms showed CTCAE grade 1 or grade 2 aspartate aminotransferase increase ( 14–29% ) and grade 1 hypomagnesaemia ( 14–20% ) ( Table 8 ) . The only arm to contain SSG ( combined with AmBisome ) showed low levels of cardiac disorders ( <5% ) , similar to that of the AmBisome + MF arm . PKDL occurred in eight Sudanese patients , 2 in the AmBisome+SSG arm , 1 in the AmBisome+Miltefosine arm and 5 in the miltefosine monotherapy arm . All cases were mild or moderate and did not require treatment . Miltefosine end of treatment concentrations were available for 42 patients from the AmBisome + miltefosine arm and for 45 patients from the miltefosine alone arm . Miltefosine keeps accumulating until the end of treatment , so the end-of-treatment concentration ( day 11 for the AmBisome + miltefosine arm and day 28 for the miltefosine alone arm ) is representative for total exposure . The mean ( SD ) miltefosine concentration at the end-of-treatment was 16 . 47 ( 5 . 89 ) μg/mL for the AmBisome + miltefosine arm and 26 . 42 ( 8 . 66 ) μg/mL for the miltefosine alone-arm . There were no significant differences in miltefosine end-of-treatment concentrations between Sudanese and Kenyan patients . As depicted in Fig 3 , patients with a body weight <30 kg ( n = 38 , of which 97% aged ≤12 yrs ) were significantly less exposed to miltefosine than patients with a higher body weight of ≥30 kg ( n = 49 , of which 84% aged >12 yrs ) : for the AmBisome + miltefosine arm the difference in end-of-treatment concentration was 36% ( p<0 . 0001 ) and for the miltefosine alone arm 32% ( p<0 . 0001 ) . In total , for 77% of all patients there was a blood parasite load available at baseline , with various reasons for non-availability ( e . g . no baseline sample available , issues with DNA extraction or no parasite load detectable ) . Only the first week of treatment was taken into account to assess the parasite clearance rate , since levels dropped to undetectable levels >1 week of treatment for many patients . The effect of the treatment arm on the parasite clearance rate in the first week of treatment was evaluated and is illustrated in Fig 4 . The parasite clearance rate was slower in the miltefosine alone arm compared to the AmBisome + miltefosine arm ( p<0 . 0001 ) , while there was no significant difference between the two combination arms ( p = 0 . 605 ) . After 1 week of treatment , almost all individuals in both combination treatment arms had regressed towards 0% parasite load , while the miltefosine monotherapy arm was on average still >10% of the initial parasite load level ( Fig 4 ) . A more extensive in-depth analysis of the pharmacokinetic and pharmacodynamics results of this study will be reported elsewhere .
This was the first phase II randomized trial of short course combination therapies containing AmBisome for VL in Africa . Neither of the two AmBisome combinations ( single daily dose of AmBisome with 10 days of SSG or miltefosine ) nor the 28 day miltefosine monotherapy ( previously used in India ) achieved more than 90% cure . There were no unexpected safety signals detected in the trial , although there is very little power to detect unexpected events given the small number of patients treated . Our recent study showed that multiple daily doses of 3 mg/kg body weight of AmBisome maybe more beneficial to parasite clearance than a single 10 mg/kg dose at day 1 [8] , suggesting that a more frequent administration of AmBisome may result in higher cure rates . Pharmacodynamic data showed faster parasitic clearance with both AmBisome combinations than with miltefosine monotherapy . Our study was not designed or powered to detect differences in efficacy between adults and children , however patients with low body weight ( <30kg ) , almost all of whom were children under 12 years of age , were found to be significantly underexposed to miltefosine compared to those with higher body weight , whether treated with miltefosine alone or in combination with AmBisome . The lack of efficacy compared to that seen previously in Southeast Asia , particularly with regard to miltefosine , could be due to genetic diversity and differences in drug susceptibility between east African and Indian Leishmania strains . However , results from Nepal and India recently demonstrated a high relapse rate 6–12 months after miltefosine monotherapy ( 75–90% efficacy ) , and more frequently in patients under the age of 15 years , suggesting a possible drug underexposure effect [21 , 22] . Pharmacokinetic studies on miltefosine exposure in adults and children from both countries show that the current 2 . 5 mg/kg/day miltefosine monotherapy dose results in low exposure in children and that a proposed allometric dosing algorithm could provide a higher and more optimal exposure in both adults and children [23] . Similar lower drug exposure in children has been observed for antimonials ( meglumine antmoniate ) in cutaneous leishmaniasis using a linear weight-adjusted dosage[24] . Our results confirmed a good tolerability of all treatments tested and no major safety concerns were identified . Despite the higher exposure of miltefosine in adults , this does not seem to be associated with higher safety risks . The frequency and severity of increase in creatinine observed in this trial were not different from what had been already described . In conclusion , as none of the treatment regimens tested achieved target efficacy they will not be developed further . Miltefosine , as an oral drug treatment , is still of interest and so an allometric dosing study is now underway to assess the safety and appropriate treatment dose of miltefosine , particularly in children , in eastern Africa ( NCT02431143 ) . This study highlights , once again , the difficulties faced when developing treatments for VL in eastern Africa . In addition to improvements based on existing drugs , early drug discovery efforts are ongoing , and several new chemical entities have been identified which will hopefully give rise to safe , effective , oral treatments for the region in the future . | Visceral leishmaniasis , or kala-azar , is a parasitic disease which is fatal without treatment . A 17-day treatment of sodium stibogluconate ( SSG ) with paromomycin ( PM ) is the recommended treatment in eastern Africa , but requires painful injections , causes adverse events , and patients need to stay in the hospital during treatment . An affordable , safe and effective oral treatment would be preferable . Whilst research to identify entirely new drugs is underway , existing treatments are being optimized as a short-term solution . Combination regimens based on AmBisome and miltefosine have been shown to be safe and effective in treating Indian patients , but there are no published data from use of these drugs in combination regimens from Africa , where efficacy of treatments can be different from India . Three regimens were evaluated for treating VL in eastern Africa , using AmBisome in combination with SSG or miltefosine , or miltefosine alone . Once again , drugs which are effective in India were found to be less so in African patients , and none of the regimes tested showed sufficiently high definitive cure rates to evaluate in Phase III trials . The results also suggest miltefosine was under-dosed in children and so allometric dosing , which takes into account the differences in drug metabolism seen in children compared to adults , needs to be studied . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"kala-azar",
"body",
"weight",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"clinical",
"research",
"design",
"tropical",
"diseases",
"parasitic",
"diseases",
"research",
"design",
"physiological",
"parameters",
"pharmaceutics",
"neglected",
"tropical",
"diseases",
"pharmacology",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"zoonoses",
"adverse",
"events",
"protozoan",
"infections",
"hematology",
"pharmacokinetics",
"blood",
"anatomy",
"physiology",
"leishmaniasis",
"body",
"mass",
"index",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy"
] | 2016 | Efficacy and Safety of AmBisome in Combination with Sodium Stibogluconate or Miltefosine and Miltefosine Monotherapy for African Visceral Leishmaniasis: Phase II Randomized Trial |
IgG antibodies can organize into ordered hexamers on cell surfaces after binding their antigen . These hexamers bind the first component of complement C1 inducing complement-dependent target cell killing . Here , we translated this natural concept into a novel technology platform ( HexaBody technology ) for therapeutic antibody potentiation . We identified mutations that enhanced hexamer formation and complement activation by IgG1 antibodies against a range of targets on cells from hematological and solid tumor indications . IgG1 backbones with preferred mutations E345K or E430G conveyed a strong ability to induce conditional complement-dependent cytotoxicity ( CDC ) of cell lines and chronic lymphocytic leukemia ( CLL ) patient tumor cells , while retaining regular pharmacokinetics and biopharmaceutical developability . Both mutations potently enhanced CDC- and antibody-dependent cellular cytotoxicity ( ADCC ) of a type II CD20 antibody that was ineffective in complement activation , while retaining its ability to induce apoptosis . The identified IgG1 Fc backbones provide a novel platform for the generation of therapeutics with enhanced effector functions that only become activated upon binding to target cell–expressed antigen .
Target cells are flagged for destruction by antibodies bound to their cognate antigen on the cell surface . Elimination of antibody-opsonized cells is mediated by the innate immune system . The cellular branch of this system includes NK cells , monocytes , macrophages , and neutrophils that are activated via specific IgG Fc receptors ( FcγR ) sensing surface-bound IgG antibodies . The molecular branch of innate defense includes the complement system , which consists of an amplifiable cascade of soluble zymogens that are abundant in blood and other extracellular fluids . We recently showed that IgG antibodies organize into ordered hexamers on cell surfaces following antigen binding . These IgG hexamers bind and activate C1 , the first component in the classical complement pathway that leads to target cell killing by complement-dependent cytotoxicity ( CDC ) via membrane attack complexes ( MACs ) that breach the cell membrane [1] . In addition , complement activation generates chemoattractants , anaphylatoxins , and opsonins that serve to attract and activate immune effector cells and induce additional killing [2] . In immunotherapy , we leverage these natural defense mechanisms by marking specific target cell populations for elimination by passively administered therapeutic antibodies . These antibodies may be engineered to enhance their ability to activate effector cells or complement . For example , amino acid residues in IgG that affect binding in an FcγR-specific fashion can be modified to promote more efficient antibody-dependent cellular cytotoxicity ( ADCC ) or antibody-dependent cellular phagocytosis [3–5] . C1 binding and CDC may be increased by reshuffling IgG1 and IgG3 or by mutating amino acid positions adjacent to the lower hinge [6–11] . In contrast to IgG molecules , IgM antibodies already pre-exist as pentameric or hexameric oligomers that are kept together via covalent bonds . Exposure of the C1 binding site and the activation of complement is regulated via conformational changes upon antigen binding [12 , 13] . This concept to enhance complement activation has been exploited by the covalent association of IgG monomers via disulfide bonds between cysteine residues in an IgM-derived 18 amino acid carboxyterminal extension and additionally between cysteine residues introduced at position 309 [14] . The ability and potency of monoclonal antibodies ( mAbs ) to induce complement activation and CDC is dependent on IgG isotype and on the characteristics of both the antigen ( i . e . , size , flexibility , and mobility ) and the epitope ( i . e . , accessibility and distance to the membrane ) . The potential of complement for rapid and effective cell killing , as well as its capacity for attracting and modulating both innate and adaptive cellular immune responses , provide a strong rationale for the development of therapeutic antibodies with optimized complement-activating characteristics [2] . However , few therapeutic antibodies generated to date induce potent CDC [2 , 15–17] , and the availability of technologies to facilitate the generation and development of IgG antibodies that are capable of effective complement activation are therefore highly sought after . We recently demonstrated that the assembly of IgG molecules into hexamers may be enhanced by specific mutations in the Fc domain [1] . Here , we identify two preferred IgG1 backbones , harboring a single amino acid mutation , that strongly enhance Fc-mediated assembly at the cell surface , while retaining all the qualities required for the development of biopharmaceuticals . We demonstrate applicability to a range of targets and cells including hematological and solid tumor indications as well as cell lines and patients’ cells . The conditional nature of the technology provides a novel route for the identification and safe development of potentiated Fc-based immunotherapeutics .
Previously , we showed that antigen-bound IgG molecules organize into hexamers at the cell surface to mediate optimal C1q binding and complement activation . We observed an extensive Fc:Fc interface between IgG molecules , and it was shown that a glutamic acid into arginine ( E345R ) mutation at this interface enhanced IgG clustering , C1q binding , and CDC [1] . To determine whether this observation could be broadly applied , we introduced the E345R mutation in a panel of IgG antibodies targeting different antigens ( CD20 [Uniprot P11836] , CD52 [P31358] , CD38 [P28907] , and epidermal growth factor receptor [EGFR] [P00533] ) and assessed CDC of cell lines with varying expression of antigen and membrane complement regulatory proteins ( mCRPs ) CD46 ( P15529 ) , CD55/DAF ( P08174 ) , and CD59 ( P13987 ) . Introduction of the E345R mutation into the chimeric CD20 antibody rituximab ( RTX ) as well as the human CD20 antibody 11B8 increased lysis of both Wien 133 and Daudi cells ( Fig 1 , S1 Table ) . The difference was particularly apparent for wild-type 11B8 , which , as expected for a type II CD20 antibody , did not induce complement-mediated lysis ( Fig 1A ) . Introducing E345R in the CD52 antibody alemtuzumab ( ALM ) strongly enhanced CDC of both Wien 133 and Raji cells ( Fig 1B ) , and the mutation also potentiated both the CD38 antibodies IgG1-005 and IgG1-003 ( Fig 1C ) . Interestingly , antibody 2F8 directed against the solid-tumor antigen EGFR also induced potent CDC when containing E345R ( Fig 1D ) . IgG1-003 and 2F8 represent antibodies that were unable to induce CDC in previous studies [18 , 19] . From these data , we conclude that the Fc:Fc interface mutation E345R is able to convert antibodies against a range of targets and epitopes into potent mediators of CDC , even for antibodies that do not induce any complement-mediated lysis as a wild-type IgG1 molecule . To analyze the impact of target expression , we performed CDC assays using a panel of human tumor cell lines with increasing CD20 expression . Cells were selected for a comparable ( intermediate ) expression of total mCRPs resulting in increasing CD20:mCRP ratios ( S2 Table ) . Only highly CD20-overexpressing SU-DHL-4 cells were lysed efficiently by RTX , whereas lower expression resulted in minimal lysis . In contrast , introduction of E345R in RTX yielded strong lysis for all three cell lines tested ( Fig 2A ) . The potential of mCRPs to limit CDC by wild-type and E345R-mutated RTX was assessed with cell lines that express similar levels of the CD20 target antigen and increasing levels of mCRPs , resulting in decreasing CD20:mCRP ratios ( Fig 2A , S2 Table ) . RTX-E345R demonstrated increased lysis for all cell lines tested , indicating that the mutation reduced sensitivity to mCRP inhibition . The in vivo efficacy of E345R-potentiated IgG1 antibody 7D8 , directed against CD20 , was evaluated in severe combined immunodeficiency ( SCID ) mice inoculated subcutaneously with luciferase-expressing Raji cells expressing high numbers of complement defense molecules ( Fig 2B ) . After the tumor volume reached approximately 40–150 mm3 , mice were randomized at an average tumor volume of 85 mm3 per group and treated intraperitoneally with a single dose of 50 μg 7D8 , 7D8-E345R , 7D8-K322A , or an irrelevant IgG1 isotype control antibody ( IgG1-b12 ) . 7D8-K322A contains a mutation that abrogates C1q binding and CDC and was used as an additional control . In agreement with its increased ability to activate complement , only 7D8-E345R showed significant antitumor efficacy . The wild-type 7D8 showed a trend of activity , but did not reach statistical significance compared to controls . Statistical analysis of tumor inhibition was performed on day 22 when all mice in all groups were still in the study ( S3 Table ) . 7D8-E345R inhibited tumor growth vs control ( p = 0 . 0044 ) and compared to 7D8-K322A ( p = 0 . 0474 ) . In addition , in a Kaplan Meier analysis performed until the end of the experiment on day 44 ( S3 Table and Fig 2B ) , 7D8-E345R significantly inhibited progression as defined by tumors growing > 700 mm3 ( p = 0 . 019 ) , whereas the wild type did not . To better understand the structural constraints of IgG hexamerization and to identify mutations other than E345R that promote Fc:Fc interactions , we generated an antibody mutant library focused on two regions of interest . Firstly , amino acid substitutions were introduced at , or proximal to , the intermolecular Fc:Fc interface observed in the hexameric crystal packing of human IgG1 [1 , 20] . Secondly , amino acid substitutions were introduced at the intramolecular CH2–CH3 interface to alter Fc domain flexibility , which could potentially modulate Fc:Fc interactions allosterically ( Fig 3A ) . We employed screening conditions that allowed for the identification of inhibitory and stimulatory mutations . Mutants of CD38 antibody IgG1-005 containing single amino acid mutations were screened with the complement-sensitive cell line Daudi at a concentration that induced maximal lysis for the wild-type antibody to identify inhibitory positions ( Fig 3B; S4 Table ) . Stimulatory mutations were screened for using the complement-refractory cell line Wien 133 at a concentration that induced minimal CDC for the wild-type antibody ( Fig 3C , S5 Table ) . As shown by the orange- and red-colored residues in Fig 3B and S4 Table , most mutations at the Fc:Fc interface inhibited CDC , corroborating our previous observations [1] and confirming that Fc:Fc interactions are a prerequisite for efficient complement activation . The consensus IgG interaction site for FcRn , Protein A , and Protein G ( S1A Fig ) [21] was found to play a crucial role in the interaction between antibody Fc domains , as exemplified by the abundance of CDC-inhibiting mutations in this region ( S1B Fig ) . The C-terminal β-strand represents another critical region as mutations of all amino acids in the stretch 437–440 inhibited CDC , demonstrating it provides important contacts for an efficient Fc:Fc interaction ( S1C Fig ) . Mutations promoting Fc:Fc interactions were relatively sparse and predominantly confined to the periphery of direct Fc:Fc contacts as indicated by the green colored residues in Fig 3C and S5 Table . A selection of the stimulating mutations were separately introduced in RTX and , next to the IgG1-005 mutants , assessed in dose response . This analysis demonstrated that the E345 , E430 , and S440 mutations that enhanced CDC of Wien 133 cells by IgG1-005 ( Fig 3D ) also consistently stimulated CDC of Ramos cells by RTX ( Fig 3E ) and Daudi cells by IgG1-005 and RTX variants ( S2 Fig , S6 and S7 Tables ) . The observation that all mutations at amino acid positions E345 and E430 stimulate CDC is surprising ( S5 Table ) . A close-up of the structure surrounding the E345 Fc:Fc interface residue shows that its side chain is directed towards residue G385 at the terminus of CH3 β-strand C of the facing antibody ( Fig 3F ) , according to the Ig fold nomenclature of Halaby [22] . Residue E430 forms a salt bridge stabilizing the CH2–CH3 interface packing ( Fig 3G ) . Since even conservative mutations into aspartic acid , preserving the negative charge , resulted in enhanced CDC , glutamic acid residues at these positions appear to restrain IgG molecules in a conformation that inhibits Fc:Fc interactions and CDC . In summary , a number of CDC-enhancing point mutations were identified , with amino acids E345 and E430 acting as hotspots controlling antibody hexamerization and complement activation . For therapeutic application of the mutants , nonspecific complement activation in the extracellular fluid is undesired and hexamerization should therefore only occur after the antibody binds its target at the cell surface . We therefore incubated the panel of mutants in human serum ( 90% ) at a high , 100 μg/ml , concentration , and we assessed the generation of complement activation product C4d ( Fig 4A ) . C4d generation by heat aggregated IgG ( HAG ) was used as a positive control . Complement activation , indicative of hexamer formation in solution , was only observed for mutants E345R and E430F , whereas all others did not induce C4d generation . To fully exclude hexamerization in vivo , we used pharmacokinetic analyses in C . B-17 SCID mice as an additional assessment . Larger molecular species of IgG , potentially interacting with C1 , would be expected to clear more rapidly than monomeric IgG molecules . The observed clearance rates in mice were consistent with the observed C4d generation in human serum ( Fig 4B ) . Whereas mutants E345R and E430F displayed somewhat faster clearance rates , the other variants displayed clearance rates similar to wild-type IgG1-005 . These findings were corroborated by the pharmacokinetic behavior observed for RTX variants ( S3 Fig ) . To assess the suitability of the IgG mutants for their potential development as biopharmaceuticals , we analyzed their biophysical characteristics with an emphasis on methods that could detect solution-phase multimers . Firstly , we analyzed the mutants by native mass spectrometry ( MS ) [23] . Similar to wild-type IgG1-005 , the mutants E345K and E430G only showed a charge envelope around m/z of 5 , 500 , corresponding to a 147 kDa molecular weight ( Mw ) IgG monomer . Mutant E345R , in contrast , yielded an additional charge envelope around m/z of 12 , 500 , corresponding to an 890 kDa Mw demonstrating that it forms some hexamers in solution , albeit at very low abundance ( 1 . 2% of total antibody mass ) ( Fig 5A ) . As a reference , we used the triple mutant IgG1-005-E345R/E430G/S440Y ( RGY ) that was previously shown to form hexamers in solution by HP-SEC , native MS and cryo electron tomography [1] . An E345R-E430G double mutant and the RGY triple mutant displayed a further increase in solution-phase hexamer formation , amounting to about 7 . 7% and 73% of total antibody mass , respectively . We were unable to detect single mutant species with increased Mw by high-performance size exclusion chromatography ( HP-SEC ) as well as multiangle laser light scattering analysis of hollow fiber flow field-flow fractionation separated fractions ( S4 Fig ) . To obtain sufficient material for the analysis of highly concentrated mAb solutions , we next generated three stable CHO cell lines producing 7D8 wild-type and the mutants E345K and E430G . Purified antibodies were concentrated to 100 mg/mL and analyzed by dynamic light scattering ( DLS ) , since it is exquisitely sensitive to the presence of high molecular weight species and tolerates highly concentrated protein solutions . Regression analysis of the diffusion coefficient [24] demonstrated slightly increased attractive interactions for both the E345K and E430G mutant ( −17 . 4 x 10−3 mL·g−1 and −12 . 6 x 10−3 mL·g−1 , respectively ) compared to wild-type 7D8 ( −7 . 57 x 10−3 mL·g−1 ) ( Fig 5B ) . At concentrations above 10 mg/ml , the Rhs of the two mutants increased more strongly than the wild-type antibody ( Fig 5C ) . To assess whether these slightly increased self-interactions impacted the stability of highly concentrated liquid antibody formulations , 100 mg/ml formulations in PBS of 7D8 and the mutants were incubated at 5°C for three months and monitored by biophysical methods ( Fig 5D , S5 Fig ) . The aggregation and other biophysical characteristics for both mutants could not be distinguished from the wild-type IgG1 , indicating that the increased self-interactions at high concentration were fully reversible . The potential of the IgG1-E430G and IgG1-E345K mutants was further assessed by studying anti-CD20 antibody 11B8 , for which we could address the impact of hexamerization-enhancing mutations on CDC , ADCC , as well as the induction of programmed cell death ( PCD ) . The type II CD20 antibody 11B8 induces ADCC and PCD ( in the absence of exogenous crosslinking ) , but not CDC . Consistent with the data above , both the E345K- and the E430G-mutated 11B8 induced potent CDC , whereas the wild-type antibody did not ( Fig 6A ) . Interestingly , the ability of 11B8 to induce ADCC was also significantly improved by both mutations ( Fig 6B ) . To study PCD induction by 11B8 , Daudi cells were stained for Annexin V ( P08758 ) positivity and active , cleaved Caspase 3 ( P42574 , Fig 6C ) . The magnitude of PCD was similar to that induced by the wild-type 11B8 antibody and to obinutuzumab ( OBN ) that was used as a positive control . In summary , both the E430G- and E345K-mutated IgG1 displayed increased CDC and ADCC , while retaining the type II CD20 antibody-specific ability to induce PCD . Based on their strong enhancement of antigen-dependent CDC , favorable biophysical characteristics , stability , monomericity , absence of solution-phase complement activation , regular pharmacokinetics , and retained effector functions , we selected IgG1-E430G and IgG1-E345K as the preferred mutants . The therapeutic potential of these mutant IgGs was assessed in ex vivo CDC assays with tumor cells obtained from patients with chronic lymphocytic leukemia ( CLL ) . Patient CLL cells usually are relatively resistant to anti-CD20-induced CDC due to their low CD20 and high mCRP expression [16] . Interestingly , the E345K and E430G mutations , introduced into CD20 mAb 7D8 , provided strongly enhanced CDC of patient cells ( Fig 7 ) . Moreover , both mutations enabled killing of 5 out of 6 patient CLL cells by RTX , where wild-type RTX was unable to induce CDC .
Activation of complement factor C1 by IgG antibodies via the classical pathway requires antigen binding and hexamerization of the antibody at the cell surface . Here , we show that the ability of IgG1 antibodies to form hexamers can be enhanced by single point mutations at specific amino acid positions both at the intermolecular Fc:Fc and the intramolecular CH2–CH3 interface . Several IgG1 mutants , exemplified by E430G and E345K , displayed strongly enhanced CDC that remained conditional on antigen binding at the target cell . Increased self-association of these mutants was only apparent at high ( 10–100 mg/mL ) concentrations in solution . This self-association was fully reversible and did not result in the formation of aggregates . Furthermore , the slight increase in self-association for these mutants did not lead to solution-phase complement activation in serum or accelerated pharmacokinetics . Mutations of the Fc:Fc interface residue E345 yielded potent activation of CDC irrespective of the amino acid substitution . It therefore appears unlikely that mutation-specific interactions drive hexamerization . We hypothesized that the wild-type E345 side chain might restrict the conformational freedom of this residue by side chain-specific interactions with neighboring residues . Although the closest hydrogen bond donor , the H433 backbone amide , seemed too distant for direct interaction , a water-mediated hydrogen bond could possibly bridge this distance . Both E345 side chain rigidity and possibly associated water were therefore analyzed for several high-resolution , nonmutated Fc structures containing defined water molecules ( S6A Fig ) . Indeed , these structures displayed limited variation in E345 carboxylic acid positioning , and approximately half contained a water molecule potentially bridging the E345 carboxylic acid group and the H433 backbone amide . E345 mutations might therefore increase conformational flexibility or potentially facilitate increased movement of H433 enabling optimized interactions with the opposing Fc . CDC enhancement was more sidechain-specific for other stimulatory mutations at the Fc:Fc interface . I253V , S254L , Q311L , and Q311I likely contributed to optimization of the hydrophobic consensus region interaction site at the Fc:Fc interface ( S6B Fig ) . Mutations S440Y and S440W also promoted hexamerization , possibly by stacking their aromatic moieties . However , hydrogen bonding to opposing Fc backbone atoms may also be important , given the poor efficacy of S440F ( S6C Fig ) . CH2:CH2 distances can vary well over 10 Å when captured by crystallization of different glycan forms [25–27] . We hypothesized that modifying the CH2–CH3 interface could lead to allosteric induction of antibody hexamerization via modulation of this dynamic conformational landscape . Functional screening identified a number of CH2–CH3 interface residues that displayed increased CDC efficacy in which residue E430 stood out ( S5 Table ) . Remarkably , substitutions of this glutamic acid to all possible amino acids stimulated CDC , suggesting that new side chain-specific effects are unlikely explanations for the allosteric stimulation . Rather , the highly conserved salt bridge formed between CH3 residue E430 and CH2 residue K338 might limit the population of CH2 domains occupying a hexamerization-favorable state ( S6D Fig ) . The disruption of the salt bridge alone , however , appeared insufficient , as modification of the pairing residue K338 only had a very limited effect on CDC ( S5 Table ) . The glutamic acids at positions 345 and 430 are conserved in all human IgG subclasses . Since a substitution into any other amino acid results in increased hexamerization and complement activation , it may therefore be speculated that glutamic acid at these positions may play a role in restricting exaggerated complement activation . In addition , some substitutions at these two positions increased the propensity to bind C1q in solution , as measured by a target independent C4d ELISA and native MS , and led to increased clearance from the murine circulation . The prevention of IgG self-association by E345 and E430 might thus enable natural access to a more extensive fraction of the antibody repertoire expressed as soluble IgG . The results described therefore provide a number of amino acid positions in the antibody Fc region that enhance the ability of IgG molecules to form hexamers , which provide a docking and activation structure for avid C1 binding and efficient complement activation . The mutants E430G and E345K exemplify the preferred IgG1 backbones . These mutants enable potent CDC for a range of antibodies against different targets , while preventing target-independent complement activation in solution at therapeutically applied mAb concentrations , which are generally > 20-fold lower than natural IgG1 titers . In designing the enhanced hexamerization antibody platform , we selected backbones with optimal manufacturability and developability characteristics including criteria requiring an absence of antigen-independent complement activation . When applying the hexamerization-enhanced backbones in a novel antibody or Fc-fusion protein , a careful assessment of safety and potential efficacy will need to be performed to establish the therapeutic window , as with any new drug . For each specific disease target , it will therefore be important to determine the potential utility of enhanced IgG hexamerization as a therapeutic strategy . This potential is envisaged to be dependent on e . g . , expression of antigen and its ability to multimerize , the potential downstream effects of multimerization , the expression of complement regulatory proteins on target cells , the availability of complement factors , as well as the expression of antigen on normal tissue . The IgG1 Fc backbones identified provide a novel platform for the generation of therapeutics with enhanced effector functions that only become activated upon target-cell binding and hexamerization . When compared to protein- and glyco-engineered therapeutic antibody technologies based on increased binding to effector molecules with “always-on” characteristics , it is this conditional enhancement that uniquely differentiates this novel platform inspired by the naturally occurring hexamerization of IgG antibodies .
Antibodies were expressed recombinantly in-house using an IgG1 heavy chain with the allotype G1m ( f ) , or bought via the pharmacy ( ALM , OBN ) . The following mAbs were used: CD20 mAbs 7D8 , 11B8 , RTX and OBN [16 , 28 , 29] , CD38 mAbs IgG1-005 and IgG1-003 [18] , EGFR mAb 2F8 [30] , HIV-1 gp120 mAb IgG1-b12 [31] , CD52 mAb ( ALM ) [32] . ALM variant E345R contained an additional K409R mutation known not to impact CDC . Codon-optimized antibody genes encoding heavy and light chains ( GeneArt , Germany ) were cloned separately into pcDNA3 . 3 ( Life Technologies , US ) . Mutations were introduced in heavy chain expression vectors either using Quikchange technology ( Agilent Technologies , US ) , or via gene-synthesis ( Geneart , Germany ) , at the indicated positions numbered according to EU nomenclature . Antibodies were expressed in HEK293 FreeStyle cells by transfection of light chain and heavy chain expression vector DNA using 293fectin essentially , as described [33] . Antibodies were purified by Protein A affinity chromatography ( rProtein A FF; GE Healthcare ) , dialyzed overnight to PBS , and filter-sterilized over 0 . 2-μM dead-end filters . The concentration of purified IgGs was determined by absorbance at 280 nm . Quality assessment of purified antibodies was performed by SDS/PAGE ( >90% intact IgG , >95% HC + LC under reducing conditions ) , ESI-TOF MS ( identity confirmation ) , and HP-SEC ( aggregate level <5% ) . All discussed proteins were purified and met these quality criteria unless explicitly stated otherwise . A focused library of mutations at the positions indicated in S4 and S5 Tables was generated by site-directed mutagenesis . Mutations were introduced into the IgG1-005 Fc region using the Quikchange site-directed mutagenesis kit ( Agilent , US ) . Briefly , for each desired mutation position , a forward and a reverse primer encoding a degenerate NNS codon at the desired location were used to replicate a full length pcDNA3 . 3 ( Life Technologies , US ) plasmid DNA template containing the IgG1-005 heavy chain with IgG1m ( f ) allotype . The resulting DNA mixtures were digested using DpnI to remove source plasmid DNA and used to transform Escherichia coli . Plasmid DNA was extracted from bacterial cultures inoculated with colonies pooled per position , and retransformed into E . coli to obtain clonal colonies . Mutant plasmid DNA isolated from resulting individual colonies was checked by DNA sequencing ( LGC genomics , Berlin , Germany ) . Expression cassettes were amplified from hit picked DNA plasmids by PCR and DNA mixes containing both a mutant heavy and a wild-type light chain of IgG1-005 were transiently transfected to Freestyle HEK293F cells ( Invitrogen , US ) using 293fectin ( Invitrogen , US ) essentially as described [33] . Supernatants of transfected cells containing antibody mutants were collected . Mutant antibody supernatants were screened in CDC assays as follows . 0 . 1 x 106 Daudi or Wien 133 cells were preincubated in round-bottom 96-well plates with 1 . 0 μg/ml of unpurified antibodies in a total volume of 100 μL for 15 min on a shaker at RT . As controls we included: 40 μL mock transfected HEK293 supernatants or PBS and IgG1-b12 as an isotype control antibody . Next , 30 μL normal human serum was added as a source of complement ( 30% final concentration ) and incubated in a 37°C incubator for 45 min . The reaction was stopped by putting the plates on ice , followed by adding 10 μl propidium iodide ( PI ) and determination of cell lysis by FACS . The A431 human epidermoid cell line and DOHH-2 , MEC-2 , SU-DHL-4 , and WSU-NHL human lymphoma cell lines were obtained from the Deutsche Sammlung von Mikroorganismen und Zellkulturen ( cell line numbers ACC 91 , ACC 47 , ACC 500 , ACC 495 , and ACC 58 respectively; Braunschweig , Germany ) . ARH-77 , Daudi , Raji , Ramos , and WIL2-S cell lines ( human lymphoma ) were obtained from the American Type Culture Collection ( ATCC no . CRL-1621 , CCL-213 , CCL-86 and CRL-1596 respectively; Rockville , MD ) . Wien 133 cells ( human Burkitt’s lymphoma ) were kindly provided by Dr . Geoff Hale ( BioAnaLab Limited , Oxford , UK ) . Raji-luc cells were generated by electroporation of Raji cells with gWIZ luciferase ( Aldevron , Fargo , ND ) and pPur vector ( BD Biosciences , Alphen aan de Rijn , The Netherlands ) in a 4:1 ratio and , after 48 h , puromycin was added for selecting a stably transfected clone ( Raji-luc #2D1 ) . Daudi , Raji , WIL2-S , ARH-77 , DOHH-2 , Ramos , WSU-NHL , and SU-DHL-4 were cultured in RPMI 1640 supplemented with 10% heat-inactivated CCS , 1 U/mL penicillin , 1 μg/mL streptomycin , and 4 mM L-glutamine . Raji-luc #2D1 cells were supplemented with 1 μg/mL puromicin . MEC-2 and Wien 133 were cultured in IMDM supplemented with 10% heat-inactivated FCS , 1 U/mL penicillin , and 1 μg/mL streptomycin ( all media and supplements were obtained from Lonza , Vervier , Belgium ) . HEK293 Freestyle cells were obtained from Life Technologies ( formerly Invitrogen , Paisley , UK ) . All cell lines were routinely tested for mycoplasma contamination and generally aliquoted and banked to allow in vitro assays to be performed from frozen cells instead of continuously cultured systems to ensure authenticity of the cell lines . Pooled normal human serum ( NHS ) AB was obtained from Sanquin ( The Netherlands ) . Primary B cells from CLL patients were obtained from AllCells ( Alameda , CA ) . Eight- to 11-wk-old female SCID mice ( C . B-17/Icr- Prkdcscid ) were obtained from Charles River Laboratories and housed in a barrier unit of the Central Laboratory Animal Facility . The mice were kept in IVC cages with water and food provided ad libitum . Mice were checked daily for clinical signs of disease and discomfort . All animal experiments were performed in compliance with the Dutch animal protection law ( WoD ) translated from the directives ( 2010/63/EU ) and if applicable , the Code of Practice “animal experiments for cancer research” ( Inspection V&W , Zutphen , The Netherlands , 1999 ) . The animals were housed and handled in accordance with good animal practice as defined by FELASA , in an AAALAC and ISO 9001:2000 accredited animal facility . The local animal welfare body as well as the Animal Welfare Committee Utrecht University ( DEC-Utrecht ) approved all the animal experiments ( 2012 . II . 08 . 123 for the pharmacokinetic analysis , and 2011 . III . 03 . 036 for in vivo efficacy studies ) . The ADCC assay was performed as described by Overdijk et al . [34] . Briefly , Raji cells were labeled with 100 μCi 51Cr ( Amersham Biosciences , Uppsala , Sweden ) and 4 h ADCC assays were performed according to standard procedures , using peripheral blood mononuclear cells ( PBMC ) from healthy donors as effector cells at a 100:1 effector:target ratio . The percentage NK cells was determined by staining for anti-CD56 ( BD 555516 , BD Biosciences , Aalst , Belgium ) and analyzing on flow cytometer ( FACS canto II , BD Biosciences ) . FcγRIIa-H131R ( rs1801274 ) and FcγRIIIa-V158F ( rs396991 ) genotyping in the PBMC were determined by predesigned SNP genotyping TaqMan SNP assays according to the manufacturer’s protocol ( Thermo Fisher Scientific , Waltham , MA USA ) . Purity and fragmentation of the samples were analyzed using CE-SDS on the Labchip GXII ( Caliper Life Sciences ) . Sample preparation was performed in 96-well Bio-Rad HSP9601 plates using the HT Protein Express Reagent Kit according to manufacturer’s instructions ( High Sensitivity protocol ) with few modifications . Both nonreduced and reduced samples ( addition of DTT ) were prepared and denatured by incubation at 70°C for 10 min . The chip was prepared according to manufacturer’s instructions , and the samples were run with the HT antibody analysis 200 high sensitivity settings . Data were analyzed for molecular weight and purity ( fraction of total ) with Labchip GXII software . CDC assays were performed as described [35] with an antibody concentration series or a fixed antibody concentration and normal human serum ( 20% final concentration unless indicated otherwise ) as a source of complement . Killing was calculated as fraction TOPRO-iodide+ cells ( % ) determined by a Celigo imaging cytometer ( Brooks Life Science Systems ) for A431 carcinoma cells , and as the fraction PI+ cells ( % ) determined by a BD FACSCanto II flow cytometer for all other cells . Complement activation in the absence of target was determined by measuring C4d concentrations , a marker for classical complement pathway activation , after incubating 100 μg antibody per mL in 90% normal human serum for 1 h at 37°C . C4d concentrations were measured in an ELISA ( MicroVue C4d EIA kit , Quidel Corporation , San Diego , US ) according to the manufacturer’s instructions . Dynamic Light Scattering measurements were performed on a DynaPro Plate Reader II ( Wyatt Technology ) with Dynamics 7 software . Samples were applied in duplicate in a 384 well plate ( Black , Round 384 IQ-LV , Aurora Biotechnologies , Cat no . 1011–00110 ) at 30 μL volume and covered with paraffin oil . Prior to the measurement the plates were centrifuged for 3 min at 2 , 500 xg . Samples were kept at 25°C , and 10 acquisitions of each 5 sec were recorded for each sample with auto-adjustment of attenuator and laser power . The cumulant fit procedure was used to analyze the data . A refractive index of 1 . 333 for PBS buffer at 20°C was used and a viscosity of 1 . 019 cP ( standard software values supplied in Dynamics software ) . The apparent Rh of mutants was divided by the Rh of wild-type mAb , each averaged over three experiments . Hollow Fiber flow Field Flow Fractionation analysis was performed using an Agilent 1100 HPLC and an Eclipse DualTec system ( Wyatt , US ) , monitoring UV at 280 nm ( Agilent ) and connected to a multiangle laser light scattering MiniDawn Treos detector ( Wyatt , US ) . 4 . 0 μL protein samples of 1 . 0 mg/mL concentration were separated in 8 . 7 mM Na2HPO4/1 . 8 mM NaH2PO4/200 mM NaCl buffered at pH 7 . 4 in a 10 kDa PES hollow fiber with a radius of 400 μm and 17 cm length , and performed in 3-fold . Data was processed using Astra software version 6 . 1 ( Wyatt , US ) . Cross-flow , focus time , elution time and flow rate optimization for IgG1-005 as well as data collection were performed by Coriolis Pharma ( Martinsried , Germany ) . HP-SEC fractionation was performed using a Waters Alliance 2975 separation unit ( Waters , Etten-Leur , The Netherlands ) connected to a TSK HP-SEC column ( G3000SWxl; Toso Biosciences , via Omnilabo , Breda , The Netherlands ) , a Waters 2487 dual λ absorbance detector ( Waters ) , and a Mini Dawn Treos MALS detection unit ( Wyatt ) . 50 μL samples containing 1 . 25 mg/mL protein were separated at 1 mL/min in 0 . 1 M Na2SO4 /0 . 1 M sodium phosphate buffered at pH 6 . 8 . Results were processed using Empower software version 2002 and expressed per peak as percentage of total peak area . Imaged Capillary Isoelectric Focusing ( icIEF ) was performed using an iCE280 Analyzer ( Convergent Biosciences ) according to the manufacturer’s instructions . Samples were not desalted before use . Methyl Cellulose and pI markers were purchased from Convergent Bioscience , Carrier Ampholytes ( Pharmalytes ) from GE Healthcare . Focusing was performed for 7 min at 3 , 000 V , and the whole-capillary absorption image was captured by a charge-coupled device camera . After calibration of the peak profiles , the data were analyzed for pI and fractional area ( % ) by EZChrom software . 5E+06 luciferase expressing Raji cells were injected subcutaneously on the right flank of 8–9 wk old , female C . B-17/IcrPrkdc-scid/CRL mice ( Charles-River Laboratories , Maastricht , the Netherlands ) . When tumor volume reached an average of 85 mm3 , mice were randomized and treated with a single intraperitoneal dose of 50 μg IgG1 7D8 WT , 7D8-E345R , 7D8-K322A or IgG1-b12 control antibody . Correct antibody administration was monitored by analysis of IgG levels seven days after treatment . Tumor volume was measured at least twice per week using caliper ( PLEXX , Elst , The Netherlands ) measurements and calculated as 0 . 52 x ( length ) x ( width ) 2 . IgG1 oligomerization was studied by native MS as described previously [36] . The constructs were analyzed in 0 . 15 M ammonium acetate ( pH 7 . 5 ) at an antibody concentration of 2 μM . This protein preparation was obtained by five sequential concentration and dilution steps at 4°C using a centrifugal filter with a cut-off of 10 kDa ( Millipore ) . Samples were sprayed from borosilicate glass capillaries and analyzed on a modified LCT time-of-flight instrument ( Waters , UK ) adjusted for optimal performance in high mass detection [37 , 38] . Instrument settings were as follows; needle voltage ~1 . 4 kV , cone voltage ~100 V , source pressure 8 . 5 mbar . The extent of oligomerization was estimated by summing the areas under the curves as described previously [39] . Antibody concentrations were determined by UV spectroscopy using a Nanodrop photospectrometer ( Thermo Scientific , De Meern , Netherlands ) . The spectrophotometer was adjusted to baseline using PBS at 280 nm , and the absorbance of duplicate sample preparations were determined on the spectrophotometer at both 280 nm ( A280 ) and 350 nm ( A350 ) . Sample concentrations were calculated using Beer’s law , the extinction coefficient for the specific molecule , and the A280 value . Antibodies ( 500 μg per mouse ) were administered intravenously to groups of mice ( n = 3 ) , and blood samples were drawn from the submandibular or saphenous vein at 10 min , 3 h , and 1 , 2 , 7 , 14 , 21 , and 28 d after administration . Blood was collected in heparin-containing vials and centrifuged ( 5 min at 10 , 000 × g ) to separate plasma from cells . Plasma was transferred to a new vial and stored at −20°C . Total human IgG concentration in plasma samples was analyzed by ELISA . Plates were coated with 2 ug/mL m-anti-HuIgG ( clone MH16-1 ( Sanquin , The Netherlands ) and plasma human IgG was detected by G-a-HuIgG-HRP ( 109-035-098 , Jackson ) . Injected material was included as reference curve . AUC up to day 21 was addressed using Graphpad Prism and clearance was calculated as ( Dose ( mg . kg−1 ) * 1 , 000 / AUC ) . Daudi cells were incubated for 24 h with indicated antibody ( 1 μg/mL ) . Percentage PCD markers were determined using Annexin V ( BD Biosciences cat . 556547 ) and cleaved Caspase 3 apoptosis Kits ( BD Biosciences cat . 550914 ) via flow cytometry ( FACS canto II , BD Biosciences ) according to the manufacturer’s protocols ( BD Biosciences ) . Schematic views of the structure of human monoclonal IgG1 antibody b12 were based on the IgG1-b12 crystal structure deposited under PDB access code 1HZH , but with amino acids renumbered according to Eu nomenclature [40] , using PyMOL version 1 . 5 . 0 . 4 ( Schrödinger LLC ) . Mutant models were generated by selecting the rotamers with minimal steric clashes without subsequent energy minimization . | Immunotherapy is a powerful and rapidly expanding field that makes use of the body’s natural defense mechanisms to eliminate disease entities such as infectious agents or cancer cells . Circulating antibodies bind aberrant structures in a highly target-specific manner and “flag” disease cells for destruction by killing machineries that are present in the bloodstream . We recently showed that activation of one of these immune defense mechanisms , the complement system , is most efficiently initiated by binding of the first complement component C1q to a ring of six antibodies . Since antibody hexamerization occurs naturally only after binding to surface antigens , complement activation and subsequent complement-mediated cell killing is therefore restricted to these antibody-flagged cells . Now , with a mutational screening approach , we identified structural entities in the antibody backbone that potentiate this antigen binding–induced hexamer formation . We identified mutations that enhance the hexamer formation and complement activation by IgG1 antibodies against a wide range of targets on varying cancer cells . Based on our findings , we present a broadly applicable platform for the generation of therapeutic antibodies with enhanced ability to promote hexamerization-induced complement activation and target cell killing only after surface antigen binding . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2016 | A Novel Platform for the Potentiation of Therapeutic Antibodies Based on Antigen-Dependent Formation of IgG Hexamers at the Cell Surface |
V-ATPases are part of the membrane components of pathogen-containing vacuoles , although their function in intracellular infection remains elusive . In addition to organelle acidification , V-ATPases are alternatively implicated in membrane fusion and anti-inflammatory functions controlled by ATP6V0d2 , the d subunit variant of the V-ATPase complex . Therefore , we evaluated the role of ATP6V0d2 in the biogenesis of pathogen-containing vacuoles using ATP6V0d2 knock-down macrophages infected with the protozoan parasite Leishmania amazonensis . These parasites survive within IFNγ/LPS-activated inflammatory macrophages , multiplying in large/fusogenic parasitophorous vacuoles ( PVs ) and inducing ATP6V0d2 upregulation . ATP6V0d2 knock-down decreased macrophage cholesterol levels and inhibited PV enlargement without interfering with parasite multiplication . However , parasites required ATP6V0d2 to resist the influx of oxidized low-density lipoprotein ( ox-LDL ) -derived cholesterol , which restored PV enlargement in ATP6V0d2 knock-down macrophages by replenishing macrophage cholesterol pools . Thus , we reveal parasite-mediated subversion of host V-ATPase function toward cholesterol retention , which is required for establishing an inflammation-resistant intracellular parasite niche .
Vacuolar H+-ATPases ( V-ATPases ) are membrane-associated ATP-dependent multimeric enzymes responsible for pumping protons from the cytosol into the lumen of intracellular organelles , thus controlling the acidification of lysosomes , endosomes , the trans-Golgi network and other intracellular vesicles [1 , 2] . V-ATPases display two functionally distinct domains composed of several subunits: the cytosolic domain V1 , composed of eight subunits ( A , B , C , D , E , F , G and H ) and that is implicated in ATP hydrolysis , and membranal domain V0 , which is composed of subunits a , d , e , c , c’ , and c” and is implicated in proton transport across the vesicle membrane [1] . Acidification of intracellular compartments is the canonical function of V-ATPases , which are largely implicated in diverse cellular processes , such as maturation and degradation of proteins , receptor-mediated endocytosis , receptor recycling and endocytic traffic [3 , 4] . At the crossroads of innate immunity and endocytosis , V-ATPases are responsible for phagolysosome acidification in macrophages and other professional phagocytes , a key feature in the immune response against intracellular pathogens [5] . Maintenance of an acidic pH controlled by V-ATPases is required for the optimal activity of lysosomal digestive enzymes and production of hydrogen peroxide and other reactive oxygen species directly involved in pathogen killing [6] . Pathogens have nevertheless evolved strategies to evade phagolysosome acidification and killing , including targeting and subverting V-ATPase functions , thus improving their adaptation inside the hostile environment of host cells [7] . The pathogen-mediated subversion of V-ATPases may involve the interference of one or several subunits that compose the two functional domains , inhibiting proton pump activity or driving V-ATPases to target different organelles . The bacterial pathogens Legionella pneumophila and Mycobacterium tuberculosis , for instance , have the ability to secrete virulence factors that directly target the H-subunit of the V1 domain of host cell V-ATPases , blocking the acidification of bacteria-containing vacuoles in which they multiply by V-ATPase exclusion [8–10] . Conversely , Yersinia pseudotuberculosis does not exclude V-ATPases from the bacteria-containing vacuole but decreases their activity during intracellular infection [11] . In addition to coupling with the V1 domain and its proton translocation canonical function , the V0 membrane domain interacts with Soluble NSF Attachment Protein Receptors ( SNAREs ) , thus being implicated in membrane fusion and exocytosis [12 , 13] . These noncanonical functions of V-ATPases can take place when V0 domains are dissociated from V1 and directed to different organelles or when V-ATPases are composed of alternative isoforms of some of their subunits [4 , 14 , 15] , a feature that could be exploited by intracellular pathogens . The a subunit from the V0 domain , for example , has four different isoforms , each one expressed in different specialized cell types and distinct organelles [16] . The d subunit , also from the V0 domain , is expressed either as a ubiquitous isoform d1 , which is implicated in the regular proton pumping activity of V-ATPases , or as an alternative isoform d2 ( ATP6V0d2 ) , which is highly expressed in restricted tissues , such as bones , kidney and lungs [17] , and specialized cell types , such as osteoclasts [18] and macrophages [19] , where it acts as a membrane fusogen [20–22] . The isoform ATP6V0d2 is implicated in counteracting macrophage inflammatory responses [23 , 24]; therefore , the pathogen-induced production of this subunit isoform may constitute a mechanism by which intracellular pathogens multiply in macrophages despite inflammatory stimuli . Accordingly , ATP6V0d2 is upregulated in macrophages upon in vitro intracellular infection with the protozoan parasite Leishmania ( Leishmania ) amazonensis [25] . Leishmania spp . are trypanosomatid parasites , which induce tegumentary or visceral leishmaniasis in humans and other animals , a major health problem in poor and developing countries [26] . They are dimorphic parasites found extracellularly in the midgut of insect vectors as flagellated and elongated promastigotes and intracellularly in mammalian host macrophages , neutrophils and dendritic cells as round-shaped amastigotes [27] . Species from the L . mexicana complex , such as L . amazonensis , L . mexicana and L . pifanoi , are known to multiply within large and fusogenic pathogen-containing vacuoles or parasitophorous vacuoles ( PV ) [28] , which are acidic compartments displaying functional V-ATPases [29] . Compared to other species , they also display , at least in vitro , a remarkable resistance to parasite killing mechanisms mediated by interferon-γ ( IFN-γ ) and lipopolysaccharide ( LPS ) within macrophages or by direct treatment with reactive oxygen species ( ROS ) [30–32] . A causal relationship between large PV development and parasite resistance to inflammatory macrophages remains elusive especially in vivo . Considering that ATP6V0d2 participates in both membrane fusion and anti-inflammatory processes , we evaluated the participation of this subunit isoform in the biogenesis of pathogen-containing vacuole formation . ATP6V0d2 participation in L . amazonensis resistance to inflammatory macrophages upon stimulation with IFN-γ/LPS or treatment with inflammatory , oxidized lipoproteins ( ox-LDL ) was also approached . Here , we demonstrate that ATP6V0d2 is upregulated by intracellular parasites as a countermeasure to macrophage inflammatory immune responses , controlling the volumetric expansion of the pathogen-containing vacuole by regulating macrophage intracellular cholesterol levels . ATP6V0d2 does not participate in parasite survival within inflammatory macrophages classically activated by IFN-γ/LPS . ATP6V0d2 is required , however , for parasite survival within macrophages that scavenge ox-LDL via parasite-mediated increased expression of LOX-1 and CD36 scavenger receptors .
The subunit d ( ATP6V0d ) connects the two functionally distinct subunit V-ATPase complexes V0 and V1 , which are responsible for the acidification of intracellular compartments . The subunit d from V-ATPase V0 complex occurs as two variants , ATP6V0d1 ( ubiquitous ) and ATP6V0d2 , which expression is restricted to certain tissues and cells , expressed in parallel with ATP6V0d1 variant [17 , 21] . V-ATPases will be thus composed of either d1 or d2 variant filling the space for the d subunit of V0 complex . To evaluate the role of isoform d2 in this canonical function of V-ATPases , we stably knocked-down ATP6V0d2 in RAW 264 . 7 macrophages ( ATP6V0d2-KD ) and evaluated phagolysosomal acidification using fluorescein ( FITC ) -tagged latex beads ingested by the phagocytes [33 , 34] . We have stably and specifically knocked down the d2 variant ( ATP6V0d2 ) , not the ubiquitous ATP6V0d1 variant which predominates over ATP6V0d2 on nonsilenced control macrophages ( Fig 1A ) . The expression of another V-ATPase subunit , ATP6V0a1 , remains unaltered upon ATP6V0d2 knock-down ( Fig 1B ) , demonstrating that this and likely all other subunits compose a functional V-ATPase in ATP6V0d2-KD macrophages . After phagosomal pH measurements using FITC-tagged beads internalized by nonsilenced and ATP6V0d2-KD macrophages ( S1 Fig ) , we observed that , although ATP6V0d2 is efficiently knocked-down ( Fig 1A and 1B ) , phagolysosomes containing FITC-tagged beads reach an acidic pH of approximately 5 . 2 in both nonsilenced and ATP6V0d2-KD macrophages , activated or not by IFN-γ/LPS treatment ( Fig 1C–1E ) . Thus , the knock-down of ATP6V0d2 does not interfere in V-ATPase canonical function of phagolysosomal acidification as corroborated by others using different methods [21 , 24] . Despite demonstrating that ATP6V0d2 does not participate in the V-ATPase canonical function of phagolysosome acidification , ATP6V0d2-KD macrophages display impaired lysosomal functions as assessed by analysis of the activity of some lysosomal enzymes . Cathepsin D ( CTSD ) , one of the most well-studied lysosomal enzymes whose activity is a direct indicator of lysosomal functions [35 , 36] , was more abundantly associated with lysosome-associated membrane protein 1 ( LAMP-1 ) -positive compartments as assessed by fluorescence colocalization analysis ( S2A Fig ) , although cleaved , “mature” functional forms of CTSD were absent in ATP6V0d2-KD ( S2B Fig ) . The activity of enzymes involved in lysosomal storage diseases that could indicate lysosome impairment was also evaluated: lysosomal acid lipase ( LAL ) , implicated in Wolman and cholesteryl ester storage diseases , displayed the same activity in both nonsilenced and ATP6V0d2-KD macrophages; activity of α-galactosidase ( α-Gal ) , implicated in Fabry Disease , was increased in ATP6V0d2-KD macrophages , while β-glucocerebrosidase ( GCase ) activity , whose activity deficiency is observed in Gaucher Disease , was decreased compared to nonsilenced macrophages ( S2C Fig ) . All tested enzymes are acid hydrolases only active at acidic pH; considering that LAL activity does not depend on ATP6V0d2 , we excluded an impairment of lysosome acidification in the lysosome dysfunction displayed by ATP6V0d2-KD macrophages . Therefore , ATP6V0d2 does not participate in the canonical V-ATPase function of phagolysosome acidification , instead exerting a pH-independent regulation of lysosomal enzymatic functions . To evaluate the participation of ATP6V0d2 in the innate immune response of macrophages , we assessed the expression of ATP6V0d2 mRNA transcripts ( relative to expression of its alternative ubiquitous isoform ATP6V0d1 ) , in nonsilenced and ATP6V0d2-KD macrophages ( Fig 2A ) . Macrophages were activated or not by IFN-γ/LPS treatment and cultured with or without the intracellular parasite L . amazonensis ( S3A Fig ) . In nonsilenced macrophages , expression of ATP6V0d2 was upregulated upon Leishmania infection . We reproduced the remarkable decrease of ATP6V0d2 expression upon classical activation with IFN-γ/LPS as demonstrated by others [24] , to levels comparable to those obtained in ATP6V0d2-KD macrophages . ATP6V0d2 expression is partially recovered by Leishmania intracellular infection , suggesting that Leishmania stimulates the expression of ATP6V0d2 as a countermeasure to the macrophage immune response . However , ATP6V0d2 is not directly implicated in the macrophage responses related to parasite intracellular multiplication , namely: i ) production of nitric oxide ( NO ) inferred by expression of the inducible isoform of nitric oxide synthase ( iNOS , NOS2 ) , the main effector of innate immunity against intracellular pathogens [37]; and ii ) expression of arginase , which is involved in polyamine synthesis and is exploited by pathogens to establish intracellular infection [38] . NOS2 expression was increased upon IFN-γ/LPS treatment in ATP6V0d2-KD as compared with nonsilenced macrophages , indicating that ATP6V0d2 buffers this activation pathway in non-infected macrophages ( Fig 2B , first graph ) . In infected macrophages , however , NOS2 expression was equally decreased upon IFN-γ/LPS treatment in nonsilenced and ATP6V0d2-KD macrophages harboring Leishmania , indicating that other host factors induced by the parasite , such as arginase , are more determinant in downregulating iNOS expression . Since arginase expression was increased in macrophages hosting the parasite independently of ATP6V0d2 knock-down or macrophage activation with IFN-γ/LPS ( Fig 2B , second graph ) , the previous data showing decreased NOS2 expression upon IFN-γ/LPS treatment may be related to this increased arginase expression due to the presence of Leishmania . Multiplication of intracellular Leishmania was assessed by quantitative live imaging and microscopic counting ( Fig 2C–2E ) . Cultures of macrophages infected with Leishmania were recorded by live imaging for 36 hours , and the numbers of macrophages per microscopic field and parasites per macrophage were quantified by image segmentation ( Fig 2C ) . Independently of ATP6V0d2 knock-down , activation with IFN-γ/LPS inhibited RAW 264 . 7 cell proliferation ( Fig 2D , upper graph ) but increased Leishmania intracellular multiplication ( Fig 2D , lower graph ) , as demonstrated by others upon IFN-γ-only treatment [30] . At the end of 72 hours after administration of parasites to macrophage cultures , samples were fixed , and the numbers of macrophages and parasites hosted per macrophage were converted into an infection index , which revealed that activation with IFN-γ/LPS increased parasite multiplication independently of ATP6V0d2 ( Fig 2E ) . Next , we evaluated L . amazonensis PV features , such as acidification and PV volumetric enlargement [28] , in nonsilenced and ATP6V0d2-KD macrophages . Intracellular parasites are sequestered within acidified PVs independently of ATP6V0d2 , as assessed by lysosomotropic probes retained in acidic compartments ( Fig 3A ) . Complete abrogation of probe fluorescence of the L . amazonensis PV in macrophages treated with the alkalinizer agent ammonium chloride ( NH4Cl ) functionally confirmed the acidified content of PVs formed independently of ATP6V0d2 . In addition , the trafficking of LAMP-1 to the L . amazonensis PV membrane , a distinguishing feature of lysosomes , phagolysosomes and Leishmania PVs [28] , was not altered by ATP6V0d2 knock-down in control or IFN-γ/LPS-activated macrophages ( Fig 3B ) . In addition , the frequency of L . amazonensis PVs displaying the late endosomal SNARE VAMP8 in their membranes is not altered by ATP6V0d2 knock-down ( S4D Fig ) . Concerning PV morphology , however , L . amazonensis PV developed in ATP6V0d2-KD macrophages did not enlarge in size as compared with nonsilenced macrophages according to three-dimensional projections of images obtained from infected samples ( Fig 3C and S4A Fig ) . To further investigate this impairment in PV enlargement , ATP6V0d2-KD macrophages hosting L . amazonensis PV were dynamically tracked by live imaging ( Fig 3D , S1 Movie ) . The parasite developed enlarging PVs in nonsilenced macrophages ( Fig 3D , arrowheads , upper row ) ; this was in contrast to ATP6V0d2-KD macrophages , in which PV dimensions are smaller and often fit parasite size , promoting PV fissions as the parasite multiplies ( Fig 3D , arrowheads , lower row ) . Using fluorescent lysosomal probes and image segmentation analysis [28] , we dynamically assessed PV volumetric enlargement in parasite-infected macrophages activated or not with IFN-γ/LPS , demonstrating that L . amazonensis PV enlargement depends on ATP6V0d2 ( Fig 3E–3G ) . On average , infected nonsilenced and ATP6V0d2-KD macrophages do not differ in or change their cell sphericity over the course of 36 hours of multidimensional ( S4B Fig ) and , in contrast to PV area measurements , PV volumetric assessment is nevertheless not influenced by cell sphericity effects ( S4B and S4C Fig ) . These results demonstrate the participation of ATP6V0d2 in controlling L . amazonensis PV volumetric expansion . The biogenesis of large L . amazonensis PVs is accompanied by upregulation of host macrophage genes implicated in lipid metabolism , specifically cholesterol homeostasis [25] , suggesting the participation of cholesterol in the intracellular establishment of this parasite . Therefore , we evaluated the intracellular levels of free cholesterol/cholesteryl esters in the studied macrophages , demonstrating that macrophages displayed a 40% decrease in cholesterol levels when ATP6V0d2 was knocked-down as detected by ELISA ( Fig 4A , nontreated group ) and confirmed by mass spectrometry ( S5A Fig ) . To functionally assess the participation of cholesterol in the ATP6V0d2-dependent biogenesis of L . amazonensis PVs , we envisioned a protocol for cholesterol repletion by adding oxidized low-density lipoprotein ( ox-LDL ) to macrophage cultures ( S3B Fig ) , as performed previously [39–41] . Modified LDL , such as ox-LDL , is more efficiently taken up by macrophages through scavenger receptors and induces higher accumulation of intracellular cholesterol than native LDL [41 , 42] . Among three different strategies to replenish macrophage intracellular cholesterol levels decreased in ATP6V0d2-KD–namely , treatment with methyl-β-cyclodextrin/cholesterol complexes [43] , with LDL [41 , 42] or with ox-LDL [39 , 41]–ox-LDL was the most effective method to replenish intracellular cholesterol with less cytotoxicity in both nonsilenced and ATP6V0d2-KD macrophages ( Fig 4A and S5B and S5C Fig ) . Accumulation of ox-LDL-derived cholesterol in macrophages leads to the formation of foamy macrophages , which are full of lipid-laden vacuoles ( lipid droplets ) [44 , 45] that could reconstitute L . amazonensis PV volumes in ATP6V0d2-KD macrophages . Accordingly , exogenous ox-LDL traffics into PVs independently of ATP6V0d2 ( Fig 4B , arrowheads ) , and the ox-LDL-mediated intracellular cholesterol repletion in ATP6V0d2-KD macrophages hosting L . amazonensis increased the PV volume to dimensions comparable to those measured in nonsilenced macrophages ( Fig 4C and S4C FIg ) . There is a negative correlation between PV size and the amount of ox-LDL accumulated within PVs , demonstrating that smaller PVs like those formed in ATP6V0d2-KD macrophages accumulate more ox-LDL ( Fig 4D ) . Importantly , PVs formed in ATP6V0d2-KD macrophages—which recover their dimensions by ox-LDL treatment—retain more ox-LDL per μm3 as compared with PVs formed in nonsilenced macrophages ( Fig 4E ) . This ox-LDL-mediated PV dimensional recovery was accompanied by a decrease in the intracellular survival of L . amazonensis specifically within ATP6V0d2-KD macrophages , as assessed by comparing infection indexes under two different concentrations of ox-LDL ( Fig 4F and 4G ) . Parasites hosted within PVs formed in ATP6V0d2-KD macrophages and enlarged after treatment with ox-LDL displayed aberrant morphology suggestive of parasite killing [46] in contrast to parasites multiplying in nonsilenced macrophages under the same ox-LDL treatment ( Fig 4F and S2 Movie ) . The ox-LDL-mediated PV size recovery observed in ATP6V0d2-KD macrophages is not related to differential expression of ATP6V0d subunit isoforms d1 and d2 ( S6A Fig ) or the differential expression of the lysosomal traffic regulator LYST/Beige ( S6B Fig , right graph ) involved in PV biogenesis [47] . In addition , the impaired intracellular establishment of L . amazonensis in ATP6V0d2-KD macrophages treated with ox-LDL was not due to increased production of reactive oxygen species [48] or inflammatory cytokines upon cellular uptake of ox-LDL [49] at the evaluated ox-LDL concentration ( S6C Fig ) . Finally , the enzymatic activities of α-Gal and GCase lysosomal enzymes after ox-LDL-mediated cholesterol replenishment were assessed and do not explain neither the ox-LDL-mediated recovery of PV dimensions in ATP6V0d2-KD macrophages ( compare infected macrophages treated or not with ox-LDL , S2D Fig ) . The cholesterol intracellular homeostasis in macrophages can be regarded as a balance between cholesterol biosynthesis that generates cholesterol precursors involved in the cholesterol biosynthetic pathways , cholesterol catabolism , and cholesterol uptake/efflux promoted by receptors for non-modified LDL and scavenger receptors for modified LDL [50] . To approach the participation of ATP6V0d2 in cholesterol homeostasis , we have evaluated the mRNA levels of scavenger receptors and of the sterol regulatory element-binding protein 2 ( SREBP2 ) which controls expression of genes involved in cholesterol synthesis [51] , in the context of ATP6V0d2 knock-down , infection with Leishmania and treatment with ox-LDL . The non-altered mRNA expression of SREBP2 observed in the conditions studied ( S6B Fig , left graph ) and the non-altered abundance of the cholesterol biosynthetic precursors squalene and lanosterol observed by mass spectrometry comparing nonsilenced and ATP6V0d2-KD macrophages ( S5A Fig ) indicate that ATP6V0d2 does not associate with cholesterol biosynthesis . An increased gene expression for LDL receptor ( LDL-R ) in ATP6V0d2-KD macrophages as compared with nonsilenced ones was observed independently of the conditions studied , with ox-LDL treatment decreasing the mRNA levels ( Fig 5A , upper left graph ) . This is compatible with LDL-R stimulated expression upon lower intracellular cholesterol levels as displayed by ATP6V0d2-KD [52–54] and reinforces the role of ATP6V0d2 in the influx of cholesterol . Considering the scavenger receptors for modified LDL , CD36 is decreased by ATP6V0d2 knock-down ( Fig 5A upper right graph and 5B-C ) . RT-qPCR for CD36 , covering the detection for all 5 isoforms of murine CD36 , was the more efficient technique to detect these differences . The decrease of total ( Fig 5B ) and membrane surface ( Fig 5C ) CD36 levels was not so marked as the decrease observed in mRNA levels ( Fig 5A ) . Recovery of PV dimensions by ox-LDL-mediated cholesterol replenishment in ATP6V0d2-KD occurs in parallel with increasing in CD36 gene expression specifically in infected ATP6V0d2-KD macrophages ( Fig 5A and 5B , red arrowhead ) in both mRNA and protein levels ( Fig 5A upper right graph and 5B ) . Considering that the ox-LDL-mediated parasite killing occurs exclusively in ATP6V0d2-KD macrophages ( parasites hosted by nonsilenced macrophages are resistant to ox-LDL intake ) and that CD36 is known to control PV enlargement [55] , we infer that CD36 participates in the recovery of PV dimensions upon ox-LDL uptake , what is detrimental to the parasite only in the absence of ATP6V0d2 . Other scavenger receptors implicated in ox-LDL intake display a non-altered expression in the conditions studied ( Scavenger Receptor class A , Msr1/SRA , Fig 5A lower left graph ) or display an increased expression specifically in infected ATP6V0d2-KD macrophages , although independent of ox-LDL treatment , such as the lectin-type oxidized LDL receptor 1 , LOX-1 ( Fig 5A , lower right graph ) . The membrane surface expression of scavenger receptors involved in cholesterol efflux , namely Scavenger receptor class B type 1 ( SR-BI ) and its alternative isoform SR-BII , was not altered by ATP6V0d2 knock-down ( Fig 5D ) . Again , it reinforces the role of ATP6V0d2 in cholesterol intake in infected macrophages .
We report the participation of an alternative isoform of the V-ATPase subunit d , the isoform d2 ( ATP6V0d2 ) in controlling the biogenesis of pathogen-containing vacuoles generated by L . amazonensis in macrophages . ATP6V0d2 , whose expression is restricted to certain cell lineages , including macrophages , does not participate in phagolysosome acidification , indicating that the ubiquitous isoform d1 ( ATP6V0d1 ) participates exclusively in the canonical function of this V-ATPase , while isoform d2 switches the V-ATPase toward noncanonical , acidification-independent functions , such as membrane fusion , regulation of lysosome enzymatic activities and downregulation of macrophage inflammatory burst [4 , 21 , 24 , 56] . Therefore , the variant ATP6V0d1 is still expressed in ATP6V0d2 knock-down macrophages ( ATP6V0d2-KD ) , capable of composing functional V-ATPases that acidify phagolysosomes and parasite-containing vacuoles . The preservation of phagolysosome acidification in the absence of the d2 variant demonstrated by us here and by others [21 , 24] is a solid evidence that V-ATPases in ATP6V0d2-KD macrophages are functional and thus composed of all subunits required for their canonical functions . ATP6V0d2 is involved in the function of important lysosomal enzymes , such as cathepsin D ( CTSD ) , whose cleavage into mature forms depends on this V-ATPase subunit isoform . Inhibition of CTSD activity was demonstrated to either increase [57] or decrease [58] cholesterol intracellular levels depending on the studied models and a definitive participation of CTSD in cholesterol homeostasis remains to be established . Sphingolipid metabolism is also likely to be disturbed by ATP6V0d2 knock-down: β-glucocerebrosidase ( GCase ) , whose activity is decreased in ATP6V0d2-KD macrophages and is responsible for breaking down glucosylceramide into ceramide [59] , is also implicated in CTSD processing [60 , 61] , and α-galactosidase ( α-Gal ) , whose activity is increased in ATP6V0d2-KD macrophages , participates in the production of glucosylceramide [62] . Hence , in addition to a 40% decrease in intracellular cholesterol levels , ATP6V0d2-KD macrophages could accumulate glucosylceramide ( glucocerebroside ) in detriment to ceramide and its incorporation into macrophage membranes . The data therefore indicate that ATP6V0d2 participates in lysosomal metabolic processes involved in the homeostasis of important membrane components , such as cholesterol and ceramide , which ultimately interfere in the biogenesis of pathogen-containing vacuoles in macrophages . The regulation of lysosome function is coordinated by multiple factors , including proper assembly , trafficking and function of V-ATPases in the membrane of lysosomes and phagolysosomes . These lysosome-associated V-ATPase features could be controlled by ATP6V0d2 in macrophages reacting to pathogens and/or inflammatory stimuli . ATP6V0d2 is implicated in buffering inflammatory responses in macrophages , particularly upon TLR4 stimulation by LPS treatment [23]; however , the conclusion that this anti-inflammatory role of ATP6V0d2 is due to an ATP6V0d2-dependent vesicle acidification contrasts with our results and previous works showing that ATP6V0d2 depletion does not interfere in V-ATPase canonical functions such as ATP hydrolysis and H+ transport [21 , 24] and that depletion of one particular subunit isoform does not interfere in V-ATPase-mediated phagosomal acidification , what would be compensated by expression with other variants ( the case of subunit ATP6V0a3 [63] ) . We demonstrated that ATP6V0d2 is upregulated by the parasite in IFN-γ/LPS-treated classically activated or M1-differentiated macrophages [64] , e . g . , macrophages that trigger an intra and extracellular inflammatory environment producing nitric oxide ( NO ) and reactive oxygen species ( ROS ) , which is recognized as the most effective macrophage response against intracellular pathogens both in vitro and in vivo [30] . In contrast with Leishmania major parasites , which multiply in macrophages sheltered by tight-fitting pathogen-containing vacuoles and are sensitive to NO and ROS generated by classical macrophage activation , L . amazonensis and L . mexicana multiply within spacious and communal vacuoles and are resistant to M1 macrophage activation , that exerts cytostatic effects on intracellular L . amazonensis [28 , 30 , 31 , 65 , 66] . Conversely , our in vitro study demonstrated that macrophage stimulation with IFN-γ/LPS increased parasite multiplication independently of ATP6V0d2 . The persistence of this intracellular parasite despite inflammatory scenarios could be related to parasite-mediated counteraction of macrophage innate immune responses and microbicidal activities , e . g . , by production of antioxidant enzymes to cope with oxidative burst [67] and establishment of a safe , customized intracellular niche where the parasite multiplies sheltered from ROS activity and antigen presentation [68 , 69] . We reproduced the drastic downregulation of ATP6V0d2 expression upon LPS stimulation of macrophages as demonstrated by others [24] , what is partially recovered by Leishmania infection . ATP6V0d2 is thus one of the several factors upregulated by the parasite in response to ( or counteracting ) the hostile environment of inflammatory macrophages . The ATP6V0d2-dependent volumetric expansion of pathogen-containing vacuoles may represent one additional countermeasure , possibly diluting phagolysosome hydrolases to concentrations innocuous to the parasite [70] , thus favoring L . amazonensis multiplication . However , we observed that inhibition of PV volumetric enlargement by ATP6V0d2 knock-down did not interfere with parasite multiplication in either non-activated or IFN-γ/LPS-activated macrophages , suggesting that PV enlargement is not crucial for parasite intracellular multiplication and does not account for parasite persistence in NO-producing inflammatory macrophages , at least for a short 72-hour in vitro infection . The ATP6V0d2-dependent PV expansion and parasite-mediated upregulation of ATP6V0d2 in IFN-γ/LPS-activated macrophages indicate that intracellular pathogens exploit ATP6V0d2 as a countermeasure to inflammatory scenarios . Although ATP6V0d2 does not participate in parasite resistance to the classical in vitro IFN-γ/LPS model of inflammatory macrophages , this V-ATPase subunit isoform was required for parasite survival in macrophages stimulated with ox-LDL , a potent inflammatory stimulus mainly studied in the context of atherosclerotic lesions but that has also been implicated in chronic psoriatic skin inflammation [71 , 72] . Our results contrast with other mechanistic studies of L . amazonensis PV enlargement , which have established that interfering with the expression of host macrophage genes , such as the lysosomal traffic regulator LYST/Beige or some members of membrane fusion SNAREs machinery impact PV expansion and directly influence parasite multiplication [47 , 73] . Parasite factors also account for this direct correlation between PV expansion and intracellular multiplication , as L . mexicana establishment in macrophages depends on Cysteine Peptidase B-mediated modulation of host cell membrane fusion machinery via the parasite GPI-anchored metalloprotease GP63 [73] . The observed PV impairments in these studies could be , however , the effect rather than the cause of parasite killing or inhibition of multiplication . We demonstrate that recruitment of late endosome-associated VAMP8 [74] to PVs and expression of LYST/Beige [47] are not associated with PV size impairments nor in the ox-LDL-mediated PV recovery observed in ATP6V0d2-KD macrophages . On the other hand , the main scavenger receptor for ox-LDL , CD36 , was demonstrated to participate in the complex machinery that regulates PV biogenesis [55] and might be implicated in the ox-LDL-mediated PV dimensional recovery . The decreased CD36 expression in ATP6V0d2-KD macrophages together with increased LDL-R expression reinforce the central role of ATP6V0d2 gene on cholesterol intake and PV size . In addition , ATP6V0d2 knock-down , infection or ox-LDL treatment do not influence expression of SREBP2 , which controls expression of genes involved in cholesterol synthesis [51] . Therefore , the ATP6V0d2-dependent PV biogenesis is unlikely to be related to cholesterol biosynthetic pathways but rather to cholesterol flux mechanisms . The similar expression of receptors involved in cholesterol efflux ( SR-BI and SR-BII ) in non-silenced and ATP6V0d2-KD macrophages , and the differences observed in the expression of receptors involved in cholesterol uptake strongly suggest that ATP6V0d2 participates in cholesterol influx . While the precise molecular mechanisms controlling ox-LDL-mediated PV dimensional recovery and parasite killing working in cooperation with ATP6V0d2 remain to be elucidated , a model summarizing our results is presented in Fig 6 . ATP6V0d2-KD macrophages displayed a 40% reduction in intracellular cholesterol levels , suggesting that the d2 subunit participates in cholesterol influx , which impacts the biogenesis of host cell membranes , including the formation of pathogen-containing vacuoles . Replenishment of ATP6V0d2-KD macrophage intracellular cholesterol levels with ox-LDL , modified LDL known to be more readily absorbed by macrophages compared with native LDL [42] , partially reconstituted PV enlargement in parallel with parasite killing . The smaller the volume of PVs , the more ox-LDL is retained in these compartments , suggesting that as pathogen-containing vacuoles expand in volume , exogenous modified LDL internalized by macrophages are filtered out from or diluted within PVs . In this scenario , we speculate that , rather than induce an inflammatory cytokine microenvironment ultimately beneficial to the parasite [49 , 75] , the uptake of ox-LDL at the concentrations employed may induce the intracellular accumulation of oxygen radicals [76] , oxidized phospholipids [77] and cholesterol crystals [71] . These compounds could access the parasites , and the potential anti-parasitic effects would be controlled by ATP6V0d2 . The hypothesis that ATP6V0d2 induced by parasites during inflammation would , at the PV membrane level , restrict the access of LDL-derived components potentially toxic to intracellular parasites is in line with the demonstration that Leishmania does not have de novo cholesterol synthesis [78] . Furthermore , similar to other protozoan parasites , such as Toxoplasma gondii , Trypanosoma cruzi and Cryptosporidium parvum [79 , 80] , the parasite is able to salvage and incorporate host cell cholesterol through endocytosis of LDL [81 , 82] . Importantly , L . mexicana is able to sequester host cell cholesterol directly from the large PV membrane built from exogenous LDL-derived components [83] . Therefore , PVs reconstituted in size by ox-LDL-mediated cholesterol influx in ATP6V0d2-KD macrophages ( but not in nonsilenced macrophages ) would be built up from ox-LDL-derived components potentially absorbed by the parasite , leading to parasite killing . ATP6V0d2 would participate in the selective features of Leishmania PV biogenesis , sparing the parasite from contacting and incorporating inflammation-derived toxic macrophage cargo . This ATP6V0d2-mediated PV selectivity for ox-LDL-derived components could play an important role in vivo: Leishmania parasites developing large PVs are clinically associated with persistent diffuse granulomatous lesions in humans ( diffuse cutaneous leishmaniasis ) , causing chronic damage to skin deep tissues despite only moderate inflammation in terms of NOS2 and IFN-γ expression compared to other disease manifestations [84 , 85] . This context of persistent inflammation may favor the oxidative damage of proteins and lipids , resulting in oxidation and accumulation of modified LDL in tissues [48 , 86] , thus promoting an environment in which the ATP6V0d2-mediated selective PV biogenesis would account for Leishmania intracellular persistence . Therefore , ATP6V0d2 interference represents an unexplored therapeutic target for chronic diseases caused by inflammation-resistant intracellular pathogens . Altogether , our results demonstrate that host macrophage V-ATPase functions can be subverted by the intracellular protozoan parasite L . amazonensis , thus establishing an intracellular niche in macrophages and allowing parasites to persist despite inflammatory environments .
All experiments involving animal work were conducted under the guidelines approved by the Committee on the Ethics of Animal Experiments of the Institutional Animal Care and Use Committee at the Federal University of Sao Paulo ( CEUA/UNIFESP n° 3398150715 ) in accordance with the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) . Wild-type MHOM/BR/1973/M2269 or DsRed2-transfected MPRO/BR/72/M1841 L . ( L . ) amazonensis amastigote parasites were derived from BALB/c mice footpad lesions and were maintained and obtained as described [87] . RAW 264 . 7 cells ( macrophage-like cells , BALB/c origin and donated by Prof . Michel Rabinovitch , EPM-UNIFESP , São Paulo ) were cultivated in RPMI medium supplemented with 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin , 100 μg/ml streptomycin ( complete medium ) and were incubated at 37°C in a humidified air atmosphere containing 5% CO2 . Macrophages were stably silenced for ATP6V0d2 using GIPZ Lentiviral shRNAi transduction following the manufacturer’s instructions ( Dharmacon , Inc . ) . Efficient transduction was monitored by GFP reporter gene expression . From three oligonucleotides tested ( V2LMM_88448 , V2LMM_194889 and V2LMM_88451 ) , oligonucleotide V2LMM_88451 yielded >90% of ATP6V0d2 silencing , thus providing the preferred model of ATP6V0d2 knock-down ( ATP6V0d2-KD ) macrophages . Nonsilenced macrophage controls are macrophages stably expressing the GFP reporter gene and a nonsilencing shRNA which is processed by the endogenous RNAi pathway but its processed siRNA will not target any mRNA in the mammalian genome . The nonsilencing shRNA sequence is verified to contain no homology to known mammalian genes . Nonsilenced or ATP6V0d2-KD macrophages were cultivated in complete medium supplemented with 10 μg/ml puromycin until intracellular infection experiments . ATP6V0d2 efficient knock-down was confirmed up to 72 hours of intracellular infection or up to 96 hours after puromycin removal . L . amazonensis amastigotes were added to nonsilenced or ATP6V0d2-KD macrophages at a multiplicity of infection ( MOI ) of 20 parasites to 1 macrophage ( 20:1 ) for 6 hours of interaction at 34°C , 5% CO2 . Macrophages were washed with phosphate-buffered saline ( PBS ) for the removal of non-internalized parasites , and complete medium was replenished without puromycin . Infected macrophages were maintained at 34°C , 5% CO2 . The infection index was calculated 72 hours post-infection ( p . i . ) by multiplying the percentage of macrophages containing at least one parasite ( % of infected macrophages ) and the number of parasites per macrophage , as quantified after Giemsa counterstaining performed as described [88] . Macrophages were treated with 20 ng/ml interferon-γ ( IFN-γ ) ( R&D Systems , Inc . ) and 1 μg/ml lipopolysaccharide ( LPS ) ( Sigma-Aldrich Inc . ) overnight and washed out before adding parasites to the macrophage cultures . Macrophages were infected for 24 hours prior to treatment with human high-oxidized low-density lipoprotein ( ox-LDL , Kalen Biomedical , LLC , USA ) diluted in complete medium for an additional 48 hours . Macrophage cultures were then washed with PBS and either incubated for 30 minutes with 200 nM Lysotracker Red DND-99 Invitrogen probe ( for assessment of the volume of parasitophorous vacuoles ) or proceeded to Giemsa staining for assessment of infection index . When indicated , infected macrophages were incubated with 50 μg/ml of fluorescent Dil-ox-LDL ( Invitrogen L34358 ) for 48 hours . Images of paraformaldehyde 4%-fixed ( PFA , Electron Microscopy Sciences ) or live macrophage cultures infected with L . amazonensis were acquired with a Leica SP5 II Tandem Scanner System confocal unit ( Leica Microsystems IR GmbH ) coupled to a microincubator controlling the temperature and CO2 pressure conditions to 34°C , 5% CO2 ( Tokai Hit Co . , Japan ) . Fluorescence and Differential Interference Contrast ( DIC ) were acquired in the resonant scanning mode at 512 x 512 or 1024 x 1024 resolution using the 63× ( HCX PL APO 63×/1 . 40–0 . 60 CS ) or 100× ( HCX PL APO 100×/1 . 44 CORR CS ) immersion oil objectives , z-stacks between 0 . 5 to 0 . 8 μm and hybrid detectors enabled . During live imaging acquisitions , the lasers were adjusted to levels below 5% of laser power , and the duration of z-stacks was reduced to less than 30 seconds per recorded position to minimize phototoxicity . Images were processed by Imaris v . 7 . 4 . 2 software ( Bitplane AG , Andor Technology ) . Cells were stained for 15 minutes with Hoechst 33342 live cell nuclear dye ( Thermo Fisher Scientific Inc . ) as indicated . Macrophages cultivated in ibiTreat-sterile tissue culture-treated HiQ4 multichamber dishes ( ibidi GmbH ) were infected with fluorescent L . amazonensis expressing DsRed2 . These multichamber units allow for acquisition of four different experimental conditions at the same live imaging session , namely , infected nonsilenced or ATP6V0d2-KD macrophages activated or not with IFN-γ/LPS . Macrophage cultures were placed in the microincubator coupled to the confocal unit , and serial images of live , infected macrophages were acquired each 30 minutes during 36 hours in 8 microscopic fields per microchamber . A counting algorithm adapted from previous studies [28] was established using Imaris software as follows: i ) isospots built based on parasite DsRed2 signals allowed for dynamic quantification of parasites per microscopic field during the acquisition period; ii ) isosurfaces built based on macrophage GFP signals allowed for dynamic quantification of macrophages per microscopic field in the same acquisition period; iii ) the ratio between these two variables per microscopic field provided the dynamic quantification of parasites per macrophages in infected cultures . The number of parasites in each analyzed macrophage was graphically represented by a color scale applied to each macrophage isosurface , ranging from cyan ( no parasite ) to magenta ( >8 parasites per macrophage ) . Macrophages cultivated in the HiQ4 multichamber dishes and infected with DsRed2-expressing L . amazonensis for 24 hours were incubated with 200 nM of Lysotracker Red DND-99 probe ( Invitrogen ) for a pulse of 30 min , washed and given fresh medium in the microincubator coupled to the confocal unit . The dynamic measurement of PV volumetric enlargement was performed as described [28] , acquiring 10 microscopic fields per experimental condition . PV volumes in μm3 in each analyzed macrophage were graphically represented by a color scale applied to each PV isosurface , ranging from cyan ( smaller ) to magenta ( larger PV ) . PV volume isosurfaces were also obtained from Dil-ox-LDL fluorescence for correlations between PV size and ox-LDL PV accumulation , using the same methodology . Similar to volume , cell sphericity is a measure obtained from three-dimensional image reconstructions assessed as described [87] . Macrophages cultivated on 13 mm circular coverslips were fixed with 4% PFA in PBS and blocked for 30 minutes with 0 . 25% gelatin , 0 . 1% NaN3 and 0 . 1% saponin PBS solution prior to 1-hour incubation with primary antibodies , including 1:2 ( v/v ) rat anti-LAMP-1 ( Developmental Studies Hybridoma Bank 1D4B ) or 1:1000 ( v/v ) anti-cathepsin D ( Abcam ab75852 ) . Next samples were treated for 1 hour with a 1:100 ( v/v ) solution of anti-rat or anti-rabbit AlexaFluor-568 secondary antibodies ( Invitrogen ) . Samples processed for confocal microscopy were treated for 15 min with 10 μM 4’ , 6-diamindino-2-fenilindol hydrochloride ( DAPI ) to stain macrophages and parasite nuclei . The coverslips were mounted with Dako Fluorescent Mounting Medium ( Dako ) before image acquisition under the confocal unit . Zymosan ( Zymosan A Z-4250 , Sigma-Aldrich Inc . ) were administrated to macrophage cultures for 6 hours ( 50 particles per macrophage ) for generation of 48-hours phagolysosomes used as positive control for VAMP8+ phagosomes , immunostained as described[68] . Samples processed for flow cytometry analysis were centrifuged 300g at 4°C for 5 minutes and incubated with BALB/c mouse serum for 1 hour to block Fc receptors in MACS buffer ( PBS pH 7 . 2 , 0 . 05% BSA , 2 mM EDTA ) . Then , cells were fixed by adding 400 μl of 1% PFA in 100 μl of MACS buffer for 30 minutes , washed and incubated with primary antibodies anti-CD36 ( cat 552544 BD ) 1:40 ( v/v ) , anti-SR-BI ( bs-1186R Bioss ) 1:50 ( v/v ) or anti-SR-BII conjugated with AlexaFluor-647 ( bs-7545R Bioss ) 1:100 ( v/v ) in MACS buffer for 1 hour at 4°C . Fluorescence-coupled secondary antibodies were incubated for additional 1 hour at 4°C and include biotin anti-mouse IgA ( cat 556978 BD ) 1:500 ( v/v ) plus streptavidin-APC ( cat 17-4317-82 eBioscience ) 1:500 ( v/v ) ( for CD36 antibody ) and anti-rabbit AlexaFluor-568 1:100 ( v/v ) ( for SR-BI antibodies ) . Then , cells were washed , centrifuged and resuspended in MACS buffer for analysis on LSR Fortessa cytometer ( BD Biosciences ) . Unstained cells and cells treated with secondary antibodies alone were used as controls . Macrophage lysates were obtained by treating cultures with lysis buffer ( Tris-HCl 50 mM pH 7 . 4 , NaCl 150 mM , EDTA 1 mM , Triton X-100 1% ) supplemented with a protease inhibitor cocktail ( Halt Protease Inhibitor Cocktail , Thermo Fisher Scientific Inc . ) at 4°C for 30 min and processed as described [88] . The membranes were blocked with TBS-Tween 0 . 1% buffer supplemented with 5% bovine serum albumin ( BSA ) for 1 hour . The primary antibodies rabbit anti-ATP6V0d2 ( Sigma-Aldrich Inc . SAB2103220 ) 1:1000 ( v/v ) , rabbit anti-ATP6V0a1 ( Synaptic Systems cat 109 003 ) 1:1000 ( v/v ) , rabbit anti-LAMP-1 ( Cell Signaling 9091S ) 1:1000 ( v/v ) , mouse anti-CD36 ( BD cat 552544 ) 1:1000 ( v/v ) , mouse anti-β-actin ( Cell Signaling 8H10D10 #3700 ) 1:5000 ( v/v ) and rabbit anti-cathepsin D ( Abcam ab75852 ) 1:1000 ( v/v ) were incubated in TBS-Tween 0 . 1% supplemented with 5% bovine serum albumin overnight at 4°C . Anti-rabbit ( A6154 , Sigma-Aldrich Inc . ) and anti-mouse ( Sigma-Aldrich Inc . A4416 ) IgG peroxidase 1:8000 ( v/v ) secondary antibodies were incubated with 5% BSA in TBS-Tween 0 . 1% for 1 hour at room temperature . Biotin anti-mouse IgA ( BD cat 556978 ) 1:8000 and Streptavidin-HRP ( Southern Biotechnology Assoc . Inc cat 7100–05 ) 1:8000 ( v/v ) secondary antibodies were used to detect CD36 and incubated with 5% BSA in TBS-Tween 0 . 1% for 1 hour at room temperature . The membrane images were acquired using ECL Prime reagent ( GE Healthcare Life Sciences ) and analyzed on a UVITEC photodocumentator ( Cleaver Scientific Ltd ) . Protein bands were quantified by densitometry using AlphaEaseFC software 3 . 2 beta version ( Alpha Innotech Corporation , San Leandro , CA , USA ) , and the results are expressed in arbitrary units , which were calculated by integrating the intensity of each pixel over the spot area and normalizing to the gel background . Macrophage messenger RNA ( mRNA ) was obtained and processed for quantitative RT-PCR as described [89] . The following primers for mouse sequences were employed in the RT-PCR analysis: Mus musculus ATPase , H+ transporting , lysosomal V0 subunit D2 ( Atp6v0d2 ) —GenBank ( access number: NM_175406 . 3 ) , Forward: 5'- TGT GTC CCA TTC TTG AGT TTG AGG -3' and Reverse: 5'- AGG GTC TCC CTG TCT TCT TTG CTT -3'; subunit d1 ( NM_013477 . 3 ) , Forward: 5’-ATT GGC CAG GAA GTT GCC ATA AT-3’ and Reverse: 5’-GTC GTT CTT CCC GGA GCT CTA TTT-3’; Arginase 1 ( NM_007482 . 3 ) Forward: 5′-AGC ACT GAG GAA AGC TGG TC- 3′ and Reverse: 5′-CAG ACC GTG GGT TCT TCA CA-3′; Nos2 ( NM_010927 . 4 ) Forward: 5′- AGA GCC ACA GTC CTC TTT GC- 3′ and Reverse: 5′- GCT CCT CTT CCA AGG TGC TT- 3′; Lysosomal trafficking regulator ( NM_010748 . 2 ) Forward: 5´- GCC TGG ATG AAG AAT TTG ATC TGG-3´and Reward: 5´- ATT AGT CCG AGA ACG GGA ATG ACA-3´; Sterol regulatory element binding factor 2 ( Srebf2 ) ( NM_033218 . 1 ) Forward: 5´- ACC AAG CAT GGA GAG GTA GAC ACC-3´ and Reverse: 5´- GGA AGA CAG GAA AGA GAG GGG AAG-3´; CD36 molecule ( NM_001159558 . 1 ) Forward: 5´- GGC TAA ATG AGA CTG GGA CCA TTG-3´ and Reverse: 5´- AAC ATC ACC ACT CCA ATC CCA AGT-3´; Low density lipoprotein receptor ( LDLR ) ( NM_010700 . 3 ) Forward: 5´- AAC CTG AAG AAT GTG GTG GCT CTC-3´and Reverse: 5´- CAT CAG GGC GCT GTA GAT CTT TTT-3´; Lectin-like oxidized low-density lipoprotein receptor-1 ( Lox-1 ) ( NM_138648 . 2 ) Forward: 5’- TCT TTG GGT GGC CAG TTA CTA CAA -3’ and Reverse: 5’-GCC CCT GGT CTT AAA GAA TTG AAA-3’; Scavenger receptor class A ( SRA ) ( NM_031195 . 2 ) Forward: 5’- CTA CAG CAA AGC AAC AGG AGG ACA– 3’ and Reverse: 5’–TGC GCT TGT TCT TCT TTC ACA GAC- 3’ . For all experiments , β-actin and HPRT were used as the endogenous gene . β-actin ( NM_007393 . 5 ) Forward: 5´-GCC TTC CTT CTT GGG TAT GGA ATC-3´ and Reverse: 5´-ACG GAT GTC AAC GTC ACA CTT CAT -3´; HPRT ( NM_013556 . 2 ) Forward: 5´-TCA GTC AAC GGG GGA CAT AAA AGT-3´and Reverse: 5´- ACC ATT TTG GGG CTG TAC TGC TTA-3´ . Gene expression analysis is in accordance with the MIQE guidelines [90] . We present results using two endogenous genes , i . e . β-actin and HPRT , showing that the profile of the results is similar using both endogenous genes ( S7D Fig ) . The efficiency of all the primers used is shown as values of slope , R2 and percentage of efficiency ( S7A and S7B Fig ) . The parameter between the curves of target and endogenous genes of a standard curve is used to calculate the amplification efficiency of the reaction , according to the equation: E = [10 ( -1 / slope ) – 1] x 100 . The standard curve is obtained by linear regression of the Ct amplification ( cycle threshold ) value on the log of the initial cDNA amount . An angular coefficient of the standard curve of -3 . 32 indicates a reaction with 100% efficiency . Reactions are considered efficient when amplification efficiencies of the target and endogenous gene are very close , with a tolerance of ± 10% of variation [91] . The specificity of the qPCR reaction was demonstrated by the melt curves of each gene ( S7C Fig ) . The data were presented as a relative quantification and were calculated using 2−ΔΔCt [92] . To confirm acidification of L . amazonensis PVs , macrophages cultivated in HiQ4 multichamber dishes were infected for 24 h and then incubated for 20 minutes with 200 nM Lysotracker Red DND-99 or 100 μg/ml of Neutral Red dye before direct observation by confocal or bright-field microscopy , respectively . To test the specificity of the Lysotracker lysosomal probe for acidic pH , macrophages were treated with 10 mM ammonium chloride ( NH4Cl ) during probing . To assess phagolysosomal pH , ATP6V0d2-KD or nonsilenced macrophages were cultivated in HiQ4 multichamber plates in the presence of FITC-coated latex beads ( 20 beads per macrophage ) for 24 hours at 34°C , 5% CO2 . Fluorescein fluorescence intensity decreases in direct correlation with acidic pH [33] and we have explored the differences in the excitation maximum of turboGFP ( ex . max = 482 nm ) and FITC ( ex . max = 495 nm ) to specifically detect fluorescence from FITC using Leica hybrid photodetectors ( Leica HyD ) . When excited by a 496 nm laser ( 400 Hz frequency and 10% laser power ) , FITC is detected by Leica HyD 2 . 7 more efficiently then turboGFP using an emission range of 520–537 nm ( S1A Fig ) , allowing us to adjust the voltage ( gain ) of photodetectors to threshold out turboGFP emission ( S1B Fig ) . The raw acquired image of FITC beads are cleared from turboGFP fluorescence overlap ( S1B Fig ) , and the fluorescence intensities per FITC-tagged bead are retrieved ( in arbitrary units generated by Leica system , Fig 1C–1E ) . For each field , a z series of 18 images ( steps ) in resolution of 512 x 512 pixels and an average of 3 scans per line ( line average ) were established . FITC fluorescence intensity per bead was retrieved from bead isospots built using Imaris software as described [28] . This approach was applied to FITC-tagged beads internalized by GFP-expressing non-silenced and ATP6V0d2-KD macrophages incubated in complete medium adjusted to different pH ranging from 6 . 5–5 ( buffered with 15 mM HEPES ) to 4 . 5–3 . 0 ( buffered with 30 mM citrate buffer ) and in the presence of 10 μM of the ionophore nigericin ( Sigma-Aldrich Inc . ) , which will rapidly equilibrate the pH within phagosomes with that of the extracellular medium [5] . A standard curve of pH measurement was then obtained using both non-silenced and ATP6V0d2-KD macrophages ( Fig 1D ) , generating very similar functions positively correlating pH and the FITC fluorescence acquired that validate the method applied in this particular condition ( i . e . , FITC-tagged beads within GFP-expressing cells ) . The mean FITC fluorescence intensities retrieved in each experimental group were applied to the standard curve to obtain phagosomal pH . α-galactosidase and β-glucocerebrosidase activities were determined as described [93 , 94] , with modifications . The determination of the activity of these enzymes is based on its action on the fluorogenic substrate 4-methylumbiliferiferone-D-galactopyranoside/4-methylumbiliferone-D-glucopyranoside ( Sigma-Aldrich Inc . ) , resulting in release of the 4-methylumbiliferone molecule ( 4MU ) and allowing for inference of the enzymatic activity in nmol per mg of protein per hour . Determination of the activity of the lysosomal acid lipase ( LAL ) enzyme in cells was performed as described [95] , with modifications . For this , the fluorogenic substrate 4-methylumbiliferone palmitate ( 4MU palmitate , Santa Cruz Biotechnology ) was used in the presence of an LAL activator , cardiolipin , and an inhibitor , Lalistat ( Sigma-Aldrich Inc . ) , that allows quantification of the enzymatic activity in nmol per mg of protein per hour . Replenishment of macrophage intracellular cholesterol levels was performed as previously described using methyl-β-cyclodextrin/cholesterol complexes [43] , with LDL [41 , 42] or ox-LDL [39 , 41] . Methyl-β-cyclodextrin/cholesterol complexes were obtained by mixing 5 mM cholesterol ( Sigma Aldrich C-8503 ) and 40 mM MβCD ( Sigma Aldrich M-4555 ) in serum-free and non-antibiotic medium ( macrophage-SFM 1X Gibco 12065–074 ) . The solution was subjected to sonication for complete solubilization and incubated under shaking at 37°C overnight . Next , solution was filtered through a 0 . 45 μm filter and used in macrophage cultures . The concentrations of methyl-β-cyclodextrin/cholesterol complexes employed in the study refers to the 5mM cholesterol concentration used for composing the complexes . LDL was generously provided by Dr . Magnus Gidlund and Dr . Henrique Fonseca ( University of São Paulo ) . For intracellular cholesterol measurement , macrophages were lysed with lipid buffer ( 0 . 5 M potassium phosphate pH 7 . 4 , 0 . 25 mM cholic acid and 0 . 5% Triton X-100 ) and sonicated in three high intensity cycles for 10 seconds [96] , and cell lysates were then assessed for cholesterol levels by the Amplex Red Cholesterol Assay Kit ( Thermo Fisher Scientific Inc . ) according to the manufacturer's instructions . The results were normalized by the amount of protein obtained in lysates , as assessed by the Bradford method [97] . Total lipids were obtained from 2 x 107 macrophages as described [98] . Purification of sterols was performed in a 10 x 2 . 5 cm silica gel 60 column ( Merck Millipore ) . Samples were prepared using 10 μL of the sterol fraction ( resuspended in 100 μL of methanol for each 107 cells ) in 2 ml acetonitrile:water ( 3:1 v/v ) solution and infused with a syringe pump at flow rate of 30 μl/minute . The analyses were performed on a triple quadrupole instrument ( model 310 , Varian Inc . /Agilent Technologies ) with atmospheric pressure chemical ionization ( APCI ) source . The data were scanned in the range of 360–450 m/z . Nitrogen was used as nebulizer ( 275 . 8 KPa ) and drying gas ( 68 . 9 KPa ) . Vaporization temperature was set at 300°C with the following conditions: capillary voltage set at 56 V , housing temperature set at 50°C , corona at 1μA and shield at 600 V . Sterol masses were retrieved from values of [M+H–H2O] and sterol abundance was assessed in non-saturated conditions . Data were acquired and analyzed with the Varian Workstation software version MS 6 . 9 and the amount of cholesterol and its precursors was assessed qualitatively comparing nonsilenced and ATP6V0d2-KD macrophages . Nonsilenced and ATP6V0d2-KD macrophages cultivated in 96-well plates were treated or not with different concentrations of ox-LDL for 48 hours . Next , samples were cultivated in a solution of 1 mg/ml 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT , Sigma-Aldrich Inc . ) for 2 hours in 37°C and 5% CO2 . Macrophage supernatant chromogenic reaction was read at 540 nm in a micro ELISA reader ( Multiskan MS–LabSystems , Finland ) . Cytotoxicity was assessed at cellular level by FACS using 1:1000 ( v/v ) of the viability dye eFluor780 ( eBiosciences ) following manufacturer instructions . For cell death positive control , macrophages were first fixed with 4% PFA for 15 minutes and then labeled with viability dye . Nonsilenced and ATP6V0d2-KD macrophages were cultivated in 24-well plates in complete medium stimulated or not with IFN-γ/LPS or ox-LDL for 48 hours . Cell culture supernatants were collected and stored at -80°C until analysis . Nitric oxide concentrations from 25 μl of supernatants were assessed as described [99] using the chemoluminescence reader Nitric Oxide Analyzer ( NOA 208i –Sievers ) . To determine cytokine concentrations , supernatants were loaded with the Milliplex Map Mouse Cytokine/Chemokine Magnetic Bead Panel and Milliplex Map TGFβ1 Single Plex Magnetic Bead Kit ( MCYTOMAG-70K and TGFBMAG-64K-01 , Merck Milipore ) , following the manufacturer’s instructions . Samples were then analyzed by Luminex MAGPIX System 40–072 ( Merck Millipore ) . The NO and cytokine concentrations were normalized according to the macrophage protein lysate concentration , as assessed using the Bradford method . The experiments were repeated independently at least twice using experimental replicates . The results were represented as the means with respective standard errors . Statistical tests were performed by SPSS software ( IBM ) , considering normal ( parametric tests ) or nonnormal distributions ( nonparametric tests ) , and significant differences were indicated by p values below 0 . 05 . Data were normalized by nonsilenced or nontreated controls as indicated . | V-ATPases control acidification and other processes at intracellular vesicles that bacteria and parasites exploit as compartments for replication and immune evasion . We report that the protozoan intracellular parasite Leishmania amazonensis resists inflammatory macrophage immune responses and upregulates an alternative isoform of subunit d of V-ATPase ( ATP6V0d2 ) . Leishmania are still sequestered within acidified parasitophorous vacuoles ( PVs ) in cells lacking ATP6V0d2 , but these PVs do not enlarge in volume , a distinguishing feature of intracellular infection by these parasites . Cholesterol levels in ATP6V0d2-deficient cells are reduced and exogenous cholesterol repletion can restore vacuole size , leading to enhanced parasite killing . This study demonstrates the ATP6V0d2-mediated interplay of macrophage cholesterol retention and control of the biogenesis of large pathogen-containing vacuoles . The study provides grounds for the development of new therapeutic strategies for diseases caused by intracellular pathogens sheltered in host cell compartments . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"methods"
] | [
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"lysosomes",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"intracellular",
"pathogens",
"vacuoles",
"pathogens",
"immunology",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"protozoans",
"signs",
"and",
"symptoms",
"leishmania",
"cellular",
"structures",
"and",
"organelles",
"lipids",
"white",
"blood",
"cells",
"inflammation",
"animal",
"cells",
"immune",
"response",
"cholesterol",
"biochemistry",
"eukaryota",
"diagnostic",
"medicine",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"macrophages",
"organisms"
] | 2019 | ATP6V0d2 controls Leishmania parasitophorous vacuole biogenesis via cholesterol homeostasis |
Pathogenic fungi constitute a growing threat to both plant and animal species on a global scale . Despite a clonal mode of reproduction dominating the population genetic structure of many fungi , putatively asexual species are known to adapt rapidly when confronted by efforts to control their growth and transmission . However , the mechanisms by which adaptive diversity is generated across a clonal background are often poorly understood . We sequenced a global panel of the emergent amphibian pathogen , Batrachochytrium dendrobatidis ( Bd ) , to high depth and characterized rapidly changing features of its genome that we believe hold the key to the worldwide success of this organism . Our analyses show three processes that contribute to the generation of de novo diversity . Firstly , we show that the majority of wild isolates manifest chromosomal copy number variation that changes over short timescales . Secondly , we show that cryptic recombination occurs within all lineages of Bd , leading to large regions of the genome being in linkage equilibrium , and is preferentially associated with classes of genes of known importance for virulence in other pathosystems . Finally , we show that these classes of genes are under directional selection , and that this has predominantly targeted the Global Panzootic Lineage ( BdGPL ) . Our analyses show that Bd manifests an unusually dynamic genome that may have been shaped by its association with the amphibian host . The rates of variation that we document likely explain the high levels of phenotypic variability that have been reported for Bd , and suggests that the dynamic genome of this pathogen has contributed to its success across multiple biomes and host-species .
A diverse cadre of fungi and fungal-like oomycetes have recently taken centre stage as emerging infectious diseases ( EIDs ) owing to their increasing impact on animals , plants and wider ecosystem health [1] . The widespread emergence of this class of pathogens shows that they are able to successfully adapt to infect diverse hosts and ecological niches , suggesting that their genomes are able to respond rapidly to natural selection [1] , [2] . This idea finds widespread support; for example , horizontal transfer of whole chromosomes [3] and accelerated evolution across functional domains in effector genes [4] are associated with rapid host-adaptation and changes in virulence across lineages and species . Maintaining the pool of genetic diversity necessary to respond to selection is facilitated by the ability of fungi to utilise multiple reproductive modes , including cryptic recombination that enables inbreeding , outcrossing , hybridization , and the generation of diversity via parasexual mechanisms [5] . These features are suspected to have contributed to the rise of contemporary fungal EIDs , which play a major role in host population declines across a broad swathe of plant and animal species [1] , [6] , [7] . In recent years , whole genome sequencing has led to the characterization of novel mechanisms driving dynamic genome structure in microbial eukaryotes . In particular , it is increasingly apparent that pathogenic fungi manifest highly plastic genome architecture in the form of variable numbers of individual chromosomes , known as chromosomal copy-number variation ( CCNV ) or aneuploidy . This feature has been identified across the fungal phylum Ascomycota , ranging from Botrytis cinerea [8] , Histoplasma capsulatum [9] , Saccharomyces cerevisiae [10] , Candida albicans [11] and the Basidiomycota Cryptococcus neoformans [12] , [13] , [14] . The mechanism ( s ) generating chromosomal CCNV in fungi are not yet well understood , but are thought to occur as a consequence of nondisjunction following meiotic or mitotic segregation [15] , followed by selection operating to stabilise the chromosomal aneuploidies [13] . Although stress occurring as a consequence of either host response or exposure to antifungal drugs has been linked to a rapid rate of CCNV in Candida [16] , it is currently unclear to what extent this contributes to broader rates of CCNV in fungi . However , dynamic numbers of chromosomes could offer routes to potentially advantageous phenotypic changes via several mechanisms such as over expression of virulence-factors [13] or drug efflux pumps [17] , the maintenance of diversity through homologous recombination [18] , increased rates of mutation and larger effective population sizes [19] , or by purging deleterious mutations through non-disjunction during chromosomal segregation [20] . Thus , CCNV likely represents an important , yet uncharacterized , source of de novo variation and adaptive potential in many fungi and other non-model eukaryote microbial pathogens . A contemporary EID that is gaining substantial notoriety is the aquatic chytrid fungus Batrachochytrium dendrobatidis ( Bd ) , which has so far been identified in over 50 countries worldwide and infecting over 500 species of amphibians [21] ( http://www . bd-maps . net ) . One of the most enigmatic aspects of Bd's population genetic structure has been the low levels of genetic variation identified between globally distributed isolates . However , recent studies have shown the existence of up to five separate lineages [22] , [23] , [24] , one of which is shown to have undergone a worldwide range expansion in the 20th Century . We recently compared the genomic diversity of this ‘Global Panzootic Lineage’ ( BdGPL ) against that of a separate , distantly related ( ∼1 , 000 ybp ) lineage that appears to have originated in South Africa ( named BdCAPE ) , using SOLiD sequencing . BdGPL was found to harbour evidence of historical recombination , manifested as patchily distributed heterozygosity , and phylogenetic incongruency across small spatial scales that we hypothesised has resulted from ongoing recombination [22] . Therefore , despite the lack of any known sexual meiotic mechanisms in its life cycle , Bd clearly has a more dynamic genome than a purely clonal , mitotic mode of reproduction would suggest . Here , we describe a new global panel of isolates that were subjected to high-depth Illumina sequencing in order to better understand cryptic genomic features that are associated with the rapid ascendancy of this pathogen .
Comparing the depth of read coverage over each chromosome using 10 Kb non-overlapping sliding windows revealed CCNV present in isolates belonging to all three lineages of Bd and affecting nine of the largest fifteen supercontigs ( Figs . 1 and S4 ) . t-tests on the mean depths across windows compared with those in the largest supercontig confirmed a significant increase in read-depth across 36 supercontigs , and a significant decrease in depth across 25 supercontigs in 18 of the 22 sequenced isolates ( Fig . S5 ) . To further verify relative ploidy within an isolate and the order of ploidy-changes , we inferred whether individual bases were ‘evenly’- or ‘oddly’-distributed across Illumina reads within a single genome by binning their frequencies into histograms for each chromosome . The expectation here is that a chromosome with an even ploidy will tend towards a 50∶50 distribution across each single SNP , while chromosomes with an odd ploidy will tend towards a 33∶66 or 33∶33∶33 ratio across SNP-calls ( Figs . S5 , S6 ) . This method identified even- or odd-ploidies for 92% of the chromosomes tested with >95% bootstrap support ( Table S2 ) . Thirteen BdGPL and two BdCAPE isolates had greater numbers of bi-alleles than tri-alleles ( corresponding to an even ploidy that most parsimoniously corresponds to diploidy ) ( Table S2 ) , and six isolates belonging to all three separate lineages that had greater numbers of tri-alleles than bi-alleles ( corresponding to an odd ploidy that most parsimoniously corresponds to triploidy ) . The remaining four isolates ( BdGPL JEL423 & MODS27 , BdCAPE SA1d & SA4c ) had significant p-values showing between 1–3 chromosomes in lower ploidy levels relative to the remaining bi-allelic genome . Over these lower-ploidy chromosomes we observed greater numbers of tri-alleles than bi-alleles and no decrease in heterozygous base-calls ( both of which should occur if these chromosomes were haploid ) . We therefore conclude that these four isolates have tetraploid genomes with the identified losses in read-depth corresponding to chromosomes that have lost a single copy and are now trisomic . We were able to take advantage of replicate lines of BdCH , which were passaged for 40 generations with and without exposure to skin antimicrobial peptides collected from the water frog Pelophylax esculentus . In these culture lines , the ancestral putatively triploid isolate ( BdCH ACON ) differentially lost and gained copies of supercontig IV and V respectively when passaged without selection ( BdCH CON2A ) , and gained a copy of supercontig V following treatment with antimicrobial peptide ( BdCH APEP ) , which resulted in a significant reduction in mean growth inhibition ( Text S1: In vitro Divergence of Independent Replicate Lines of BdCH; Fig . 2 ) . Due to the fact that most of our isolates exhibiting CCNV were sequenced shortly following isolation from nature without sequential passage , we know that CCNV is occurring frequently in both wild and cultured isolates . The rapidity that these mutations are accumulating across our isolates shows that aneuploidies in Bd are occurring at rates that will generate genome diversity within the timescale of a single host infection . In order to detect the presence and frequency of recombination events we determined the phase of bi-allelic heterozygous polymorphisms ( Table S1 , Figs . S7 , S8 , S9 ) . We focused our attention on SNPs that were supported by a high percent of uniquely mapped reads ( Table S1 ) and reads agreeing with the phasing ( Fig . S9 ) . By performing pairwise comparisons of shared phased positions between each of our isolates , we found >99% of these sites remained in the same phase for intra-lineage comparisons and >92% for inter-lineage comparisons ( Fig . S10 ) . However , we also identified 4 , 974 haplotypes demonstrating crossovers ( Fig . S11 ) where all four pairwise combinations of bases were observed . Of these , 2 , 007 occurred at unique positions/loci in the genome . Every pair of isolates that we compared ( except between BdGPL isolates MAD ( FR ) and AUL ( FR ) ) showed at least one haplotype that included an inferred crossover ( Fig . S11 ) . This was surprising given many of the isolates share a very recent common ancestor . For instance , we found that two isolates ( MODS27 and MODS28 ) which were recovered from Discoglossus sardus at a single site in Sardinia on a single collection trip and are closely related ( Fig . S2 ) had accumulated three crossovers . This shows that recombinant genotypes can accumulate even within highly-related free-living populations of Bd , a feature of this chytrid's population genetics that was first remarked upon by Morgan et al . in populations of Sierra Nevadan Rana muscosa [28] . The greatest proportion of phased positions demonstrating crossovers were found to occur between the three lineages , demonstrating an accumulation of recombinant haplotypes that scales with time of divergence ( Fig . S11 ) . For example , as many as 7 . 3% of the phased positions revealed crossovers that have accumulated since isolates BdGPL AP15 ( IT ) and BdCH ACON ( CH ) were separated . Crossovers were identified in every major chromosome , and predominantly identified in intergenic regions ( 143 Mb−1 compared with 57 Mb−1 for coding regions and 65 Mb−1 for introns ) ( Figs . 3A , S12 , S13 ) . Crossovers were also found to occur with a higher frequency amongst isolates belonging to the lineages BdCAPE and BdCH ( between 0 . 6 and 1 . 1% of phased positions , respectively ) compared with 0–0 . 2% in BdGPL . This was a surprising finding given that the three BdCH isolates were separated by only 40 passages in the lab and were derived from a single isolate that had been relatively recently isolated in 2007 . This suggests two hypotheses: Either in vitro passage under selective conditions promotes rapid recombination , or our isolate of BdCH is descended from a population of Bd that is more recombinogenic than BdGPL . To further study the amount of recombination within lineages and between isolates , we extracted haplotypes that were phased across all of the isolates within a given lineage and contained at least two alleles per loci ( ranging in length from 11 nt to 33 . 3 Kb: Fig . S12 ) . Because only 35 haplotypes were retained for the entire panel of BdGPL isolates , we also extracted haplotypes from two BdGPL subsets consisting of 3 and 5 isolates respectively , thus allowing higher numbers of crossovers to be retained . From each of these sets of haplotypes , we calculated a multilocus measure of linkage disequilibrium ( the standardised index of association rBarD [29] ) and applied Hudson's four-gamete test [30] in order to quantify the amount of recombination amongst isolates within each lineage ( Table 2 ) . Across the BdGPL groups , >30% of phased positions were in significant disequilibrium compared with 16% and 11% for BdCH and BdCAPE respectively . RbarD appeared to be robust against sample size differences , and gave values from BdGPL values of 0 . 79–0 . 82 compared against 0 . 58 and 0 . 61 for BdCH and BdCAPE . Finally , a smaller proportion of BdGPL subset haplotypes failed the four-gamete test compared with BdCAPE or BdCH isolates . Each of these findings shows that recombination is causing diversity within each of the lineages . However , the emergent BdGPL is far more clonal than either of the other two lineages . We next investigated whether recombination had occurred between these three lineages since their divergence , by calculating θ , Weir's [31] formulation of Wright's fixation index ( FST ) for pairwise comparisons of each lineage across window lengths of 1 . 4 Kb and 10 Kb ( Figs . S14 and 3B ) . We found that all three lineages were highly differentiated from one another across each chromosome , with only minor intra-chromosomal regions of high similarity ( which mainly comprised a long stretch of rDNA located at the start of chromosome 14 ) . This indicates that recombination amongst these lineages has not occurred since their separation . We then determined whether certain categories of genes were associated with higher-than-average rates of recombination using t-tests on numbers of crossovers after accounting for differing levels of heterozygosity and density of phased-sequences ( Text S1: Identifying gene groups and names; Table S3 , Figs . 4 and S15 , S16 ) . Surprisingly , we found only one group showing significant enrichment for crossovers: those showing homology to the C-terminal of the Crinkler ( CRN ) family of oomycete effector proteins found in the Phytophthora genus [32] , [33] . Enrichment was found in both BdGPL and BdCAPE , whilst not in BdCH . Haplotypes that failed the four-gamete test were predominantly from coding-regions , but had no clear pattern of enrichment for any gene category ( Table S4 ) . To identify genes that are present in the reference sequence and absent in our panel of isolates ( presence/absence polymorphism ) , we examined the read-depth across each of the genes . Only five genes were identified from our panel ( Table S5 ) , including three amongst BdCAPE isolates and two amongst BdCH isolates . Therefore , whilst high-levels of aneuploidy are occurring , it does not appear to be resulting in frequent gene loss . To study the patterns of mutation across the nuclear genome , we categorized each of the mutations by their location in the genome in terms of coding regions ( CDS ) , introns and intergenic regions ( Table S6 ) . In every isolate we sequenced , every variant type was found in greater abundance per kilobase in the non-coding regions ( with the exception of 0 . 01 Kb−1 fewer heterozygous positions in the introns compared with the CDS for isolate MG1 ) . This overall pattern can be explained through selection purging deleterious mutations from the CDS . In addition , we found homozygous polymorphisms to be highly supported in all lineages in terms of uniquely mapped reads , whilst un-phased bi-allelic heterozygous positions had a smaller total proportion in the divergent lineages compared with BdGPL , suggesting some heterozygous positions may be miscalled due to paralogs . We categorised each of the mutations within the CDS into synonymous and non-synonymous mutations ( Table S6 ) . SNPs were responsible for 169 , 000 synonymous changes and 197 , 000 non-synonymous changes . Genes with putative roles in pathogenicity were grouped by searching for secretion signals , protease domains and carbohydrate binding domains ( Text S1: Identifying gene groups and names ) , and tested each of these for enrichment of homozygous SNPs ( Tables S7 and S8 ) and heterozygous positions ( Table S9 ) using hypergeometric tests . We found that gene groups that carried a secretion signal ( proteases , chitin-binding and uncharacterized secreted ) as well as CRN-like genes , were significantly enriched for both homozygous and heterozygous polymorphisms relative to the whole set of genes . Predicted chitin-binding proteins that lacked a secretion peptide were not enriched for either homozygous SNPs or heterozygous positions ( Tables S7 and S9 ) , and non-secreted proteases were only enriched for synonymous amino acid changes . Conversely , CRN-like genes are only enriched for non-synonymous homozygous SNPs and not synonymous SNPs . We next measured the rates of synonymous substitution ( dS ) , non-synonymous substitution ( dN ) and omega ( dN/dS = ω ) for every gene in every isolate and compared values by grouping isolates into their lineages ( Fig . 4 , Table S10 ) . In total , we identified 1 , 450 genes with ω≥1 in at least one of our isolates ( BdCAPE = 816; BdCH = 746; BdGPL = 283 ) , suggesting positive or diversifying selection . Although no clear pattern could be distinguished within BdGPL ( Fig . S17 ) owing to the high degree of relatedness amongst isolates and thus relative paucity of polymorphism , CRN-like genes in both BdCAPE and BdCH had the greatest median , upper quartile and upper tail values of omega ( Fig . S18 ) . In addition , average ω values for secreted chitin-associated genes and proteases were marginally higher than their non-secreted counter parts . Uncharacterized secreted genes also had a greater ω than either of those non-secreted gene groups . Finally , a significant enrichment of both CRN-like genes and uncharacterized ( secreted ) genes with ω≥1 were identified in both BdCAPE and BdCH ( Table S11 ) . By analysing each of these 1 , 450 genes with ω≥1using branch site models ( BSM ) in PAML along each of the three lineages of Bd , we identified a subset of 482 genes that show evidence for positive selection in at least one of the lineages . For BdCAPE and BdCH , a greater percent of each of the secreted gene categories were found to have accumulated an excess of non-synonymous mutations compared with their non-secreted counterpart gene categories ( Table S11 ) . Nine genes were also identified in all three lineages ( Fig . S19 ) , including four uncharacterized secreted and five uncharacterised non-secreted genes . However , the most striking finding of this analysis was found among BdGPL isolates where 349/482 ( 72% ) of the genes showed a signature of positive selection compared with only 23% for each of the other two lineages . This finding suggests that BdGPL has been undergoing greater levels of positive selection than either BdCAPE or BdCH , despite the low numbers of sites under selection owing to the high levels of relatedness within this lineage .
Recent studies have attributed aspects of Bd's pathogenesis to the presence of a number of putative virulence factors that include proteases and chitin-binding proteins [32] , [33] , [34] . The former category contain M36 or S41 domains that are thought to degrade host-cellular components , and these protease families are known to have undergone extensive expansions in Bd since its divergence from free-living saprobes such as Homolaphlyctis polyrhiza [32] . Chitin binding proteins are thought to be involved in pathogenesis by allowing Bd to bind to keratinized host cells and to subsequently enter the host cells [34] . To date , the functional nature of the crinkler-like family in Bd has only been inferred owing to their homology to host-translocated proteins of known virulence in oomycetes [33] . Our data show that , across this global panel of 22 isolates and three lineages , the secretome and crinkler-like family of genes manifest higher diversity of homozygous and heterozygous SNPs , enrichment for non-synonymous mutations and greater dN/dS ( ϖ ) ratios when compared against classes of genes that do not contain a signal peptide . This shows that these gene families are evolving most rapidly in Bd , and that gene-products that interact with the amphibian host are undergoing diversifying ( or reduced purifying ) selection when compared with those gene-products that remain intracellular . Our findings suggest that Bd has had an evolutionary association with amphibians that predates the radiation of the lineages that we have characterised here , and is further evidence that this chytrid has an obligate rather than an opportunistic association with its amphibian hosts . By mapping read-depth and SNPs across these genomes , we discovered that widespread genomic variation occurs within and amongst Bd isolates from the level of SNPs up to heterogeneity in ploidy amongst genomes and amongst chromosomes within a single genome . Individuals from all three lineages harboured CCNV along with predominantly or even entirely diploid , triploid and tetraploid genomes . Recent research by Rosenblum et al . [35] has also identified widespread CCNV across diverse lineages of Bd recovered largely from infected amphibians in the Americas , including a single haploid chromosome in isolate BdGPL JEL289 . This variation may itself , reflect only part of the full diversity in Bd , pathogensas +2/+3 shifts in ploidy , whole genomes in tetraploid , or chromosomes in pentaploid or greater , may occur and await discovery . Chromosomal genotype was shown to be highly plastic as significant changes in CCNV occurred in as few as 40 generations in culture . It is not known whether other chytrid species also undergo CCNV , or if this is a unique feature of Bd and hence may be intrinsic to its parasitic mode of life . Currently , CCNV is known to occur in a variety of protist microbial pathogens , including fungi , however it is currently not known whether this genomic-feature is specific to a parasitic life-style , or is a more general feature of eukaryote microbes; identifying the ubiquity of CCNV or otherwise across nonpathogenic species will therefore be of great interest . Further , the manner in which the plasticity of CCNV in Bd affects patterns of global transcription and hence the phenotype of each isolate also remains to be studied . However , it is clear from research on yeast , Candida and Cryptococcus , that CCNV significantly contributes to generating altered transcriptomic profiles , phenotypic diversity and rates of adaptive evolution even in the face of quantifiable costs; understanding the relationship between CCNV and Bd-phenotype will therefore likely be key to understanding its patterns of evolution at both micro- and macro-scales . Whilst differing numbers of individual chromosomes presents a potential barrier to the standard model of meiosis , homologous recombination may still be occurring via mitotic processes within compatible genomes . In order to study recombination amongst our isolates , we determined the phase of our reads and constructed haplotypes that were suitable for traditional population genetic tests . This showed that , whilst the majority of the genomes from all three lineages manifest widespread linkage disequilibrium , recombination could still be detected across each chromosome and in all genomes . Crossovers ( measured both as the proportion of SNPs that change phase and the numbers of haplotypes failing Hudson's four-gamete test ) were found to occur much more frequently within the BdCAPE and BdCH lineages compared to BdGPL , and these two lineages accordingly manifest lower average linkage disequilibrium . All of the BdCH genomes that we sequenced stem from a single isolate collected in 2007 . This suggests that either the high rates seen here have accrued since the isolate was taken into culture ( suggesting a very rapid rate of in vitro recombination ) , or that we are characterising recombination events that occurred prior to the isolation of BdCH and are segregating as a consequence of the multiple-ploidy nature of Bd . In support of the latter hypothesis , comparisons between population-level data for BdGPL and BdCAPE show that BdGPL is far less recombinogenic and has been undergoing a largely clonal expansion since its emergence , consistent with previous observations made by James et al . [36] . These data suggests that the global BdGPL population is derived from a less recombinogenic ancestor than either BdCH or BdCAPE , that contemporary recombination is not occurring at a rapid rate and , where it occurs , is the result of a selfing rather than outcrossing events . The discovery of a lower proportion of variable sites across haplotypes in addition to the lower proportion of heterozygous positions in BdGPL compared against BdCAPE or BdCH does not support the notion that BdGPL is an outbred hybrid lineage as previously proposed [22] . The discovery of a new Bd lineage found in Brazil ( BdBrazil ) along with an isolate that is a likely BdGPL/BdBrazil recombinant [24] strongly implies that Bd retains the ability to outcross , despite having a primarily clonal genome and life cycle . However , values of FST across our dataset show no introgression between the three lineages; this demonstrates that they have remained largely separate since their divergence and suggests that outcrossing between lineages of Bd is rare or , if it has occurs , remains spatially restricted . Further broad-scale collections of isolates and extension of our comparative-population genomic analyses will allow the assignment of more accurate rates of introgression across evolutionary timescales . We show that rates of recombination are uneven across the genome , with CRN-like genes enriched for crossovers , suggests that either CRN-like genes might have features that favour recombination or that recombinants of these genes have a fitness advantage and are thus more likely to reach fixation than recombinants at other locations in the genome . CRNs were also enriched for non-synonymous polymorphisms , are characterised by a signal of directional selection , and are amongst the most polymorphic genes in Bd's genome . Within the oomycete genus Phytophthora , CRNs manifest diverse carboxy-terminal domains and high rates of homologous recombination targeted to the conserved HVLVXXP motif , suggesting that the mosaic domains of CRNs are being shuffled by recombination [2] . Recently , a number of Bd CRNs have been shown to be highly expressed on host tissue in vitro [37] . Therefore , whilst these genes in Bd lack a secretion signal , their expression , accumulation of genetic variation in terms of recombination and ϖ values , and similarities with oomycete CRNs strongly suggest that a number of these CRN-like genes are functional in Bd . However , whether they contribute directly or indirectly to the virulence of Bd remains to be determined . Our demonstration of multiple hierarchies of cryptic genomic variation in Bd in terms of CCNV , ubiquitous and potentially targeted recombination , and natural selection , points to an ability to generate diversity without the necessity of an obligate sexual stage . Our study has uncovered high levels of genotypic plasticity that are likely to cause widespread phenotypic plasticity even without the need to invoke outcrossing . These large and small-scale changes are therefore likely to contribute to rapid evolutionary rates in the face of an effective host response . Such ‘genomic instability’ may explain the diverse phenotypic responses observed in Bd [38] , and may also explain the enormous diversity of hosts and biomes that this generalist pathogen has managed to infect .
Full details are given in Text S1 , Supplemental Materials and Methods . Briefly , twenty-two isolates that had been collected from nine countries and four continents were chosen for sequencing ( Table 1 ) . Paired-end Libraries were constructed according to the protocols provided by Illumina sequencing ( Truseq kit ) . The genome sequence and feature file for the chytrid fungus Batrachochytrium dendrobatidis ( Bd ) strain JEL423 was downloaded from http://www . broadinstitute . org/ ( GenBank project accession number AATT00000000 ) . The feature file for JEL423 had all but the longest splice variants removed for each gene leaving 8794/8819 genes . We aligned our reads to the genome sequence using Burrows-Wheeler Aligner ( BWA ) v0 . 5 . 9 [26] with default parameters , converted to Samtools mpileup format using SAMtools v . 0 . 1 . 18 [39] and polymorphisms called using the Binomial SNP-Caller from Pileup ( BiSCaP ) v0 . 11 [27] . For phylogenetic analysis we extracted polymorphisms covered ≥4 reads in all 22 isolates . FASTA files were converted into Nexus files and trees constructed using the Un-weighted Pair Group Method with Arithmetic Mean ( UPGMA ) algorithm in PAUP and visualised using Figtree [40] ( Fig . S3 ) . Gene groups were identified using gene-annotations , blastx searches ( 1e−05 e-value cut-off ) to the non-redundant BLAST database , SignalP3 . 0 [41] , Merops [42] and Procarb604 v1 [43] . Chromosome copy number variation ( CCNV ) was identified using changes in both depth of coverage and percent of reads specifying two most frequent alleles at any locus . To quantify these changes , we first performed t-tests ( with a cut-off of p<5−10 ) on the mean depths across the largest supercontig ( supercontig 1 ) against each subsequent supercontig for each isolate ( Fig . S5 ) . Next , we calculated the percent of reads specifying the two most frequent alleles ( Fig . S7 ) for each chromosome in each isolate separately using a minimum depth of 4 reads for both alleles and binned values falling between 47–53% ( expected even ploidy/bi-allelic ) and 30–36% and 63–69% ( expected odd ploidy/tri-allelic ) . To account for depth and mutation variation within a chromosome , we performed 1000 bootstraps for either predominance of bi-allelic or tri-allelic peaks ( Table S2 ) . Using a 5% cut-off ( 5%<x<95% ) we found 305/330 largest 15 chromosomes gave confident odd or even allelic peaks and was largely concordant with changes predicted by t-tests . To detect recombination , we identified haplotypes using reads that overlapped two or more bi-allelic heterozygous positions . Haplotypes from each isolate were then compared to haplotypes in other isolates . We also calculated the Index of association ( IA ) , detecting linkage disequilibrium for a given set of haplotypes if VD>L ( Lold ) . We also calculated rBarD values and performed 4 gamete tests between every combination of loci in a haplotype ( Fig . S9 ) to quantify the amount of recombination occurring within populations . In addition , we applied Weir's [30] estimator of Wright's Fixation Index ( FST ) according to the equations given in Multilocus 1 . 3 [29] . For selection , we used the yn00 and codeml programs of PAML [44] implementing the Yang and Nielsen method [45] on every gene in every isolate and those with ω≥1 respectively . For codeml , we used the Branch site model ( BSM ) A ( model = 2 , NSsites = 2 , fix_omega = 0 ) compared with the null model ( model = 2 , NSsites = 2 , fix_omega = 1 , omega = 1 ) . Next , we calculated 2 * the log likelihood difference between the two compared models ( 2D′ ) with two degrees of freedom , and identified any with values greater than 8 . 1887 and 11 . 4076 ( 5% and 1% significance after Bonferroni correction ) . Enrichment for crossovers and polymorphisms was detected using hypergeometric tests and t-tests . For in vitro divergence , an isolate of B . dendrobatidis from a Swiss Alytes obstetricans ( isolate 0739 ) was subcultured into control ( ACON ) and peptide-treated ( APEP ) culture flasks containing 10 ml 1% tryptone media supplemented with 1% penicillin-streptomycin ( Sigma ) to reduce the risk of bacterial contamination . Cultures were incubated at 18°C and passaged every 4–5 d by scraping the side of the flask and transferring 1 ml into 9 ml fresh media . Peptide-treatment included addition to the media of 80 µg ml-1 skin defense peptides collected from Pelophylax esculentus ( n = 15 combined ) according to Daum et al . ( 2012 ) [46] . This was equivalent to the IC50 , or the concentration at which growth of Bd was inhibited by 50% . | Pathogenic fungi constitute a growing threat to both plant and animal species on a global scale . However , many features of the fungal genome that enable them to successfully adapt to infect diverse hosts and ecological niches remain cryptic , especially for newly evolved emerging lineages . In this paper , we report three novel features of genome diversity linked to pathogenicity in the emerging amphibian pathogen , Batrachochytrium dendrobatidis ( Bd ) . Firstly , we identified widespread chromosome copy number variation ( CCNV ) across our lineages , with individual isolates harboring between 2 to 5 copies of each chromosome and rapid rates of CCNV occurring in culture . In addition , by using in vitro divergence of replicate lines of Bd , we showed that changes in ploidy can occur within as few as 40 generations . Secondly , we identified uneven rates of recombination across the genomes and lineages , revealing hot spots in known classes of virulence factors . Finally we identified significant evidence of diversifying selection across the secretome of Bd , and showed that selection also targets putative virulence factors . These findings add to our knowledge of genome-dynamicity and modes of evolution manifested by eukaryote microbial pathogens , and may explain the varied phenotypic responses observed in Bd . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"mutation",
"haplotypes",
"genome",
"evolution",
"natural",
"selection",
"genetics",
"population",
"genetics",
"ploidy",
"biology",
"genomics",
"evolutionary",
"biology",
"genomic",
"evolution",
"comparative",
"genomics"
] | 2013 | Chromosomal Copy Number Variation, Selection and Uneven Rates of Recombination Reveal Cryptic Genome Diversity Linked to Pathogenicity |
To date , Alphavirus infections and their most prominent member , chikungunya fever , a viral disease which first became apparent in Tanzania in 1953 , have been very little investigated in regions without epidemic occurrence . Few data exist on burden of disease and socio-economic and environmental covariates disposing to infection . A cross-sectional seroprevalence study was undertaken in 1 , 215 persons from Mbeya region , South-Western Tanzania , to determine the seroprevalence of anti-Alphavirus IgG antibodies , and to investigate associated risk factors . 18% of 1 , 215 samples were positive for Alphavirus IgG . Seropositivity was associated with participant age , low to intermediate elevation , flat terrain and with IgG positivity for Rift Valley fever , Flaviviridae , and rickettsiae of the spotted fever group . When comparing the geographical distribution of Alphavirus seropositivity to that of Rift Valley fever , it was obvious that Alphaviruses had spread more widely throughout the study area , while Rift Valley fever was concentrated along the shore of Lake Malawi . Alphavirus infections may contribute significantly to the febrile disease burden in the study area , and are associated with several arthropod-borne infections . Their spread seems only limited by factors affecting mosquitoes , and seems less restricted than that of Rift Valley fever .
Alphaviruses form a genus in the family Togaviridae . About 40 different viruses including type and sub-type viruses are members of the genus . Among them are major human pathogens such as chikungunya virus ( CHIKV ) and viruses of veterinary importance , e . g . Venezuelan equine encephalitis virus . The currently most important Alphavirus of human pathogenicity is CHIKV , which causes significant morbidity and economic losses [1] . Although it has been described and isolated first in 1953 from a febrile person in Tanzania , East Africa [2] , currently only few data on the distribution and medical importance of CHIKV and other Alphaviruses in Africa are available . Since the 1960s , especially CHIKV was repeatedly isolated throughout African and Asian countries [3] , and small outbreaks were frequently reported . The virus gained notoriety , when in the years 2004–2007 an outbreak was noticed of so far unknown dimension . Starting in Kenya , a severe epidemic hit the islands of the Indian Ocean in 2005/2006 , with nearly 280 . 000 people infected on the island of La Reunion alone [1] , [4] , [5] . Transmission to the Indian sub-continent resulted in chikungunya fever in an estimated 1 . 3 million people [6] . The enormous scientific interest in this outbreak led to several new findings concerning viral molecular biology and ecology [3] , [7]–[9] . Investigations regarding the climatic conditions before the outbreak revealed unusual warm and dry conditions along the Kenyan coast in 2004 [10] , [11] . Infrequent replenishment of domestic water stores due to these dry conditions may have facilitated the transmission of the virus . Despite this increased research interest , the role of CHIKV as well as other Alphaviruses in endemic regions , especially in sub-Saharan Africa , remains unclear . Recent studies concentrated mainly on areas of the latest CHIKV pandemic . The disease burden and the epidemiology in local populations not affected by the devastating outbreak in 2004–2007 is largely unknown . In a small study in Guinea , arboviruses as causative agent for febrile disease were identified by neutralization assays in 63% of 47 patients [12] . 17% of these had acute CHIKV infections . In a clinical study conducted in Northern Tanzania with 870 febrile patients , PCR-confirmed acute CHIKV infections were diagnosed in 7 . 9% of all cases [13] . However , surveillance of other Alphaviruses is even less developed as most of these studies are targeting CHIKV using PCR . A serosurvey in rural Kenya revealed a seropositivity prevalence of 34% for anti-Alphavirus IgG , which was not associated with age , indicating frequently occurring smaller epidemics rather than endemic cycling [14] . Although CHIKV is expected as the main pathogen , other Alphaviruses cannot be excluded since a broadly cross-reactive ELISA was used . With the recent outbreak of CHIKV in Italy , and detection of autochthonous transmission in southern France , it is clear that Alphaviruses and especially CHIKV have the potential to become endemic in areas in Europe where Aedes albopictus is already established [15] , [16] . In this study we aimed to assess the epidemiological patterns of Alphavirus infections in the Mbeya Region in Tanzania , by measuring seroprevalence in 1215 individuals participating in an epidemiological survey in the Region . This region was not affected by the 2004–2007 outbreak , and diagnosis or laboratory verification of acute chikungunya fever or other Alphavirus infection does not occur locally . The survey gave us the opportunity to study the role of this pathogen genus and its dependence on certain social and ecological factors in an endemic transmission cycle in a typical local setting .
Both EMINI and this sub-study were approved by local and national Tanzanian ethics committees . Each EMINI participant had provided written informed consent before enrolment , with parents consenting for their children . The EMINI population survey had the objective to create the infrastructure to Evaluate and Monitor the Impact of New Interventions in the Mbeya Region of south-western Tanzania . Financed by the European Union over five years ( 2006 to 2011 ) , the strengthening of the local health infrastructure and the establishment of a cohort which could be followed up on an annual basis created a platform on which the impact of improved health care infrastructure and new interventions could be monitored and evaluated . Embedded into the EMINI project were several focused studies such as this sub-study , which determined seroprevalences for a number of tropical arthropod-borne diseases . In preparation of the EMINI survey , a census of the entire population in nine geographically distinct and ecologically different sites of the Mbeya Region was carried out . Study sites were selected to reflect the wide range of different conditions within the region in terms of elevation , population density and development ( urban versus rural ) . Basic information regarding the households and their inhabitants was collected and all household positions were recorded with handheld GPS receivers . Ten percent of the surveyed households were then chosen by geographically stratified random selection for inclusion into the EMINI survey , to obtain a representative sample of the population from each site . The resulting EMINI cohort included all consenting participants of 4 , 283 households . Over the following five years annual visits at the same time of the year were conducted , during which structured interviews with all household members were performed , and blood , urine and stool specimens collected . For this sub-study , we stratified the 17 , 872 participants , who had provided a blood sample during the second EMINI survey in 2007/2008 , by age , gender , altitude of residence and ownership of domestic mammals . To be able to assess factors of interest that were identified in the literature but might have been underrepresented in the study population , we employed disproportionate random sampling with equal participant numbers for each stratum to identify 1 . 226 samples from participants above the age of 5 years to be tested for IgG antibodies against Alphaviruses and other tropical arthropod-borne diseases . To characterize the socio-economic situation of each household , the head of each household was asked for the following information during each annual EMINI visit: Presence/absence of different items in the household ( clock or watch , radio , television , mobile telephone , refrigerator , hand cart , bicycle , motor cycle , car , savings account ) , sources of energy and drinking water , materials used to build the main house , number of persons per room in the household and availability and type of latrine used . Based on the provided information , a socio-economic-status ( SES ) score was established , using a modified method originally proposed by Filmer and Pritchett which has frequently been employed to characterize the SES of people living in developing countries [17]–[19] . Population- and livestock-densities were calculated using data and household positions collected during the initial population census . Elevation data were retrieved from the NASA Shuttle Radar Topography Mission ( SRTM ) global digital elevation model , version 2 . 1 [20] , [21] . Land surface temperature ( LST ) and vegetation cover ( EVI = enhanced vegetation index ) data for the years 2003 to 2008 were retrieved from NASA's Moderate-resolution Imaging Spectroradiometer ( MODIS ) Terra mission which “are distributed by the Land Processes Distributed Active Archive Center ( LP DAAC ) , located at the U . S . Geological Survey ( USGS ) Earth Resources Observation and Science ( EROS ) Center ( lpdaac . usgs . gov ) ” [22] . These data were used to produce long-term averages of day and night LST and EVI . Population- , household- , and livestock-densities , LST , EVI , and elevation data were averaged for a buffer area within 1000 meter radius around each household in order to characterize the ecological situation around the household . This approach was preferred to using the respective spot values at the household position , because spot data are more prone to random error than averages for a wider area . Detection of anti-Alphavirus IgG , anti-Yellow fever virus IgG , anti-dengue 1–4 virus IgG , and anti-West Nile virus IgG on bio-banked samples were performed as described previously for Rift Valley fever virus ( RVfV ) IgG [23] . A commercially available biochip ( Euroimmun , Lübeck , Germany ) , containing infected and non-infected Vero E6 cells or only non-infected Vero E6 cells ( negative control ) , was used for indirect immunfluorescence testing ( IIFT ) , following a standard protocol . All serum samples were heat-inactivated and diluted tenfold prior to testing . Further dilutions of positive sera were carried out in the range of 1∶20 to 1∶640 . A rabbit anti-human IgG FITC-labelled antibody ( DAKO , Hamburg , Germany ) served as conjugate . To decrease the known subjectivity of reading IIFT results to the best objective level , fluorescence microscopy was carried out independently by two experienced observers . In case of discrepancies ( positive vs . negative; difference >1 titer step ) the testing was repeated . A sample was classified as positive , if at a screening dilution of 1∶20 a typical fine granular cytoplasmatic fluorescence was detected in around 20% of the cells on the positive field of the biochip with a typical location and morphology of infected cells , while no signal was detectable in the negative field . IIFT was repeated in case of indeterminate results , i . e . in cases where samples differed clearly from the negative control but did not match the criterion “positive” . Ultimately , 1 , 215 definitive results were available from the selected samples . Fresh EDTA-blood was used for malaria testing using a rapid test ( ICT , South Africa ) for each participant . Stata statistics software ( version 12 , Statacorp , College Station , TX , USA ) was used for all statistical analyses , and Manifold System 8 . 0 Professional Edition ( Manifold Net Ltd , Carson City , NV ) was used for processing of geographical data and to produce maps . In order to identify possible risk factors for anti-Alphavirus IgG positivity , we analysed seropositivity as the binary outcome in uni- and multi-variable Poisson regression models with robust ( or Huber-White ) variance estimates adjusted for household clustering [24] , [25] . Initial uni-variable models for all factors that we deemed as possibly related to CHIKV infection were used to identify variables with a uni-variable p-value < = 0 . 1 for further multi-variable evaluation . Stepwise backward and forward regression , the Akaike and Bayes information criterion and various assessments of model-fit were used to identify the best multi-variable model , where only variables with a multi-variable p-value <0 . 1 were retained . Associations of anti-Alphavirus IgG positivity with other diseases were assessed in uni-variable Poisson regression models with anti-Alphavirus IgG as the binary outcome , and the respective disease as the only predictor variable . In addition we ran the same models adjusted for those risk factors that had been retained in the above described multi-variable models regarding risk factors for anti-Alphavirus IgG positivity . In the analysis , positivity for at least one of dengue , West Nile and Yellow fever antibodies , was categorized as “Flavivirus IgG” positive .
219 of 1 , 215 ( 18 . 0% ) samples reacted positive for anti-Alphavirus IgG . The estimated overall population prevalence , predicted from our stratified sample by direct extrapolation , is 11 . 8% for the population of our 9 sites ( fig . 1 ) . Seropositivity increased with participant age , both in uni- and in multi-variable analysis ( table 1; prevalence ratio ( PR ) for a 10-year increase in multi-variable analysis: 1 . 26 , 95% confidence interval ( CI ) 1 . 20 to 1 . 32 , p<0 . 001 ) . Gender was not significantly associated with seropositivity . We found a significant association of anti-Alphavirus IgG status with elevation above sea level , with significantly higher seroprevalence in the strata below 1 , 198 m , both in uni-variable and multi-variable analysis ( fig . 2 ) . The median elevation of the Kyela site is 487 m ( Interquartile range IQR 483 m–514 m ) , and that of Igurusi is 1 , 193 m ( IQR 1 , 156–1 , 205 m ) . Not only elevation , but also slope of the terrain was negatively associated with seropositivity in uni- and multi-variable analysis , even when adjusted for age and elevation ( PR 0 . 86 per degree , 95% CI 0 . 77 to 0 . 95 , p = 0 . 004 ) , with the highest anti-Alphavirus IgG prevalence occurring on terrain with a slope of less than ∼1 . 6° ( fig . 2 ) . Several social , economic and behavioural factors showed significant association in uni-variable analysis but were rendered non-significant in multi-variable analysis when adjusting for age , elevation and slope of terrain . Factors associated with higher anti-Alphavirus IgG prevalence in uni-variable analysis included a lower socio-economic status , lower population density , higher vegetation density and higher land surface temperatures , especially night temperatures . Also , bed net ownership and higher frequency of use , which occurred in areas with higher mosquito burdens , were associated with a higher seropositivity in uni-variable analysis . Anti-Alphavirus IgG status was not associated with animal ownership , including cattle , sheep , goats and chicken ( data not shown ) . Next , we analysed correlations between anti-Alphavirus IgG status and other infectious diseases throughout the survey . Uni-variable analyses showed significant positive associations of anti-Alphavirus IgG with P . falciparum malaria RDT positivity , antibody positivity for spotted fever group rickettsiae ( SFG ) and Rift Valley fever virus ( RVFV ) [23] , and any of the tested Flaviviridae ( table 2 ) . In separate models for each of these pathogens , that were adjusted for age , elevation and slope of terrain , a significant positive association was retained for SFG IgG , Flavivirus IgG and RVFV IgG , while the association with P . falciparum disappeared . A comparison of the spatial distribution of RVFV IgG and anti-Alphavirus IgG shows that for both viruses , Kyela site has the highest seroprevalences , but a wider occurrence is seen for anti-Alphavirus IgG when compared to RVFV IgG ( fig . 1 ) . HIV status was unrelated with individual anti-Alphavirus IgG status in uni- and multi-variable analysis ( data not shown ) .
In the current study we present high rates of IgG antibodies against an Alphavirus . Cross reactions mainly occur in IFAT between antibodies against CHIKV and other viruses of the Semliki-Forest virus complex of Alphaviruses , while cross reactivities against the Venezuelan equine , the Eastern equine and the Western equine encephalitis group are rare and low ( ≥4 titer steps; Dobler , unpublished observations ) . Therefore we assume that cross reactions may mainly occur between Semliki Forest complex viruses like O′nyong nyong virus or Semliki Forest virus . Other non-African Semliki-Forest virus complex viruses , like Ross River virus or Mayaro virus do not seem to be responsible for the antibodies as the inhabitants of the areas tested did not leave the region . However , we cannot exclude that a so far unknown Alphavirus of the Semliki Forest virus complex is circulating and may cause infection with or without clinical symptoms . The question can only be answered by virus detection by isolation or molecular detection and characterization of genome parts . This analysis of the seroprevalence for Alphaviruses adds to the picture of arthropod-borne infectious diseases in our study population . Together with previous reports on RVFV , rickettsiae of the typhus group and spotted fever group , we are demonstrating comparably high seroprevalences which could be caused by considerable exposure of the population to arthropod-borne infections other than malaria [23] , [26] , [27] . Akin to RVFV , a near-linear correlation of anti-Alphavirus IgG prevalence with age suggests endemic exposure rather than single or few epidemic events . Acute Alphavirus infections such as chikungunya fever are neither known nor regularly diagnosed in the health facilities in the region , and might be overlooked by medical staff as a possible causative agent for febrile illness , leading to presentation at the health facility . Febrile disease in the area is mostly regarded as malaria by treating clinicians , despite the fact that our survey showed a marked reduction of P . falciparum infection since the introduction of artemether - lumefantrine as first line therapy in 2006 [28] . Therefore , the awareness for zoonoses as a possible underlying cause of febrile illness should be increased . Our analysis shows that anti-Alphavirus IgG prevalence is associated with geographical features related to favourable mosquito breeding conditions . These include low to moderate elevations and flat terrain , which disposes to the formation of surface water collections [4] . Climate has been consistently pointed out as one of the major determinants for the distribution of vector borne diseases . Although lower larval rearing temperatures result in increased likelihood of adult female mosquitoes becoming infected with CHIKV or other arboviruses in laboratory experiments [29] , [30] , it is higher temperatures which are generally linked to more efficient disease transmission in laboratory and epidemiological investigations [31] . However , the temperature variables examined here dropped out of the multi-variable model due to lack of multi-variable significance , with the elevation variable obviously producing a better fit than the satellite-measured land surface temperatures . In La Reunion , the spread of Ae . albopictus has been found to be limited to elevations <1200 m in summer [32]; in Gharwal/India , spread was limited to <1 . 400 m . This corresponds well with the drop in seroprevalence in strata above 1197 m ( table 1 ) . We still assume that temperature is the causal limiting factor in higher elevations , not other elevation-dependent factors such as radiation or atmospheric pressure . It should thus be kept in mind that our elevation results should not be generalized to predict infection risk in other climatic settings . Comparisons with our data on spread of other mosquito-borne infections , namely RVF , Flaviviridae and P . falciparum malaria , and the tick-borne spotted fever group ( SFG ) rickettsiae , produce interesting findings . The uni-variable association of P . falciparum to anti-Alphavirus IgG disappears when adjusting for age , elevation and slope , suggesting that this association was due to factors supporting the breeding of the different mosquito vectors alike . We did not test to distinguish between anti-CHIKV IgG and o′nyong′nyong virus IgG due to lack of capacities to perform the neutralization test , so it is possible that the seroprevalence is caused by more than one virus . A similar association exists between bednet ownership and anti-Alphavirus IgG . Bednet ownership is not homogenous over the study area , but more frequent in areas of higher malaria transmission , which in our data are characterised by low elevation and even terrain , favouring standing surface water as mosquito breeding grounds . This not only supports Anopheles but also other mosquito species , so the use of bednets can be seen as a proxy for general abundance of mosquitoes – hence the positive association in uni-variable analysis . When corrected for elevation and slope of terrain , the association with bednets disappeared – showing that in malaria endemic areas , bednet ownership neither favours nor protects against Alphavirus infection . This may point towards a diurnally active vector such as Aedes spp . , against which bednets do not protect . The association of anti-Alphavirus IgG with RVFV and Flavivirus IgG , viruses sharing Aedes spp . as vector , is however retained in multivariable analysis . This shows that in addition to age , elevation and slope , additional relevant factors still influence the spread of these Aedes – borne infections which remain to be identified . Others have found higher seroprevalences in Cameroonians living under corrugated iron roofs vs . thatched grass roofs; furthermore , living in rural areas was associated with higher seroprevalences [33] . SFG rickettsiae and Alphaviruses are transmitted by completely different vectors ( cattle ticks and mosquitoes respectively ) , therefore the reason for the observed association is not clear . Rural living conditions , defined by low population density and long distances to roads , was a risk factor in our analysis of SFG rickettsia IgG [27] . Others authors also found this to be a risk factor for anti-Alphavirus IgG [33] , so it is possible that rural conditions are the factor which increases the risk for both of these diseases which do not have much else in common . Interesting are the differences in geographical spread of anti-Alphavirus IgG versus RVFV IgG in our population . Anti-Alphavirus IgG is more evenly distributed in the two Kyela sub-sites , and is also common in other sites . RVFV IgG on the other hand concentrates along the shore of Lake Malawi and nearby watercourses . The preferred occurrence of RVFV along water bodies has been demonstrated in other settings as well [34] , but does not seem to apply to anti-Alphavirus IgG positivity in our setting . This observation may imply that the vectors of RVFV in Kyela region are floodwater mosquito species and therefore need the shore of Lake Malawi , whilst the vectors of the Alphavirus may show an anthropophilic behaviour . The strong affinity to water , which applies for RVFV , but not for the Alphavirus , may also be related to a higher density of cattle as RVFV reservoir hosts along the water , or with the transovarial transmission of RVFV in diapausing Aedes mosquitoes along waterbodies [35] . It is also possible that RVFV requires a temperature optimum as suggested by our previous work , with a direct correlation with higher minimum temperatures , lower maximum temperatures , and positive influence of dense vegetation , conditions that are fulfilled mainly at the lakeshore [23] . The causative Alphavirus , despite probably limited to a smaller number of vector species compared to RVFV , seems to be less specific in terms of ecological conditions and seems to show a more anthropophilic behaviour , leading to a wider spread of the virus throughout the study area . In summary , our data suggest that CHIKV or a closely related Alphavirus like o′nyong′nyong virus is circulating in the study area . If this virus causes disease , it could be an important cause of febrile illnesses in the region , and may be currently underdiagnosed . The linear relation of seropositivity to age suggests endemic rather than epidemic cycling , opposed to a study from Kenya where seropositivity was linked by the authors to epidemic exposure [14] . Kenya was reportedly affected by past CHIKV outbreaks , while there are no reports of outbreaks from our study area . A study from northern Tanzania reported acute CHIKV infection in 7 . 9% of febrile hospitalized patients , demonstrating that CHIKV circulates between epidemics in the country and may well be responsible for the seroprevalences in our study [13] . The power of our statements is further limited by the stratified nature of our study cohort , which results in prevalence levels slightly different from the general population . Further , the serological method used does not allow distinguishing between different Alphavirus species , and only gives information on cumulative lifetime infection risk . Therefore , prospective studies are needed to establish the rate of acute fever caused by CHIKV or other Alphaviruses in febrile patients . If the infecting Alphavirus is shown to be CHIKV or another Alphavirus of human medical importance these results should lead to a re-assessment of the local diagnostic algorithm for febrile illnesses , to take into account the endemic presence of the causative Alphavirus ( es ) in the area . These studies would also have to answer the question whether endemic strains do have a reduced pathogenicity , and have evaded detection by not causing the typical symptoms . | The origin of febrile disease is often difficult to diagnose . In tropical countries , viral infections that are transmitted by arthropods include , among others , Alphavirus infections ( e . g . chikungunya fever ) , dengue , West Nile , Yellow Fever and Rift Valley fever . In malaria endemic areas , these diseases are often mis-diagnosed and treated as malaria . Our study examined serum samples from 1 , 215 participants of a population survey from the Mbeya region , south-western Tanzania , for antibodies against Alphaviruses of the Semliki forest group as a sign of past infection . We found 18% of study participants positive , a surprisingly high number which points to a hitherto undetected circulation of Alphaviruses in the region . Among examined risk factors , even terrain , low to moderate elevation and participant age were associated with antibody positivity . Comparison with the distribution of Rift Valley fever seropositivity showed that Alphaviruses are more widely distributed throughout the study area , while Rift Valley fever seems to occur in a limited area close to Lake Malawi only . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"travel-associated",
"diseases",
"infectious",
"disease",
"control",
"viral",
"diseases"
] | 2014 | Seroprevalence of Alphavirus Antibodies in a Cross-Sectional Study in Southwestern Tanzania Suggests Endemic Circulation of Chikungunya |
The immune system can recognize virtually any antigen , yet T cell responses against several pathogens , including Mycobacterium tuberculosis , are restricted to a limited number of immunodominant epitopes . The host factors that affect immunodominance are incompletely understood . Whether immunodominant epitopes elicit protective CD8+ T cell responses or instead act as decoys to subvert immunity and allow pathogens to establish chronic infection is unknown . Here we show that anatomically distinct human granulomas contain clonally expanded CD8+ T cells with overlapping T cell receptor ( TCR ) repertoires . Similarly , the murine CD8+ T cell response against M . tuberculosis is dominated by TB10 . 44-11-specific T cells with extreme TCRβ bias . Using a retrogenic model of TB10 . 44-11-specific CD8+ T cells , we show that TCR dominance can arise because of competition between clonotypes driven by differences in affinity . Finally , we demonstrate that TB10 . 4-specific CD8+ T cells mediate protection against tuberculosis , which requires interferon-γ production and TAP1-dependent antigen presentation in vivo . Our study of how immunodominance , biased TCR repertoires , and protection are inter-related , provides a new way to measure the quality of T cell immunity , which if applied to vaccine evaluation , could enhance our understanding of how to elicit protective T cell immunity .
The adaptive immune system can generate 1014 unique TCRs , which provides the capacity to recognize an enormous universe of distinct antigens [1–4] . Despite our understanding of the genetic and structural basis for TCR diversity and antigen recognition , it remains challenging to predict the magnitude and diversity of T cell responses . The size of the T cell response to model antigens generally correlates with the abundance of antigen-specific T cells in the naïve repertoire ( e . g . , precursor frequency ) [5–7] . Paradoxically , pathogen-specific T cell responses are often focused on a small number of the available antigenic epitopes and use a narrow TCR repertoire , a phenomenon termed “immunodominance” . Pathogens have numerous strategies to evade host immunity , hindering our ability to determine a priori how T cell diversity relates to antimicrobial immunity . Thus , the relationship between immunodominance and host defense during infection is incompletely understood . For pathogens that rapidly mutate , such as human immunodeficiency virus 1 ( HIV-1 ) , a diverse T cell response could benefit the host by efficiently detecting escape mutants , while a biased response could be detrimental . For slowly replicating pathogens that encode numerous antigens , the relation between diversity and protection is less clear . The M . tuberculosis genome contains hundreds of epitopes that can potentially be recognized by murine and human CD8+ T cells [8] . The CD8+ T cell response against M . tuberculosis focuses on the TB10 . 4 protein ( EsxH; Rv0288 ) in people as well as experimentally infected animals [8–13] . Following aerosol infection of C57BL/6 mice , 30–50% of the responding CD8+ T cells in the lungs recognize the Kb-restricted epitope TB10 . 44–11 ( amino acid sequence IMYNYPAM ) , defining it as an immunodominant epitope [14–16] . Immunodominant T cell responses in patients with tuberculosis have been suggested to be both a correlate of protection and a marker of disease progression [17–20] . Elucidating how immunodominance arises and affects resistance to infection is crucial for developing successful vaccines , which usually target a limited number of antigens . Here , we investigated the origin and protective capacity of immunodominant T cell responses following M . tuberculosis infection in both humans and mice . Extreme TCR bias , the presence of public TCRs , and strong selection of a complementarity determining region 3 ( CDR3 ) β motif were shown by TCR sequencing of sorted tetramer+ cells from the lungs of infected mice . We discovered that TCR bias emerges soon after T cell priming in the lymph node ( LN ) and becomes more extreme during chronic infection . Cloning TB10 . 44-11-specific TCRs allowed us to develop retrogenic ( Rg ) mice to study immunodominant TCRs in vivo . Competition studies using TB10 . 44-11-specific Rg CD8+ T cells showed that small differences in T cell affinity lead to clonal dominance in vivo . Finally , TB10 . 44-11-specific CD8+ T cells transferred IFNγ-dependent protection against M . tuberculosis infection that required TAP1-dependent antigen presentation .
Deep sequencing of the TCRβ repertoire was performed on CD8+ T cells purified from 11 lung granulomas and one LN obtained from 5 patients undergoing lung resection for medically non-responsive tuberculosis ( see Table 1 , S1 Data ) . The CD8+ T cells in the granulomas were more clonal than the peripheral blood TCRβ repertoire of healthy individuals ( Fig 1A and S2A Data ) . TCRβ expansions were detected in all lung samples ( Fig 1B and S3 Data ) . Abundant clonotypes were detected in anatomically distinct granulomas from the same patient ( Fig 1B ) . There was extensive overlap of the TCR sequences between granulomas from the same patient , although most sequences detected in each granuloma were unique to that lesion ( Fig 1C ) . However , it was the shared sequences that were most abundant . For example , of 217 TCRβs common to all three granulomas from patient #23 , 143 were the most abundant in each lesion ( the top 10 are shown in Fig 1C and S2B Data ) . Thus , the same T cells clonotypes were abundant in different lesions from the same subject despite their varied pathology ( see Table 1 ) . Some TCRβs appeared to have undergone antigen-selection , as sequences with identical CDR3β amino acid sequences were encoded by distinct recombination events . For example , five distinct DNA recombination events led to the CDR3β sequence ‘CASSVDGGTEAFF’ and two occurred at a high frequency ( 1 . 16% and 0 . 9% , Fig 1D ) . Thus , CD8+ T cells in human granulomas undergo clonal expansion , and while there is considerable heterogeneity between distinct lesions , the most abundant clonotypes were shared between granulomas . As the antigen-specificity and the HLA restriction of these T cells were unknown , the inferences that we can make are limited . Furthermore , co-infection with HIV , present in four of the five subjects , could potentially confound the analysis , since HIV itself can alter the TCR repertoire of CD8+ T cells . Indeed , we cannot be certain whether these TCRs are specific for Mtb . Therefore , we next turned to an animal model of tuberculosis to address the origin and consequences of clonal CD8+ T cell expansions . The M . tuberculosis epitope TB10 . 44–11 elicits an immunodominant CD8+ T cell response in both people and mice [10 , 21] . To determine whether the number of distinct clonotypes among the responding T cells was limited or diverse , we sorted TB10 . 44-11-tetramer+CD8+ T cells from the lungs of six individual mice , infected with M . tuberculosis for nine weeks . Deep sequencing showed that the CD8+ T cell response to TB10 . 44–11 was significantly more clonal than the T cell repertoire from uninfected mice ( Fig 2A ) . The TCRβ repertoire of each infected mouse was dominated by large expansions of two or three clones , although the dominant Vβ gene varied between individuals ( Fig 2B ) . The dominance of TCRβ chains with identical CDR3β sequences indicated that the TB10 . 44-11-specific CD8+ T cell response was oligoclonal ( Fig 2C ) . To determine whether TCR bias was established during priming in the LN or after T cell trafficking to the lungs , we used monoclonal antibodies ( mAbs ) specific for a subset of the known Vβ families ( see Table 2 ) . All 5 Vβ families were expressed by CD8+ T cells obtained from LNs of mice 21 days after infection , the majority of which are not specific for Mtb ( Fig 2D ) . The distribution of these 5 Vβ families among tetramer+ cells in the LN was similar to the bulk CD8+ T cell population ( Fig 2D ) . Starting on day 21 in the lung , and more dramatically by day 28 in the LN and lung , significant Vβ family bias was detected among TB10 . 44-11-specific CD8+ T cells . For example , Vβ4 ( mouse ‘A’ and ‘B’ ) , Vβ7 ( mouse ‘B’ ) , or Vβ10 ( mouse ‘C’ ) dominated the TB10 . 44-11-specific CD8+ T cell response by day 21 post infection ( Fig 2E ) . The dominant Vβ family was frequently expanded in both the LN and lung , suggesting that TCR bias developed early after T cell priming . Overtime , biases became more dramatic , suggesting ongoing preferential expansion of certain clones . The TB10 . 44-11-specific CD8+ T cells in one infected mouse ( mouse ‘H’ ) were >80% Vβ7+ after 25 weeks of infection ( Fig 2E and 2‘H’ ) . Thus , after an initial priming of a broad repertoire in response to antigen , TCR bias among TB10 . 44-11-specific CD8+ T cells develops in the draining LN early after T cell priming and becomes established during the chronic phase of infection . The highly oligoclonal response and the dominance of CDR3β amino acid sequences suggest that the immunodominant T cell clonotypes undergo selection during infection . Two general mechanisms can explain how TCR bias develops during the TB10 . 44–11 response: 1 ) TB10 . 44-11-specific CD8+ T cells are drawn from a limited pool of naïve precursors; or 2 ) the naïve TB10 . 44-11-specific CD8+ T cells pool is diverse but competition leads to selection and bias . To discriminate between these possibilities , we measured the precursor frequency of TB10 . 44-11-specific CD8+ T cells in uninfected mice . Using sequential tetramer staining and enrichment of antigen-specific T cells from the naïve repertoire [5 , 22] , we determined that approximately 1:13 , 000 CD8+ T cells were specific for the TB10 . 44–11 epitope in the C57BL/6 naïve repertoire , or about 857 cells per mouse ( Fig 3A and 3B ) . This precursor frequency is among the highest recorded for antigen-specific CD8+ T cells in the mouse [7] . The high frequency of TB10 . 44-11-specific CD8+ T cell precursors in C57BL/6 mice contrasts with the limited number of unique T cell clonotypes that comprise the post-infection TB10 . 44-11-specific CD8+ T cell repertoire . To estimate the frequency of TB10 . 44-11-specific TCRβ clones in the naïve repertoire , TB10 . 44-11-specific sequences from infected mice were used to interrogate data sets from three uninfected C57BL/6 mice ( referred to below as spleen A , B , or C ) each containing more than a million TCRβ sequences ( Provided by David Hamm , Adaptive Biotechnologies reference data "Mus musculus TCR Beta from spleen" , 2014 ) . By performing a pairwise comparison , we identified TB10 . 44-11-specific TCRβ DNA sequences in the naïve T cell repertoire of uninfected mice ( Fig 3C ) . For example , an abundant TCRβ from the lung of infected mouse #2 ( 17 . 8% ) was present in spleen B ( 0 . 0026% ) ; however , none of the abundant ( >0 . 1% ) TCRβs from the lung of infected mouse #2 were present in spleen A or C ( Fig 3C ) . When the translated CDR3β amino acid sequences were used to query the naïve repertoire , the number of matches increased from 40 to 345; and several of the most highly represented CDR3βs were identified ( Fig 3D ) . This raises the possibility that there are multiple DNA recombination events that can generate CDR3β regions capable of recognizing TB10 . 44–11 in the naïve repertoire . Importantly , the frequencies of TB10 . 44–11–associated TCRβs in the naïve repertoire were similar to the median frequency determined for the entire naïve T cell population ( Fig 3E ) . For example , the median frequency of the clonotypes detected in naïve spleen #B was 0 . 0011% . The frequencies of “CASSQDRENSDYTF” and “CASSRDRENSDYTF” in the naïve spleen #B repertoire were 0 . 00425% and 0 . 00106% , respectively . Thus , the low frequency of these two CDR3β regions in the naïve TCR repertoire does not predict their massive clonal expansion after infection . To make this analysis more quantitative , we determined the frequencies of all TB10 . 44-11-associated CDR3β sequences that could be identified in any of the three uninfected spleens ( A , B , or C ) . Those sequences ( “TB10-associated” , Fig 3E ) had a slightly higher median frequency than the median of the entire splenic TCR repertoire ( “uninfected spleen ) ( 0 . 001845% vs . 0 . 001142% , P < 0 . 0001 ) . We next focused on the subset of “TB10-associated” CDR3βs that were present in at least 2 of the 6 infected lungs , and defined these as “shared sequences” ( Fig 3E ) . The median frequency of the “shared sequences” was 0 . 003614% , which was increased compared to “uninfected spleen” ( P < 0 . 0001 ) ; this is in agreement with previous observations describing a higher frequency of public TCRs in the naïve repertoire [2] . Finally , the CDR3βs that we defined as “expanded sequences” ( those with a frequency >1% among CD8+ T cells in the Mtb-infected lung and that account for 75% of the total TB10 . 44-11-associated sequences ) had a frequency similar to “uninfected spleen” ( 0 . 001559% , not significant ) . Thus , TB10 . 44-11-associated sequences do not appear to be over-represented in the naïve repertoire compared to other sequences . Interestingly , the individual frequency of highly represented sequences within an individual naive mouse varied by more than 10-fold , raising the possibility that the precursor frequency of individual CDR3β clonotypes could affect their representation in the post-immune repertoire ( e . g . , after infection ) . Interestingly , “CASSRDRENSDYTF , ” which was one of the more successful CDR3βs as it was commonly detected during Mtb infection , was not present at a greater frequency than other TCRs in the naïve repertoire ( Fig 3E ) . Although T cells specific for the immunodominant epitope TB10 . 44–11 have a high precursor frequency in the naïve repertoire of C57BL/6 mice ( Fig 3B ) , extreme TCR bias emerges during the CD8+ T cell response to TB10 . 44–11 ( Fig 2b ) . While heterogeneity in the frequency of TB10 . 44-11-specific clonotypes found in the naïve repertoire could account for some of the bias ( Fig 3E ) , the precursor frequency of individual clonotypes in the naïve repertoire does not appear to be the dominant factor influencing representation in the post-infection repertoire . We next evaluated the possibility that clonotypic dominance occurs because of clonal selection during infection . The CDR3 length of the unique TCRβ sequences from the infected and uninfected mice was similar with a median CDR3β length of 36–39 bases ( Fig 4A ) . In contrast , 56% of the highly represented TCRs ( e . g . , frequency >1% ) had a CDR3β length of 42 . Analysis of the amino acid sequences with this CDR3β length ( accounting for 36% of all TB10 . 44-11-specific sequences ) revealed a strong consensus motif—“CASSxDReNsdytF” ( Fig 4B ) . This motif was present in clonal expansions if different mice , and several distinct DNA recombination events generate the conserved aspartic acid ( Asp , “D” ) at CDR3β residue 6 ( e . g . , V-D recombination , germline encoded , N region addition; Fig 4C ) . Thus , we conclude that this residue is under strong selection . In addition to the selection of the conserved Asp , the over-representation of certain CDR3β regions in their entirety appeared to be the consequence of selection . In Mouse #1 , 63% of the CDR3β sequences encoded the amino acid sequence “CASSLDRENSDYTF . ” Remarkably , three distinct VDJ recombination events generated this CDR3β in Mouse #1 ( Fig 4C ) . These data strongly suggest that these TCRs were selected . Not only were the TCRβ expansions extremely biased within each individual mouse , but some abundant sequences shared identical CDR3βs between mice , constituting so called “public” TCRs [2 , 23] ( Fig 4C ) . The “DREN” motif was also detected among TCRs from human lung granulomas in three of the five patients , and was expanded in patient #24 ( Fig 4D ) . Two different patients share the motif “CASSxDRENTEAFF , ” which is similar to the murine motif ( Fig 4D ) . While the “DREN” motif was detected in the peripheral blood TCRβ repertoire of normal donors , those clonotypes had a 10-fold lower average frequency ( Fig 4D and S4 Data ) . Some clonotypes found in the Mtb granulomas were 1000-fold enriched compared to the average “DREN” frequency in peripheral blood of normal donors ( Fig 4D ) . These data are consistent with selection since distinct recombination events generate different TCRs with the “DREN” motif ( Fig 4D ) . Finally , there is evidence for public TCRs as patients #23 and #26 have clones with the identical CDR3β sequence “CASSSDRENTEAFF” . The extreme TCR bias ( Fig 2C ) and the identification of multiple VDJ recombination events encoding identical CDR3βs indicate that dominant T cell clonotypes undergo selective expansion during infection . To quantify selection during the response to M . tuberculosis , we calculated the ratio of unique amino acid sequences to unique nucleic acid sequences . For example , the same immunodominant CDR3β was encoded by three unique DNA sequences in mouse #1 . Shared sequences ( >2 mice ) and abundant sequences ( >1% ) had lower median values ( 0 . 158 and 0 . 154 , respectively ) than the bulk population of TB10 . 44-11-specific CD8+ T cells ( 0 . 868 ) or T cells from uninfected mice ( 0 . 845 ) , indicating strong selection at the level of the CDR3β amino acid sequence ( p< 0 . 0001; Fig 4E ) . Thus , our TCR analysis indicates that clonotypic dominance occurs because of strong selection of certain CDR3β amino acid sequences . To study the biology of TB10 . 44-11-specific CD8+ T cells and to delineate the mechanism ( s ) responsible for the development of TCR bias during M . tuberculosis infection , we developed retrogenic mice . To ensure that we could track the recombinant TCR-expressing CD8+ T cells , we used Vα2var mice , which have a single Vα gene ( Vα2 ) but can still generate diversity through limited VαJα recombination [24] . Vα2var mice resisted tuberculosis and generated a dominant TB10 . 44-11-specific CD8+ T cell response , although it was more variable than in C57BL/6 mice ( see S5 Data ) . Single cell sorting of H2-Kb/TB10 . 44–11 tetramer+ cells from M . tuberculosis-infected Vα2var mice was followed by single cell PCR to determine the TCRα and TCRβ sequences . Three mice were analyzed and the TB10 . 44-11-specific CD8+ T cell response in each mouse was dominated by one or two TCR clonotypes ( Fig 5A ) . Each identified dominant TCRβ paired with a single TCRα chain , supporting that these were true clonal expansions . Despite the constrained Vα2 , the VαJα recombination site was remarkably diverse . In contrast , there were structural parallels between the CDR3β sequences from Vα2var ( Fig 5B ) and C57BL/6 mice ( Figs 2 and 3 ) . While the dominant TCRβs from the Vα2var mice used distinct Vβs and Jβs , there was enrichment of arginine ( “R” ) and aspartic acid ( “D” ) in the CDR3β ( Fig 5B and 5C ) . To determine whether the enrichment of “R” or “D” was significant , we assessed their occurrence in the normal TCRβ repertoire . We queried the splenic TCRβ repertoire from three C57BL/6 mice representing over 1 . 1 million reads and ~53 , 000 unique sequences each ( Fig 2A ) . The average frequency of “R” , “D” , or “RD” at CDR3β position 6 , 7 , or 6–7 , was 10 . 1% , 7 . 5% , and 2 . 5% respectively , indicating “R” , “D” , and “RD” were significantly enriched among the clonally expanded TB10 . 44-11-specific CD8+ T cells ( P < 0 . 0001; see S6 Data ) . The four dominant TCRs were cloned into retroviral vectors linked by the 2A sequence allowing the production of four different retrogenic mice ( named Rg1 to Rg4 , see S10 Data ) that expressed greater numbers of CD8+ T cells specific for TB10 . 44–11 ( Fig 5D ) . We confirmed that the correct TCRs were expressed based on expression of the expected Vα and Vβ chains by CD8+ T cells from the retrogenic mice ( Fig 5D ) . The four TCRs cloned were shown to be specific for TB10 . 44–11 based on their binding to tetramers and activation of effector functions following stimulation with the TB10 . 44–11 peptide ( Fig 5E–5G ) . Naïve ( CD44loCD62Lhi ) Rg3 CD8+ T cells were purified and transferred into congenically marked recipient mice infected with M . tuberculosis . Immediately after transfer ( d6 or d7 after infection ) very few Rg CD8+ T cells were detected ( ~100–200 cells per lung ) and they were mostly naïve ( Fig 6A ) . Rg CD8+ T cells began to acquire an activated phenotype ( CD44hiCD62Llo ) starting on d11 in the LN . Associated with their activation , the numbers of Rg3 CD8+ T cells in the LN dramatically increased during days 11–13 post-infection ( Fig 6A ) . Subsequently , these Rg CD8+ T cells began to accumulate in the lung by day 13–15 , and the Rg3 CD8+ T cell numbers continued to increase through day 18 ( Fig 6A ) . The increase in cell numbers in the LN and lung correlated with proliferation , occurring first in the LN , then in the lung , and finally in the spleen ( Fig 6B ) . Thus , priming of Rg3 CD8+ T cells tracks the kinetics that has been established for the IFNγ-response in intact mice and activation of transferred transgenic CD4+ T cells [25–28] . Following priming , the Rg3 CD8+ T cells rapidly acquired the ability to produce IFNγ in both the LN and lung ( Fig 6C ) . Thus , Rg3 CD8+ T cells are primed in the LN and are subsequently recruited to the lung , where they express a variety of effector functions including the production of IFNγ , TNF , and granzyme B ( see S7 Data ) , similar to endogenous TB10 . 44-11-specific CD8+ T cells . It is unknown whether the immunodominant CD8+ T cell response to TB10 . 4 is protective . Therefore , we activated Rg3 CD8+ T cells in vitro with TB10 . 44–11 peptide , IL-2 and IL-12 and after 60–72 hours , transferred them into sublethally irradiated mice and infected them with M . tuberculosis . We compared the protective capacity of Rg3 CD8+ T cells with ovalbumin-specific CD8+ T cells ( e . g . , OT-I cells ) . When a large number of activated cells ( e . g . , 106 ) were transferred , both Rg3 and OT-I cells transferred considerable protection ( Fig 7A ) . The ability of large numbers of OT-I cells to transfer protection has not been directly investigated , but may be due to their highly activated state and could involve the production of IFNγ , has shown previously for ovalbumin-specific CD4+ T cells [29] . As the number of transferred cells was titrated down , protection mediated by OT-I cells diminished ( Fig 7A ) . In contrast , Rg3 cells continued to mediate significant protection even when as few as 10 , 000 cells were transferred ( Fig 7A ) . Transfer of Rg4 T cells also conferred protection against M . tuberculosis challenge ( see S8A Data ) . To demonstrate that protection required TCR dependent recognition of antigen , we transferred activated Rg3 cells ( 106 cells/mouse ) into sublethally irradiated WT or TAP1-/- recipient mice . Rg3 effector CD8+ T cells were able to transfer protection to WT but not TAP1-/- recipients ( Fig 7B ) . As in vitro activation bypasses the need for in vivo priming , these results show that protection mediated by Rg3 CD8+ T cells requires recognition of antigens processed by the TAP-dependent class I MHC antigen-processing pathway . We next determined which effector functions were required for protection . Rg3 mice were produced in an IFNγ-/- background and IFNγ-/- Rg3 CD8+ T cells compared to WT Rg3 CD8+ T cells for their ability to transfer protection . Under our experimental conditions , activated WT Rg3 CD8+ T cells ( 106 cells/mouse ) transferred significantly more protection than OT-I or IFNγ-/- Rg CD8+ T cells ( Fig 7C ) . These results show that transfer of protection by TB10 . 4-specific Rg CD8+ T cells requires IFNγ production ( Fig 7C ) . Furthermore , naïve Rg3 CD8+ T cells ( 105 cells/mouse ) prolonged the survival of M . tuberculosis-infected TCRα-/- mice in an IFNγ-dependent manner ( Fig 7D ) . Collectively , these data show that TB10 . 4-specific CD8+ T cells can control M . tuberculosis infection and IFNγ production after recognition of antigen presented in vivo is required for protection . Two of the cloned TCRs ( Rg3 and Rg4 ) differed in their binding to Kb/TB10 . 44–11 tetramers , revealing a difference in avidity ( Fig 8A ) . No difference in the level of TCR level was detected ( see S8B Data ) . Importantly , when transferred separately into intact mice , both Rg3 and Rg4 were primed , underwent expansion and trafficked to the lung with similar kinetics , and mediated protection ( Figs 6 and 7 and see S8 Data ) . To investigate whether TCR avidity could affect immunodominance , we co-transferred naïve Rg cells expressing either TCR3 or TCR4 at a 1:1 ratio into congenic recipients and analyzed their relative abundance following infection . Following co-transfer into M . tuberculosis infected mice , Rg3 and Rg4 T cells initially maintained a 1:1 ratio and importantly , both Rg3 and Rg4 T cells were primed in the LN and increased in number ( Fig 8B and 8C ) . However , by day 11 post infection , Rg4 T cells began to outnumber Rg3 T cells , and by day 14 , Rg4 T cells outnumbered Rg3 T cells by a ratio of 10:1 in the LN ( Fig 8B and 8C ) . In the lung , there was no change in the ratio or cell count until day 14 , at which point Rg4 CD8+ T cells accounted for >90% of the Rg CD8+ T cells ( Fig 8B ) . While the absolute number of both Rg3 and Rg4 CD8+ T cells increased in the lung by day 20 , Rg4 CD8+ T cells outnumber Rg3 CD8+ T cells by 100:1 ( Fig 8C ) . Although both Rg3 and Rg4 were able to expand and confer protection when transferred separately , Rg4 T cells dominated when in competition with Rg3 T cells . When co-transferred , both Rg3 and Rg4 CD8+ T cells were primed in the LN and proliferated; however , small differences in their affinity led to the establishment of clonotypic dominance by Rg4 CD8+ T cells during the T cell response to M . tuberculosis infection .
We report that extreme TCR bias develops during the polyclonal CD8+ T cell response to a single immunodominant epitope during tuberculosis in humans and in mice . In mice , preferential Vβ use by TB10 . 44-11-specific CD8+ T cells is detected in the LN within 3 weeks of infection , indicating that bias develops soon after T cell priming . With time , TCR clonality becomes more extreme . Why do a few clonotypes dominate the TB10 . 44-11-specific CD8+ T cells during M . tuberculosis infection ? TCR diversity arises by three principal mechanisms: 1 ) V , D , and J segments generate combinatorial diversity; 2 ) imprecise recombination and insertion of non-templated ‘N’ sequences at the VβD , DJβ and VαJα junctions; and 3 ) random assortment between TCRα and TCRβ chains [30 , 31] . By these mechanisms , people have the potential to generate >1014 unique TCRαβ receptors [1 , 32] . Thymic selection constrains the repertoire by ensuring that T cells that are unable to recognize MHC and the T cells that recognize self-antigens with high affinity are deleted . As a result , people have ~1010 T cells and each clonotype is represented by 10–500 T cells; therefore , the number of unique TCRs in any individual ( ~2 . 5 x 107 ) is far fewer than the number of potential TCRs [2 , 3] . Although sharing of TCRs between people is improbable , such ‘public’ TCRs are detected and may have special significance [23] . If the number of antigen-specific T cells in the naïve repertoire is limiting , clonotypic dominance could arise by a “founder” effect in which few T cells are primed and expand , leading to T cell populations of restricted diversity . A founder effect is unlikely to explain the TCR bias among TB10 . 44-11-specific CD8+ T cells because we detect ~900 naïve T cells in each C57BL/6 mouse , a precursor frequency that is among the highest recorded for antigen-specific CD8+ T cells in the mouse [7] . TB10 . 44-11-tetramer+ CD8+ T cells from the LNs of infected mice use many Vβ families , which also argues against a founder effect . Another possibility is that the frequency of each clonotype in the naïve repertoire is skewed and results in TCR bias after infection . We observe considerable heterogeneity in the relative abundance of naïve precursors with the potential to recognize the TB10 . 44–11 . However , skewing in the naïve repertoire cannot solely explain the selection that we observe for TB10 . 44-11-specific CD8+ T cells following infection . After infection , TB10 . 44-11-specific CD8+ T cells express a limited number of CDR3β sequences . Given the high precursor frequency , we predicted a priori that the response would be dominated by private clonotypes , i . e . , TCRs unique to each individual . Instead , common CDR3β motifs are generated by independent VDJ recombination events , within the same mouse and among different individuals . The most frequently used and shared TCRβs are under selective pressure , determined using a modification of Warren’s method [33] . Developing retrogenic mice that over-produce CD8+ T cells specific for TB10 . 44–11 permitted us to perform competition experiments with naïve TB10 . 44-11-specific CD8+ T cells . A small difference in affinity significantly affects clonal representation and establishment of hierarchical dominance during infection . These data provide crucial experimental support for the idea of antigen-driven selection based on our TCR analysis . Although TCR affinity seems to be a major factor driving immunodominance , other factors can contribute . For example , the inflammatory environment and tissue-specific cues influence the fate of individual CD8+ T cells during infection [4] , and these factors may contribute to the establishment of immunodominant T cell responses during tuberculosis . Why is TB10 . 4 an immunodominant antigen ? While there is a high precursor frequency , few of these T cell clones are significantly represented in the final immune response . The lack of a correlation between the precursor frequency and immunodominance in C57BL/6 and BALB/c mice [10] indicates that a high precursor frequency is not a prerequisite for immunodominance during chronic infection . TB10 . 4 belongs to a larger family of ESAT6-related proteins that are secreted by specialized type VII secretion systems , and many of the secreted proteins are immunodominant in different animal species and humans [34–36] . While the abundance of TB10 . 4 during infection is unknown , paucity of this antigen during T cell priming or limited antigen presentation in the infected lung could drive clonotypic dominance by selecting for higher affinity T cells . Whether certain mycobacterial antigens that elicit immunodominant T cell responses act as “decoys” to distract the immune response from responding to subdominant epitopes that might be more important targets of protective immunity was raised by Baena and Porcelli [37] . The finding that portions of the mycobacterial genome that encode T cell epitopes was more evolutionarily conserved has fueled this idea and raised the possibility that T cell immunity could benefit M . tuberculosis , possibly by creating an inflammatory environment that facilitates transmission [38] . Work done by the Andersen group finds that cryptic epitopes of ESAT6 are minor components of natural immune response to M . tuberculosis but specific vaccination strategies that elicit CD4+ T cells specific for the subdominant epitopes generate more durable protection against tuberculosis [39–41] . Could TCR diversity ( or bias ) be a surrogate for the quality or effectiveness of T cell immunity ? Spectratyping of peripheral blood T cells from tuberculosis patients reveals TCR skewing compared to healthy controls [20 , 42 , 43] . Extreme TCR bias ( e . g . , clonality ) was noted primarily in the setting of severe clinical disease , raising the possibility that TCR bias is associated with disease progression [19 , 20] . Arguing against this interpretation is the presence of highly skewed TCR repertoires in lung granulomas from patients with latent tuberculosis [17] . In our cohort of patients , we also see the emergence of clonal T cell expansions in the lungs of patients with active disease , compared to the frequency of T cells in the peripheral blood of normal donors . Importantly , clonal expansions can be detected among peripheral blood CD4+ and CD8+ T cell in uninfected healthy individuals , with memory T cells being 50-fold less diverse than naive T cells [33] . While these data indicate that T cell expansions can occur independently of active infection , clonotypes expressing the CDR3β motif “DREN” were 1000-fold enriched in Mtb granulomas compared to their average frequency in the peripheral blood of normal donors . Thus , these expansions are several orders of magnitude greater than the expansions reported in uninfected individuals [33] . This may indicate that the "DREN" expansions are driven by Mtb infection . Furthermore , based on our murine data , we infer that T cell selection , possibly driven by affinity , is occurring . We are confident that we captured the majority of lung TB10 . 4-specific CD8+ T cells present in each individual mouse . In contrast , we observed considerable heterogeneity in the TCR repertoire obtained from distinct granulomas in each human subject . While this heterogeneity is not surprising based on the work on Flynn and Barry [44 , 45] , it is important to recognize that TCR bias at the level of the granuloma may be driven by heterogeneity in bacteria and bacterial antigens , as well as the persistent immune response . Therefore , the links between TCR bias , functional capacity of T cells and protection during tuberculosis are still unclear , and longitudinal studies coupled with the functional study of antigen-specific T cells are needed to define what constitutes a protective T cell response against tuberculosis in both people and in experimental animal models , both in terms of TCR diversity and T cell function [46] . While antigen choice is a key part of vaccine development , predicting which immunogenic epitopes elicit memory responses that generate protective immunity during infection has not been straightforward . Some pathogens mutate to escape T cell surveillance; other pathogens avoid immune detection by sequestering their antigens . CD4+ and CD8+ T cells specific for TB10 . 4 are elicited following clinical tuberculosis infection . Based on the ability of TB10 . 4-specific CD4+ T cells to mediate protection in animal models , the TB10 . 4 antigen has been incorporated into subunit vaccines . Our data is the first to show that TB10 . 4-specific CD8+ T cells transfer protection and that protection requires antigen presentation by TAP1-dependent pathways . This implies that the TCR-mediated recognition of infected cells is a prerequisite for the antimicrobial activity of CD8+ T cells . Furthermore , IFNγ is a key mediator of bacterial control . Why then did a vaccine designed to elicit TB10 . 4-specific CD8+ T cells fail to protect mice against M . tuberculosis [47] ? We chose to measure protection in immunocompromised ( e . g . , sublethally irradiated ) mice , as it is difficult to demonstrate CD8+ T cell mediated protection in mice with an intact CD4+ T cell compartment . Another factor that may impair the ability of TB10 . 4-specific CD8+ T cells to protect mice with an intact immune system is inefficient presentation of IMYNYPAM . Lindenstrom et al show that vaccination with native TB10 . 4 protein does not elicit TB10 . 4-specific CD8+ T cells because the amino acid sequence surrounding the epitope ( e . g . , IMYNYPAML ) is inefficiently processed and presented . In contrast , a homologous protein ( TB10 . 3 , EsxR ) contains a homologous sequence ( e . g . , IMYNYPAMM ) , which is more efficiently processed and presented . Less TB10 . 3 is produced by the bacterium than TB10 . 4 , and because of this , Lindenstrom speculates that IMYNYPAM is not presented efficiently by infected macrophages . Our own data showing protection ( Fig 7 ) used cells activated in vitro prior to adoptive transfer , bypassing the need for priming . TAP1-dependent protection mediated by these TB10 . 4-specific CD8+ T cells implies that infected cells in the lung present IMYNYPAM . However , the source of the antigen ( e . g . , TB10 . 3 vs . TB10 . 4 ) is uncertain . If IMYNYPAM-specific CD8+ T cells recognize the small amount of TB10 . 3 expressed by infected macrophages , then selecting CD8+ T cells with a high affinity for IMYNYPAM will be even more important for host resistance . Our study took advantage of the well-characterized CD8+ T cell response to Mtb in mice , where responses to an epitope of TB10 . 4 elicit an immunodominant T cell response . This allowed us to track and purify antigen-specific CD8+ T cells during infection . We find that the TB10 . 4-specific CD8+ T cell response is characterized by extreme clonality despite originating from a high-frequency naïve precursor pool . We were able to show that structural features of the CDR3β region were important for epitope recognition , most likely because of clonal competition and affinity selection . Similarly , we found that human CD8+ T cells also undergo selection and clonal expansion . Although the clonal expansions we detected in humans were not as dramatic as in the mouse model , we believe that this is partially because we were unable to purify human antigen-specific CD8+ T cells , due to the lack of appropriate reagents . The ability to use tetramer-sorted cells allows one to analyze T cells that are all specific for a single epitope , which adds considerable power to the TCR analysis . Therefore , it is uncertain whether the extreme immunodominance we observe in the murine system will be found in humans , and this is the subject of active investigation . For example , greater T cell diversity is theoretically expected in humans , due to the contributions of HLA variability , but also empirically , as demonstrated by the large-scale studies to date that have generally found a more diverse T cell response in infected individuals [48 , 49] . However , this may also be due to technical barriers; while antigen-specific CD8+ T cell expansions are found during tuberculosis , they often appear to be unique to individuals and their private repertoires , which further complicates TCR analysis of antigen-specific T cells [48–50] . If this holds true , it could be difficult to fully exploit TCR analysis as a biomarker for following disease progression or treatment efficacy , although if expanded T cell clones from the lung are correlated with those that are present ( and can be detected ) in peripheral blood , algorithms might be developed that are independent of antigen-specificity . Finally , based on the propensity of Mtb to drive clonal selection of CD8+ T cells , we infer that there is a paucity of antigen presentation in the infected lung . Such a state may arise because of inefficient cross-presentation of antigens by the class I MHC-processing pathway and could explain why CD8+ T cells have not proven to be as protective as CD4+ T cells . Similarly , if low levels of antigen presentation rapidly select for high affinity CD8+ T cells during infection , an effective vaccine will need to elicit similarly high affinity T cells , rather than large numbers of diverse T cells , if they are to be effective in controlling bacterial replication .
The University of KwaZulu Natal ( UKZN ) Biomedical Research Ethics Committee ( BREC ) approved the study protocol , its associated informed consent documents and data collection tools . Written informed consent was obtained for all research subjects . All animal experiments were performed in accordance with National and European Commission guidelines for the care and handling of laboratory animals . The studies were approved by the Institutional Animal Care and Use Committee at the Dana Farber Cancer Institute and the University of Massachusetts Medical School ( Animal Welfare Assurance no . A3023-01 [DFCI] or A3306-01 [UMMS] ) , under Public Health Service assurance of Office of Laboratory Animal Welfare guidelines ) . Lung tissue of approximately 3 cm3 was isolated from different areas of resected lungs , corresponding to the most diseased ( A ) , intermediate ( B ) , and healthiest tissue ( C ) , typically corresponding the upper ( A ) , lower ( B ) and middle lobe ( C ) ( Table 1 ) . The operating surgeon classified the tissue based on their experience and the pre-operative radiological data . Each sample was washed in multiple changes of HBSS and then diced into approximately 1 mm3 pieces , which were strained , re-suspended in 7mls of pre-warmed digestion media ( R10 ( RPMI supplemented with 10% FCS , 2 mM L-glutamate , 100 U/ml Penstrep ) , containing 0 . 5 mg/ml collagenase D ( Roche ) and 40 U/ml DNaseI ( Roche ) , and transferred to GentleMACS C-tubes ( Miltenyi ) for mechanical digestion per manufacturers instructions . The resultant suspension was incubated for 60 min at 37 oC , subjected an additional mechanical digestion step . The resulting suspension was strained through a 70 μm cell strainer , washed twice in HBSS , stained and CD8+ T-cells sorted using the FACS ARIA system , gating on the live ( nearIR , Invitrogen ) singlet , CD45+ , CD3+ and CD4- population . Cells were sorted directly into RLT buffer , and genomic DNA extracted using the DNeasy Minikit ( Qiagen ) as per manufactures instructions . C57BL/6 ( WT ) , CD45 . 1 ( B6 . SJL-PtprcaPepcb/BoyJ ) , CD90 . 1 ( B6 . PL-Thy1a/CyJ ) , OT-I ( C57BL/6-Tg ( TcrαTcrβ ) 1100Mjb/J ) , TCRα KO ( B6 . 129S2-Tcrαtm1Mom/J ) , IFNγ KO ( B6 . 129S7-Ifngtm1Ts/J ) and TAP KO ( B6 . 129S2-Tap1tm1Arp/J ) mice were purchased from Jackson Laboratories ( Bar Harbor , ME ) . Vα2var mice [24] were bred at Jackson Laboratories ( Bar Harbor , ME ) . Mice were 6 to 10 weeks old at the start of all experiments . Mice infected with M . tuberculosis were housed in a biosafety level 3 facility under specific pathogen-free conditions at DFCI or at UMMS . M . tuberculosis ( Erdman strain ) infection was performed via the aerosol route , and mice received 50–200 CFU/mouse . A bacterial aliquot was thawed , sonicated twice for 10 s in a cup horn sonicator , and then diluted in 0 . 9% NaCl–0 . 02% Tween 80 . A 15 ml suspension of M . tuberculosis was loaded into a nebulizer ( MiniHEART nebulizer; Vortran Medical Technologies ) and mice were infected using a nose-only exposure unit ( Intox Products ) . Alternatively , the bacterial aliquot was diluted in a final volume of 5ml , and mice were infected using a Glas-Col aerosol-generation device . At different times post-infection , mice were euthanized by carbon dioxide inhalation , organs were aseptically removed , individually homogenized and viable bacteria were enumerated by plating 10-fold serial dilutions of organ homogenates onto 7H11 agar plates . Plates were incubated at 37°C and M . tuberculosis colonies were counted after 21 d . Cell suspensions from lung , spleen and LNs were prepared by gentle disruption of the organs through a 70 μm nylon strainer ( Fisher ) or using the GentleMacs apparatus ( Miltenyi Biotec , Germany ) according to the manufacturer instructions . For lung preparations , tissue was digested for 30–60 min at 37 °C in 1 mg/mL collagenase ( Sigma ) prior to straining . Erythrocytes were lysed using a hemolytic solution ( 155 mM NH4Cl , 10 mM KHCO3 , 0 . 1 mM sodium EDTA pH 7 . 2 ) and , after washing , cells were resuspended in supplemented RPMI ( 10% heat inactivated FCS , 10 mM HEPES , 1 mM sodium pyruvate , 2 mM L-glutamine , 50 mg/ml streptomycin and 50 U/ml penicillin , all from Invitrogen ) or MACS buffer ( Miltenyi Biotec , Germany ) . Cells were enumerated in 4% trypan blue on a hemocytometer or using a MACSQuant flow cytometer ( Miltenyi Biotec , Germany ) . Surface staining was performed with antibodies specific for mouse CD3 ( clone 17A2 ) , CD3ε ( clone 145-2C11 ) CD4 ( clone GK1 . 5 ) , CD8 ( clone 53–6 . 7 ) , CD19 ( clone 6D5 ) , CD44 ( clone IM7 ) , CD62L ( clone MEL-14 ) , CD45 . 1 ( clone A20 ) , CD45 . 2 ( clone 104 ) , CD90 . 1 ( clone OX-7 ) , CD90 . 2 ( clone 53–2 . 1 ) , Vα2 ( clone B20 . 1 ) , Vβ4 ( clone KT4 ) , Vβ5 ( clone MR9-4 ) , Vβ7 ( clone TR310 ) , Vβ10 ( clone B21 . 5 ) , Vβ11 ( clone RR3-15 ) ( from Biolegend or BD Pharmingen , CA , USA ) . The specificity of the anti-Vα and-Vβ mAbs are shown in Table 2 . The tetramers TB10 . 44–11-loaded H-2 Kb were obtained from the National Institutes of Health Tetramer Core Facility ( Emory University Vaccine Center , Atlanta , GA , USA ) . All stainings were performed for 20 min on ice and , unless stated , cells were fixed before acquisition with 2% formaldehyde in PBS for 30–60 min . Cell analysis was performed on a FACS Canto ( Becton Dickinson , NJ , USA ) or on a MACSQuant flow cytometer ( Miltenyi Biotec , Germany ) . Data were analyzed using FlowJo Software ( Tree Star , OR , USA ) . For cell sorting , stained and non-PFA fixed cells were suspended in MACS buffer ( Miltenyi Biotec , Germany ) and deposited in collection tubes using a BD Canto flow cytometer ( Becton Dickinson , NJ , USA ) . For both FACS analysis and cell sorting , single-lymphocyte events were gated by forward scatter versus height and side scatter for size and granularity . For TCRβ high-throughput sequencing , genomic DNA was purified from sorted cell populations and sequenced by Adaptive Biotechnologies Corp . ( Seattle , WA ) using the ImmunoSEQ assay ( http://www . immunoseq . com ) as previously described[51] . Data were analyzed using the ImmunoSEQ analyser toolset . Clonality is the entropy of the TCRB frequency distribution and is calculated as 1- ( entropy/log2[# unique TCRs] ) . Here entropy , a measure of diversity within a complex data set , is also known as the Shannon-Wiener index , Shannon’s diversity index or Shannon’s entropy [52 , 53] . Thus “0” represents a diverse repertoire and “1” is a completely clonal repertoire . Analysis of the precursor frequency of naïve T cells was performed as previously described [54] . Briefly , the spleen and axillary , mesenteric , cervical , inguinal , popliteal , and salivary LN were harvested from individual mice , dispersed , and filtered through a 70-mm mesh and enumerated for total and CD8+ T cell composition . The cell suspension was then costained with identical PE- and APC-conjugated tetramers and then purified via anti- PE magnetic bead selection ( Miltenyi Biotec , Germany ) . Positive and negative fractions were then surface stained with anti-MHC II , CD11b , CD19 , and CD4 as a “dump” channel , and anti-CD8α , CD3 and CD44 . Flow cytometry counting beads were added immediately before samples were collected by the cytometer to determine the fraction of tetramer+ events collected and used to determine the total number and frequency of tetramer+ cells in each animal . For analysis purposes , naïve cells were cells that were present in the bound fraction , costained with PE- and APC-conjugated tetramers , and did not express CD44 . Live lymphocytes were sorted as TCRβ+CD8+Tet+CD19-CD4-CD11b-CD11c- as individual cells into wells of 96-well PCR plates containing 10 μl of reverse transcriptase buffer ( 50 mM Tris-HCl , 75 mM KCl , and 3 mM MgCl2 ) , 2% Triton X-100 , 500 μM dNTP with 1 μg BSA , 50 ng of oligo ( dT ) ( 12–18 ) , 500 μM dTT , 50 μM of TCRα-specific primer , 50 μM of TCRβ-specific primer , 8 U of RNaseOUT , and 30 U of Moloney murine leukemia virus reverse transcriptase ( Invitrogen Life Technologies ) . The plates were incubated for 90 min at 37°C , then heat inactivated for 20 min at 80°C . 2 μl of the cDNA were used for each of the nested PCRs for TCRα or TCRβ ( see S9 Data for list of primers ) . The first round of each nested PCR amplification was performed by combining 2 μl of cDNA with 9 μl of Taq buffer ( 50 mM KCl , 10 mM Tris-HCl ( pH 8 . 3 ) , and 2 . 5 mM MgCl2 ) , 500 μM dNTP , 0 . 3 U of Taq polymerase , 50 μM of TRACext- or TRBCext-specific primer for the constant region and an oligonucleotide mixture of 23 TRAVext or 19 TRBVext primers ( each 50 μM final concentration ) . For the second round of the nested reaction , 2 μL of the first reaction were combined with 18 μL of Taq buffer ( 50 mM KCl , 10 mM Tris-HCl ( pH 8 . 3 ) , and 2 . 5 mM MgCl2 ) , 500 μM dNTP , 0 . 6 U of Taq polymerase , 50 μM of TRACint- or TRBCint-specific primer for the constant region and an oligonucleotide mixture of 23 TRAVint or 19 TRBVint primers ( each 50 μM final concentration ) . The PCR conditions for the first round of PCR were 94°C for 3 min followed by 35 cycles of 94°C for 20 s , 52°C for 45 s , and 72°C for 60 s , with a final extension at 72°C for 7 min . For the second round of PCR conditions were the same , but only for 26 PCR cycles . Contamination was monitored for all steps ( sorting , reverse transcriptase , and PCR ) , by leaving 16 control wells empty per 96-well PCR plate sorted . For TCR product sequencing , a total of 12 μl of the products from the second round of the nested PCR amplification was combined with 1 . 5 μl of 10X shrimp alkaline phosphatase reaction buffer ( 200 mM Tris-HCl ( pH 8 . 0 ) and 100 mM MgCl2 ) , 1 U of shrimp alkaline phosphatase ( Amersham Biosciences ) , and 1 U of exonuclease I ( New England Biolabs ) , and water to total 15 μl . The reaction was then heated to 37°C 30 min , 80°C 10 min , and cooled to 4°C , and the product was subjected to automated sequencing ( Dana-Farber/Harvard Cancer Center High-Throughput Sequencing Core ) . The sequences of the four TCRs cloned are shown in S10 Data . TCR retroviral constructs were generated as 2A-linked single open reading frames using PCR and cloned into a murine stem cell virus-based retroviral vector with a GFP marker as previously described [55 , 56] . Details of cloning strategies and primer sequences are available upon request ( samuel . behar@umassmed . edu ) . Retroviral-mediated stem cell gene transfer was performed as previously described [55 , 56] . 5×105 cells were plated in each well of a round bottom 96-well plate and incubated in the presence of TB10 . 44–11 peptide ( 10 μM; New England Peptide ) . Incubation in the presence of αCD3/αCD28 ( 1 μg/mL; BioLegend ) or in the absence of stimuli were used as positive and negative controls , respectively . Cells were incubated for 1 h at 37° C , at which point Golgi Stop solution ( BD Pharmingen , CA , USA ) was added to each well for the remaining 4 h . Cells were collected after the 5 h stimulation and then surface stained with the antibodies described above , followed by intracellular staining for IFNγ ( clone ) using BD Permwash Kit ( BD Pharmingen , CA , USA ) as per manufacturer’s instructions . In vitro cytotoxicity was determined using peptide- coated EL4 target cells differentially labeled with the cell proliferation dye efluor 450 ( eBiosciences ) as previously described [21] . Briefly , target cells were pulsed with 10 μM of TB10 . 4 peptide at 37°C for 1 h in complete medium or left unpulsed ( as controls ) , followed by extensive washing . Target cell populations were then labeled with either 10 μM or 100 nM e450 dye in PBS for 20 min at room temperature , followed by extensive washing . Labeled populations were mixed at an equal cell ratio with effector retrogenic cells at 1:1:1 ratio in round bottom 96-well plates ( 100 , 000 cells/population , in triplicate ) . Plates were incubated for 4–12 h at 37°C , in the dark . After incubation , the cells were analyzed by flow cytometry , and the ratios of recovered GFP ( retrogenic , effector ) and e450-labeled target EL4 populations were determined . Single cell suspensions of pools of spleens and LNs from naive retrogenic mice ( 6 to 10 wks post reconstitution ) were prepared . CD8+ T cells were purified from each suspension using the CD8+ T cell isolation kit and magnetic separation ( Miltenyi Biotec , Germany ) . After purification , cells were counted and transferred via the tail vein into congenically marked recipients ( CD90 . 1 ) , which had been infected 6–7 d earlier with virulent M . tuberculosis ( Erdman ) via the aerosol route . For priming experiments , 104 to 105 cells were transferred into each recipient . For competition experiments , cells were mixed at a 1:1 ratio ( confirmed by FACS analysis prior to injection ) and then transferred into each recipient . For analysis of cell proliferation of retrogenic cells after adoptive transfer , bead-purified naïve Rg cells ( see above ) were labeled with 10 μM of the cell proliferation dye efluor 450 ( eBiosciences ) in PBS for 20 min at room temperature , followed by extensive washing . Single cell suspensions of pools of spleens and LNs from naive retrogenic or OT-I mice were prepared . CD8+ T cells were purified from each suspension using the CD8+ T cell isolation kit and magnetic separation ( Miltenyi Biotec , Germany ) . After purification , cells were counted and mixed at a 1:1 ratio with peptide coated APCs in media containing 100U/mL of IL-2 and 10U/mL of IL-12 . APCs used were red blood cell lysed splenocytes from naïve C57Bl6/j mice , pulsed with 10 μM of TB10 . 4 or SIINFEKL peptide at 37°C for 1 h in complete medium , followed by irradiation with 3200 Rads from a cesium-137 source and extensive washing . 2x106 cells were plated , 1 mL into each well of a 24 well plate . After 24 h , cells were fed with 1 mL of fresh media , containing 100U/mL of IL-2 and 10U/mL of IL-12 . 48 h after the initial stimulation , cells were fed by removing 1 mL of culture media and addition of 1 mL of fresh media , containing 100U/mL of IL-2 . 60–72 h after the initial stimulation , cells were extensively washed with complete media , and used for adoptive transfer experiments . An adoptive transfer model was used to analyze the ability of T cells to mediate protection against pulmonary M . tuberculosis infection as previously described [21] . Briefly , C57BL/6 or TAP-ko mice were sublethally irradiated with 600 rad using a cesium-137 source . The next day , 104 to 106 bead-purified activated Rg T cells ( or OT-1 cells , as controls ) were transferred via the tail vein . Mice were infected with virulent M . tuberculosis ( Erdman ) via the aerosol route within 24 h of the adoptive T cell transfer . Three weeks after infection , the bacterial burden in the lung and spleen was determined . For survival experiments , naïve T cells ( 105 per mouse ) were transferred into TCRα KO mice via the tail vein; following transfer , mice were infected with virulent M . tuberculosis ( Erdman ) via the aerosol route . Population medians were used to compare TCR frequencies . Other data are represented as mean + SEM . For data with a verified for Gaussian distribution , a t-test was performed to compare two groups; otherwise , a Mann-Whitney U test was used . To compare more than 2 groups , one-way ANOVA , followed by Bonferroni post-hoc test was performed . Differences with a p<0 . 05 were considered significant and represented by * . | While T cells are required for protection against Mycobacterium tuberculosis infection , attempts to prevent tuberculosis by vaccines designed to elicit memory T cells have only been partially successful . Several vaccine candidates are in clinical trials , but progress has been slow because their ability to prevent disease must be empirically tested . There is little understanding of why certain antigens are targets of protective immunity . We have characterized an immunodominant CD8+ T cell response to the M . tuberculosis antigen TB10 . 4 ( EsxH ) . CD8+ T cells specific for the TB10 . 44–11 epitope are primed early during infection and account for 30–50% of lung CD8+ T cells during chronic infection . Now we have used deep sequencing to characterize the TCR repertoire of TB10 . 44-11-specific CD8+ T cells in the lungs of infected mice . Interestingly , TB10 . 44-11-specific CD8+ T cells exhibit extreme clonal expansion of certain TCRβ with common structural features , most likely because of affinity selection . Affinity selection of T cells is more important when antigen presentation is limiting . Although the lung contains numerous bacteria during infection , antigen-presentation by infected APC may be limiting , mimicking a “low antigen” state . Thus , even T cells that have the potential to mediate protection may function inefficiently because of suboptimal T cell activation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Human and Murine Clonal CD8+ T Cell Expansions Arise during Tuberculosis Because of TCR Selection |
Coordination of growth between and within organs contributes to the generation of well-proportioned organs and functionally integrated adults . The mechanisms that help to coordinate the growth between different organs start to be unravelled . However , whether an organ is able to respond in a coordinated manner to local variations in growth caused by developmental or environmental stress and the nature of the underlying molecular mechanisms that contribute to generating well-proportioned adult organs under these circumstances remain largely unknown . By reducing the growth rates of defined territories in the developing wing primordium of Drosophila , we present evidence that the tissue responds as a whole and the adjacent cell populations decrease their growth and proliferation rates . This non-autonomous response occurs independently of where growth is affected , and it is functional all throughout development and contributes to generate well-proportioned adult structures . Strikingly , we underscore a central role of Drosophila p53 ( dp53 ) and the apoptotic machinery in these processes . While activation of dp53 in the growth-depleted territory mediates the non-autonomous regulation of growth and proliferation rates , effector caspases have a unique role , downstream of dp53 , in reducing proliferation rates in adjacent cell populations . These new findings indicate the existence of a stress response mechanism involved in the coordination of tissue growth between adjacent cell populations and that tissue size and cell cycle proliferation can be uncoupled and are independently and non-autonomously regulated by dp53 .
In multicellular organisms , coordination of growth between and within organs contributes to the generation of well-proportioned organs and functionally integrated adults . Although the mechanisms that help to coordinate the growth between different organs start to be unraveled [1] , the underlying molecular mechanisms that contribute to generating well-proportioned adult organs remain largely unknown . In Drosophila , primordia of the adult head , thorax , and terminalia are established in the embryo as imaginal discs , which grow and proliferate within the feeding larva , fuse during metamorphosis , and give rise to the adult animal [2] . Even though the size , shape , and pattern of each adult structure are genetically determined in an autonomous manner by each imaginal disc , several humoral mechanisms contribute to coordinating the growth between imaginal discs to generate a well-proportioned adult fly . Insulin-like growth factors ( IGF ) and Target of Rapamycin ( TOR ) kinase signaling couples the nutritional status of the animal with the growth of each imaginal disc , and steroid and neuropeptide hormones coordinate the termination of growth with developmental timing ( reviewed in [3] ) . Damage or growth retardation of imaginal tissue induces , through the activity of steroid and neuropeptide hormones , a larval developmental delay to ensure that termination of growth is coordinated among growing tissues and all organs attain a characteristic final size [4] , [5] . Similarly , the activity of IGFs modulates the growth of each imaginal disc to give rise to well-proportioned adult flies , with variable sizes depending on nutritional status ( reviewed in [6] ) . Here we used the wing imaginal disc to first analyze whether adjacent cell populations within an organ grow in a coordinated manner to give rise to a well-proportioned structure and afterwards to determine the molecular mediators involved in this coordination . The wing disc is a mono-layered epithelium that grows about a thousand-fold in mass and cell number during larval development . After metamorphosis , it gives rise to the adult wing , a flat structure with a species-specific shape , size , and pattern . By reducing the growth rates of defined territories within the developing wing primordium and analyzing the non-autonomous response throughout development of the adjacent cell populations , we demonstrate that adjacent cell populations respond as a whole by decreasing their growth and proliferation rates . This non-autonomous response occurs independently of where growth is affected , and it is functional throughout development . We underscore a central and non-autonomous role of Drosophila p53 ( dp53 ) and the apoptotic machinery in these processes . While the decrease in growth and proliferation rates are regulated in a coordinated and non-autonomous manner by the activity of dp53 , effector caspases have a non-autonomous role in reducing proliferation rates . These new findings indicate the existence of a stress response mechanism involved in buffering local variations in growth in order to maintain the relative contribution of each cell population to the final organ and that tissue size and cell cycle proliferation can be uncoupled and are regulated by two different mechanisms downstream of dp53 .
In order to address whether adjacent cell populations grow and acquire a final size in a coordinated manner , we quantified the size and shape of adult wings when tissue growth was reduced in defined territories of the developing wing . Growth , defined as accumulation of cell mass , can be modulated by changing the biosynthetic capacity of cells ( reviewed in [7] ) . In Drosophila larvae , starvation of dietary nutrients leads to smaller adult flies . Starvation modulates tissue size by reducing insulin/TOR signaling , which leads to the inhibition of ribosome synthesis , a decrease in nucleolar size , and a reduction in protein synthesis capacity [7] , [8] . Thus , a set of genes with well-known growth inhibitory functions in ribosome function , protein biosynthesis , or insulin signaling were expressed with the Gal4/UAS system in specific territories of the developing wing primordium ( Figure 1A ) . A cold-sensitive version of the Ricin toxin A chain ( Ricincs ) , a protein toxin that belongs to the ribosome inactivating proteins that binds and reduces the translational activity of 28S rRNA and thus protein synthesis [9] , was used to impair growth , as its activity can be easily modulated in time by changes in temperature [10] . 4E-BP is an important repressor of translation levels and can be inactivated by the protein kinase TOR [11] . 4E-BP binds eIF4E and impairs the recruitment of the 40S ribosomal subunit to the cap structure present at the 5′-end of all eukaryotic cellular mRNAs . To reduce elF4E activity and impair growth , we used a constitutively active form of 4E-BP ( 4EBPAA , [12] ) that cannot be phosphorylated by TOR . Finally , the activity of the insulin/TOR pathway was reduced by expression of the tumor suppressor gene PTEN , a conserved negative regulator of the pathway [13] , [14] . A collection of Gal4 drivers expressed in distinct domains of the developing wing primordium was used to induce the expression of Ricincs , PTEN , and 4EBPAA ( Figures 1B , C and S1 ) . Larvae containing the Gal4 driver and the UAS-Ricincs transgene were initially grown at 18°C ( restrictive temperature ) until early second instar and then switched to 29°C ( permissive temperature ) until eclosion of the resulting adult flies . Expression of PTEN and 4EBPAA was achieved by growing the larvae at 25°C . Larvae expressing an UAS-GFP transgene and the corresponding Gal4 driver were subjected to the same temperature schemes and used as controls . Expression of Ricincs , 4EBPAA , or PTEN caused a clear reduction in total wing area in all the genotypes analyzed ( Figures 1B , C , S1 and Table S1 ) . Interestingly , these wings conserved normal shape , proportions , and vein patterning . The maintenance of shape and wing proportions suggests that the size of the neighboring cell populations not expressing the transgene is reduced in a non-autonomous manner . In order to quantify these non-autonomous effects , we focused our attention on those Gal4 drivers expressed either in the anterior ( A , ci-Gal4 , ptc-Gal4 , and dpp-Gal4 ) or posterior ( P , en-Gal4 , and hh-Gal4 ) compartments , cell populations that do not mix and give rise to defined structures of the adult wing ( [15] , e . g . Figure 1C ) . Both compartments , the one expressing the growth-reducing transgene and the adjacent one , decreased in size ( Figures 1E and S1 ) . A size reduction of 11%–14% was observed in the A compartment when Ricincs , 4EBPAA , or PTEN were expressed in P cells with the en-gal4 driver ( p<10−3 , Table S2 ) . A similar reduction was detected in the P compartment ( 14%–32% ) when these transgenes were expressed in A cells with the ci-Gal4 driver ( p<10−2 , Table S2 ) . Similar results were obtained with hh-Gal4 , ptc-Gal4 , and dpp-Gal4 drivers ( Figure S1 ) . The observed non-autonomous effects in tissue size are most likely a local response to a signal coming from the growth-depleted territory rather than a more general response of the whole larvae induced by the insult to the imaginal cells , since expression of PTEN with a Gal4 driver expressed in the developing wing but not in other tissues ( spaltPE-gal4 , [16] , Figure 1G ) gave rise to smaller wings ( 82 . 3%±2 . 1%; p<10−9 ) without a large impact on the overall size of the animal , as visualized by quantification of adult weights ( 103%±8%; p = 0 . 02 ) and pupal lengths ( 101%±4%; p = 0 . 08 ) ( Figure 1G–I and Table S1 , see also [4] ) . We next took advantage of the temperature sensitivity of the Ricincs transgene to conduct time-lapse experiments . Larvae expressing Ricincs in the A compartment with the ci-gal4 driver were initially grown at 18°C and switched to 29°C at different developmental stages until adult eclosion . The size of the resulting adult wings was measured . The reduction in size of the nearby P compartment ranged from 5% to 32% depending on when larvae were switched to the restrictive temperature ( Figures 1D , S1 and Table S4 ) . Longer exposure to Ricincs expression gave rise to the strongest phenotypes , thereby suggesting that the non-autonomous effects are operative throughout development ( see below ) . We also noted that the non-autonomous reduction in size of the P compartment was proportional to the reduction in size of the A compartment ( Figure 1D ) . The non-autonomous reduction in tissue size could be a consequence of reduced cell growth , reduced cell number , or both . To address this issue , we quantified cell densities in the adult wing by counting the number of hairs ( each cell differentiates a hair ) in a defined area in the two compartments . Cell densities were slightly increased to various degrees in the transgene-expressing compartment ( Figures 1F , S1 ) . The increase in cell densities ranged from 1 . 7% to 19% , depending on the Gal4 driver and the UAS-transgene used ( Table S3 ) . In the non-expressing transgene compartment , a significant increase in cell densities was observed mainly in those wing discs with the highest reduction in tissue size ( ci-gal4; UAS-Ricincs , 111 . 6%±2 . 2%; p<10−13 ) . Thus , changes in cell size contributed in a smaller extent to the non-autonomous reduction in tissue size and mainly in those situations where this reduction was above a certain threshold . The results presented so far indicate that growth depletion in defined territories of the wing induces a non-autonomous reduction in tissue size in nearby territories . In order to gain insight into whether this non-autonomous response is an active mechanism that takes place throughout development , we monitored the size of the A and P compartments throughout development after targeted expression of Ricincs in P cells . Larvae expressing Ricincs or GFP in the P compartment ( with the en-Gal4 driver ) were grown at 18°C and switched to 30°C from early second instar to late third instar stages . The size ratio between the transgene-expressing ( P ) and non-expressing ( A ) compartment was first quantified at a range of time points after induction of Ricincs expression . The P/A ratio was roughly maintained throughout the induction period in GFP-expressing wing discs ( blue dots in Figure 2A ) . In Ricincs-expressing discs , this ratio was smaller in the first 48 h after transgene expression but reached a similar value in mature wing discs ( red dots in Figure 2A , see also [17] ) . These results suggest that during development the Ricincs-expressing P compartment was relatively smaller than the nearby compartment , but both compartments reached a similar size ratio to that observed in control wing discs at the end of larval development . We next quantified and compared the absolute size of both compartments in GFP- and Ricincs-expressing wing discs . Interestingly , not only was the Ricincs-expressing P compartment already smaller 24 h after transgene expression , but also the adjacent A compartment showed a decrease in size when compared to GFP control wing discs ( Figure 2B ) . As observed in adult wings , the A and P compartments of Ricincs-expressing mature wing discs were smaller than those of control GFP-expressing wing discs ( Figure 2B ) , even though the Ricincs-expressing animals extended their larval period for about 24 h before entering metamorphosis ( Figure S2 ) . We next induced neutral clones of cells at the beginning of the Ricincs induction period ( early second instar ) and examined the size of these clones 72 and 96 h later in third instar wing discs . The size of the clones in the A compartment was measured in en-gal4; UAS-Ricincs wing discs and was compared to the size of control clones induced in the A compartment of en-gal4; UAS-GFP wing discs subjected to the same temperature schemes . In Ricincs-expressing wing discs , the size of the clones visualized 72 or 96 h after clone induction was , respectively , one-half or two-thirds smaller than the size of clones quantified in GFP-expressing discs ( Figure 2C ) . All together , the results presented so far indicate that growth rates are non-autonomously reduced when growth is depleted in defined territories of the developing wing . In order to gain insight into whether cell proliferation rates are also regulated in a non-autonomous manner , we analyzed cell cycle progression in developing wing primordia exposed to transgene expression . Larvae expressing Ricincs in the P compartment ( with the en-Gal4 driver ) were grown at 30°C from early second ( 48 h AEL ) to mid-late third instar stages ( 96 h AEL ) and the expression of markers for each cell cycle stage was then analyzed . Larvae expressing GFP in the P compartment were subjected to the same temperature schemes and used as controls . S phase progression was first monitored . Imaginal discs were exposed to BrdU for 45 min , and its incorporation was subsequently analyzed . BrdU incorporation was strongly reduced in the adjacent A compartment , while it was not significantly affected in the Ricincs-expressing domain ( Figure 2D , E ) . Similar non-autonomous effects in BrdU incorporation were observed by expression of PTEN or 4E-BPAA in the A or P compartment ( with the en-Gal4 and ci-Gal4 drivers , respectively; Figure 2N–P ) . Consistent with the observation that the non-autonomous effects in tissue size were not exclusive to the A and P compartments ( Figure 1 ) , a non-autonomous reduction in BrdU incorporation levels was also observed when driving expression of Ricincs in other domains in the wing primordium ( Figures 2M and S2 ) . Similarly , the non-autonomous reduction in BrdU incorporation were also observed at earlier stages of wing development ( Figure 2Q ) , thereby indicating that the non-autonomous effects in proliferation rates were operative throughout development ( Figure 1D ) . The observed non-autonomous effects in BrdU incorporation are most likely a local response to a signal coming from the growth-depleted territory rather than a more general response of the whole larvae , since targeted expression of Ricincs with the ap-Gal4 driver , which is expressed in the dorsal compartment of the developing wing , caused a non-autonomous reduction in BrdU incorporation in wing cells while other imaginal tissues not expressing the transgene ( e . g . eye ) or expressing it late in development ( e . g . leg ) incorporated BrdU at normal levels ( Figure 2R , S ) . Next , using in situ hybridization , we monitored the expression levels of cyclin E ( cycE ) and string ( stg , the Drosophila cdc25 homolog ) , two genes that act in wing disc cells as rate-limiting factors of G1/S and G2/M transitions , respectively [18]–[20] . Consistent with the non-autonomous reduction in BrdU incorporation levels , cycE mRNA levels were reduced in the A compartment of wing discs expressing Ricincs in P cells ( with the en-Gal4 driver , Figure 2F , G ) . Interestingly , stg mRNA levels were also reduced in these cells ( Figure 2H , I ) , suggesting that the G2/M transition was also compromised or delayed in a non-autonomous manner . Consistent with this view , mitotic activity , monitored with an antibody against a phosphorylated form of histone H3 at serine 10 ( PH3 ) that labels mitotic figures , was reduced in these cells ( Figure 2J , K ) . The number of PH3-positive cells observed in the A compartment of Ricincs-expressing wing discs decreased by 50% compared to GFP-expressing discs ( p = 0 . 02 , Figure 2L ) , whereas a similar number of PH3-positive cells was observed in the P compartment of both genotypes ( p = 0 . 6 , Figure 2L ) . Although we noted a slight increase in stg and cycE mRNA levels in cells expressing Ricincs ( Figure 2G , I ) , no detectable changes in mitotic activity or BrdU incorporation were observed ( Figure 2E , J , L ) . The non-autonomous reduction in mitotic activity , BrdU incorporation , and cycE and stg mRNA levels observed suggest that a general reduction in proliferation rates , without any obvious arrest in any particular cell cycle stage , is non-autonomously induced when growth is impaired in a defined territory of the developing wing . In order to confirm this hypothesis , we used a fluorescence-associated cell sorter ( FACS ) to collect data about the cellular DNA content of dissociated cells from 96 h AEL wing discs and analyzed the cell cycle profile of these cells . A forward scatter ( FSC ) analysis was also carried out to compare cell sizes . Cell cycle profiles and cell sizes of A cells dissociated from wing discs expressing Ricincs and GFP or GFP alone ( with the en-Gal4 driver ) in the P compartment were very similar ( Figure S2 ) . Comparable results were obtained with the ci-Gal4 driver ( Figure S2 ) . All together these data indicate that upon growth depletion in defined territories of the wing primordium , the adjacent cell populations reduced their growth and proliferation rates , giving rise to smaller structures with a smaller number of cells . The slight non-autonomous reduction in cell size contributes to a small extent to the non-autonomous reduction in adult tissue size ( Figure 1E , F ) and is most probably occurring during post-larval stages , since no major change in cell size was observed in imaginal tissues ( Figure S2 ) . We noticed that the levels of BrdU incorporation , stg and cycE expression , and mitotic activity were largely unaffected in the transgene-expressing compartment when compared to GFP control wing discs ( Figure 2 ) . This might reflect a process of compensatory proliferation due to the large number of cells being lost by cell death ( see below ) or by other means . In mammalian cells , inhibition of the insulin/TOR pathway or inhibition of protein biosynthesis increases the activity of the tumor suppressor gene p53 ( reviewed in [21] ) and induces the activation of the apoptotic machinery that breaks down cells in a highly controlled fashion by the action of caspases , a specialized class of cysteine proteases . Non-apoptotic functions of activated caspases have been previously described , and Drosophila and vertebrate caspases have been reported to regulate cell proliferation in various ways ( reviewed in [22] , see also [23] ) . Thus , we first monitored the contribution of the apoptotic machinery to the non-autonomous regulation of tissue growth and cell proliferation rates observed in developing wing discs and resulting adult wings . We performed a TUNEL assay to label DNA strand breaks induced by apoptotic cell death . A clear increase in TUNEL-positive cells was observed in those territories expressing Ricincs ( Figure 3A , B ) , PTEN , or 4E-BPAA ( Figure 3D , E ) as well as in adjacent cells . We next used an antibody against the activated form of human Caspase 3 , a marker of Caspase-9-like Dronc activity in Drosophila tissues [24] . We observed increased levels of Dronc activity in those territories expressing the transgene as well as in adjacent cells ( Figure 3C and unpublished data ) . The increase in TUNEL-positive cells and Dronc activity raises the question of whether apoptosis participates in the non-autonomous response observed in developing wing primordia and adult wings . Caspase activities are regulated by inhibitor-of-apoptosis proteins ( IAPs ) , and in Drosophila , DIAP1 binds and inhibits Dronc and the effector caspases DrICE and Dcp-1 ( Figure 3Q , reviewed in [25] ) . The Drosophila pro-apoptotic genes hid , grim , and reaper bind and repress DIAP1 , thus alleviating repression of initiator and effector caspases . In order to analyze the contribution of caspases to the non-autonomous effects in proliferation rates , we tested the requirement of various elements of the genetic cascade that drives apoptosis in Drosophila ( Figure 3Q ) . When apoptosis was reduced in the whole tissue by halving the dose of hid , grim , and reaper ( in Df ( H99 ) /+ wing discs ) or by depleting dronc expression ( in droncl29 wing discs ) , a clear reduction in TUNEL-positive cells was observed upon Ricincs expression ( compare Figure 3B with 3H , I ) . Interestingly , the non-autonomous reduction in BrdU incorporation levels caused by Ricincs expression was largely rescued in these discs ( Figure 3M , N and compare with Figure 2E ) . We then expressed DIAP1 and p35 in the same domain as Ricincs to analyze whether caspase activation is required in the growth-depleted territory or in the neighboring cell populations . DIAP1 represses both DrIcE and Dronc , while the baculovirus protein p35 specifically represses effector caspases DrIce and Dcp-1 and maintains fully active Dronc ( reviewed in [25] , Figure 3Q ) . Expression of either DIAP1 or p35 in the same domain as Ricincs caused a clear autonomous reduction in the number of TUNEL-positive cells ( Figure 3J and unpublished data ) as well as a clear non-autonomous rescue of BrdU incorporation levels ( Figure 3O and unpublished data ) . A similar non-autonomous rescue in the expression levels of cycE and string mRNA and in the number of mitotic figures was observed upon p35 expression ( Figure 3K , L , P ) . Similar results were obtained when PTEN or 4E-BPAA were co-expressed with p35 in different domains of the wing disc ( Figure 3F , G ) . We then used FACS and FSC analysis to characterize the cell cycle profile and the size of A cells upon expression of Ricincs and GFP , or Ricincs and p35 in the P compartment ( with the en-Gal4 driver , Figure S3 ) . Similar cell cycle profiles and cell sizes were obtained in both genotypes . Comparable results were obtained with the ci-Gal4 driver ( Figure S3 ) . The capacity of p35 expression to rescue the non-autonomous reduction in BrdU , string , and cycE levels and in mitotic activity caused by Ricincs expression , even though Dronc is fully active under these conditions [25] , suggests that effector caspases , such as DrIce and Dcp-1 , are required in the growth-depleted territory to induce a non-autonomous reduction in cell proliferation rates in the nearby cell populations . We next analyzed the resulting adult wings when the activity or expression of various elements of the apoptotic machinery was depleted in Ricincs-expressing larvae . The autonomous reduction in tissue size was not rescued and the cell densities were either unaffected or increased when apoptosis was reduced ( Figure 3R , S ) , thereby suggesting that effector caspases do not play a major role in the autonomous reduction in tissue size caused by Ricincs expression . Surprisingly , the non-autonomous reduction in tissue size was not rescued either ( Figure 3R ) . Nevertheless , and consistent with the role of effector caspases in regulating cell proliferation rates in nearby territories , cell densities were significantly increased in these territories in all the genetic backgrounds tested ( Figure 3S ) . The non-autonomous reduction in cell size is most probably occurring during post-larval stages , since no major change in cell size was observed in imaginal tissues ( Figure S2 ) . These results imply that growth and proliferation rates are independently regulated and that the decrease in cell proliferation rates does not play a major role in the observed reduction in tissue size . While effector caspases are required in the growth-depleted territory to induce a non-autonomous reduction in cell proliferation rates in the nearby cell populations , the non-autonomous reduction in tissue size relies on a caspase-independent mechanism . The transcription factor and tumor suppressor p53 , a short-lived , non-abundant protein in healthy cells , plays a major role in regulating the response of mammalian cells to stress , in part through the transcriptional activation of genes involved in apoptosis and cell cycle regulation [26] . Impaired TOR signaling , ribosomal biogenesis , and protein translation increase p53 activity [21] , [27] . Although the regulation of dp53 in Drosophila has not been fully elucidated , the biological function of p53 is well-conserved between flies and mammals [28] . dp53 mediates a variety of stress responses by inducing the expression of the pro-apoptotic gene reaper [29] , [30] . Interestingly , expression of reaper was induced in Ricincs- and in PTEN-expressing cells and this expression depended on the activity of dp53 ( Figure 4A-D ) . We did not find any evidence of increased dp53 mRNA levels in the transgene-expressing cells ( unpublished data ) , thus suggesting that the activation of dp53 is post-transcriptional . The increase in the number of TUNEL-positive cells caused by expression of Ricincs was largely rescued by reducing the activity of dp53 in the whole wing disc ( in dp53ns mutant larvae ) or in the transgene-expressing domain ( co-expressing dp53DN or dp53dsRNA; Figure 4E–H ) . Most interestingly , the effects of Ricincs on the levels of BrdU incorporation and on the number of mitotic PH3-positive cells were also largely rescued by dp53 depletion ( Figure 4I–M ) . These results indicate that dp53 and the effector caspases are required in the growth-depleted territory to non-autonomously reduce the proliferation rates in neighboring cell populations . We next analyzed the resulting adult wings when the activity or expression of dp53 was depleted in the domains expressing Ricincs . Consistent with the observation that the apoptotic machinery and effector caspases do not play a major role in the autonomous reduction in tissue size caused by expression of these transgenes , the autonomous reduction in tissue size was still observed and the cell densities were either unaffected or increased when dp53 activity was reduced ( Figure 5A ) . However , and in contrast to the apoptotic machinery and the effector caspases , dp53 exerts a fundamental role in the non-autonomous regulation of tissue size . The non-autonomous reduction in tissue and cell size caused by Ricincs expression was either weaker or not observed when dp53 activity was depleted in the transgene-expressing domain ( Figure 5A ) , and the resulting adult wings were not well-proportioned and exhibited a clear asymmetric shape with respect to the AP boundary ( Figure 5B ) . In other words , in a situation of reduced dp53 activity in the Ricincs-expressing compartment , the neighboring compartment exhibited a size nearly identical to the one observed in GFP control wings . These results indicate that , upon growth depletion , dp53 exerts a fundamental and non-autonomous role in reducing the size of the adjacent cell populations . In order to address whether growth rates are also regulated in a non-autonomous manner by the activity of dp53 , we induced neutral clones of cells at the beginning of the Ricincs induction period ( early second instar ) and examined , 72 h and 96 h later , the size of clones located in the A compartment of wing discs expressing GFP , or Ricincs and GFP , or Ricincs and dp53dsRNA in the P compartment ( with the en-gal4 driver , Figure 4N ) . Interestingly , the non-autonomous reduction in clone size caused by Ricincs expression was largely rescued by co-expression of dp53dsRNA ( Figure 4N ) .
The Drosophila wing primordium increases about a thousand-fold in cell mass and cell number during larval development . Even though molecules of the Wnt , TGF-β , and Hedgehog families are well known to organize tissue growth and patterning of this primordium and to play a fundamental role in the generation of an adult wing with a species-specific size , shape , and patterning [31] , the molecular mechanisms that contribute to buffering local variations in tissue growth caused by different types of stress and help to generate well-proportioned adult wings under these circumstances remain largely unknown . Here we underscore a new role of dp53 and the apoptotic machinery in these processes . Depletion of the insulin pathway or the protein biosynthetic machinery in defined territories of the developing wing primordium induces an autonomous reduction in growth rates as well as a non-autonomous decrease of growth and cell proliferation rates in the adjacent cell populations . This non-autonomous response occurs independently of where growth is affected and is functional throughout development . We present evidence that targeted depletion of the insulin pathway or the protein biosynthetic machinery induces the activation of dp53 and consequently the apoptotic machinery . While growth and proliferation rates are regulated in a coordinated and non-autonomous manner by the activity of dp53 , effector caspases have a unique role , downstream of dp53 , in reducing proliferation rates in adjacent cell populations ( Figure 5C ) . Thus , tissue growth and proliferation rates can be uncoupled and are regulated by two different mechanisms downstream of dp53 . These new findings underscore a new role of dp53 and effector caspases in buffering local variations in tissue growth and in maintaining the relative proportions of distinct cell populations within a tissue to give rise to a functional adult structure . Independent lines of evidence support the view that adjacent cell populations are able to buffer local variations in tissue growth caused by different means , and not only when the activities of the insulin pathway or the protein biosynthetic machinery are compromised . The halteres and wings of Drosophila are homologous thoracic appendages , which share common positional information provided by signaling pathways . The activity in the haltere discs of the Ultrabithorax ( Ubx ) Hox gene establishes the differences between these structures , their different size being an obvious one ( reviewed in [32] ) . In Contrabithorax mutant wings in which one compartment is reduced in size due to the transformation to haltere by the ectopic expression of Ubx , the adjacent compartment adjusts its final size and results in a smaller wing territory [33] . Similar non-autonomous effects in tissue size were observed when inducing clones of cells with reduced EGF-Receptor activity [34] , upon depletion of the dMyc proto-oncogene or over-expression of the hippo tumor-suppressor gene ( Figure S5 ) . Nevertheless , we cannot rule out the possibility that in other situations in which growth is being challenged , the tissue might not respond as a whole . In these situations , we speculate that dp53 is inactive or not activated . We would like also to point out that the dp53 dependent mechanism described in this work might be functional to buffer local and slight variations in growth rates . Above a certain threshold in the reduction of tissue size , this mechanism might not be sufficient to generate well-proportioned organs . In vertebrates and invertebrates , p53 and caspase-dependent cell death also play a fundamental role in regeneration ( reviewed in [35] ) as well as in response to stress or tissue damage , whereby the damaged tissue undergoes extra cell proliferation to compensate for cell loss [23] , [36]–[38] . Upon tissue damage in Drosophila tissues , a feedback loop mediated by dp53 and the initiator caspase Dronc is required in undifferentiated dying cells to induce cell proliferation in surrounding cells [30] . A specific role of effector caspases has been described in differentiated neurons to induce , upon tissue damage , cell proliferation in surrounding cells [39] . These non-autonomous effects are mediated by the ectopic activation of signaling molecules of the Wnt , TGF-β , and Hedgehog families . In contrast , we did not find any clear change in the expression of Wnt , TGF-β , or Hedgehog in the growth-depleted territory or in the activity of their signaling pathways in adjacent cell populations ( Figure S4 and unpublished data ) . Also , while dp53 and caspases have a role in inducing cell proliferation within the damaged tissue [30] , our data indicate that the same molecules reduce growth and proliferation rates in adjacent unaffected cell populations . These observations suggest that different types of signaling molecules induced or activated by dp53 and caspases exert different effects in the damaged tissue and in nearby ones . It is interesting to note in this context that effector caspases have been recently shown to promote wound healing and tissue regeneration in mice by mediating the cleavage of iPLA2 ( Calcium-independent Phospholipase A2 ) to trigger the production of the growth signal Prostaglandin E2 [23] . Whether the non-autonomous reduction in cell proliferation rates is due to the caspase-dependent cleavage and activation of specific signaling molecules or whether dying cells release a variety of signaling molecules as a consequence of cell demolition [40] that could have a general role in the reduction of cell proliferation rates remains to be solved . Similarly , the signaling molecules that mediate the non-autonomous role of dp53 in regulating tissue growth remain to be identified . Finally , we would like to highlight the central role of p53 in tissue homeostasis and stress response . While the role of p53 in regulating the response of mammalian cells to stress through the transcriptional activation of genes involved in apoptosis and cell cycle arrest is cell-autonomous [41] , we speculate that the non-autonomous role of dp53 defined in this study also makes a major contribution to stress response and tissue homeostasis . Upon tissue damage , neighboring populations of healthy cells might reduce their growth and proliferation rates until damaged cells have been recovered . The combined autonomous and non-autonomous activities of p53 might be fundamental in tissue homeostasis .
The Drosophila strains include: UAS-Ricincs ( [10] , a cold sensitive version of the Ricin toxin A Chain ) , UAS-4EBPAA ( a 4E-BP constitutively active form with two threonine-to-alanine substitutions ( T37A , T46A ) [12] ) , UAS-PTEN [13]; salPE-Gal4 [16]; UAS–dp53RNAi ( ID 10692 , VDRC ) ; and dp53ns ( a knock out of dp53 created by ends-in homologous recombination , Flybase ) . Other stocks are described in Flybase or Text S1 . Mouse anti-BrdU ( Developmental Studies Hybridoma Bank ) ; rabbit anti-PH3 ( Upstate Biotechnology ) ; rabbit anti-GFP ( Abcam ) ; rabbit anti-cleaved-Caspase 3 ( Asp175 , Cell Signaling Technology ) . Other antibodies are described in Text S1 . Secondary antibodies were obtained from Molecular Probes . TUNEL analysis , BrdU staining , and in situ hybridization were performed as described in [20] . A Digoxigenin-RNA Labeling kit ( Roche ) was used to synthesise probes of string , cycE , and reaper . Wing size and size of the A and P compartments were measured using the Image J Software ( NIH , USA ) . Cell density was measured as the number of hairs ( each wing cell differentiates a hair ) per defined area . Two conserved regions between veins L4 and L5 ( P compartment ) and veins L2 and L3 ( A compartment ) in both dorsal and ventral surfaces of the wings were used to measure cell densities . The final area and cell density values were normalized as a percent of the control Gal4-driver; UAS-GFP values . At least 10 adult wings per genotype were scored . Only adult males were scored . The average values and the corresponding standard deviations were calculated and t test analysis was carried out . en-Gal4/SM6a-TM6b/UAS-Ricincs or en-Gal4 , UAS-GFP females were crossed with w1118/Y males and allowed to lay eggs for 5 h at 18°C . After 6 days at 18°C , the resulting larvae were transferred to 29°C . Wing discs were dissected after different time points of Ricincs induction and tissue size was measured from confocal images by means of Image J software ( NIH , USA ) . At least 10 wing discs were used per time point . Average values and the corresponding standard deviations were calculated and t test analysis was carried out . Note that , consistent with the temperature sensitivity of Ricincs , en-gal4;UAS-Ricincs adult wings grown at the restrictive temperature ( 18°C ) showed a size nearly identical to the one of en-gal4;UAS-GFP adult wings ( Figure S1 ) , hs-FLP , ubi-GFP , FRT19 or hs-FLP , ubi-GFP , FRT19; +/+; UAS-p53RNAi females were crossed with control FRT19/Y and experimental FRT19/Y; en-Gal4/SM6a-TM6b/UAS-Ricincs males and allowed to lay eggs for 5 h at 18°C . After 6 days at 18°C , the resulting larvae were heat-shocked at 37°C for 30 min and then transferred to 29°C . Wing discs were dissected 72 h and 96 h after clone induction . The size of clones was quantified from confocal images with Image J software ( NIH , USA ) . At least 15 clones were quantified per time point . Average values and the corresponding standard deviations were calculated and t test analysis was carried out . Wing discs of larvae carrying different UAS transgenes and the en-Gal4 or ci-gal4 drivers were dissected and cells were dissociated according to the protocol described in [42] . Hoescht and GFP fluorescence were determined by flow cytometry using a MoFlo flow cytometer ( DakoCytomation , Fort Collins , CO ) . Excitation of the sample was carried out using a Coherent Enterprise II argon-ion laser . Excitation with the blue line of the laser ( 488 nm ) permits the acquisition of forward-scatter ( FS ) , side-scatter ( SS ) , and green ( 530 nm ) fluorescence from GFP . UV emission ( 40 mW ) was used to excite Hoescht blue fluorescence ( 450 nm ) . Doublets were discriminated using an integral/peak dotplot of Hoechst fluorescence . Optical alignment was based on optimized signal from 10 µm fluorescent beads ( Flowcheck , Coulter Corporation , Miami , FL ) . DNA analysis ( Ploidy analysis ) on single fluorescence histograms was done using Multicycle software ( Phoenix Flow Systems , San Diego , CA ) . Forty hatched first-instar larvae were sorted from egg laying plates ( egg laying period of 5 h ) , transferred to fresh tubes , and kept at 18°C . Then , animals of the different genotypes were transfer to 29°C from early second instar and the number of pupae was counted daily . Results were expressed as percentage of the individuals from each genotype that attained the pupal stage . | The coordination of growth within and between organs contributes to the generation of functionally integrated structures and well-proportioned animals and plants . Though these issues have fascinated biologists for centuries , the responsible molecular mechanisms remain largely uncharacterized . In this work , we have used the Drosophila wing primordium to show that adjacent cell populations grow and proliferate in a coordinated manner . By reducing growth rates in specific territories within the developing wing , we showed that the tissue responds as a whole and that in adjacent cell populations the growth and cell cycle rates are concomitantly reduced , thus maintaining tissue proportions and normal wing shape . Interestingly , we show that the Drosophila tumour suppressor protein dp53 and apoptotic machinery play a key role in coordinating this tissue-wide response . Both growth and proliferation rates are regulated in a coordinated and non-autonomous manner by the activity of dp53 , whilst the apoptotic pathway has a specific and non-autonomous role in regulating cell proliferation rates . Our studies describe a novel mechanism for regulating tissue growth in developing organs that may ultimately be relevant for other processes involving coordination of growth , such as tissue renewal , regeneration , and cancer . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology/molecular",
"development",
"developmental",
"biology/organogenesis"
] | 2010 | A dp53-Dependent Mechanism Involved in Coordinating Tissue Growth in Drosophila |
Upon viral infection , the production of type I interferon ( IFN ) and the subsequent upregulation of IFN stimulated genes ( ISGs ) generate an antiviral state with an important role in the activation of innate and adaptive host immune responses . The ubiquitin-like protein ( UBL ) ISG15 is a critical IFN-induced antiviral molecule that protects against several viral infections , but the mechanism by which ISG15 exerts its antiviral function is not completely understood . Here , we report that ISG15 plays an important role in the regulation of macrophage responses . ISG15−/− macrophages display reduced activation , phagocytic capacity and programmed cell death activation in response to vaccinia virus ( VACV ) infection . Moreover , peritoneal macrophages from mice lacking ISG15 are neither able to phagocyte infected cells nor to block viral infection in co-culture experiments with VACV-infected murine embryonic fibroblast ( MEFs ) . This phenotype is independent of cytokine production and secretion , but clearly correlates with impaired activation of the protein kinase AKT in ISG15 knock-out ( KO ) macrophages . Altogether , these results indicate an essential role of ISG15 in the cellular immune antiviral response and point out that a better understanding of the antiviral responses triggered by ISG15 may lead to the development of therapies against important human pathogens .
The host innate immune response represents a critical initial line of defense against invading pathogens , and the magnitude of this early response can influence the course of disease progression . One of the earliest host responses to viral infection is the production of type I interferon ( IFN-α and-β ) and the subsequent upregulation of IFN-stimulated genes ( ISGs ) [1] , [2] . These ISGs generate an antiviral state and play an important role in determining the host innate and adaptive immune responses . One of the most highly induced genes in the IFN response is ISG15 , which encodes a small UBL protein of 17 kDa that forms covalent conjugates with cellular proteins mediating considerable antiviral responses [3] . During viral infection in mice , ISG15 exists in three different forms: unconjugated within the cell , conjugated to target proteins and released into the serum [4] . When ISG15 is secreted , free ISG15 can function as a cytokine that modulates the immune response . For example , free ISG15 can activate natural killer ( NK ) and cytotoxic T-cells , stimulate IFN-γ production and induce dendritic cell maturation and neutrophil recruitment [5] , [6] . In addition , antiviral activity associated with protein ISGylation has been described in vitro and/or in vivo for both DNA and RNA viruses , including influenza A and B viruses [7] , Sindbis virus [7] , [8] , hepatitis B virus [9] , herpes simplex type-1 virus [7] , vaccinia virus [10] , vesicular stomatitis virus [11] , [12] , lymphocytic choriomeningitis virus [13] , respiratory syncytial virus [14] , HIV-1 [15] and Ebola virus [16] . In contrast , free ISG15 , but not ISGylation , promotes antiviral responses against Chikungunya virus infection [17] . Mice lacking UbE1L , the ISG15 E1 enzyme required for ISG15 conjugation , are more susceptible to influenza virus infection , indicating that ISGylation is essential for ISG15 antiviral activity [18] . ISG15 was shown to conjugate to over 160 host and viral proteins [19] , especially those undergoing active translation [20] . Some of these host target proteins are downstream effectors of the interferon signaling , such as double-stranded RNA-activated protein kinase ( PKR ) [21] , Myxovirus resistance protein A ( MxA ) , and Retinoic acid inducible gene-I ( RIG-I ) [4] , while others are involved in the regulation of type I IFN signaling , e . g . Janus kinase 1 ( JAK1 ) , extracellular signal-regulated kinase ( ERK1 ) , interferon regulatory transcription factor 3 ( IRF3 ) and signal Transducers and Activator of transcription 1 ( STAT1 ) [22] . The ISGylation of these cellular proteins may also contribute to the antiviral activity of ISG15 . Moreover , it has been described that ISG15 expression blocks the virus-budding process by different mechanisms such the blockage of Endosomal Sorting Complexes Required for Transport ( ESCRT ) machinery for HIV [15] , or in the case of Ebola and other enveloped viruses infections , inhibiting the Nedd4 E3 ubiquitin ligase [23] . Furthermore , several viral proteins have been shown to be conjugated to ISG15 , such as the NS1 protein from influenza virus , NS5A from Hepatitis C virus [24] and gag from HIV virus [25] . It has been proposed that conjugation to viral proteins inhibits specific viral functions or virions assembly , causing a block in viral infection progression [26] , [27] . Concerning VACV replication , previous publications from our group showed that ISG15 exerts antiviral activity against this virus , and that viral E3 protein can bind to ISG15 , counteracting its activity . Thus , a VACV strain lacking E3 ( VVΔE3L ) was unable to replicate in ISG15+/+ cells , but was able to replicate in ISG15−/− deficient cells [10] . Moreover , infection of ISG15−/− mice with VVΔE3L resulted in significant disease and mortality , which was not observed in ISG15+/+ mice infected with this attenuated virus [10] . Besides the antiviral activity of ISG15 , it has been recently described that a mutation in human ISG15 correlates with “Mendelian susceptibility to mycobacterial disease” ( MSMD ) , a rare disorder that manifests in severe clinical symptoms following infection with weakly virulent mycobacterial strains and other intracellular pathogens [28] . Although the role of ISG15 as a defense molecule against the infection by several pathogens is accepted , the mechanisms by which its antiviral properties are exerted and the main cellular populations responsible for these activities are still weakly defined . In order to characterize whether the ISG15 antiviral activity observed in animal models is cell-type specific and to further characterize its implication in the immune response , we have now analyzed the impact of ISG15 deficiency on VACV replication using different primary cell lines ( fibroblasts , dendritic cells and macrophages ) derived from ISG15+/+ and ISG15−/− mice . Here , we have observed that macrophages are key effectors of ISG15-mediated antiviral responses during VACV infection and , very importantly , that one of the most essential functions of the macrophages , the phagocytosis , is dramatically diminished in the absence of ISG15 . These results provide valuable information on the underlying mechanisms governing the suppression of viral infection by ISG15 .
In order to characterize if different ISG15 deficient cell types exhibit variable susceptibility to viral infection , we examined VACV replication in different primary cells , derived from both ISG15+/+ and ISG15−/− mice . As a first indicator , we analysed the cytopathic effect ( CPE ) induced in fibroblasts ( MEFs ) or peritoneal F4/F80 positives macrophages ( Figure S1 ) after VACV infection ( 5 PFU/cell ) . CPE was estimated by cell rounding and alterations in cell morphology . While no differences in the VACV-induced CPE were observed between wild type and ISG15−/− MEFs ( Fig . 1A ) , VACV-induced CPE was clearly detectable in ISG15+/+ macrophages ( Fig . 1B and Movie S1 ) , but not in ISG15−/− cells ( Fig . 1B and Movie S2 ) . In order to confirm these observations and to get a more quantitative result , we measured the cellular mortality produced by VACV infection in the different cell lines by a cellular viability assay . In agreement with the previous results , in the case of VACV-infected MEFs ( Fig . 1C ) , no differences in the percentage of cellular viability at 24 hours post-infection were observed in any case and , as expected , percentage of cell death correlated with the multiplicity of infection used both in for ISG15+/+ and ISG15−/− cells . However , in the infected peritoneal macrophages ( Fig . 1E ) , a higher percentage of ISG15−/− cells survived to the infection , at each multiplicity of infection ( MOI ) analyzed , in comparison to the results in ISG15+/+ macrophages . In order to evaluate if the differences in cell viability in response to VACV infection were due to variations in the viral production in the absence of ISG15 , we quantified virus titers in both ISG15+/+ and ISG15−/− MEFs and peritoneal macrophages infected with VACV in the same conditions . Upon VACV-infection , no significant differences in viral titters were observed between ISG15+/+ and ISG15−/− MEFs . Furthermore , and as expected , the viral titers increased with time ( Fig . 1D ) and correlated with the observed increase in cellular mortality ( Fig . 1C ) . In contrast , in macrophages from both ISG15−/− or ISG15+/+ mice , and according to previously published results [29] , the infection with VACV was abortive and viral titers did not increase over time ( Fig . 1F ) . In addition , viral protein synthesis in both VACV-infected ISG15+/+ and ISG15−/− MEFs and peritoneal macrophages was evaluated by Western blot using specific antibodies for early p25 ( E3L ) , intermediate p39 ( A4L ) , and late viral proteins p14 ( A27L ) . Proteins encoded by E3L and A4L genes were efficiently detected in lysates of all the infected cells ( Fig . 2A–B ) . However the relative levels were significantly lower in the ISG15−/− macrophages , suggesting that viral infection could be blocked in an early step in these cells . Regarding the late proteins encoded by A27L genes , they were detected only in lysates from infected MEFs ( Fig . 2A ) and not in infected macrophages ( Fig . 2B ) , confirming that VACV infection is abortive in macrophages . To further characterize these results , the kinetics of viral protein synthesis and the viral induced shut-off , as indicators of the infection progression , were evaluated by metabolic labeling at 3 , 6 and 9 h after VACV infection in the different cell lines . In ISG15+/+ and ISG15−/− MEFs , the viral protein synthesis pattern presented similar kinetics , strongly suggesting that ISG15 does not significantly alter VACV replication in this cell type ( Fig . 2C ) . However , in macrophages , kinetics of viral gene expression was strongly affected by the lack of ISG15 . Whereas in ISG15+/+ macrophages , the synthesis of viral proteins was clearly detectable at 3 hpi , a clear delay in the course of infection and viral protein synthesis was observed in ISG15−/− macrophages . To further analyse the putative cell-specific role of ISG15 on viral replication , we carried out similar experiments in ISG15+/+ and ISG15−/− bone marrow-derived dendritic cells ( BMDC ) ( data not shown ) . As observed in MEFs , similar levels of viral protein were expressed in ISG15+/+ and ISG15−/− BMDC . The above stated results could be explained by differences in ISG15 expression levels among MEFs and macrophages in response to viral infection . To evaluate this hypothesis , we analyzed the ISG15 expression levels ( both non-conjugated and conjugated to its cellular protein target ) , which was clearly higher in VACV-infected macrophages than in the infected-MEFs ( Fig . 2A–B ) . In summary , VAVC infection appears to be similar in ISG15+/+ and ISG15−/− MEFs , but appears to be reduced in ISG15−/− macrophages , which correlates with lower cell virus-induced death . Although these data appear counterintuitive , as ISG15 would have been expected to inhibit viral infection , and not to promote viral infection , these results suggest that ISG15 expression might be crucial for macrophage activation in response to viral infection and allow us to speculate that ISG15 could modify host factors involved in viral cycle modulation in macrophages . The above exposed results indicated that the infection of VACV is modulated in macrophages by an uncharacterized ISG15 dependent mechanism . Moreover , clear differences in viral-induced cell death were observed in these cells in the absence of ISG15 , indicating that ISG15 could regulate macrophages programmed cell death in response to viral infection . To analyze this hypothesis , we first evaluated cell death by apoptosis in response to VACV infection , by the cleavage of the poly ( ADP-ribose ) polymerase-1 ( PARP-1 ) using an AB that recognizes both full-length and cleaved forms of PARP-1 [30] . In infected MEFs , derived either from ISG15+/+ or from ISG15−/− mice , despite cell death ( Fig . 1A ) , no evident signs of apoptosis were observed ( Fig . 3A ) . However , an 89 kDa PARP-1 cleavage product , indicative of activation of the apoptotic cascade , was clearly observed at 6 hpi in ISG15+/+ macrophages . Remarkably , this fragment was only weakly detectable in the ISG15−/− infected macrophages ( Fig . 3B ) , suggesting that activation of apoptosis was impaired in infected macrophages in in the absence of ISG15 . To further confirm these observations , additional apoptosis activator markers were evaluated in ISG15+/+ and ISG15−/− MEFs or peritoneal macrophages infected with VACV . A clear activation of caspase 3 and 9 was only observed in ISG15+/+ infected macrophages , which correlated with lower levels of the anti-apoptotic factor B-cell lymphoma 2 ( Bcl-2 ) . In order to get a more quantitative and physiological indicator of the apoptosis activation in these cells , the Caspase-Glo 3/7 assay kit was used following manufacturer's instructions . As shown in Fig . 3C , VACV-induced apoptosis was almost undetected in ISG15+/+ and ISG15−/− MEFs , further validating the observations described above . However , when cells were treated with the apoptosis activators epopside and staurosporine , apoptosis in ISG15+/+ and ISG15−/− treated-MEFs reached similar levels , confirming that ISG15 does not play any role in the activation of the apoptosis cascade in MEFs . In contrast , VACV-induced apoptosis activation was only detected in ISG15+/+ cells but not in macrophages lacking ISG15 . In order to exclude that ISG15 generally regulates macrophage apoptosis rather than controlling apoptosis induction upon viral infection , we again used the general apoptosis activators epopside and staurosporine . A slight diminution of the apoptosis induced by these compounds was detected in the absence of ISG15 , indicating that , although it could be implicated in general apoptosis , ISG15 mainly regulates the apoptosis in response to viral infection ( Fig . 3D ) . Although these data might be explained by a higher level of viral infection and viral protein synthesis in ISG15+/+ macrophages , resulting in enhanced apoptosis as compared to ISG15−/− macrophages , these results also suggest that in macrophages but not in MEFs ISG15 is involved in the specific activation of programmed cell death upon VACV infection . The data presented above suggest that the virus infection itself could be the signal that triggers apoptosis in macrophages , as the VACV cycle appears to be prematurely inhibited in the absence of ISG15 in these cells . Given that ISG15−/− infected macrophages showed an increase in cellular survival , accompanied by a reduction in viral protein levels and a delayed kinetic of viral protein synthesis , we decided to analyze whether VACV entry was impaired in ISG15−/− macrophages . For that , we used a recombinant virus in which the viral structural protein A3L was labeled with the yellow fluorescent protein ( YFP ) , allowing the visualization of the viral particles by time-lapse microscopy . When macrophages were infected with the VACV-YFP , we observed a delay in the entry of the virions inside the cell in the absence of ISG15 ( Fig . 4 ) . While in wild type ( WT ) macrophages the fluorescent signal increased with time inside the cell ( Fig . 4A ) , in ISG15−/− macrophages , the signal was localized mainly outside the cell even after 2 hours of adsorption ( Fig . 4B ) . These results indicate that , in the absence of ISG15 , infection was blocked at an early step , which might also be the reason for the delay in the kinetics observed in the viral protein radiolabeling experiment ( Fig . 2D ) . These results suggest that , in the absence of ISG15 , the virus entry is impaired and made us to consider whether this mechanism was exclusive for VACV or if it also observed upon infection with other viruses . Therefore , we performed similar experiments using FluV , a completely different virus with different cellular receptor . In a first approach , we infected ISG15+/+ and ISG15−/− MEFs or peritoneal macrophages with FluV ( 5 PFU/cell ) and , as above described for VACV , evaluated de novo viral protein synthesis using 35S-Met at different times post-infection . As observed for VACV infection , there are no variations in viral protein synthesis in MEFs in the absence of ISG15 ( Fig . 5A ) , but a clear delay in the overall viral protein synthesis is observed in the infected ISG15−/− macrophages ( Fig . 5B , compare 3 hpi in ISG15−/− and ISG15 +/+ macrophages ) . Although no viral production was observed in both ISG15+/+ and ISG15−/− macrophages ( Figure S2 ) , when we visualized the effect of FluV infection by phase-contrast microscopy , a clear CPE was evident in the ISG15+/+ macrophages with the course of infection , whereas the ISG15−/− macrophages remained unaltered with no obvious signs of viral infection-induced cell death ( Fig . 5C ) . Since the block in infection and the differences in viral induced CPE were observed with two very distinct viruses , we next investigated whether other cellular entry processes , such as phagocytosis , might be regulated by ISG15 in macrophages . A major function of macrophages is the phagocytosis of pathogens , antigens , and infected or apoptotic cells , which is critical for innate as well as for adaptive immunity . Taking into account that in ISG15−/− macrophages the early events in the infection cycle of two completely different viruses , VACV or FLuV , were aborted , and that this was at the level of virus endocytosis , at least for VACV , we considered that other cellular entry processes inherent to macrophage function , such as phagocytosis , could be controlled by ISG15 . Latex beads are a common model substrate in biochemical studies of macrophage phagosome composition and maturation [31] . To determine whether ISG15 is critical for macrophage phagocytosis capacity , we analyzed the intake of GFP labelled latex beads in ISG15+/+ and ISG15−/− macrophages by confocal and time-lapse microscopy , as described in Material and Methods . To potentially enhance the impact of ISG15 , which is an IFN induced protein , macrophages were treated with type I IFN alpha ( 100 units/ml for 16 hours ) or left untreated [32] . The time-lapse microscopy images showed a marked decrease of latex beads phagocytosis in ISG15−/− macrophages when compared to the ISG15+/+ cells . This difference was even more evident upon IFN treatment , further implicating a role of ISG15 in this process ( Fig . 6A–C and Movie S3-S4-S5-S6 ) . Quantification of these results revealed that , after IFN incubation , the phagocytic capacity of ISG15+/+ cells was about 100 times higher than that observed in ISG15−/− cells ( Figs . 6B ) . Moreover , and pointing out the biological relevance of ISG15 in phagocytosis , IFN treatment increased the latex bead uptake in ISG15+/+ macrophages but not in ISG15−/− cells ( Fig . 6B ) . Representative confocal immunofluorescence images of macrophages with internalized beads are shown in Fig . 6D . Furthermore , to check if the treatment with IFN also enhance the entry of VACV in macrophages , we monitored by immunofluorescence the amount of virus inside the cell after 2 hpi in permeabilized ISG15+/+ and ISG15−/− cells treated or not with IFN . A clear increase of viral entry was observed after IFN treatment in ISG15+/+ infected macrophages ( Figure S3 ) . All together , these results strongly suggest that ISG15 has an important role in the phagocytic activity of macrophages that is in correspondence with an increase of viral entry , and that IFN enhances both processes through the induction of ISG15 . The activation of Phosphoinositide 3-kinase/ ( PI3K ) - A protein-serine/threonine kinase ( AKT ) signaling has previously been shown to be required for macrophage phagocytosis [31] , [33] . Therefore , we examined whether the activation of this signaling cascade could be affected in the absence of ISG15 . The phosphorylation of AKT , mammalian Target of Rapamycin ( m-Tor ) and ERK1-2 in ISG15+/+ and ISG15−/− macrophages in response to VACV infection was monitored by Western blot . ISG15+/+ macrophages have some basal levels of AKT phosphorylation under the used conditions , which were increased at early times upon exposure to VACV . By contrast , basal levels of P-AKT were clearly reduced in ISG15−/− macrophages , when compared to ISG15+/+ cells , and these did not increased upon exposure to VACV ( Fig . 7A ) . In contrast , phosphorylation levels of m-Tor or ERK1-2 were similar in both types of cell populations . Similar results were obtained when macrophages were incubated with latex beads ( data not shown ) , indicating that the underlying ISG15-dependent mechanism of phagocytosis/endocytosis activation by both particles and viruses could be similar and related to AKT . To analyze whether the reduction in AKT phosphorylation observed in ISG15−/− macrophages is involved in the phagocytosis blockage , the phagocytosis capacity of WT macrophages was analyzed by confocal immunofluorescence using different inhibitors of the PI3K pathway . Treatment of IFN-exposed ISG15+/+ macrophages with wortmannin , an inhibitor of AKT phosphorylation ( Fig . 7C ) , considerably reduced their phagocytic capacity ( Fig . 7B and Movie S8 ) in comparison to untreated cells ( Fig . 7B and Movie S7 ) . In contrast , the mTOR inhibitor rapamycin , which preserves AKT phosphorylation ( Fig . 7C ) , had no effect on the phagocytosis activity . We also checked if treatment with lipopolysaccharide ( LPS ) , a well characterized macrophage activator , affected phagocytosis in both ISG15−/− and ISG15+/+ macrophages ( without previous interferon treatment ) . The WT macrophages activated with LPS showed similar phagocytosis levels as controls ( Fig . 7B and Movie S10 ) . Strikingly , in ISG15−/− macrophages , no activation of phagocytosis was observed after LPS treatment ( Fig . 7B ) . In addition to the confocal analyses , time-lapse microscopy was performed and representative images of the different treatments are shown in Figure 8 . Only wortmanin treatment clearly reduced phagocytosis capacity of ISG15+/+ macrophages , as indicated by the high number of non-internalized and surface-bound beads in these cells . Collectively , these results indicate that ISG15 plays a critical role in AKT-phosphorylation , and that this pathway is essential to ensure proper phagocytosis in macrophages . These findings describe a novel function of ISG15 promoting macrophage phagocytosis . As ISG15 plays a critical role in latex beads and virus entry in macrophages , we wanted to analyze if ISG15 also participates in the phagocytosis of infected cells by macrophages . A function of ISG15 in this process would also at least partially explain the high susceptibility of ISG15−/− mice to viral infections . To assess this possibility , ISG15−/− MEFS were infected with VACV-YFP at 1 PFU/cell for 8 hours , and subsequently added to macrophages cultures at a ratio of one MEF to four macrophages . The mixed culture was incubated for 2 , 5 hours at 37°C and the number of macrophages containing fluorescent signal was determined . When VACV-YFP-infected MEFs were mixed with ISG15+/+ peritoneal macrophages , efficient phagocytosis was observed ( Fig . 9A ) . In contrast , a massive decrease in phagocytosis of infected cells was found when infected MEFs were mixed with ISG15−/− macrophages ( Fig . 9A ) . Quantification of these results revealed that the phagocytic capacity of the ISG15+/+ macrophages was about 20 times higher than that observed in ISG15−/− ( Fig . 9F ) . More than 40% of the ISG15+/+ macrophages incorporated virus-infected cells , whereas only about 2% of the ISG15−/− macrophages phagocyted VACV infected cells . These results show that in macrophages ISG15 is necessary to phagocytize VACV-infected cells . To study whether the phagocytosis is or not exclusively due to the macrophages in these co-cultures experiments , we decided monitored the phagocytosis capacity in MEFs using GFP-latex . A clear absence of latex bead was observed inside the cells indicating that MEFs do not exhibit phagocytic ability ( Figure S4 ) . However , although the MEFs were not able to phagocyte latex beads the infection with the VACV-YFP showed that virus entry occurred normally in these cells ( Figure S4 ) . Phagocytosis of infected cells could contribute to the virus clearance and , if during this process the macrophage gets infected , it could commit suicide , blocking further possible propagation of the virus infection . Thus , we decided to examine whether virus growth in MEFs is affected in the presence of macrophages , as a simulation of macrophage mediated viral clearance . For this purpose , ISG15−/− MEFs were infected with VACV and added to a macrophage culture after 8 hpi . The co-culture was further maintained and the virus titer in the culture medium was determined at 24 and 48 h after infection . The virus titer in the infected cells of the co-cultures that contains ISG15 +/+ macrophages gradually decreased , while that in the control culture with DMEM and in the co-culture with ISG15−/− macrophages clearly increased the time ( Fig . 9B ) . Furthermore , in the co-culture experiments with ISG15+/+ macrophages , the reduction in viral titer was accompanied with a reduction in VACV viral protein expression ( Fig . 9C ) , an increase in ISG15 levels ( conjugated and non-conjugated ) ( Fig . 9D ) and a clear increase in the AKT phosphorylation level ( Fig . 9-E ) . These results clearly indicate that the production of VACV into the cells is inhibited in the presence of high levels of ISG15 in macrophages and strongly suggest that this was due to phagocytosis of infected cells . However , the possibility remained that soluble factors , secreted from macrophages and could cause a decrease in viral replication . To investigate the influence of cell-to-cell contact in these observations and to rule out the contribution of soluble antiviral molecules secreted by the macrophages in the above described results , we performed similar co-culture assays in which MEF and macrophages were physically separated in special plates but shared a common culture medium . When macrophages and infected cells were cultured in plates with two isolated compartments , no antiviral effect was observed in any case ( Figure S5 ) . These results indicate that direct contact between virus-infected cells and macrophages is required for the clearance of viral infection . This observation implies that cytokines or other soluble mediators , like secreted free ISG15 or IFNs , were not directly responsible for the ISG15 mediated antiviral effect of macrophages . Finally , to confirm that the ISG15-mediated phagocytic capacity regulates the viral clearance , we studied the effect of wortmannin treatment in virus clearance . ISG15−/− MEFS were infected with VACV-YFP at 1 PFU/cell for 8 hours , and added to the macrophages cultures , which were previously treated or not with wortmannin for 1 hour . The mixed culture was incubated for 2 , 5 hours at 37°C and the number of macrophages containing fluorescent signal was determined . When the infected MEFs were mixed with ISG15+/+ peritoneal macrophages , efficient phagocytosis was observed . As expected , intake of infected cells was completely abrogated when AKT phosphorylation was blocked by wortmannin treatment in the peritoneal macrophages , resembling the phenotype observed in ISG15−/− macrophages ( Fig . 9F–G , upper panels ) . Moreover , and further confirming the role of AKT phosphorylation in the phagocytosis mediated viral clearance , the reduction of virus production was completely inhibited upon wortmannin treatment ( Fig . 9F–G , lower panels ) , resembling the observation in co-culture experiments with ISG15−/− macrophages . These results indicate that phagocytosis of virus-infected cells and the subsequent virus clearance is strongly dependent on AKT phosphorylation . In summary , these studies established a novel mechanism by which ISG15 controls viral-induced macrophage phagocytosis and its antiviral activities through AKT dependent pathway in response to VACV infection .
The host innate immune response , including the production of type-I IFN , represents the primary line of defense against viral pathogens . Of the hundreds of IFN-stimulated genes ( ISGs ) discovered to date , ISG15 was one of the firstly identified and shown to encode an ubiquitin-like protein modifier [34] . ISG15 knock-out mice are more susceptible to infection by several viruses , pointing out the relevance of this molecule in the antiviral response in animal models [35] . However , the underlying causes of the enhanced susceptibility of ISG15−/− mice and the cell types involved are only weakly defined . In order to clarify these questions , we evaluated the role of ISG15 in VACV replication in different cells types . Both MEFs and dendritic cells ( not shown ) VACV-infected were unaffected by the lack of ISG15 in the course of infection . In contrast , ISG15−/− peritoneal macrophages showed a clear resistance to VACV-induced cell death in comparison with wild type control macrophages where VACV replication is abortive and infectious progeny is not released [29] . Whereas WT macrophages became apoptotic and die after infection , in ISG15−/− macrophages , in which VACV infection is also abortive , no apoptosis was observed , as evidenced by the absence of PARP cleavage and CPE ( Fig . 2 ) . Apoptosis after infection with many types of viruses is generally considered as a self-defense mechanism [36] , as loss of host cell activity should impair virus propagation . For instance , apoptotic cells are engulfed and digested in lysosomes of phagocytes [36] . Moreover , a higher increase in the expression of ISG15 and the protein ISGylation levels were observed in ISG15+/+ infected macrophages when compared to infected MEFs , indicating that the factor involved in the different phenotype among VACV infection in these cells could be modified by ISG15 . In addition to the differences in the VACV-induced apoptosis between ISG15+/+ or ISG15−/− macrophages , we observed clear differences in the phagocytosis ability of the macrophages . Apoptosis and phagocytosis are two interconnected pathways , which are very important for the efficacy of the innate immune response against pathogen infection [36] . Phagocytic elimination of invading microbes represents an important innate immune mechanism , as it contributes to the clearance of the virus . Moreover , if during this process the macrophage gets infected , it will commit suicide by apoptosis , blocking further possible propagation of the viral infection . In contrast , viruses appear to resist this host action by inhibiting apoptosis using anti-apoptotic proteins encoded by viral genes [37] . Moreover , the phagocytosis of apoptotic bodies seems to be an important mechanism to cross-prime DCs , as extracellular antigens gain access to MHC I molecules for priming of cytolytic T-cells [38] . Some microbes including viruses are phagocytized not directly but indirectly as microbe-infected cells [33] . Phagocytosis of immune complexes , opsonized virus , and infected host cells represents another important connection between the adaptive and innate immune systems , with potential roles both in priming of the adaptive immune response and in clearance of virus . Phagocytosis not only may rapidly remove virus or virally infected cells from the circulation but it could also affect immune complex-induced inflammation , which is implicated in driving disease progression . Importantly , there is evidence that disease susceptibility and severity in numerous autoimmune diseases and infectious diseases are regulated by phagocytosis [39] . While ISG15−/− mice were not more susceptible to VACV wild type virus infection , this might be explained by the capacity for VACV to inhibit IFN signaling and therefore ISG15 induction during infection in vivo . In fact , a VACV with enhanced IFN induction due to the deletion of E3L displayed higher pathogenicity in ISG15−/− mice [10] . It is attractive to speculate that the increased susceptibility of ISG15−/− mice to several virus infections , including herpesvirus and influenza virus , is mediated at least in part by a defect in macrophage phagocytosis . Further experimentation is required to demonstrate that this is the case . As we mentioned before , our results reflect a diminution in the apoptosis and in the phagocytosis of ISG15−/− macrophages . Interestingly , type I IFN increases the phagocytic ability of macrophages in an ISG15 dependent manner , strongly suggesting that the ability of IFN to activate phagocytosis by macrophages is mediated by upregulation of ISG15 . The phosphatidylinositol 3-kinase ( PI3K ) pathway is an important signaling pathway that modulates diverse cellular activities , including cell survival , growth , proliferation , metabolism , migration , and apoptosis [40] . A great number of viruses utilize the PI3K-AKT cell signaling pathway to promote various steps in their replication cycle , such as the regulation of gene expression and the genome replication . Some bacteria and a few non-enveloped viruses also utilize this pathway to trigger their invasion and phagocytosis into cells [41]–[43] . Recently , it has been published that VACV induces AKT phosphorylation to allow viral entry in an integrin β1-dependent manner , suggesting that integrin β1-mediates PI3K/AKT activation induced by VACV [44] . Interestingly , when we examined the phagocytic capacity of ISG15−/− macrophages , the deficiency in the uptake of latex beads , VACV and VACV-infected MEFs was accompanied by a decrease in the phosphorylation levels of AKT . Moreover , the inhibition of the PI3K/AKT pathway in macrophages leaded to a reduction of phagocytosis and virus clearing capacity . These results further confirm that the initial transient activation of AKT is required for macrophages antiviral activities . Currently , there is increasing evidence of the involvement of AKT pathway in the biological effects of IFNs [45] . In this sense , our data indicate a clear connection between this pathway and ISG15 in the control of phagocytosis and antiviral defense . In particular , we concluded that the regulation of the macrophage antiviral response by ISG15 is dependent on AKT phosphorylation . Consequently , our results suggest that the interconnection between IFN response and the AKT pathway could be at the base of the increased susceptibility to viral infection in the absence of ISG15 . Optimal phagocytosis by macrophages is likely necessary to exert the antiviral action and to allow the clearance of the virus to take place . These mechanisms could be crucial for the ISG15 antiviral activity in animal models . Our results clearly indicate that a physical contact between the macrophages and the infected cells is required for antiviral effects , suggesting that intracellular ISG15 regulate this process . However , ISG15 is also secreted by neutrophils , monocytes and lymphocytes , and this released ISG15 controls T and natural killer ( NK ) lymphocytes , principal inductors of interferon ( IFN ) -γ [28] . In addition , the lack of secreted ISG15 is associated with severe mycobacterial disease in both mice and humans [28] . Taking into account that the intracellular survival of Mycobacterium tuberculosis depend on its ability to arrest phagolysosome biogenesis blocking the efficient antigen processing and presentation in macrophages [46] , it is likely that this novel mechanism described here might also play a role in the increased susceptibility of ISG15 deficient humans to bacterial infections , although further investigations will be needed to evaluate this possibility . In summary , we have identified a key pathway that links the macrophage phagocytosis capacity with a post-phagocytic signaling event . This , in turn , could be required for the macrophage-mediated antiviral activity essential for the virus clearance and the immune response following infection . These novel findings further underline the importance of ISG15 in the control of innate immunity . Further investigations on how these different processes are regulated by ISG15 and how these events affect downstream immune functions in vivo will be required to understand the role of ISG15 in the generation of tissue protective responses during infection with different pathogens .
ISG15−/− MEFs and their wild type counterpart were generated by Osiak et al . [13] and cultured in DMEM with 10% fetal calf serum ( FCS ) . VACV wild-type Western Reserve strain ( WR ) was grown on monkey BSC-40 cells ( African green monkey kidney cells , ATCC number CRL-2761 ) , purified by sucrose gradient banding and titrated in BSC-40 cells as described [47] . VACV-YFP , a generous gift of Michael Way , was grown as described [48] . Influenza virus A/Wilson Smith N ( WSN ) /1933strain was grown on MDCK cells ( ATCC number CCL-34 ) and titrated in MDCK cells as described [49] . Resident peritoneal macrophages were isolated from mice by peritoneal lavage using 10 ml DMEM . Lavage fluid was centrifuged ( 500× g , 5 min ) and cells were cultured in Petri dishes in DMEM containing 10% fetal bovine serum , 100 U/ml penicillin and 100 µg/ml streptomycin ( 3 h , 37°C , 5% CO2 ) . Non-adherent cells were removed by extensive washing with DMEM as described [50] , this protocol showed that the purity of peritoneal macrophage was ≥95% and their immunophenotype profile was typical of F4/80 positive ( see Figure S1 ) . All animals were handled in strict accordance with good animal practice as defined by the relevant national , international , and/or local animal welfare bodies , and with the Spanish Royal Decree ( RD 1201/2005 ) . All animal work was approved by the Ethical Committee of Animal Experimentation ( CEEA-CNB ) of the Centro Nacional de Biotecnología ( CNB-CSIC ) . Cells were grown to confluence in 96-well plates and infected with VACV virus at the indicated multiplicity of infection ( MOI ) from 0 . 01 to 10 PFU/cell . 24 hours post-infection ( hpi ) , the medium was removed and cytolysis was determined by crystal violet staining as described previously [51] . The percentage of viable cells after infection was calculated assuming the survival rate of uninfected cells to be 100% . ISG15+/+ and ISG15−/− fibroblast or peritoneal macrophages were infected ( 106 cells/time post-infection; 10 PFU/cell ) with VACV and collected at the indicated times post-infection ( 0 , 2 , 6 , and 16 hpi ) . Cell extracts were obtained using lysis buffer ( 50 mM Tris-HCl , 0 . 5 M NaCl , 10% NP-40 , 1% SDS ) and protein extraction was performed by 5 min incubation on ice . Protein lysates ( 100 µg ) were fractionated by 14% or 8% SDS-PAGE , transferred to nitrocellulose membranes , and incubated with anti-ISG15 [12] , anti-tubuline ( Sigma ) , anti-AKT ( Cell signaling ) , anti-phosphoS473-AKT ( Cell signaling ) , anti-caspase 3 ( Oncogene ) , anti-caspase 9 ( Oncogene ) , anti-bcl2 ( Santa Cruz ) , anti-eIF-2α ( Santa Cruz ) , anti-phospho-T202/Y204- Erk1/2 ( Santacruz , kindly provided by S . Alemany ) anti-anti Erk1/2 ( Santa Cruz ) , anti-anti-S35-eIF2α ( Invitrogen ) and anti-PARP ( Cell Signaling ) antibodies , followed by secondary antibodies ( mouse and rabbit peroxidase conjugates from Sigma ) . Protein expression was detected using ECL reagents ( Amersham ) . ISG15+/+ and ISG15−/− fibroblast or peritoneal macrophages were infected ( 106 cells/time postinfection; 3 PFU/cell ) with VACV ( Western Reserve strain ) or FluV ( WSN , A/WSN/1933 ) . At indicated hpi , cells were washed with methionine-free medium and incubated with methionine-free medium containing 50 µCi/ml of 35S-methionine ( 30 min , 37°C ) . Cells extracts were prepared as described in lysis buffer ( 50 mMTris-HCl , 0 . 5 M NaCl , 10% NP-40 , 1% sodium dodecyl sulfate [SDS] ) , fractionated by 12% SDS- polyacrylamide gel electrophoresis ( PAGE ) and developed by autoradiography . Apoptosis quantification was carried out using the Caspase-Glo 3/7 assay kit ( Promega ) , following the protocol recommended by the supplier . Briefly , ISG15+/+ and ISG15−/− fibroblast or peritoneal macrophages monolayers grown in 96 well plates were infected at the indicated MOI . At the specified times post-infection , 100 µl of Caspase-Glo 3/7 reagent was added to the wells under study . Plates were gently shaken and then incubated in the dark at 20°C for 60 min before recording the luciferase activity using an Orion microplate luminometer ( Berthold technologies ) . ISG15+/+ and ISG15−/− peritoneal macrophages or MEFS were seeded in 8-well μ-Slide microscopy chambers ( Ibidi , Germany ) and filmed after the incubation with 1-µm-diameter latex beads conjugated to green fluorescent protein ( GFP ) ( Sigma ) , in a ratio of 10 beads per cell . Alternatively cells were cultured on coverslips and incubated for 1 h with the beads ( 20 or 10 beads per cell , depending of the experiment ) , washed three times with PBS and incubated with DMEM medium for an additional hour as described [31] . Cells were fixed with 4% PFA and processed for immunofluorescence analysis . Briefly , cells were washed with phosphate-buffered saline ( PBS ) , fixed with 4% paraformaldehyde , and permeabilized with 0 . 1% Triton X-100 in PBS ( room temperature , 10 min ) , DNA was stained with ToPro 3 ( Life Technologies ) . Images were obtained using a Bio-Rad Radiance 2100 confocal laser microscope . The phagocytosis assay with mouse peritoneal macrophages was conducted as previously described [52] . ISG15+/+ and ISG15−/− peritoneal macrophages were seeded in 8-well μ-Slide microscopy chambers ( Ibidi , Germany ) during 48 hours and washed to remove the non-adherent cells . ISG15−/− MEFS were infected with VACV-YFP at 1 PFU/cell for 14 hours , and added to the macrophages culture at ratio of one MEF cells to four macrophages . The mixed culture was incubated for two hours at 37°C and the number of macrophages contained fluorescent signal was determined and expressed relative to the total number of macrophages as the phagocytic index . Images from live ISG15+/+ or ISG15−/− macrophages or MEFS infected with VACV-YFP or treated with latex beads were collected every 10 minutes for 2 hours using a Leica Plan APO 20×/0 . 70 objective mounted on a Leica TCS SP5 confocal system as described previously [53] . | Modification of proteins by ubiquitin ( UB ) and ubiquitin-like proteins ( UBLs ) are key regulatory processes of the innate and adaptive immune response . Interferon ( IFN ) stimulated gene product 15 ( ISG15 ) is an ubiquitin-like protein modifier , which is reversibly conjugated to different viral and cellular proteins mediating considerable antiviral responses . In turn , many viruses , including poxviruses , have evolved strategies to block the antiviral and inflammatory effects of the innate immune responses to keep cells alive until virus replication is completed . Here , we describe a novel function of ISG15 in the control of macrophages activation , phagocytosis and apoptosis in response to viral infection . These processes are essential for the self-defense mechanism to protect animals from infectious disease and could be crucial to understand the ISG15 antiviral activity described in animal models . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | ISG15 Regulates Peritoneal Macrophages Functionality against Viral Infection |
CONSTITUTIVE PHOTOMORPHOGENIC 1 ( COP1 ) functions as an E3 ubiquitin ligase and mediates a variety of developmental processes in Arabidopsis by targeting a number of key regulators for ubiquitination and degradation . Here , we identify a novel COP1 interacting protein , COP1 SUPPRESSOR 2 ( CSU2 ) . Loss of function mutations in CSU2 suppress the constitutive photomorphogenic phenotype of cop1-6 in darkness . CSU2 directly interacts with COP1 via their coiled-coil domains and is recruited by COP1 into nuclear speckles in living plant cells . Furthermore , CSU2 inhibits COP1 E3 ubiquitin ligase activity in vitro , and represses COP1 mediated turnover of HY5 in cell-free extracts . We propose that in csu2 cop1-6 mutants , the lack of CSU2’s repression of COP1 allows the low level of COP1 to exhibit higher activity that is sufficient to prevent accumulation of HY5 in the dark , thus restoring the etiolated phenotype . In addition , CSU2 is required for primary root development under normal light growth condition .
Sunlight provides not only the major energy source , but also a main environmental signal that regulates multiple developmental processes in plants , such as seed germination , photomorphogenesis , flowering , phototropism and root growth [1] . In Arabidopsis thaliana , phytochromes ( phyA-phyE ) sense red and far-red light ( 600–750 nm ) [2 , 3]; cryptochromes ( CRY1 and CRY2 ) and phototropins ( PHOT1 and PHOT2 ) perceive blue and UV-A light ( 315–500 nm ) [4 , 5]; and UVR8 acts as the UV-B ( ~280 nm ) photoreceptor [6] . In response to light , photoreceptors can directly act on numerous gene promoters throughout the genome to regulate the expression of their target genes in order for plants to rapidly adapt to their changing light environment [7–9] . In the absence of light , plants develop long hypocotyls , apical hook , unopened cotyledons and etioplastids , a unique developmental program known as skotomorphogenesis or etiolation . In the light , plants undergo photomorphogenesis , which is characterized by short hypocotyls , expanded cotyledons , and developed chloroplasts [1] . The skotomorphogenesis program is vital for terrestrial plants when their lives often start in the darkness of soil . The program prepares the plants for exposure to sunlight with vigor ( a process known as greening ) , while inability to etiolate in darkness would be lethally damaged when exposed to light irradiation . The CONSTITUTIVELY PHOTOMORPHOGENIC 1 ( COP1 ) gene is essential for etiolation by acting as a repressor of photomorphogenesis , and its loss of function mutant display a constitutive photomorphogenic phenotype in darkness [10] . COP1 protein contains a RING finger , a coiled-coil domain , and WD-40 repeat domain , and it functions as an E3 ubiquitin ligase that targets a subset of photomorphogenic promoting factors for ubiquitination and degradation . In plant cells , COP1 exists as homodimers , which further stably associates with two SPA proteins , forming a tetrameric protein complex [11 , 12] . Both COP1 dimerization and the interaction with SPA proteins are mediated through the coiled-coil domain of respective proteins . Association with SPA proteins enhances the activity of COP1 to targets substrate ubiquitination [12–14] . The substrates of COP1 in seedlings include LONG HYPOCOTYL ( HY5 ) , HY5 HOMOLOG ( HYH ) , LONG HYPOCOTYL IN FAR-RED 1 ( HFR1 ) , LONG AFTER FAR-RED LIGHT 1 ( LAF1 ) , SALT TOLERANCE HOMOLOG 3 ( STH3/BBX22 ) and PHYTOCHROME INTERACTING FACTOR 3-LIKE1 ( PIL1 ) [14–20] . Besides seedling photomorphogenesis , COP1 also mediates the degradation of CONSTANS ( CO ) , GIGANTEA ( GI ) , EARLY FLOWERING 3 ( ELF3 ) , HYPERSENSITIVE RESPONSE TO TCV ( HRT ) , SCAR1 , GATA TRANSCRIPTION FACTOR 2 ( GATA2 ) and MYC2 , and plays critical roles in various developmental processes including flowering time , circadian clock , viral defense , root development , hormone signaling and controlling miRNA biogenesis [21–27] . COP1 is evolutionarily conserved from plants to animals . Mammalian COP1 has been reported to act as a tumor suppressor that targets oncoproteins c-Jun and ETS via its E3 ubiquitin ligase activity [28–31] . As a key regulator , COP1 protein level , activity , and localization are tightly controlled to ensure appropriate protein accumulation of its targets in response to developmental and environmental cues . In the dark , COP1 is enriched in the nucleus where it targets substrates for ubiquitination . Light triggers photoreceptors , including phyA , phyB , CRY1 and CRY2 , to associates with SPA proteins or COP1 , resulting in repression of the COP1-SPA E3 ubiquitin ligase activity [32–36] . This event is then followed by repartitioning of COP1 from the nucleus to the cytoplasm [37–40] . In addition , recent studies reveal that CSU1 , SPAs and PIFs contribute to the modulation of COP1 protein level and activity as well [12 , 14 , 41 , 42] . In search of novel factors that modulate COP1 function or mediate its output , we have conducted a genetic screen for suppressors of cop1-6 , a hypomorphic allele of cop1 mutants [43] . This screen has previously led to successful identification of CSU1 , an E3 ubiquitin ligase that targets COP1 [41] . Here we report another novel COP1 suppressor , designated as CSU2 . Mutations in CSU2 nearly completely suppress the constitutive photomorphogenic phenotype of cop1-6 in darkness . CSU2 physically interacts and co-localizes with COP1 in nuclear speckles via a coiled-coil domain association . CSU2 is able to repress the COP1 E3 ubiquitin ligase activity . In addition , CSU2 has an important role in root development . Collectively , our genetic and biochemical data demonstrate that Arabidopsis CSU2 functions as a negative regulator of COP1 , which serves to optimize the development of plants .
A forward genetic screen was performed to explore cop1 suppressors as described previously [41] . Six independent recessive alleles , located at a novel extragenic locus ( At1g02330 ) named cop1 suppressor 2 ( csu2 ) , were recovered from this screen ( Fig 1 ) . Each of the mutant alleles ( csu2-1 to csu2-6 ) nearly completely suppressed cop1-6 constitutive photomorphogenic phenotype in the dark ( Fig 2 ) . Since the mutation in cop1-6 causes a splicing defect that leads to low expression of the COP1 gene product [41 , 43] , we first tested whether mutations in CSU2 affected cop1-6 splicing profiles by a RNA pattern analysis . csu2 cop1-6 produced five cryptically spliced profiles at intron 4 of COP1 , similar to cop1-6 ( S1 Fig ) , suggesting that csu2 suppressed cop1-6 not by correcting its splicing defect . Thus , csu2 was further characterized . Via a combined chromosomal mapping and re-sequencing approach ( see materials and methods for detail ) , we found that the csu2-4 mutation changes the splicing junction “AG” at the 3' end of intron-2 to “AA” , thus disrupting the splicing principles of CSU2 . Five additional mutant alleles from the same genetic complementation group were analyzed by PCR amplification followed by sequencing , which led to identification of distinct point mutation in each of the csu2 mutant allele in At1g02330 ( Fig 1B ) . Thus , At1g02330 defines the CSU2 gene . CSU2 is a single-copy gene encoding a predicted 279 amino acid protein in Arabidopsis ( Fig 1C ) . Only one putative domain , a coiled-coil domain , was identified in CSU2 . CSU2 is evolutionarily conserved . The amino acid sequence identity of Arabidopsis CSU2 to its orthologs from Homo sapiens , Mus musculus , Danio rerio , Drosophila melanogaster , and Oryza saliva is 34% , 34% , 35% , 40% and 61% respectively , with the coiled-coil domain being the most conserved region ( S2 Fig ) . cop1-6 mutant is unable to etiolate in darkness [43] , and is defective in greening upon transfer to white light [44] . Mutations in CSU2 almost completely restored cop1-6 constitutive photomorphogenic phenotype to WT phenotype in the dark ( Fig 2 ) . Hypocotyl length of all six different csu2 cop1-6 mutant lines was essentially indistinguishable from that of WT seedlings ( Fig 2A and 2B ) . Although the cotyledons of csu2 cop1-6 were slightly open , the cotyledon apertures of all six independent csu2 cop1-6 mutant lines were significantly smaller than that of cop1-6 ( Fig 2C and 2D ) . Moreover , although most dark-grown cop1-6 seedlings were unable to green when transferred from dark to white light , the greening rate of etiolated csu2 cop1-6 mutant seedlings was restored to a level comparable to that of WT ( Fig 2E ) . To verify that the suppression of the cop1-6 phenotype in csu2 cop1-6 etiolated seedling was indeed caused by the mutation in CSU2 gene only , we introduced CSU2-GFP and YFP-CSU2 into the csu2-2 cop1-6 double mutant . Consistently , CSU2-GFP csu2-2 cop1-6 and YFP-CSU2 csu2-2 cop1-6 transgenic seedlings displayed constitutive photomorphogenic phenotype similar to that of cop1-6 single mutant in the dark , indicating that a functional CSU2 could complement the phenotype conferred by csu2-2 in cop1-6 background in darkness ( Fig 2F ) . Not only did csu2 rescue the dark phenotype of cop1-6 , csu2 also partially suppressed the short hypocotyl phenotype of cop1-6 seedlings grown under various light conditions tested ( white , red , far-red and blue ) ( S3 Fig ) . The dwarf phenotype of cop1-6 adult plants under the long-day condition ( 16 h light / 8 h dark ) for 30 days was also partially suppressed by csu2 ( S4 Fig ) . All together , these genetic data suggest that csu2 almost completely suppress cop1-6 in the dark and partially in the light . To examine whether mutations in CSU2 have defect in light responses by themselves , single mutants of all six alleles ( csu2-1 to csu2-6 ) were isolated from the F2 generation of csu2 cop1-6 crossed with Col and grown under various light conditions ( dark , white , blue , red and far-red ) for five days . At low fluence rate of white light ( 15 . 7 μmol/m2/s ) , the hypocotyl length of csu2 mutant seedlings was indistinguishable from that of WT ( S5 Fig ) . At the higher fluence of white light ( 33 . 3 and most evidently 112 . 5 μmol/m2/s ) , all six independent csu2 single mutants displayed statistically significantly longer hypocotyls than did WT seedlings ( Fig 3 and S5 Fig ) . However , csu2 mutant seedlings did not differ significantly from WT seedlings under all monochromatic light ( blue , red and far-red ) conditions tested ( S6 Fig ) . The fact that csu2 mutant seedlings were specifically hyposensitive to higher fluence rate of white light suggests that CSU2 acts as a positive regulator in the high fluence white light induced inhibition of hypocotyl elongation . HY5 transcription factor is a major downstream effector of COP1 , whose mutation can also suppress cop1-6 [15 , 44 , 45] . The hypocotyl length of hy5-215 csu2 double mutant seedlings was similar to that of hy5-215 single mutants in all light conditions tested including high fluence of white light , in which csu2 exhibited longer hypocotyls than WT ( Fig 3B–3K ) . This result indicates that hy5-215 is epistatic to csu2 in the control of hypocotyl growth . Although either csu2 or hy5 alone only partially suppressed cop1-6 in the light , both mutations together ( hy5 csu2 cop1-6 ) restored cop1-6’s hypocotyl length to that of WT seedlings under all light conditions tested ( white , blue , red and far-red ) ( Fig 3B–3K ) . It appeared that CSU2 and HY5 act additively in the suppression of cop1 hypocotyl phenotype in the light . We suggest from these genetic data that CSU2 and HY5 work independently and additively , with HY5 acting downstream of CSU2 , to counter COP1’s action in the control of hypocotyl elongation . To understand the mechanism of CSU2 , we examined a possible protein-protein interaction between CSU2 and COP1 by a yeast-two-hybrid assay . As shown in Fig 4 , CSU2-COP1 interaction was evident as indicated by increased β-galactosidase activity compared to BD-CSU2 and AD-COP1 alone . COP1 possesses three protein-protein interaction domains , Ring-finger , coiled-coil and WD40 domains , while CSU2 contains only one predictable coiled-coil domain . To identify which COP1 domain is responsible for the interaction with CSU2 , a deletion analysis of the COP1 fragment was carried out . Interestingly , COP1 N282 , COP1 Δring and COP1 coil containing the COP1 coiled-coil domain , showed even stronger interaction with CSU2 than full-length COP1 ( Fig 4 ) . In contrast , COP1 Ring and COP1 WD40 , which lack COP1 coiled-coil domain , were unable to interact with CSU2 . Thus , the coiled-coil domain of COP1 is necessary and sufficient for interaction with CSU2 . Next , we examined whether the coiled-coil domain of CSU2 was sufficient for the CSU2-COP1 interaction . Similar to the full-length CSU2 , the CSU2 coil domain was capable of interacting with COP1 , COP1 N282 , COP1 Δring and COP1 coil , but not COP1 Ring and COP1WD40 . In addition , CSU2 Δcoil , which lacks the coiled-coil domain , was unable to interact with full-length COP1 or any of the COP1 deletion constructs ( Fig 4 ) . Taken together , those data indicates that CSU2 interacts with COP1 through their respective coiled-coil domains . We next performed the Bimolecular Fluorescence Complementation Assays ( BiFC ) . Constructs of CSU2 fused with N-terminal of YFP ( YN-CSU2 ) and COP1 fused with C-terminal of YFP ( YC-COP1 ) were generated . When YN-CSU2 and YC-COP1 were co-transformed into onion ( Allium cepa ) epidermal cells , strong YFP fluorescence signals were observed in the nucleus , indicating that CSU2 can interact with COP1 ( Fig 5A ) . Furthermore , we examined whether Fluorescence Resonance Energy Transfer ( FRET ) could occur between the two fusion proteins CFP-CSU2 and YFP-COP1 using the acceptor photobleaching technique . Here , we co-expressed CFP-CSU2 with YFP-COP1 in onion epidermal cells and excited them with 405- and 514-nm wave lengths light sources . Both CFP and YFP fluorescence were detected before bleaching . CFP-CSU2 produced uniform fluorescence throughout the nucleus , while YFP-COP1 formed nuclear speckles ( S7A and S7B Fig ) . Since FRET occurs only at nanometer scale distances [46] , only YFP-COP1 speckles areas were chosen for bleaching by 514-nm laser . After bleach , emission of YFP-COP1 was reduced dramatically , whereas emission from CFP-CSU2 in the region of interest increased ( S7A and S7B Fig ) , indicating that FRET had occurred between the two proteins prior to the bleach . As a control , we did not detect FRET between YFP and CFP-CSU2 ( S7C and S7D Fig ) . Together , these data support a conclusion that the CSU2 interacts with COP1 in living plant cells . COP1 forms nuclear speckles in darkness and is able to recruit several interacting proteins to those loci [14 , 16 , 45 , 47] . Our FRET assay data indicated that COP1 and CSU2 might co-localize in the nuclear speckles ( S7A Fig ) . To further substantiate this finding , we performed transient co-localization assays using GFP tagged CSU2 fusion protein in onion epidermal cells ( Fig 5B and 5C ) . Unlike COP1 , CSU2 localized uniformly throughout the nucleus ( Fig 5C ) . However when we co-expressed COP1 ( 35S:COP1 ) together with CSU2-GFP , we detected consistent nuclear speckles ( Fig 5C ) . Since CSU2-GFP by itself only produces a uniform fluorescence , the observation of nuclear speckles when co-expressed with untagged COP1 suggests that CSU2 is recruited into nuclear speckles by COP1 . Moreover , untagged COP1 ( 35S:COP1 ) could confer nuclear speckle formation to a co-expressing CSU2 coil-GFP but not CSU2 Δcoil-GFP ( Fig 5C ) . These observations provide further evidence that interaction of COP1 , via the coiled-coil domain of CSU2 , is required and sufficient for recruitment of CSU2 into the nuclear speckles in living plant cells . To determine whether CSU2 is a nuclear protein in planta , we examined its localization pattern in 35S:CSU2-GFP csu2-2 transgenic Arabidopsis seedlings where CSU2-GFP has been shown to be functional ( Fig 2F ) . As shown in S8 Fig , CSU2-GFP was found within the nucleus both in darkness and light , confirming that CSU2 is a nuclear protein in planta . COP1 targets a group of interacting proteins for ubiquitination and degradation . Therefore , we investigated whether COP1 regulates CSU2 abundance . YFP fluorescence signal intensity was comparable in the YFP-CSU2 csu2-2 and YFP-CSU2 csu2-2 cop1-6 transgenic seedlings ( S9 Fig ) . In addition , similar protein levels of YFP-CSU2 were detected in these two transgenic lines grown in various light conditions tested ( dark , white , blue red and far-red ) ( S10 Fig ) . These findings suggest that COP1 does not regulate CSU2 abundance . The coiled-coil domain of COP1 is necessary for its dimerization [13] and for interacting with SPA proteins [11 , 12 , 48] . These interactions enhance COP1’s E3 ubiquitin ligase activity [12 , 14] . Given that CSU2-COP1 association is through COP1 coiled-coil domain , we wanted to test whether CSU2 can affect COP1 activity . Consistent with previously described in vitro ubiquitination assay [14 , 17] , we detected a robust COP1 dependent ubiquitination activity , and this activity was drastically inhibited when CSU2 was present in the reaction ( Fig 6A ) . Remarkably , COP1’s ubiquitination activity was not affect by CSU2 Δcoil , which lacks coiled-coil domain ( Fig 6A ) . Therefore CSU2 can inhibit COP1 E3 activity in vitro , and the inhibition is dependent on CSU2’s COP1-binding domain . HY5 is a major ubiquitination substrate of COP1 in seedlings , and its level of accumulation correlates with seedling photomorphogenesis [15 , 45] . To examine the effect of CSU2 on COP1’s activity toward a specific substrate , we performed a cell-free HY5 degradation assay in cell lysates , in which degradation of HY5 was dependent on the presence of COP1 ( Fig 6B and 6C ) . Notably , with decreasing amounts of CSU2 in the mixture , the protein level of HY5 also decreased ( Fig 6B ) . In contrast to full length CSU2 , CSU2 Δcoil had no effect on COP1 mediated degradation of HY5 ( Fig 6C ) . As a validation of the assay , degradation of HY5 protein could be blocked by proteasome inhibitor MG132 treatment . The GFP protein , as an internal control , remained relatively stable under all the tested conditions ( Fig 6B and 6C ) . Together , these data show that CSU2 represses the COP1 ubiquitination activity in vitro , and repress COP1-dependent degradation of HY5 in a cell-free degradation assay . In both cases , the coiled-coil domain of CSU2 is required for the repression of COP1 activity . Prompt by CSU2’s activity in repressing COP1’s E3 ubiquitin activity in vitro , and in inhibiting HY5 degradation in the cell-free assay , we determined the steady state levels of COP1 and HY5 proteins in the seedlings of csu2 cop1-6 compared to cop1-6 , and wild type ( Fig 6D ) . The levels of COP1 in csu2 cop1-6 appeared slightly higher than that of cop1-6 , but still substantially lower than WT in both dark- and light-grown seedlings ( Fig 6D ) . The reason of the slight increase of COP1 is discussed later . The important point is that , even with clearly reduced amount of COP1 , the dark-grown csu2 cop1-6 seedlings nevertheless managed to keep HY5 protein level as low as in WT , which was drastically decreased compared to cop1-6 ( Fig 6D ) . Presumably , despite of reduced level of COP1 in csu2 cop1-6 , but due to lack of CSU2-mediated inhibition , the total activity of COP1 seems sufficient to prevent HY5 accumulation in the dark . The slight increase of COP1 level in csu2 cop1-6 might also have contributed to the suppression of HY5 in the dark . We next asked whether csu2 mutant seedlings display altered protein accumulation of additional components of light signaling . Under both dark and light conditions , phyA , phyB , COP1 , HY5 and SPA1-4 ( dark only ) accumulated at comparable levels in WT and csu2 mutant seedlings ( S11 Fig ) . Thus we have not detected an effect of CSU2 on protein abundance of these light-signaling components under normal growth conditions . Light-grown seedlings display longer primary roots than etiolate seedlings , and cop1 mutant seedlings display an opposite root growth pattern [49] . To investigate the role of CSU2 in the root development , the six different csu2 mutant lines were germinated on vertical plates and grown for five days under dark or constant white light conditions . In the dark , cop1-6 displayed longer roots than did WT , while csu2 displayed the same root length to that of WT . csu2 cop1-6 double mutants exhibited roots similar to those of csu2 or WT seedlings , indicating that the long root phenotype of cop1-6 was completely suppressed by csu2 ( Fig 7A and 7B ) . In the light however , all six different csu2 single mutants displayed dramatically shorter roots than did WT or cop1-6 ( Fig 7C and 7D ) , and csu2 cop1-6 showed similar root length as csu2 single mutants . To further confirm that the short primary root phenotype is caused by disruption of CSU2 , we investigated the primary root phenotypes of 35S:myc-CSU2 csu2-2 as well as 35S:CSU2-GFP csu2-2 and 35S:YFP-CSU2 csu2-2 transgenic lines ( S9 Fig ) . In all cases , expression of CSU2 transgene rescued the shortened primary root phenotype of csu2-2 ( S9 Fig ) , indicating the short primary root phenotype is resulted from lack of a functional CSU2 . Taken together , these findings show that csu2 completely suppresses cop1 long primary root phenotype in the dark , that CSU2 is required for light stimulated primary root development , and that csu2 is epistatic to cop1 with respect to the primary root phenotype in the light . To further investigate the genetic relationship among csu2 , hy5 and cop1 with respect to root phenotypes , we studied the hy5 csu2 , hy5 cop1 and hy5 csu2 cop1 double and triple mutants . In the dark , all the double and triple mutants exhibited root phenotypes similar to those of WT ( Fig 8A and 8B ) . Under white light condition , the root length of hy5 csu2 , or hy5 csu2 cop1 double and triple mutant seedlings resembled csu2 short roots phenotype ( Fig 8C and 8D ) , suggesting a different genetic relationship of those three loci in mediating light regulation of root development and in hypocotyl growth . With regard to primary root growth , the requirement for functional CSU2 overrides the regulatory functions of COP1 and HY5 .
COP1 is regulated in a number of different ways . Not only is COP1 nucleocytoplasmic partitioning regulated by light , low temperature , heat shock and ethylene [37 , 52–54] , its protein abundance is regulated by CSU1 , an E3 ubiquitin ligase identified by the same screen as CSU2 [41] ( Fig 9 ) . COP1 activity is rigorously regulated as well . It has been demonstrated that PIFs and SPAs interact with COP1 , and enhance COP1 ubiquitylation activity [12 , 14 , 42] , while photoreceptor activation inhibits COP1 E3 activity [32–36] ( Fig 9 ) . In etiolated seedlings , two SPA proteins associate with COP1 homo-dimers and form stable core complexes through their respective coiled-coil domains , which in turn , serve to enhance the COP1 activity possibly by increasing substrate recruitment [11 , 12 , 14] . Upon exposure to light , phyA , phyB and CRY1 interact with SPA , while the CRY2 binds to COP1 . These interactions result in destabilization and disruption of the COP1-SPA complex , and consequently inhibition of COP1 E3 ubiquitin ligase activity [32–36] . In a similar fashion , we speculate that CSU2 mediated repression may also be directed at dismantling COP1-SPA complex and/or blocking COP1 dimerization . CSU2 and COP1 interact through their coiled-coil domains , and CSU2 coiled-coil domain is necessary for the repression of COP1 activity in vitro ( Figs 4 , 5 and 6 ) . Moreover , CSU2 can inhibit COP1-mediated HY5 turnover in a cell-free plant extract assay , also in a coiled-coil domain dependent manner . The coiled-coil domain of COP1 is responsible for its self-dimerization , a necessary conformation for its E3 ubiquitin ligase activity [13] . Thus it is possible that CSU2-COP1 association may interfere with the COP1 self-dimerization ( in vitro ) as well as COP1-SPA interaction ( in vivo ) , which may result in destabilization COP1 dimer and COP1-SPA complexes , in a similar mechanism to activated photoreceptors . We found that csu2 cop1-6 seedlings contained slightly higher amount of COP1 protein than cop1-6 alone , although still substantially lower than in wild type ( Fig 6D ) . This could also be explained by above mentioned hypothesis: lack of CSU2’s competitive binding to COP1 coiled-coil domain would stabilizes COP1 dimerization and COP1-SPA complex , which would protect COP1 protein to certain extent . Nonetheless the slight increase of COP1 protein alone cannot fully account for the complete suppression of HY5 level in cop1-6 csu2 double mutants in the dark ( Fig 6D ) . We postulate that both stabilization of COP1 , and more importantly an increase of COP1 activity , occur in the absence of CSU2 , which most likely underlie the mechanism of suppression of cop1-6 by csu2 . csu2 specifically suppresses the cop1-6 allele , but not cop1-1 and cop1-4 ( Fig 2 , S13A and S13B Fig ) . In cop1-6 , the mutation causes a splicing defect that eventually produces COP1-6 mutant protein with five additional amino acids insertion at severely decreased level [43] . COP1-6 protein is largely biologically functional [41] . The strong allele cop1-1 has a 66-bp deletion , causing a deletion from amino acid 355 to 376 ( ~74 kD ) [43] . The COP1-1 protein is produced to wild-type levels ( S13C Fig ) , but is severely functionally defective , as indicated from the mutant phenotype . cop1-4 mutant accumulates a truncated COP1 protein ( ~33 kD ) containing only the N-terminal 282 amino acids , and it is expressed at same or reduced level compared to wild type [43] ( S13C Fig ) . Interestingly , when COP1-4 ( N282 ) protein is overexpressed , it can cause a dominant negative phenotype in wild type background [55] . Thus the loss-of-function mechanism of cop1-4 mutation is rather complicated . Among the three cop1 mutant alleles , cop1-6 is the only hypomorphic allele , as it produces a functional protein at a lower level . Since csu2 suppression works by releasing the repression on a functional COP1 protein , only cop1-6 can be effectively suppressed by lack of CSU2 . The failure of suppression of cop1-1 and cop1-4 by csu2 may primarily attribute to the nature of COP1-1 and COP1-4 mutant gene products , which are functionally defective . It should be mentioned that hy5 is able to partially suppress cop1-1 and cop1-4 , as well as cop1-6 [44] , because HY5 is a downstream factor that mediates COP1’s output . Arabidopsis exhibited longer roots in the light and shorter roots in darkness , while cop1-6 displayed a revered phenotype [49] . In the dark , COP1 directly targets SCAR1 , a positive regulator of root growth for ubiquitination and protein turnover [25] , which contributes to the longer primary root phenotype of cop1 in darkness . The drastic long primary root length of cop1-6 grown in darkness was completely suppressed by csu2 ( Fig 7 ) . In the light however , both CSU2 and COP1 function as positive regulators of root development . The csu2 mutant seedlings developed severely shortened roots in the light , suggesting CSU2 is required for primary root growth in response to light ( Fig 7 ) . Our study revealed that CSU2 may act upstream of COP1 in the hypocotyls , whereas may genetically act downstream of COP1 in the roots , and that a functional CSU2 protein is required for primary root growth both in WT and in cop1-6 ( Fig 8 ) . Thus , it appears that different regulatory module of CSU2-COP1 pair may exist in the hypocotyl and root cells . Nevertheless , the exact functional relationship between COP1 and CSU2 in regulation of root growth needs further investigation .
The cop1-6 [43] , hy5-215 [44] , csu2 cop1-6 ( csu2-1 cop1-6 to csu2-6 cop1-6 ) , and csu2 ( csu2-1 to csu2-6 ) ( this study ) mutants are in the Columbia-0 ( Col-0 ) ecotype . Seeds were surface sterilized with 30% commercial Clorox bleach and 0 . 02% Triton X-100 for ten min and washed three times with sterile water , and sown on 1×Murashige and Skoog ( MS ) medium supplemented with 0 . 4% Bacto-agar ( Difco ) and 1% sucrose . The seeds were stratified in darkness for three days at 4°C , and then transferred to light chambers maintained at 22°C . The genetics screen , identification and characterization were previously described [41] . Genetic complementation tests showed that six different csu2 ( csu2-1 cop1-6 to csu2-6 cop1-6 lines ) EMS mutations were allelic to each other . Homozygous mutant suppressor plants were crossed to wild-type plants ( Col-0 ) , and segregation in the F2 generations was analyzed in the dark to distinguish between intragenic and extragenic suppressors . Meanwhile , the suppressor mutants were backcrossed to cop1-6 . The phenotype of F1 and the segregation ratio in the F2 generations in the dark were analyzed to identify whether the suppression phenotype is caused by a monogenic recessive mutation . Rough mapping was performed as described [41] . We crossed csu2-4 cop1-6 ( Col background ) with Landsberg containing a cop1-6 mutation to generate the mapping population . F2 generation seeds were sown on plates containing 1×MS medium , and grown in darkness at 22°C for five days . The suppressor seedlings with long hypocotyl and apical hook were then picked for Genomic DNA extraction and mapping . The markers used for mapping were designed based on the Arabidopsis Mapping Platform ( http://amp . genomics . org . cn ) and the standards described previously [56] . CSU2 was rough mapped to a ~250 kb region between markers 1-U89959-0145 and 1-AC022521-0169 on the left arm of chromosome 1 . SOLiD sequencing and mutation identification was performed as previously described [41] . The fragment libraries were created using the SOLiD Fragment library construction procedures according to the manufacturer’s instructions ( Life Technologies , Carlsberg , USA ) . The libraries were sequenced on a SOLiD5500 sequencer according to the manufacturer’s instructions ( Life Technologies , Carlsberg , USA ) . Mapping of sequencing reads to the Arabidopsis thaliana reference genome ( TAIR10 ) and single nucleotide polymorphism ( SNP ) calling were accomplished using LifeScope v2 . 5 . SNPs were then sorted into four categories ( EMS induced homozygous , EMS induced heterozygous , other homozygous and other heterozygous ) . Candidate homozygous EMS induced SNPs were identified in windows with reduced heterozygosity in the regions identified by physical mapping using in house scripts . To measure the hypocotyl and root length of seedlings , seeds were sown on horizontal or vertical plates and stratified at 4°C in darkness for three days , and then kept in continuous white light for eight h in order to induce uniform germination . The seeds were then transferred to dark , white , blue , red , and far-red light conditions , and grown at 22°C for five days [41] . The hypocotyl and root length of seedlings was measured using ImageJ software . The full-length CSU2 open reading frame ( ORF ) , CSU2 coiled-coil domain fragment and CSU2 lacking coiled-coil domain fragment were cloned into the pDONR-221 vector ( Invitrogen ) and introduced into the plant binary vector pEarley Gateway 103 , pEarley Gateway 104 or pEarley Gateway 203 [57] under the 35S promoter using Gateway LR Clonase enzyme mix ( Invitrogen ) . pEarley Gateway-CSU2-GFP , pEarley Gateway-YFP-CSU2 , pEarley Gateway-Myc-CSU2 , pEarley Gateway-CSU2 coil-GFP , and pEarleyGateway-CSU2Δcoil-GFP constructs were generated . pB42AD-COP1 , pB42AD-COP1N282 , pB42AD-COP1ΔRing , pB42AD-COP1 Ring , pB42AD-COP1 coil , and pB42AD-COP1 WD40 constructs were described previously [17] . To generate pLexA-CSU2 , pLexA-CSU2 coil and pLexA-CSU2 Δcoil constructs , full-length CSU2 , CSU2 coiled-coil domain and CSU2 lacking coiled-coil domain fragment were amplified by PCR with the respective pairs of primers and then cloned into the EcoRI/XhoI sites of the pLexA vector ( BD Clontech ) . To produce the constructs for BiFC assays , each full-length CSU2 or COP1 fragments was amplified by PCR with the respective pairs of primers and then cloned into the NcoI/NotI sites of pSY728 or pSY738 vector [58] , respectively . COP1-Flag construct was prepared with modified versions of pCombia1300 plasmid . pJIM-35S-HA-HY5 [59] , pCombia1300-35S-GFP [60] , and pCombia1300-35S-P19 [61] constructs were described previously . To produce pCold-TF-COP1 , full-length COP1 were amplified by PCR and then cloned into the KpnI/PstI sites of the pCold-TF vector ( Takara ) . To generate pET28a-CSU2 and pET28a-CSU2 Δcoil , full-length CSU2 or CSU2 Δcoil fragment lacking CSU2 coiled-coil domain were amplified by PCR and then cloned into the NdeI/XhoI sites of the pET28a vector , respectively . The primers used for plasmids construction were listed in S1 Table . The LexA-based yeast two-hybrid system ( BD Clontech ) was used for the assays . The respective combinations of LexA and AD fusion plasmids were co-transformed into the yeast strain EGY48 . Yeast transformation and the β-galactosidase activity assays were performed as described in the Yeast Protocols Handbook ( BD Clontech ) . Each pair of recombinant constructs encoding nYFP and cYFP fusions was co-bombarded into onion epidermal cells and incubated in 1×MS solid media containing 4% sucrose for 24 h at 22°C in darkness , followed by observation and image analysis by using confocal microscopy . FRET and co-localization assay experiments were performed according to the standards outlined in previous research [19] . For FRET assays , the pAM-PAT-35SS-YFP-COP1 [41] , pAM-PAT-35SS-CFP-CSU2 ( this study ) , overexpression constructs were introduced into onion epidermal cells by particle bombardment and incubated , and live cell images were acquired using an Axiovert 200 microscope equipped with a laser scanning confocal imaging LSM 510 META system ( Carl Zeiss ) . Cells were visualized at 24 h after particle bombardment using the confocal microscope . The multitracking mode was used to eliminate spillover between fluorescence channels . The CFP was excited by a laser diode 405 laser and the YFP by an argon-ion laser , both at low intensities . Regions of interest were selected and bleached with 100 iterations using the argon-ion laser at 100% . For co-localization assays , respective combination of pRTL2-35S-COP1 [19] , pEarly Gateway-35S-CSU2-GFP ( this study ) , pEarly Gateway-35S-CSU2 coil-GFP ( this study ) , and pEarly Gateway-35S-CSU2Δcoil-GFP ( this study ) constructs were introduced into onion epidermal cells by particle bombardment , and incubated in darkness for 24 h . The cells were then analyzed by confocal microscopy . Total RNA was extracted from five-d-old Arabidopsis seedlings grown under white light using the RNeasy plant mini kit ( QIAGEN ) . cDNAs were synthesized from 2 mg of total RNA using the SuperScript II first-strand cDNA synthesis system ( Fermentas ) according to the manufacturer’s instructions . Then , cDNA were subjected to PCR or real-time qPCR assays . Quantitative real-time qPCR was performed using the CFX96 real-time PCR detection system ( Applied Biosystems ) and SYBR Green PCR Master Mix ( Takara ) . PCR was performed in triplicate for each sample , and the expression levels were normalized to that of a PP2A gene . In vitro ubiquitination assays were performed as previously described [41] , with some minor modifications . Ubiquitination reaction mixtures ( 60 μL ) contained 30 ng of UBE1 ( E1; Boston Biochem ) , UbcH5b ( E2; Boston Biochem ) , and 500 ng of HA-tagged ubiquitin ( HA-Ub; Boston Biochem ) in a reaction buffer containing 50 mM Tris at pH 7 . 5 , 10 mM MgCl2 , 2 mM ATP , and 0 . 5 mM DTT . 500 ng 6×His-TF , 500 ng 6×His-TF-COP1 ( previously incubated with 20 μM zinc acetate ) , 500 ng 6×His-CSU2 , and 500 ng 6×His-CSU2 Δcoil were applied in the reactions as indicated . After 2 h incubation at 30°C , the reactions were stopped by adding 5×sample buffer . One-half of each mixture ( 30 μL ) was then separated onto 8% SDS-PAGE gels . Ubiquitinated TF-COP1 was detected using anti-ubiquitin ( Santa Cruz ) , and anti-HA ( Sigma-Aldrich ) antibodies , respectively . In vitro protein degradation assays were performed as described [62] with minor modification . For in vitro protein degradation analysis , Agrobacterium tumefaciens strains carrying constructs of p19 ( for suppressing PTGS ) together with HA-HY5 , COP1-Flag , myc-CSU2 , myc-CSU2Δcoil , or GFP ( internal control ) plasmids were co-infiltrated in Nicotiana benthamiana leaves , separately . One day after infiltration , a HA-HY5 sample was harvested . COP1-Flag sample , myc-CSU2 sample and GFP sample were collected after three days infiltration , individually . These four samples were separately extracted in native extraction buffer ( 50 mM Tris-MES pH 8 . 0 , 0 . 5 M sucrose , 1 mM MgCl2 , 10 mM EDTA , 5 mM DTT , 10 mM PMSF , 1×protease inhibitor cocktail ( Roche ) ) . Then , 100 μg HA-HY5 extract was mixed with 100 μg Flag-COP1 , 100 μg GFP , 100 μg or 200 μg myc-CSU2 and myc-CSU2 Δcoil extract as indicated . A final concentration of 10 μM ATP was added to the reaction samples to preserve the function of the ubiquitination and 26S proteasome . For the proteasome inhibition , a final concentration of 50 μM MG132 was added to the corresponding mixtures . The mixtures were incubated at 4°C with gentle shaking for 6 h . Reaction was stopped by the addition of 5×SDS sample buffer and boiling for 10 min before protein gel analysis . The primary antibodies used in this study were anti-Flag ( Sigma-Aldrich ) , anti-HA ( Sigma-Aldrich ) , anti-GFP ( BD Clontech ) , and anti-myc ( Sigma-Aldrich ) . Statistical analysis was performed by using GraphPad Prism 6 ( GraphPad Software ) . To determine statistical significance , we employed one-way ANOVA with Tukey’s posthoc test . The difference was considered significant at P < 0 . 05 . Sequence data from this article can be found in the Arabidopsis Genome Initiative database under the following accession numbers: CSU2 ( At1g02330 ) , COP1 ( AT2G32950 ) , HY5 ( AT5G11260 ) . | CONSTITUTIVE PHOTOMORPHOGENIC 1 ( COP1 ) is a key regulator of light mediated developmental processes and it works as an E3 ubiquitin ligase controlling the abundance of multiple transcription factors . In the work presented here , we identified a novel repressor of COP1 , the COP1 SUPPRESSOR 2 ( CSU2 ) , via a forward genetic screen . Mutations in CSU2 completely suppress cop1-6 constitutive photomorphogenic phenotype in darkness . CSU2 interacts and co-localizes with COP1 in nuclear speckles via the coiled-coil domain association . CSU2 negatively regulates COP1 E3 ubiquitin ligase activity , and repress COP1 mediated HY5 degradation in cell-free extracts . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Arabidopsis COP1 SUPPRESSOR 2 Represses COP1 E3 Ubiquitin Ligase Activity through Their Coiled-Coil Domains Association |
White blood cells ( WBCs ) mediate immune systems and consist of various subtypes with distinct roles . Elucidation of the mechanism that regulates the counts of the WBC subtypes would provide useful insights into both the etiology of the immune system and disease pathogenesis . In this study , we report results of genome-wide association studies ( GWAS ) and a replication study for the counts of the 5 main WBC subtypes ( neutrophils , lymphocytes , monocytes , basophils , and eosinophils ) using 14 , 792 Japanese subjects enrolled in the BioBank Japan Project . We identified 12 significantly associated loci that satisfied the genome-wide significance threshold of P<5 . 0×10−8 , of which 9 loci were novel ( the CDK6 locus for the neutrophil count; the ITGA4 , MLZE , STXBP6 loci , and the MHC region for the monocyte count; the SLC45A3-NUCKS1 , GATA2 , NAALAD2 , ERG loci for the basophil count ) . We further evaluated associations in the identified loci using 15 , 600 subjects from Caucasian populations . These WBC subtype-related loci demonstrated a variety of patterns of pleiotropic associations within the WBC subtypes , or with total WBC count , platelet count , or red blood cell-related traits ( n = 30 , 454 ) , which suggests unique and common functional roles of these loci in the processes of hematopoiesis . This study should contribute to the understanding of the genetic backgrounds of the WBC subtypes and hematological traits .
White blood cells ( WBCs ) mediate immune systems , and play essential roles in defending the body against invading foreign microorganisms [1] . WBCs consist of a variety of cells that mediate diverse roles , and are morphologically classified into 5 main subtypes: neutrophils , lymphocytes , monocytes , basophils , and eosinophils [1] . A number of previous studies have demonstrated significant contributions of these WBC subtypes to the regulation of innate and adaptive immune systems [2]–[6] . Since the number of WBC subtypes circulating in peripheral blood are tightly regulated , and abnormality in their numbers are closely linked to the presence and prognosis of diseases [2]–[6] , the counts of WBC subtypes are widely used as important blood markers in medical treatment . Therefore , elucidation of the mechanism ( s ) that regulates the counts of WBC subtypes would have substantial clinical impact and would provide new insights into the etiology of the immune system . WBC subtypes are known to be heritable traits and several epidemiological studies have suggested the existence of genetic factors that explain the variations in the counts of WBC subtypes , as well as a number of common environmental factors such as age , sex , and smoking [7]–[10] . Recently , genome-wide association studies ( GWAS ) have identified a number of genetic loci that affect hematological traits , but most of these identified loci were determined to be associated with red blood cell ( RBC ) or platelet ( PLT ) -related traits or total WBC count [11]–[17] . However , studies investigating WBC subtypes have yet to be further assessed [18]–[21] . Moreover , it is also of interest to evaluate whether ethnic differences underlie the genetic backgrounds that affect hematological traits . Previous studies for hematological traits have also suggested that several genetic loci have pleiotropic associations with other hematological traits [15]–[18] . Therefore , it is of interest whether the WBC subtype-associated genetic loci have pleiotropic associations with counts of other WBC subtypes , RBCs , and PLTs , when considering the biological roles of the loci in the processes of hematopoiesis . In this study , we report a large-scale GWAS for the counts of the WBC subtypes in 14 , 792 Japanese subjects enrolled in the BioBank Japan Project [22] . Subsequently , we performed pleiotropic association analysis of the identified WBC subtype-associated loci . We evaluated the associations of the loci identified in the Japanese population using data obtained by cohorts of Caucasian populations [23] , in order to highlight the ethnically common and divergent genetic backgrounds of WBC subtypes .
In the GWAS for the WBC subtypes , we enrolled 8 , 794 Japanese subjects . The counts of the 5 main WBC subtypes ( neutrophils , lymphocytes , monocytes , basophils , and eosinophils ) of the subjects were collected from medical records and summarized in Table S1 . We found moderate degree of correlation ( R2>0 . 1 ) between the neutrophil and monocyte counts , between the basophil and lymphocyte counts , and between the basophil and eosinophil counts ( Table S2 ) . These results were considered to be compatible with previous reports [7]–[9] . To relatively compare the effect sizes on the traits , we carried out normalization of the counts of the respective WBC subtypes . The subjects with normalized values beyond ±4SD were discarded , which accounted for less than 0 . 5% of the total subjects . Genotyping was performed with over 590 , 000 SNP markers using Illumina610-Quad Genotyping BeadChip ( Illumina , CA , USA ) . We applied stringent quality control criteria , including principal component analysis ( PCA ) [24] to evaluate potential population stratification , and obtained genotype data for 481 , 110 autosomal SNPs . To extend the genomic coverage , we subsequently performed the whole-genome imputation of the SNPs , using HapMap Phase II genotype data of Japanese ( JPT ) and Han Chinese ( CHB ) individuals as references [25] . After the imputation , 2 , 178 , 645 autosomal SNPs that satisfied the criteria of a minor allele frequency ( MAF ) ≥0 . 01 and an imputation score ( Rsq value by MACH software [26] ) ≥0 . 7 were obtained . The associations of these imputed SNPs with the transformed values of the counts of WBC subtypes were evaluated using a linear regression model . Quantile-Quantile plots of P-values indicated remarkable departures from the null hypothesis in their tails , except for the lymphocyte count ( Figure S1 ) . Inflation factors of P-values , λGC [27] , were as low as 1 . 024–1 . 038 , which suggested no substantial population stratification existed in our study population as previously anticipated for the Japanese population [28] . We identified 10 significant associations that satisfied the genome-wide significance threshold of P<5 . 0×10−8 in the GWAS for the counts of neutrophils , monocytes , basophils and eosinophils ( Figure 1 and Table S3 ) . We also evaluated the associations in the previously-reported WBC subtype-associated loci , and observed significant associations in six of these loci ( the PSMD3-CSF3 and PLCB4 loci for the neutrophil counts [21] , the MHC region for the lymphocyte counts [20] , the IL1RL1 , IKZF2 , HBS1L-MYB loci for the eosinophil counts [18] , P<0 . 005; Table S4 ) . We subsequently performed a replication study using independent 5 , 998 Japanese subjects , and further evaluated the associations of the loci by combining the results of the GWAS and the replication study . We selected a total of 36 genetic loci that showed P<5 . 0×10−6 in the GWAS for any of the WBC subtypes as the candidates for inclusion in the replication study . As a result of combined study , we finally identified 12 genetic loci that satisfied genome-wide significance threshold of P<5 . 0×10−8 . Of these , the top-associated SNPs in 2 loci were genotyped and the SNPs in 10 loci were imputed with imputation score of Rsq>0 . 90 . Specifically , we found 2 , 4 , 4 , and 3 loci for neutrophil , monocyte , basophil , and eosinophil counts , respectively ( Table 1 , Table S3 , and Figure 1 ) . One locus was shared between basophil and eosinophil counts . On the other hand , no significant association was detected for the lymphocyte count in the combined study . Among the identified loci in the combined study , 4 loci were the replication for the previous studies: rs4794822 in the PSMD3-CSF3 locus for the neutrophil count [21] , rs4328821 in the GATA2 locus , rs2516399 in the MHC region , and rs9373124 in the HBS1L-MYB locus for the eosinophil count [18] . Associations in the other 9 loci were novel findings to our knowledge ( Figure 2 ) . Specifically , we identified associations in one locus for the neutrophil count ( rs445 in the CDK6 locus ) , 4 loci for the monocyte counts ( rs12988934 in the ITGA4 locus , rs3095254 in the MHC region , rs10956483 in the MLZE locus , and rs10147992 in the STXBP6 loci ) , and 4 loci for the basophil count ( rs12748961 in the SLC45A3-NUCKS1 locus , rs4328821 in the GATA2 locus , rs11018874 in the NAALAD2 locus , and rs7275212 in the ERG loci ) ( Figure 2 ) . Of the associated SNPs located in the MHC region , rs3095254 was reported to be in linkage disequilibrium ( LD ) with HLA-Cw*0702 allele ( D′ = 1 and r2 = 0 . 24 ) , and rs2516399 was in LD with HLA-DRB1*0405 and HLA-DQB1*0401 ( D′>0 . 7 and r2>0 . 2 ) [29] . To highlight the ethnically common and divergent genetic backgrounds of the WBC subtypes , the associations of the 12 identified loci were further evaluated in Caucasian populations by using 15 , 600 subjects in cohorts of the CHARGE Consortium ( Table S5 ) [23] . The CHARGE Consortium consists of multiple community-based and prospectively designed cohorts from the United States and Europe [30] and has performed association studies for hematological traits , including the WBC subtypes [23] . We observed the same directional effects of the alleles in all 12 loci evaluated in the CHARGE Consortium . Furthermore , significant associations were observed in 4 loci ( the PSMD3-CSF3 locus for the neutrophil count , the ITGA4 locus for the monocyte count , and the GATA2 locus for both the basophil and eosinophil counts; P<0 . 004 ) . We also observed the suggestive associations in 2 loci ( the CDK6 locus for the neutrophil count and the HBS1L-MYB locus for the eosinophil count; P<0 . 05 ) . We next evaluated the pleiotropic associations of the WBC subtype-associated loci . For the top-associated SNPs in each of the loci that indicated significant associations in our study ( P<5 . 0×10−8 ) , we evaluated the associations with the counts of the other WBC subtypes , total WBC count , RBC-related traits ( RBC count , hemoglobin [Hb] , hematocrit [Ht] , mean corpuscular hemoglobin [MCH] , mean corpuscular hemoglobin concentration [MCHC] , mean corpuscular volume [MCV] ) , and PLT count using 30 , 454 Japanese subjects ( Table S6 ) . We found various patterns of pleiotropic associations for the loci associated with the WBC subtypes ( Figure 3A and Figure 4 ) . Three loci demonstrated specific associations with the original WBC subtypes identified in the GWAS ( rs12748961 in the SLC45A3-NUCKS1 locus , rs12988934 in the ITGA4 locus , rs11018874 in the NAALAD2 locus ) , although 9 other loci demonstrated pleiotropic associations with other traits . The most pleiotropic associations were observed in the HBS1L-MYB locus , which indicated significant associations with all of the evaluated hematological traits ( P<0 . 005 ) . The T allele of rs9373124 that increased the eosinophil count also increased the counts of the other WBC subtypes , total WBC count , RBC count , the Hb and Ht levels , and conversely decreased MCH , MCHC , MCV , and PLT count , validating its substantial role in hematopoiesis [15] , [31] . In the GATA2 locus , we observed significant associations with both the basophil and eosinophil counts ( P<5 . 0×10−8; Figure 3B ) . This locus encompassed several genes , although GATA2 seemed to be most responsible for regulating eosinophils and basophils from a functional standpoint [3] , [32] . Interestingly , the peaks of the associations of the SNPs in the basophil and eosinophil GWAS showed concordance at rs4328821 . Possession of the A allele of rs4328821 increased both the basophil and eosinophil counts ( Figure 3C ) . The subjects who were homozygous for the A allele had 1 . 28-fold and 1 . 19-fold higher basophil and eosinophil counts , respectively , compared with the corresponding levels of the subjects who were homozygous for the G allele . Moreover , rs4328821 significantly explained 2 . 7% of the correlation between the basophil and eosinophil counts ( permutation P<1 . 0×10−9 ) . Upon combining the effects of the SNPs in the identified loci , up to 2 . 1% of the variations of the counts of the WBC subtypes was explained , and up to 8 . 0% of the correlation between the WBC subtypes was explained ( Table S2 ) .
Through a GWAS and a replication study consisting of 14 , 792 Japanese subjects , our study identified 12 loci that were significantly associated with the counts of WBC subtypes . Among the identified loci , 9 loci are reported for the first time in this study . The identified loci demonstrated a variety of patterns of pleiotropic associations within the WBC subtypes and/or with total WBC count , RBC-related traits , and PLT count , which suggest they have both unique and common roles in the processes of hematopoiesis . Comparison of the loci identified in the Japanese population with those in Caucasian populations demonstrated the ethnically common and divergent genetic backgrounds of the various WBC subtypes . Two loci identified in the GWAS for the neutrophil count ( the CDK6 and PSMD3-CSF3 loci ) have previously been reported to be associated with the total WBC count [16] , [17] . Since neutrophils are the most abundant subtype of WBCs and typically comprise 50–70% of the total WBC count , the associations between these loci with the total WBC count would have reflected the associations with the neutrophil count [21] . We identified 4 novel loci associated with monocyte count ( the ITGA4 , MLZE , and STXBP6 locus and the MHC region ) . The landmark SNP in the MHC region was located near the HLA-C gene and in moderate LD with the particular HLA-C allele , which belongs to MHC class I molecules . ITGA4 encodes the α4 chain of the integrins , which mediate migration of the WBCs [33] . Previous reports have demonstrated that STXBP6 ( also known as amisyn ) binds to the components of the SNARE complex , which mediates membrane fusions including phagocytosis [34] , [35] . In response to inflammation , monocytes differentiate into macrophages and migrate into affected tissues of inflammation , and subsequently perform phagocytosis and antigen presentation using MHC molecules expressed on the cell surface [6] . Therefore , associations of the SNPs in these loci with the monocyte count would be plausible from a biological perspective . Recently , clinical benefits of inhibition of α4 integrin have been demonstrated in the treatment of autoimmune diseases [36] . Although further functional investigation is necessary , the SNP in the ITGA4 locus that was identified in our GWAS may be a promising target for pharmacogenomics of anti-α4 integrin therapy . MLZE ( also known as GCDMC ) belongs to the Gasdermin family of genes [37] , and its role in the regulation of the monocyte count should be further explored . In addition , we identified 4 novel loci associated with the basophil count ( the SLC45A3-NUCKS1 , GATA2 , NAALAD2 , and ERG loci ) and replicated associations in 3 previously-reported loci associated with the eosinophil count ( the GATA2 and HBS1L-MYB loci and the MHC region ) . Basophils and eosinophils coordinately mediate allergic inflammation [3]–[5] , and the correlation of these counts [7] suggested the existence of genetic factors that are shared between them . Our pleiotropic study demonstrated overlap of the associated loci between the basophil and eosinophil counts , which was most highlighted in the GATA2 locus . GATA2 is a well-known zinc-finger transcription factor and plays an essential role in hematopoiesis , particularly in the regulation of basophils and eosinophils [3] , [32] . The landmark SNP in the GATA2 locus was concordant in the GWAS for both the basophil and eosinophil counts , and significantly explained part of their correlation in the counts . Pleiotropic associations of the SNP with the basophil and eosinophil counts were further replicated in the Caucasian populations . These results suggested an ethnically-shared substantial functional role of the SNP in the etiology of GATA2 . ERG encodes a member of the Ets family of transcription factors , and is known to be included in the Down syndrome critical region on chromosome 21 [38] . Although its functional role ( s ) in the regulation of basophils has not been investigated to date , an essential role of ERG for definitive hematopoiesis has been demonstrated [38] . The SLC45A3-NUCKS1 locus in the present study encompassed several genes , and we submit that the functional origin of this locus should be further investigated . Interestingly , the fusion transcript of SLC45A3 and ERG is observed in prostate cancers , which have been characterized by the overexpression of ERG mRNA [39] , [40] , although we did not find any significant gene-gene interaction in the SNPs in these two loci for basophil count ( data not shown ) . Finally , NAALAD2 is a member of the N-acetylated α-linked acidic dipeptidase gene family [41] , and its role in the regulation of the basophil counts should be a topic to be further investigated in the future . In contrast to the WBC subtypes mentioned above , no significant association was detected for the lymphocyte count . One probable explanation for this finding is that lymphocytes can be further divided into a variety of subsets , such as natural killer ( NK ) cells , T cells , and B cells , which were not examined specifically in this study . Therefore , future GWAS that focus on those specific subsets of lymphocytes [20] are necessary to efficiently investigate the genetic backgrounds of the lymphocytes . Several points about this study bear discussion . First , since our study populations consisted of the disease patients , it would be useful to assess the possibility that the disease status might have confounded the results . When the respective disease groups were analyzed separately and evaluated through meta-analysis , all the identified loci satisfied the significant associations ( P<5 . 0×10−8 ) without significant heterogeneities of the effects ( α = 0 . 01 ) . None of the identified loci has been reported to be associated with the risk of the diseases enrolled in the study population , except for the MHC region with Rheumatoid Arthritis ( RA ) [42] . Moreover , after the subjects affected with RA were excluded , the significant associations of the SNPs in the MHC region with the monocyte and eosinophil counts were observed ( P = 1 . 7×10−10 for rs3095254 and P = 9 . 6×10−12 for rs2516399 , respectively ) . It would be of note that we observed concordance of the associations in the identified loci with those by CHARGE consortium , which is consisted of multiple community-based and prospective cohorts incorporating normal populations . Although further validation study using non-affected subjects would be desirable , these observations suggested that the utilization of disease patients have not induced substantial bias in our study . Second , the counts of the WBC subtypes were based on medical records . Although the data collection protocol is enormously standardized [22] , there is a possibility that unstandardized discrepancy among the medical institutes might induce bias in the phenotype distributions and impair statistical power of the study . Third , the explained proportion of the WBC subtypes by the identified loci would be estimated conservatively . Because of the stringent significance threshold adopted in the study , a number of associated loci with moderate effect sizes would be still unidentified . Further approaches , such as considering the entire SNPs simultaneously [43] , are necessary for the accurate estimation of the explained variations . Candidate gene analysis based on the biological pathway of the WBC subtypes would also be a promising approach to uncover these unidentified loci [44] . Fourth , since the correlation among the traits could modulate the pattern of the pleiotropic associations , further distinction of actual pleiotropic associations from simple associations induced by the correlations would be a topic to be investigated . In summary , our study identified 9 novel loci that are associated with the counts of the WBC subtypes . The pleiotropic association study of the identified loci demonstrated unique and common genetic backgrounds underlying the WBC subtypes . Our study should contribute to the general understanding of the etiology and regulation of the WBC subtypes .
The subjects enrolled in the GWAS , and in the replication study for WBC subtypes ( n = 14 , 792 ) , and in the pleiotropic association study for hematological traits ( n = 30 , 454 ) consisted of patients that were classified into 27 disease groups ( Tables S1 and S6 ) . The subjects in the pleiotropic association study included the subjects in the GWAS and the replication study . All subjects were collected under the support of the BioBank Japan Projects [22] . Subjects who were determined to be of non-Japanese origin by either self-report or by PCA in GWAS were excluded from analysis . Some of the subjects in this study have also been included in our previous studies [17] , [21] , [45] , [46] . All participants provided written informed consent as approved by the ethical committees of the BioBank Japan Project [22] and the University of Tokyo . Clinical information of the subjects including age , gender , and smoking history were collected by self-report on the questionnaire . The laboratory data including the counts of the WBC subtypes and other hematological traits were collected from medical records by the professional medical coordinators according to the standardized protocol [22] . The details of the study enrolled by the CHARGE Consortium , including subject details and the study design , are described at length elsewhere [23] , and are summarized in Table S5 . In the GWAS for the WBC subtypes , 592 , 232 SNPs were genotyped for 8 , 943 subjects using Illumina HumanHap610-Quad Genotyping BeadChip . We excluded 77 subjects with call rates <0 . 98 in the process of genotyping . After this initial exclusion , SNPs with call rates <0 . 99 or with ambiguous clustering of the intensity plots , or non-autosomal SNPs , were excluded . We excluded 67 closely related subjects based on the identity-by-descent ( IBD ) , which was estimated using the “–genome” option implemented in PLINK version 1 . 06 [47] . For each pair with a 1st or 2nd degree of kinship , we excluded the one member of the pair with lower call rates than the other . We then excluded subjects whose ancestries were estimated to be distinct from East-Asian populations using PCA performed by EIGENSTRAT version 2 . 0 [24] . We performed PCA for the genotype data of our GWAS along with the genotype data of Phase II HapMap populations ( unrelated European ( CEU ) , African ( YRU ) , and East-Asian ( JPT + CHB ) individuals ) ( release 24 ) [25] . Based on the PCA plot of the subjects , we visually identified and excluded 5 outliers in terms of ancestry from JPT + CHB clusters . Subsequently , the SNPs with MAF <0 . 01 or with an exact P-value of the Hardy-Weinberg equilibrium test <1 . 0×10−7 were excluded . Finally , we obtained 481 , 110 SNPs for 8 , 794 subjects . After the quality control criteria mentioned above were applied , genotype imputation was performed using MACH 1 . 0 [26] in a two-step procedure [46] . The genotype data of Phase II HapMap JPT and CHB individuals ( release 24 ) [25] were adopted as references . In the first step of the imputation , recombination and error rate maps were estimated using 500 randomly selected subjects from those enrolled in the GWAS . In the second step , genotype imputation of all subjects was performed using the estimated recombination and error rate maps . Quality control filters of MAF ≥0 . 01 and Rsq values ≥0 . 7 were applied for the imputed SNPs . The genotype data of the SNPs enrolled in the replication or pleiotropic association study were obtained from the genome-wide screening data of the BioBank Japan Project [22] . Genotyping was performed using either Illumina HumanHap550v3 Genotyping BeadChip or Illumina HumanHap610-Quad Genotyping BeadChip , and the same quality control filters and imputation procedure were applied . The common log-transformed values of the counts of each of the WBC subtypes were adjusted for gender , age , smoking history , and the affection statuses of the subjects with the disease groups ( Table S1 ) , using linear regression by R statistical software ( version 2 . 11 . 0 ) . Then the residuals were normalized as Z scores , and the subjects with Z score >4 . 0 or <−4 . 0 were excluded in each of the traits . Associations of the SNPs with the counts of the WBC subtypes were assessed by linear regression assuming the additive effects of the allele dosages on the Z scores , using mach2qtl software [26] . In the replication and pleiotropic association studies , the association of the SNPs with the normalized residuals were also evaluated by the linear regression as univariate analysis for each of the phenotypes . The transformation methods used for the hematological traits in the pleiotropic association study are summarized in Table S6 . Combined study of the results of the GWAS and the replication study was performed using an inverse-variance method from the summary statistics of beta and standard error ( SE ) . Through the combined study of the GWAS and the replication study , the locus which satisfied the genome-wide significance threshold of P<5 . 0×10−8 was considered to be significant . We did not account for multiple comparisons among the traits . These significantly associated loci were subsequently enrolled in the pleiotropic association study . For the selection of the loci that were evaluated in the replication study , we adopted less stringent threshold of P<5 . 0×10−6 to include potentially associated loci . For the evaluation of the identified loci using the Caucasian populations , Bonferroni correction based on the number of the evaluated loci were adopted ( α = 0 . 05 , n = 12 , P<0 . 004 ) . LD between the SNPs in the MHC region and HLA alleles were estimated using the genotype data of the SNPs and the high-resolution HLA alleles for Phase II HapMap JPT and CHB individuals [25] , [29] . Explained proportions of the variations of the WBC subtypes by the combination of the associated SNPs were estimated based on the differences of the coefficient of determination , R2 , in the multivariate linear regression model for common-log transformed counts of the respective WBC subtypes , including the associated SNPs as covariates , and the model additionally including age , gender , smoking history , and the affection statuses of the subjects as covariates . Explained proportions of the correlation between the two WBC subtypes by the associated SNPs were estimated based on the following statistics: ( R2resi1 - R2resi2 ) / R2nomi , where R2nomi is the R2 between the log-transformed values of the counts of the WBC subtypes , R2resi1 is the R2 between the residuals of the values adjusted for gender , age , smoking history , and the affection statuses of the subjects , and R2resi2 is the R2 between the residuals of the values adjusted for gender , age , smoking history , the affection statuses of the subjects , and the SNPs . Significance of the statistics was evaluated using permutation procedure ( × 109 iteration steps ) . The URLs for data presented herein are as follows . BioBank Japan Project , http://biobankjp . org MACH and mach2qtl software , http://www . sph . umich . edu/csg/abecasis/MACH/index . html International HapMap Project , http://www . hapmap . org PLINK software , http://pngu . mgh . harvard . edu/~purcell/plink/index . shtml EIGENSTRAT software , http://genepath . med . harvard . edu/~reich/Software . htm R statistical software , http://cran . r-project . org SNAP , http://www . broadinstitute . org/mpg/snap/index . php | White blood cells ( WBCs ) are blood cells that mediate immune systems and defend the body against foreign microorganisms . It is well known that WBCs consist of various subtypes of cells with distinct roles , although the genetic background of each of the WBC subtypes has yet to be examined . In this study , we report genome-wide association studies ( GWAS ) for the 5 main WBC subtypes ( neutrophils , lymphocytes , monocytes , basophils , and eosinophils ) using 14 , 792 Japanese subjects . We identified 12 significantly associated genetic loci , and 9 of them were novel . Evaluation of the associations of these identified loci in cohorts of Caucasian populations demonstrated both ethnically common and divergent genetic backgrounds of the WBC subtypes . These loci also indicated a variety of patterns of pleiotropic associations within the hematological traits , including the other WBC subtypes , total WBC count , platelet count , or red blood cell-related traits , which suggests unique and common functional roles of these loci in the processes of hematopoiesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"blood",
"cells",
"genome-wide",
"association",
"studies",
"medicine",
"quantitative",
"traits",
"genome",
"analysis",
"tools",
"biology",
"hematology",
"cell",
"biology",
"heredity",
"genetics",
"cellular",
"types",
"genomics",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics",
"human",
"genetics"
] | 2011 | Identification of Nine Novel Loci Associated with White Blood Cell Subtypes in a Japanese Population |
Adaptive evolution is generally assumed to progress through the accumulation of beneficial mutations . However , as deleterious mutations are common in natural populations , they generate a strong selection pressure to mitigate their detrimental effects through compensatory genetic changes . This process can potentially influence directions of adaptive evolution by enabling evolutionary routes that are otherwise inaccessible . Therefore , the extent to which compensatory mutations shape genomic evolution is of central importance . Here , we studied the capacity of the baker's yeast genome to compensate the complete loss of genes during evolution , and explored the long-term consequences of this process . We initiated laboratory evolutionary experiments with over 180 haploid baker's yeast genotypes , all of which initially displayed slow growth owing to the deletion of a single gene . Compensatory evolution following gene loss was rapid and pervasive: 68% of the genotypes reached near wild-type fitness through accumulation of adaptive mutations elsewhere in the genome . As compensatory mutations have associated fitness costs , genotypes with especially low fitnesses were more likely to be subjects of compensatory evolution . Genomic analysis revealed that as compensatory mutations were generally specific to the functional defect incurred , convergent evolution at the molecular level was extremely rare . Moreover , the majority of the gene expression changes due to gene deletion remained unrestored . Accordingly , compensatory evolution promoted genomic divergence of parallel evolving populations . However , these different evolutionary outcomes are not phenotypically equivalent , as they generated diverse growth phenotypes across environments . Taken together , these results indicate that gene loss initiates adaptive genomic changes that rapidly restores fitness , but this process has substantial pleiotropic effects on cellular physiology and evolvability upon environmental change . Our work also implies that gene content variation across species could be partly due to the action of compensatory evolution rather than the passive loss of genes .
Deleterious , but non-lethal mutations are constantly generated and can hitchhike with adaptive mutations [1] . Consequently , such deleterious alleles are widespread in eukaryotic populations [2] , [3] . For example , as high as 12% of the coding SNPs in yeast populations are deleterious [2] . Many of the observed functional variation in this species yield proteins with compromised or no activities [2] , or lead to complete loss of genes with significant contribution to fitness ( Text S1 ) . Deleterious loss-of-function variants may occasionally revert to wild type , eventually perish from the population , or become compensated by mutations elsewhere in the genome . The third possibility , termed compensatory evolution , is the focus of our study . Theoretical works suggest that mutant subpopulations can cross fitness valleys by the simultaneous fixation of a compensatory mutation in the population [4] , [5] . This process can also work in large populations and is facilitated by linkage of the two alleles [5] . Compensatory evolution appears to be common at many levels of molecular interactions . It is involved in the maintenance of RNA and protein secondary structures , it mitigates the costs of antibiotic resistance [6] , [7] , and allows rapid fitness recovery in populations with accumulated deleterious mutation loads [7]–[9] . Compensatory regulatory mutations also act to stabilize gene expression levels across species [10] , [11] , and conserve DNA-encoded nucleosome organization [12] . The most detailed experimental analyses on compensatory mutations for fixed deleterious mutations were performed in DNA bacteriophages [8] , [13]–[15] , bacteria [16] , [17] , and Caenorhabditis elegans [7] , [9] . Three major patterns emerged from these studies . As the target size for compensatory mutations is typically much larger than that for reversion , compensation is more likely than reversion of deleterious mutations [13] . The rate of compensatory evolution increased with the severity of the deleterious fitness effects , and was not limited to functionally interacting partners of the mutated gene [15] . As regards the potential pleiotropic effects of compensatory evolution , our knowledge is rather limited , not least because it demands detailed exploration of the underlying molecular mechanisms of compensation . Compensatory mutations may enhance fitness either by reducing the need for the gene with the compromised function , or by restoring the efficiency of the affected molecular function [18] . For compensation of fitness costs of antibiotic resistance conferring mutations , restoration of function was the most common mechanism [18] , but in other systems the relative importance of functional substitution and restoration is unknown . In the case of functional restoration ( e . g . , by enhanced dosage of a redundant duplicate of the disrupted gene ) , one might expect limited pleiotropic fitness effects of compensatory mutations across environmental conditions . Compensatory evolution following gene loss is of special interest [17] . Gene loss may be initiated by genetic drift and/or selection through antagonistic pleiotropy [17] , [19] . As reversion to the wild-type state is less likely , gene loss may promote genetic changes that drive the populations to new adaptive peaks ( Figure 1 ) . It's reasonable to assume that compensatory mutations are generally specific to the gene defect , and multiple molecular mechanisms can restore fitness . Therefore , independently evolving populations carrying an inactivated gene are expected to diverge from each other . Moreover , if compensation mainly proceeds by reducing the need for the disrupted molecular function then compensatory evolution could have a large impact on cellular physiology and survival upon environmental change . Accordingly , the beneficial effects of compensatory mutations may frequently be conditional , and subsequent changes to the environment can reveal the hidden genetic variation across populations ( Figure 1 ) . The goal of the current study was to test this hypothesis by an integrated systems biology approach . Specifically , we aimed to determine the potential of the Saccharomyces cerevisiae genome to compensate for gene loss through compensatory evolution and to explore the long-term consequences of this process .
We initiated laboratory evolutionary experiments with 187 haploid single gene knock-out mutant strains , all of which initially showed slow ( but non-zero ) growth compared to the wild-type control in a standard laboratory medium ( Figure 2A , for selection criteria , see Materials and Methods ) . These genes cover a wide range of molecular processes and functions ( Table S1 ) . Populations were cultivated in parallel ( four replicate populations for each null mutation ) , resulting in 748 independently evolving lines . 0 . 5% of each culture was diluted into fresh medium every 48 hours , and populations were propagated for approximately 400 generations . To control for potential adaptation unrelated to compensatory evolution , we also established 22 populations starting from the isogenic wild-type genotype , referred to as evolving wild types . Next , all starting and evolved populations were subjected to high-throughput fitness measurements by monitoring growth rates in liquid cultures . Fitness may increase during the course of laboratory evolution as a result of general adaptation to the environment and/or accumulation of compensatory mutations that suppress the deleterious effects of gene inactivation . Under the assumption that compensatory evolution was the dominant force in our experiments , fitness should not increase by the same extent in all lineages: genotypes that carry deleterious null mutations are further away from the optimal state and are hence expected to show large fitness gains ( Figure 2A ) ; this was indeed so . On average , the evolving wild-type control populations showed a small , but significant 5% fitness improvement . By contrast , the fitness of populations carrying a deleterious null mutation improved by 23% on average ( Figure 2B ) , and many of them approximated wild-type fitness ( Figure 2C; Table S1 ) . On the basis of fitness measurements at multiple time points during laboratory evolution ( see Methods ) , we also report that individual fitness trajectories often showed a saturating trend during the course of laboratory evolution ( Figure S1 ) . The difference in fitness improvement is not due to the elevated mutation rate of mutant genotypes for two reasons . First , a previous study conducted a genome-wide screen with the aim to identify genes in S . cerevisiae that influence the rate of mutations [20] . While a large number of such genes have been found , only four of them were present in our gene set ( Δrad54 , Δrad52 , Δmre11 , and Δrad50 ) . Second , fitness improvements of the corresponding single gene knock-out strains did not differ from the rest of the dataset ( one-tailed Wilcoxon rank sum test , p = 0 . 89 ) . As previously [16] , we defined compensatory evolution as a fitness increase that is disproportionally large relative to that in the evolving wild-type lines . Using this definition , 68% of the genotypes showed evidence of compensatory evolution ( i . e . , at least one of the four independently evolving populations fulfilled the above criteria ) . The corresponding genes cover a wide range of molecular and cellular processes ( Table S1 ) . Next , we compared the fitness improvements between evolved lines founded from the same gene deletion genotype versus those founded from different genotypes . This analysis revealed that not all genes were equally likely to be compensated as fitness gain differed significantly across genotypes ( ANOVA , F ( 186 ) = 3 . 9 , p<10−14 ) ( see also Figure S2 ) . It has been previously suggested that as mutations with especially large fitness effects tend to disrupt a broader range of molecular processes [21] , such mutations may influence the number of mutational targets where compensatory evolution can occur [13] . We compiled three datasets that estimate different aspects of gene pleiotropy [22] , including fitness under diverse environmental conditions ( environmental pleiotropy ) , the number of protein-protein interactions ( network pleiotropy ) , and the number of biological processes associated with a gene ( multifunctionality ) . The extent of evolutionary compensation did not depend on any of the above mentioned features ( Figure 2D ) . However , consistent with results of prior small-scale bacterial and viral evolutionary studies [13] , [16] , null mutations with more severe defects were more likely to be compensated ( Figure 2E ) . This pattern probably reflects that the availability of compensatory mutations across the genome strongly depends on the fitness effect of the deleted gene . We provide a simple explanation of this phenomenon in the Discussion . To investigate the genomic changes underlying compensatory evolution , we re-sequenced the complete genomes of 41 independently evolved lines and the 14 corresponding ancestors , all of which showed large fitness improvements ( Table S1 ) . We focused on de novo mutations that accumulated during the course of laboratory evolution . Large-scale duplications ( including segmental or whole chromosome duplication ) were observed in 22% of the laboratory evolved lines . On average , six point mutations and 0 . 5 small insertions or deletions per clone were detected ( Figure 3A; Table S2 ) . The ratio of non-synonymous to synonymous mutations was significantly higher than expected by chance ( p = 0 . 003 , see Materials and Methods ) , indicating that the accumulation of these mutations was driven by adaptive evolution . On average , pairs of evolutionary lines founded from the same genotype shared 5 . 3% of their mutated genes , while the same figure was 0 . 1% for lines founded from different genotypes ( Table S2 ) . This result is in contrast to results of a prior bacterial study [23] , where a strong signature of parallel evolution emerged at the gene level across parallel evolving laboratory populations . Despite the rarity of parallel evolution at the molecular level , a major unifying trend emerged: evolution preferentially affected genes that are functionally related to that of the disrupted gene ( Figure 3B ) . Moreover , when the null mutation affected a protein complex subunit , another subunit of the same complex was mutated 10 times more often than expected by chance ( Figure 3B ) . Taken together , these results indicate that deletion of any single gene drives adaptive genetic changes specific to the functional defect incurred . Although duplicated genes with partially overlapping function are frequent in the yeast genome , we found no evidence that genetic changes affecting a duplicate of the disrupted gene provide a general mechanism of compensation in our evolved lines . First , our dataset contains 128 genes showing evidence for compensation , and only 25% of these genes have a duplicate in the yeast genome ( i . e . , at least 30% amino acid similarity between the two copies ) . This figure is a gross overestimate , as it includes very distant duplicates that most likely diverged functionally ( Materials and Methods ) . Second , the subset of genes with a gene duplicate were not more likely to be compensated during laboratory evolution than the rest of the dataset ( Chi-squared test , p = 0 . 54 ) . Third , genome sequence analysis of the evolved lines revealed only one clear example where evolution proceeded through increasing the dosage of a gene duplicate with redundant function of the deleted gene ( Figure 3C ) . All three studied evolved lines of Δrpl6b showed an increased copy number of the left arm of Chromosome XIII ( Figure 3C ) . RPL6B is a non-essential gene and encodes a ribosomal 60S subunit protein L6B . The duplicated genomic regions of Δrpl6b evolved lines carry RPL6A , a duplicate copy of RPL6B . The two genes share 94% amino acid identity , have highly overlapping functions , and deletion of both genes confer a synthetic lethal phenotype [24] . On the basis of these observations , we propose that doubling the copy number of RPL6A through segmental duplication could be partly responsible for the improved fitness in the evolved lines carrying the RPL6B deletion . The hypothesis was tested by increasing the copy number of RPL6A in wild-type and Δrpl6b genetic backgrounds , respectively . As expected , an enhanced copy number of RPL6A substantially improved the fitness of Δrpl6b , but not that of the wild type ( Figure 3D ) . Compensatory evolution may restore wild-type physiology or generate novel alterations with respect to prior physiological states [25] . To investigate the relative contribution of these processes , eight genotypes carrying a deleterious gene deletion and one corresponding evolved line were selected for transcriptome analysis ( see Materials and Methods for selection criteria ) . Using DNA microarrays , the global gene expression states were compared between the wild-type , the ancestral line , and the evolved lines carrying the same gene deletion ( Figure 4A and 4B ) . As expected from prior studies [26] , inactivation of genes with high fitness contribution altered the expression of a large number of genes across the genome ( ranging between 81 to 588 ) ( see Table S3 ) . Next , the transcriptomic profiles were compared by calculating all pairwise combinations of Euclidean distances . The wild-type , the ancestral line , and the corresponding evolved lines generally showed substantial differences in their transcriptome profiles ( Figure 4B ) , indicating that compensatory evolution drives the cell towards novel genomic expression states . Importantly , transcriptome profile distances between different genotypes was always higher than distances between replicate measurements of the same genotype ( Figure 4B ) , implying that the substantial differences observed between evolved lines and wild-type cannot be attributed to measurement noise . As a further support , typically only 10%–30% of the genes with altered expression in the ancestral lines showed significant shift towards the wild-type expression level in the corresponding evolved lines ( Figure 4C ) . Hence , despite substantial fitness improvements ( >75% for all cases investigated ) , the majority of the gene expression changes due to gene deletion remained unrestored during evolution . These patterns were not attributable to growth rate regulated gene expression or copy number variation in the evolved lines ( Figure S3 ) . Taken together , compensatory evolution following gene loss did not restore wild-type genomic expression and promoted genomic divergence across populations . Are these evolutionary outcomes phenotypically completely equivalent ? This problem was first addressed by monitoring the fitness of 237 evolved populations in 14 environmental settings , including previously tested nutrients and stress factors [27] . Prior to evolution , genotypes carrying a gene deletion generally displayed slow growth in most environments ( Table S1 ) . The situation was far more complex following laboratory evolution . Considering all possible pairs of population-environment combinations , fitness improved in 52% , and declined in 8% of the cases ( Figure 5A ) . Moreover , independently evolved populations carrying the same disrupted gene showed more fitness variation across the 14 tested conditions than in the environment they had been exposed to during laboratory evolution ( Figure 5B , p<10−7 ) , while evolved wild-type populations did not show such a difference ( p = 0 . 93 , coefficient of variations compared by Z-test ) . Furthermore , the degree of fitness variation across conditions was especially high for gene deletions that showed large fitness gains during compensatory evolution ( Spearman rho = 0 . 36 , p = 10−4 ) ( Figure 5C ) . These results indicate that the level of discernible heterogeneity in fitness was relatively low in the evolved populations founded from the same genotype , but the variation can be uncovered upon environmental change . Finally , our analysis revealed a few instances where the laboratory evolved lines displayed significantly higher than wild-type fitness in specific environments ( Table S1 ) . Most notably , the evolved Δrpl6b and Δatp11 lines displayed 24%–26% fitness increase compared to that of the wild type in a medium containing sodium chloride ( Table S1 ) , a result that was confirmed by additional independent colony size assays with high replicate number ( n = 20 , Wilcoxon rank-sum test p<10−4 in all cases ) . Moreover , the fitnesses of these lines in this medium surpassed all that of the 22 evolved wild-type controls . These results are all the more remarkable , as the corresponding ancestral Δrpl6b and Δatp11 strains showed fitness values significantly lower than wild type under all environmental conditions considered . These preliminary results indicate that gene loss can promote adaptive evolution towards novel environments , a possibility that will be explored further in a future work . Next , we conducted an in-depth genetic analysis with the MDM34 deletion with the aim of deciphering the molecular mechanisms and/or potential fitness costs of compensatory mutations ( Text S1 ) . This gene codes for a component of the ERMES protein complex , and is involved in the exchange of phospholipids between mitochondria and the endoplasmatic reticulum ( Figure 6A ) . Disruption of this gene yields impaired cardiolipin synthesis [28] , as an insufficient amount of unsaturated fatty acids reaches the mitochondria ( Figure 6A ) . Laboratory-evolved lines carrying deletion in this gene substantially improved fitness in the medium of selection ( Table S1 ) , but the putative cellular mechanisms of compensation were remarkably different across populations ( Figures 6A and S4 ) . The native copy of MDM34 was reinserted into the ancestral line and four evolved lines carrying the same deletion ( Δmdm34 ) . The analysis revealed that the net effect of mutations in three evolved lines were deleterious in the presence of MDM34 ( Figure 6B ) . Next , we concentrated on a specific mutation observed in MGA2 , a gene involved in the regulation of unsaturated fatty acid biosynthesis ( Figure 6A; Text S1 ) . Inserting the observed mutations ( mga2-1 ) into wild type and Δmdm34 resulted in very similar conclusions . mga2-1 and Δmdm34 showed strong sign-epistasis [29]: they were independently deleterious but significantly less so when they occurred together ( Figure 6C ) . Moreover , the capacity of mga2-1 to compensate the loss of MDM34 was restricted to non-acidic conditions ( Figure 6C ) , probably because of the misregulation of the corresponding stress-induced pathway under low pH ( Text S1 ) . Our dataset contains 21 independent point mutations that occurred during laboratory evolution and generated in-frame stop codons . Most notably , a mutation in WHI2 emerged in an evolving Δrpb9 line , which shortened the coding region from 480 to 133 codons , and hence most likely resulted in a non-functional protein . To test the impact of loss of WHI2 function on fitness and compensation , Δwhi2 was introduced into Δrpb9 cells using synthetic genetic array methodology ( Figure 7A and 7B ) [30] . In agreement with expectation , deletion of WHI2 partly suppressed the harmful effect of the RPB9 deletion ( Figure 7B ) . RPB9 is an RNA polymerase II subunit , and its deletion leads to elevated transcriptional error rate [31] and in turn , to proteotoxic stress [32] , which can result in cell cycle arrest [33] . WHI2 is known to be required for general stress response [34] and cell cycle arrest [35] . We speculate that less stringent cell cycle control due to WHI2 deletion is favorable in Δrpb9 ( see also [36] ) . Next , the fitness impact of WHI2 deletion was evaluated across 14 environments . The fitnesses of the Δrpb9 Δwhi2 strain varied strongly across conditions , and showed correlation with that of the evolved Δrpb9 line , which carried the WHI2 non-sense mutations ( Spearman rho = 0 . 77 , p<0 . 005 ) ( see Figure 7C ) . Most notably , the compensation of Δrpb9 by Δwhi2 was completely abolished in the presence of cycloheximide ( Figure 7B ) . We conclude that the compensatory effect of WHI2 deletion is plastic across environments .
Our work addresses one of the most long-standing debates in evolution . Since the early 1920s , Ronald Fisher pioneered the view that adaptation is by and large a hill climbing process: it proceeds through progressive accumulation of beneficial mutations [37] , [38] . However , as slightly deleterious mutations are far more abundant , they have a significant contribution to genetic variation in natural populations [2] . In the long run , the wealth of such detrimental mutations is expected to promote fixation of compensatory mutations elsewhere in the genome . This work focused on a specific aspect of this problem , and asked whether deleterious gene loss events promote adaptive genetic changes and what the side consequences of such a process might be . To systematically study compensatory evolution following gene loss , we initiated laboratory evolutionary experiments with over 180 haploid yeast genotypes , all of which initially displayed slow growth owing to the deletion of a single gene , and investigated the genomic and phenotypic capacities of the evolved lines in detail . Thanks to the exceptionally large-scale analysis of our study , the following major conclusions can be drawn . First , compensatory evolution following gene loss was pervasive: 68% of the deleterious , but non-lethal gene disruptions were compensated through the accumulation of adaptive mutations elsewhere in the genome ( Figure 2B ) . Furthermore , in agreement with prior bacterial studies [16] , [17] , the process was strikingly rapid . As the set of disrupted genes are functionally very diverse ( Table S1 ) , it appears that defects in a broad range of molecular processes can readily be compensated during evolution . However , we and others [17] also found that not all genotypes are equally likely to be recovered during laboratory evolution . Therefore , future works should clarify the exact molecular , functional , and systems level gene properties that influence compensability . Second , our large-scale study indicates that the extent of fitness loss due to gene disruption is one if not the strongest predictor of compensatory evolution ( Figure 2E ) . Although this relationship has been observed previously in small-scale studies [16] , the reasons remained largely unknown . One may argue that the spread of compensatory mutations with mild beneficial effects would have taken many more than 400 generations to reach fixation [16] . Although this explanation cannot be excluded , there is another intriguing possibility [13] . Consistent with Fisher's geometric model [37] , [38] , fitness improvement in populations close to an optimal state can only be achieved by relatively rare mutations with small effects . However , when a population with a gene defect is further away from a fitness peak , compensatory evolution may proceed through a wider range of mutations , including ones that have deleterious side effects . Two lines of evidence are consistent with this scenario . Compensatory evolution has associated pleiotropic effects ( Figures 5 and 6C ) . Moreover , the theory predicts that compensatory mutations should be especially frequent in the case of strongly deleterious null mutations . An analysis based on data of a prior genome-wide genetic interaction study [21] suggests that it may indeed be so ( Figure 8 ) . Third , genomic analysis of the evolved lines revealed that deletion of any single gene drives adaptive genetic changes specific to the functional defect incurred ( Figure 3B ) , and consequently convergent evolution at the molecular level was extremely rare . In agreement with a prior bacterial evolutionary study [17] , we found that gene duplication has only a minor role during compensatory evolution following gene loss . A more general issue is the extent to which mutations that affect gene expression could alone recover fitness [17] , [39] . Although genetic changes in putative promoter regions were not overrepresented in our dataset ( Binomial test , p = 0 . 87 ) , 21 observed point mutations generated in-frame stop codons , most likely yielding proteins with compromised or no activities ( see also Figure 7 ) . These results indicate that fitness recovery following gene loss can partly be achieved purely through inactivation of other genes . Fourth , compensatory evolution promoted divergence of genomic diversification , and shifted the evolved population towards novel genomic expression states ( Figure 4B ) . Despite substantial fitness improvements , the majority of the gene expression changes due to gene deletion remained unrestored during evolution . This finding is consistent with prior works arguing that no clear relationship exists between the change in mRNA expression of a gene and its requirement for growth in the same condition [40] . Fifth , independently evolved populations showed substantial fitness variation across environments that they had not been exposed to during laboratory evolution ( Figure 5 ) . These results suggest that accumulation of adaptive mutations during compensatory evolution generated substantial genetic differences between populations , and this variation can be uncovered upon environmental change . Taken together , several lines of evidence indicate that fitness gains in the evolved lines reflect accumulation of gene specific compensatory mutations rather than a global adaptation: ( i ) evolving wild-type control populations showed only minor changes in fitness , ( ii ) the rate of adaptation was genotype specific , ( ii ) convergence at the molecular across genotypes was extremely rare , ( iv ) evolution preferentially affected genes that are functionally related to that of the disrupted gene , and ( v ) compensatory mutations had no beneficial impact in a wild-type genetic background . The above results encouraged us to distinguish between two evolutionary scenarios . Organisms may attempt to restore the disrupted molecular function through mutations in genes with redundant functions ( functional restoration ) . Alternatively , they may aim to minimize the cellular damage incurred by gene disruption ( functional replacement ) . While the possibility of full functional restoration cannot be excluded , the rarity of compensation through mutations in gene duplicates and the plasticity of compensatory mutational effects across environments are consistent with the second scenario . Indeed , our work demonstrates that gene loss promotes genetic changes that have a large impact on evolutionary diversification , genomic expression , and viability upon environmental change . An important implication of our study is that the beneficial effects of compensatory mutations should frequently be conditional , and subsequent changes to the environment can reveal the hidden fitness effects ( beneficial and detrimental alike ) . Lack of restoration of fitness across environments is broadly consistent with the emerging view that epistatic interactions are plastic across conditions [41] , [42] . The perspective offered in this work leads to the re-formulation of several fundamental questions . First , it sheds light on an evolutionary paradox: while core cellular processes are generally conserved during evolution [43] , the constituent genes are partly different across related species with similar lifestyles . We propose that gene content variation across species is partly due to the action of compensatory evolution and may not need to reflect changes in environmental conditions and the consequent passive loss of genes . Although the exact population genetic conditions facilitating this process remain to be elucidated , several observations are consistent with this view . Most notably , the phylogenetic conservation of indispensable genes depends on how easily the gene can be functionally replaced through enhanced expression of other genes [44] . Second , it has been suggested that deleterious mutations may act as stepping stones in adaptive evolution by providing access to fitness peaks that are not otherwise accessible [45] , [46] . Indeed , our analysis revealed a few instances where the laboratory evolved lines displayed significantly higher than wild-type fitness in specific environments . Finally , given the prevalence of gene loss events during tumorigenesis , future work should elucidate whether similar processes drive the somatic evolution of cancer [47] .
All strains used in this study were derived from the BY4741 S . cerevisiae parental strain . Non-essential single-gene deletion strains from the haploid yeast deletion collection [40] ( MATa; his3Δ 1; leu2Δ 0; met15Δ 0; ura3Δ 0; xxx::KanMX4 ) were used to systematically identify all gene disruptions with a significant growth defect . Slow-growing mutants were identified in two steps . An earlier study identified 671 gene deletants in diploid background , which showed a significant fitness defect on both rich and synthetic media [48] . We thus measured fitness of the corresponding MATa haploid strains by recording their growth curves in liquid media . We identified 187 deletants showing at least 10% growth rate defect , which constituted the set of ancestral strains subjected to laboratory evolution ( for details of growth measurements see below ) . The slow-growing yeast deletants used in this study are listed in Table S1 . The evolutionary experiment was conducted using rich liquid medium ( YPD , 1% yeast extract , 2% peptone , 2% glucose ) . Solid media were prepared using 2% agar , which were found to be optimal for reproducible colony size measurement . Details on the media used in the phenotypic profiling experiment can be found in Table S4 . Oleic acid and stearic acid was dissolved in DMSO as a 100 mM stock and added to the medium after autoclaving to a final concentration of 0 . 1 mM . Compensatory adaptation refers to fitness gains in a gene deletion strain that are greater than fitness gains occurring in an isogenic wild-type strain . We conducted a series of laboratory evolutionary experiments using four independent populations of each of the 187 slow-growing deletants along with 22 independent lineages of an isogenic wild-type strain ( referred to as evolving wild types ) . The YOR202W deletion strain was used as evolving wild-type control because the fitness of this strain is indistinguishable from the BY4741 parental wild-type strain [19] . Moreover , this strain carries the KanMX4 cassette in the nonfunctional his3Δ1 allele , thus it was possible to control for the reported mutation-generating effect of the KanMX4 cassette [36] . All strains were inoculated into randomly selected positions of 96-well plates . Four wells in different positions were not inoculated by cells to help plate identification and orientation . Cells were grown in standard laboratory rich media to minimize selection pressure originating from nutrient limitation . The presence of the KanXM4 cassette was not selected for during the evolutionary experiment , since G418 was omitted from the medium for two reasons . First , using G418 at 200 mg/l concentration decreases the growth rate of the unevolved wild-type control strain ( unpublished data ) and might lead to selection for increased resistance . Second , the usage of the drug at a growth-limiting concentration may induce mutagenesis through environmental stress response . To provide optimal growth conditions , plates were covered with sandwich cover ( Enzyscreeen BV ) , shaken at 350 rpm , and incubated at 30°C . Using a handheld replicator , ∼105 cells ( ∼0 . 5 µl sample volume ) were transferred every second day to 100 µl of fresh medium in 96-well plates resulting in ∼7 . 6 generations between transfers . The experiment was run for 104 days ( ∼400 generations total ) and samples from days 0 , 26 , 52 , 78 , and 104 were frozen in 15% glycerol and kept at −80°C until fitness measurement . Cross-contamination events were regularly checked by PCR and visual inspection of empty wells ( unpublished data ) . We used established protocols specifically designed to measure fitness in yeast populations [49] . Growth was assayed by monitoring the optical density ( OD600 ) of liquid cultures of each strain using 384-well microtiter plates containing YPD medium ( as during the evolutionary experiments ) . We used relative growth rate as a proxy for relative fitness ( see below ) . Compared to laborious competition based fitness assays , this protocol allows estimating growth rate on a relatively large scale in an environment that is nearly identical to the one used in the evolutionary experiments . Starter cultures were inoculated from frozen samples using 96-well plates . The starter plates were grown for 48 hours under identical conditions to the evolutionary experiment . 384-well plates filled with 60 µl rich medium per well were inoculated for growth curve recording from the starter plates using pintool with 1 . 58 mm floating pins . The pintool was moved by a Microlab Starlet liquid handling workstation ( Hamilton Bonaduz AG ) to provide uniform inoculum across all samples . The median blank corrected initial OD600 of the wells was 0 . 027 . Each 384-well plate were inoculated with four different starter plates: one plate having the unevolved wild-type control as a reference strain in all wells in order to estimate various within-plate measurement biases , and three plates containing the same set of mutants from three of the five time points of the evolutionary experiment . The 384-well plates were incubated at 30°C in an STX44 ( LiCONiC AG ) automated incubator with alternating shaking speed every minute between 1 , 000 rpm and 1 , 200 rpm . Plates were transferred by a Microlab Swap 420 robotic arm ( Hamilton Bonaduz AG ) to Powerwave XS2 plate readers ( BioTek Instruments Inc ) every 20 minutes and cell growth was followed by recording the optical density at 600 nm . Six technical replicate measurements were executed on all strains sampled from each time-point of the evolutionary experiment . Measurements with growth curve irregularities were automatically removed . Only those strains were further analyzed where at least four technical replicate measurements remained after this quality control step . Growth rate was calculated from the obtained growth curves following an established procedure [49] , [50] . To eliminate potential within-plate effects that might cause measurement bias , growth rates were normalized by the growth rate of neighboring reference wells that contained the wild-type controls . For each strain and each evolutionary time point , relative fitness was calculated as the median of the normalized growth rates of the technical replicates divided by the median growth rate of the wild-type controls . At day 0 , the technical replicate measurements of the isogenic independently evolving lines were combined to calculate median ancestral fitness since by that time these populations had no independent evolutionary history . Stringent criteria were used to define the set of ancestor strains with substantial growth rate defect: a minimum of 10% fitness drop was required compared to the wild-type controls; significance was determined by one-tailed Wilcoxon rank sum test , p-value was corrected with a false discovery rate of 0 . 05 . To determine whether the fitness defect of a given knock-out strain became compensated during the evolutionary experiment two criteria must have been met: First , the growth rate improvement had to be significant ( one-tailed Wilcoxon rank sum test , p-value corrected with a false discovery rate of 0 . 05 ) . Second , the growth rate increment of the knock-out strain had to be disproportionally larger than that of the evolving wild-type control strains . To test whether fitness gain in a knockout is higher than those occurring in the evolving control lines , we first fitted a normal distribution to the fitness improvement values of the evolving control lines . Next , we defined a fitness improvement cutoff , so that the probability that an evolving control line would show an improvement at least that high is less than 0 . 05 . To evaluate the extent of evolutionary compensation , a relative compensation index was calculated according to the following formula:where WT and Δ means median normalized growth rate of the evolving wild-type control and the knock-out strain , respectively , measured before ( start ) and after ( end ) the evolutionary experiment . Thus , a relative compensation of 1 indicates that the knock-out strain reached the same fitness after evolution as the evolving wild-type control strains . See Table S1 for the whole dataset . To study the pleiotropic effects of compensatory adaptation , we measured the fitnesses of 237 evolved lines carrying a single gene deletion , all evolved wild-type control lines along with the corresponding ancestors across various environmental conditions . As this experiment demands high-throughput analyses ( over 14 , 000 data points ) , fitness was estimated by colony size on solid agar media . Moreover , it allowed direct comparison of the reliability of our measurements to results of a previous study ( Figure S5 ) . We prepared solid agar media of 14 different compositions to expose the strains to fundamentally diverse environments and to obtain sufficient throughput . Our list of 14 growth media was primarily based on a previous study [27] and included various carbon sources and stress conditions ( Table S4 ) . A robotized replicating system was set up for colony size based fitness measurement . The system consists of a Microlab Starlet liquid handling workstation ( Hamilton Bonaduz AG ) equipped with a pintool with 768 pins ( S&P Robotics Inc ) and a custom-made pintool sterilization station . Several aspects of the replication procedure had been experimentally customized to achieve uniform , reproducible inoculation of yeast cells . Fitness of the ancestor ( day 0 ) and evolved strains ( day 104 ) was approximated by measuring colony sizes of ordered arrays of strains at 768 density . First , four different 96-well plates of the evolutionary experiment were scaled up to arrays of 384 colonies: one having the unevolved wild-type control in all positions , and three different plates of the mutant set from the same time point . Then pairs of 384 arrays with corresponding strains from day 0 and 104 were combined to reach 768 density . With this set up , all evolving replicate lines derived from the same ancestral genotype from both day 0 and day 104 were grown on the same 768 plate to exclude potential plate-to-plate variations when comparing colony growth of ancestor and evolved lines . Four technical replicates of these 768 arrays were transferred into each of the 14 different media . After acclimatization to the media at 30°C for 48 hours the plates were replicated again onto the same type of media and photographed after 48 hours of incubation at 30°C . Digital images were processed to calculate colony sizes , and potential systematic biases in colony growth were eliminated ( Text S1 ) . For each growth environment , fitness of each original knock-out genotype at day zero and each independently evolving line at day 104 was determined as the median of the size of replicate colonies . The reliability of our experimental setup and data processing was confirmed by comparing the fitness measurements of ancestral knock-out strains with the published data of Dudley and colleagues ( Figure S5 ) [27] . To determine whether an ancestor genotype shows a significantly altered fitness compared to the wild-type control in a given environment , we used a Wilcoxon rank sum test ( with p-value corrected for each condition with a false discovery rate of 0 . 05 ) . The same statistical test was used to determine whether the fitness of an evolved line is different from that of its ancestor in a given environment . See result in Table S1 . To reveal the underlying molecular mechanisms of compensation , we subjected 41 strains to whole-genome re-sequencing . Our list of sequenced strains primarily included genotypes with large initial fitness defect , substantial fitness improvement and gradual fitness increase over the course of evolution . To be able to detect parallel evolution at the molecular level , we selected two to four independently evolving lines of each ancestor genotype for sequencing . Overall , 41 evolved lines from 14 deletion strains were chosen along with their corresponding ancestor strains . Candidates were re-streaked and single clones were isolated and their fitness increase was confirmed by growth curve recording . Genomic DNA was prepared using a glass bead lysis protocol: clones were inoculated into 5 ml YPD+G418 ( 200 mg/l ) and grown to saturation at 30°C . Cells were pelleted and resuspended in 500 µl of lyis buffer ( 1% SDS , 50 mM EDTA , 100 mM Tris [pH 8] ) . Cells were mechanically disrupted by vortexing for 3 minutes at high speed with 500 µl glass bead ( 500 µm , acid washed ) . After adding 275 µl 7 M ammonium acetate , samples were incubated at 65°C for 5 minutes , followed by a second incubation on ice for 5 minutes . The samples were extracted with chloroform∶isoamyl alcohol ( 24∶1 ) and centrifuged for 10 minutes . The aqueous layer was transferred into a new tube and precipitated with 1 ml isopropanol , pelleted and washed with 70% ethanol , and resuspended in 500 µl RNaseA solution ( 50 ng/ml ) . After 30 minutes RNaseA treatment at room temperature , samples were chloroform∶isoamyl alcohol ( 24∶1 ) extracted , precipitated with 50 µl sodium acetate ( 3 M [pH 5 . 2] ) and 1 , 250 µl ethanol , pelleted and washed with 70% ethanol . Finally , the genomic DNA was dissolved in water . The steps of re-sequencing was done by the UD-GenoMed Medical Genomic Technologies Ltd: amplified genomic shotgun libraries were run on the Illumina HighScan SC with 1×100 bp single read module resulting in an average coverage of about 80× . Reads were aligned to the S . cerevisiae EF4 genome assembly using the BWA software package [51] having the genomic repeats masked using RepeatMasking [52] . Variant calling was performed using the GATK software package [53] . Genomic single-nucleotide polymorphisms with less than 200 phred-scaled quality score or lower than 0 . 3 mutant/reference ratio were ignored . Duplications of large chromosomal segments or whole chromosomes were identified as increased read coverage of certain regions . Elevated read coverage of regions with a minimum of 25 kb length were accepted as duplications if both the Control-FREEC [54] ( Wilcoxon rank-sum test , p<0 . 01 ) and the CNV-seq [55] ( p<0 . 0001 ) software predicted significant alteration from the read coverage of the reference genome . Our primary aim was to analyze de novo mutational events . De novo mutations were identified as alterations from the reference genome specifically found in the evolved lines but not present in the ancestral strains . Mutations , which occurred before our evolutionary experiment but after the gene knock-out , are referred to as secondary ancestor mutations . These mutations were identified in the ancestral strains as SNPs and indels present only in the corresponding ancestor strain , not in any other ancestral strains . The rationale behind this consideration is not to classify mutations accumulated in the parental strain of the mutant library prior to the generation of the knock-out strain as a secondary ancestor mutation . The list of identified mutations can be found in Table S2 . Whole-genome re-sequencing revealed that 86% of SNPs in the coding regions were non-synonymous . To statistically test whether the ratio of non-synonymous to synonymous SNPs was higher than expected based on a neutral model of evolution , we employed the method of Barrick and colleagues [56] . Briefly , we took all different point mutations observed in protein coding regions and calculated the probability that 86% or more substitutions would result in a non-synonymous substitution if it occurred in a random coding position . The excess of non-synonymous substitution observed in the evolved genomes was significant ( p = 0 . 003 ) . To test whether the extent of evolutionary compensation is influenced by the disrupted gene's pleiotropy , we used three complementary measures of gene pleiotropy . Environmental pleiotropy of a non-essential gene was defined as the number of unique conditions in which the removal of the gene resulted in a fitness defect according to Dudley and colleagues [27] . Network pleiotropy was measured as the total number of protein-protein interactions reported in the BioGRID database [57] . Finally , multifunctionality of a gene was calculated on the basis of a set of GO terms considered to be specific by yeast geneticists , as previously described [58] . To investigate whether mutations accumulated during compensatory evolution preferentially affected genes that are functionally related to the disrupted gene , we used different measures of functional relatedness: co-membership within stable protein complexes , shared functional category , genetic interaction profile similarity , co-expression , and paralogy . For protein complexes we used the manually curated dataset based on tandem affinity purification/mass spectrometry studies ( YHTP2008 ) from the Wodak lab [59] . For functional categories , the MIPS Functional Catalogue Database was downloaded [60] . Genetic interaction profile similarities were obtained from a large-scale genetic interaction screen study [21] . The authors calculated the genetic interaction profile for a given gene deletion genotype as the list of genetic interaction scores detected across all other genes in their dataset . The genetic interaction profile similarity between two genes was defined as the Pearson correlation value of the two genetic interaction profiles [21] . For calculating co-expression data , 247 normalized microarray datasets from the M3D database [61] were used to create an expression profile for each gene . In case of multiple replicates per experiment , the average normalized values were calculated , and employed further . For each gene pair , co-expression value was calculated as the Pearson correlation coefficient between the two expression profiles . Paralog gene pairs were identified by performing all-against-all BLASTP similarity searches of yeast open reading frames . We defined two genes as paralogs if ( i ) the BLAST score had an expected value E<10−8 , ( ii ) alignment length exceeded 100 residues , ( iii ) sequence similarity was >30% , and ( iv ) they were not parts of transposons . Eight evolved lines were selected for microarray analysis , all of them showing high fitness following evolution ( at least 20% initial fitness defect compared to the wild-type control and at least 20% fitness improvement as a result of the evolutionary process ) . The corresponding ancestral strains and the wild-type control were also subjected to gene expression profiling . Table S3 contains the list of strains . Candidates were re-streaked and single clones were isolated and their fitness increase was confirmed by growth curve recording . Two independent colonies of the wild-type control , evolved , and corresponding ancestor knock-out strains were inoculated into 15 ml YPD and grown overnight at 30°C . The saturated populations were diluted to an OD600 of 0 . 15 in 60 ml YPD and grown to early mid-log phase ( OD600 0 . 6±0 . 05 ) in 250 ml Erlenmeyer flasks with 220 rpm shaking at 30°C . Cells were harvested by centrifugation ( 4 , 000 rpm , 3 min , 30°C ) and immediately frozen in liquid nitrogen after removal of supernatant . Total RNA was prepared by hot acidic phenol extraction and cleaned up using the QIAGEN's RNAeasy kit . All steps after RNA isolation were automated using robotic liquid handlers as described previously [62] . Dual-channel 70-mer oligonucleotide arrays were used with a common reference pool of wild-type RNA . Quality control , normalization , and dye-bias correction was performed as described earlier [62] . The reported fold change is the average of the four replicate mutant profiles versus the average of all wild-type controls . A total of 58 transcripts showed stochastic changes in wild-type profiles and were excluded from the analyses . Differentially expressed genes were defined as those showing a 1 . 7-fold abundance change and a p-value<0 . 05 when comparing two strains . The raw dataset is available online at ArrayExpress ( http://www . ebi . ac . uk/arrayexpress/ , accession number E-MTAB-2352 ) . All transcriptome comparisons of the wild-type , knockout , and evolved strains were repeated on a dataset where CNVs , genes showing expression response to aneuploidy , and growth rate related genes were excluded . CNVs were identified on the basis of the read coverage of the genome sequence data ( Table S2 ) with the exception of one strain ( Δrpl43a ) , which was not sequenced . In the case of Δrpl43a , whole chromosome duplication was predicted on the basis of visual inspection of expression profiles . The position of partial chromosome duplication was predicted by the Charm algorithm [63] . In evolved strains carrying aneuploid chromosomes , genes showing expression response to that particular aneuploidy were excluded from the transcriptome comparisons ( data on the transcriptome effects of aneuploidy were obtained from [64] ) . Genes showing significant expression response to changes in growth rate were also excluded , as defined previously [65] on the basis of the growth rate measurements of Brauer and colleagues [66] . The evolved lines of Δmdm34 were chosen for in-depth genetic analysis . The fitness cost of the set of compensatory mutations accumulated in the evolved Δmdm34 lineages was measured in wild-type genetic background . To this end , the MDM34 gene was re-introduced into the ancestor and evolved Δmdm34 lineages according to the delitto perfetto method [67] . First , the KanMX4 cassette in the ancestor and evolved Δmdm34 lineages was swapped with the CORE-UH cassette , containing the KlURA3 and hyg markers . Then the MDM34 open reading frame with longer than 0 . 3 kb flanking regions on both sides was amplified from the unevolved wild-type control strain and transformed into the cells to replace the CORE-UH cassette . The replacement of the KlURA3 marker was counter-selected using 5-FOA containing medium . The loss of hygr was confirmed , the site and orientation of gene replacement was verified by PCR and the sequence of the MDM34 gene was determined by capillary sequencing . In a second analysis , a point mutation identified in the MGA2 gene in one of the evolved Δmdm34 lineages was reinserted into both the wild-type and ancestor Δmdm34 background . This specific point mutation changes the 750th codon of MGA2 from GAT to TAT resulting in the incorporation of tyrosine instead of aspartic acid . We refer to the mutant allele as mga2-1 . Using the delitto perfetto method [67] , we introduced this point mutation into the unevolved wild-type control strain . First , the CORE-UH cassette was inserted into the genome at the desired position of the SNP . Then , two complementary oligonucleotides of 81 bp length with the sequence of the region of interest and the SNP in the 41st position were transformed . The replacement of the KlURA3 marker with the missense SNP was counter-selected using 5-FOA containing medium , loss of hygr was confirmed , and the result of the site-directed mutagenesis was verified by capillary sequencing . Attempts to introduce the mga2-1 mutation into the ancestor Δmdm34 strain in this way were not successful , presumably due to the severe slow growth of the intermediate strain that lacks both MDM34 and MGA2 gene in a functional form . To complement this , a helper plasmid with MDM34 gene ( MoBY ORF Library [68] ) was transformed into the cells prior to the site directed mutagenesis [69] . Because of the presence of the URA3 marker on the helper plasmid , the CORE-Hp53 cassette was used in this experiment . The steps of mutagenesis were similar as without the helper plasmid , which was removed by passaging cells through 5-FOA afterwards . Yeast samples were grown in 20 ml YPD medium to mid-log phase ( 0 . 8 OD600 value ) . RNA was extracted from 107 yeast cells by acidic phenol method using TRI Reagent Protocol ( Sigma-Aldrich Co ) . The RNA samples were concentrated by the NucleoSpin RNA Plant Kit ( Macherey-Nagel ) , according to the manufacturer's instructions . A total of 500 ng RNA was used as a template to prepare cDNA using the Maxima First Strand cDNA Synthesis kit ( Thermo Scientific ) . Reactions without template were set up to detect contaminations of the reagents used in the cDNA synthesis . qPCR reactions were set up in 20 µl volume , using the following templates: no template control , 10 ng non-transcribed RNA and cDNA transcribed from 10 ng RNA . The qPCR reactions were run in a Bioer LineK Gene device , using 2× Maxima SYBR Green qPCR Master Mix ( Thermo Scientific ) . All samples had three technical replicates . Gene expression was determined in arbitrary units using a standard curve fitted on triplicates of a four-step 10-fold dilution series . OLE1 expression level was determined relative to TUB1 expression level as an internal control . All control reactions , not treated with reverse transcriptase or not having template , gave Ct values at least 10 cycles higher than the corresponding samples . | While core cellular processes are generally conserved during evolution , the constituent genes differ somewhat between related species with similar lifestyles . Why should this be so ? In this work , we propose that gene loss may initially be deleterious , but organisms can recover fitness by the accumulation of compensatory mutations elsewhere in the genome . To investigate this process in the laboratory , we investigated 180 haploid yeast strains , each of which initially displayed slow growth owing to the deletion of a single gene . Laboratory evolutionary experiments revealed that defects in a broad range of molecular processes can readily be compensated during evolution . Genomic analyses and functional assays demonstrated that compensatory evolution generates hidden genetic and physiological variation across parallel evolving lines , which can be revealed when the environment changes . Strikingly , despite nearly full recovery of fitness , the wild-type genomic expression pattern is generally not restored . Based on these results , we argue that genomes undergo major changes not simply to adapt to external conditions but also to compensate for previously accumulated deleterious mutations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"systems",
"biology",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"theory",
"evolutionary",
"biology",
"evolutionary",
"genetics"
] | 2014 | The Genomic Landscape of Compensatory Evolution |
Nuclear receptors of the Hepatocyte Nuclear Factor-4 ( HNF4 ) subtype have been linked to a host of developmental and metabolic functions in animals ranging from worms to humans; however , the full spectrum of physiological activities carried out by this nuclear receptor subfamily is far from established . We have found that the Caenorhabditis elegans nuclear receptor NHR-31 , a homolog of mammalian HNF4 receptors , is required for controlling the growth and function of the nematode excretory cell , a multi-branched tubular cell that acts as the C . elegans renal system . Larval specific RNAi knockdown of nhr-31 led to significant structural abnormalities along the length of the excretory cell canal , including numerous regions of uncontrolled growth at sites near to and distant from the cell nucleus . nhr-31 RNAi animals were sensitive to acute challenge with ionic stress , implying that the osmoregulatory function of the excretory cell was also compromised . Gene expression profiling revealed a surprisingly specific role for nhr-31 in the control of multiple genes that encode subunits of the vacuolar ATPase ( vATPase ) . RNAi of these vATPase genes resulted in excretory cell defects similar to those observed in nhr-31 RNAi animals , demonstrating that the influence of nhr-31 on excretory cell growth is mediated , at least in part , through coordinate regulation of the vATPase . Sequence analysis revealed a stunning enrichment of HNF4α type binding sites in the promoters of both C . elegans and mouse vATPase genes , arguing that coordinate regulation of the vATPase by HNF4 receptors is likely to be conserved in mammals . Our study establishes a new pathway for regulation of excretory cell growth and reveals a novel role for HNF4-type nuclear receptors in the development and function of a renal system .
Nuclear receptors ( NRs ) comprise a large family of transcription factors distinguished by a highly conserved DNA binding domain and a structurally conserved ligand-binding domain . NRs are notable for their ability to interact with small molecule ligands , enabling these factors to respond to autocrine , paracrine , and endocrine signals in order to mediate transcriptional effects at a distance [1] , [2] . The canonical NR family is exclusively found in metazoans and the number of nuclear receptor members varies dramatically depending on species; from 21 NR genes in Drosophila melanogaster , to ∼50 in rodents and humans , to over 250 NRs in Caenorhabditis elegans and related nematodes [3] . The extraordinarily large NR family of C . elegans is particularly intriguing . Of the 283 predicted NR genes , only 15 are directly orthologous to NRs found in other metazoans , including Drosophila and mammals [4] . The remaining 268 NRs are thought to be derived from extensive duplication and diversification of an ancestral gene most closely related to the mammalian and Drosophila HNF4 receptors [5] . The presence of both highly similar and divergent HNF4-type receptors in nematodes implies that many of these proteins will carry out conserved structural and physiological functions , whereas others will have evolved to adopt responsibilities more specific to the nematode lineage . This idea is supported by the fact the C . elegans NHR-49 nuclear receptor shares many of the metabolic functions of the mammalian HNF4α , but not the developmental activities [6] , [7] . Thus , study of C . elegans NRs should not only be helpful for understanding mammalian NR function and physiology , but should also reveal novel regulatory activities for the nuclear receptor family . The prospect that the responsibilities of mammalian receptors may be divided among a larger number of NRs in C . elegans may be advantageous for understanding the physiological function these complex proteins . For example , the mammalian HNF4α plays numerous roles in development , metabolism , and disease [8]; because of this widespread physiological impact , the functional and mechanistic diversity of this receptor is far from understood . Indeed , mutations in the human HNF4α are associated with maturity onset diabetes of the young ( MODY ) and late onset type II diabetes; yet , how these HNF4α lesions lead to diabetes has not been established [9]–[11] . Furthermore , there is considerable controversy over the quantity and identity of HNF4 target genes [12]–[14] . These complications may be due , at least in part , to the fact that HNF4α carries out essential functions in several different tissues , and that HNF4α likely regulates different target sets depending on metabolic , developmental , and nutritional context . HNF4α is also expressed in many cell types for which its function has not yet been established; for example , the epithelial cells of the intestine and the proximal and convoluted tubules of the kidney , and while HNF4α has been shown to regulate proliferation of transformed kidney cell lines , its role in kidney development remains to be defined [15] , [16] . The C . elegans renal system is comprised of only three cells , yet these cells carry out many of the same functions as mammalian kidneys [17] , [18] . Therefore , C . elegans might be an advantageous system in which to study the role of HNF4 receptors in renal development . The largest portion of the C . elegans excretory system consists of the excretory cell ( EC ) . The development of the EC is extraordinary , as it involves the formation and growth of four branches that project outward from a single nucleus located near the anterior bulb of the pharynx [17] . These branches grow along the length of the animal to near the tip of the head and tail in early development , and then continue to grow along with the animal until adulthood . Each branch of the EC contains an inner membrane that coalesces to form a lumen; thus , the excretory cell becomes a large , single cell tube . Consequently , the EC has been effectively used to understand the development of tubes and to investigate mechanisms involved in excretory function [17] , [19] , [20] . At this point , factors known to participate in the development and function of the C . elegans excretory cell include vATPases , WNK kinases , CLIC-like proteins , Patched related proteins , and mucins [17] , [21]–[24] . Additionally , the CEH-6 homeobox protein has also been implicated as the only transcriptional regulatory factor , thus far , involved in excretory cell development [25] . How the complex structure of the EC is developed and maintained so precisely , even at points very distant from the primary sites of gene regulation , remains a mystery . We have found a highly conserved C . elegans HNF4 paralog , NHR-31 , that is specifically expressed in the excretory cell of the nematode , suggesting that investigation of this receptor may provide unique insight into the role of nuclear receptors in renal development and tube formation . In this study , we show that NHR-31 specifically regulates the expression of genes that coordinate the synchronous growth and elongation of excretory canals , demonstrating a novel NR mediated pathway for renal system development and function .
nhr-31 is predicted to encode an HNF4α related nuclear receptor ( NR ) protein with a highly conserved DNA binding domain ( DBD ) and ligand binding domain ( LBD ) ( Figure 1A ) . To help establish the physiological function of this NR , we determined the tissues in which the nhr-31 gene is expressed . A GFP reporter construct was generated by fusing 3 . 0 kb of nhr-31 upstream regulatory sequence to the gfp gene ( Pnhr-31::gfp ) . Injection of Pnhr-31::gfp into WT worms revealed that the nhr-31 promoter drives strong expression in the excretory cell ( EC ) . In transgenic animals , GFP protein was first observed in the EC cell shortly after EC birth and persisted in the EC for the remainder of worm embryogenesis , larval development , and adulthood ( Figure 1B and data not shown ) . GFP was observed throughout the cytoplasm of the H-shaped excretory cell . Because our reporter construct was designed by fusing only the nhr-31 promoter to the gfp gene , the GFP localization pattern does not represent NHR-31 protein sub-cellular localization . Pnhr-31::gfp expression was also observed , at lower levels , in the intestine and in several unidentified cells located near the tail ( Figure 1B and data not shown ) . In C . elegans , the EC functions cooperatively with duct and pore cells , and together these cells are important for maintaining osmolarity homeostasis [26] , [27] . To determine if nhr-31 RNAi animals displayed compromised excretory function , we treated animals with nhr-31 RNAi or control RNAi from the L1 to L4 stage of development and then stressed L4 animals with acute exposure to a standard growth plate supplemented with 500 mM NaCl , and determined their ability to respond to these unfavorable conditions . 250 animals were assayed at each time point . After just two hours , less than 5% of nhr-31 RNAi animals could be rescued from 500 mM NaCl exposure . In contrast , L4 animals fed control RNAi were able to thrive for much longer under these same conditions , with over 50% of animals maintaining the ability to recover even after 8 hours of high salt exposure ( Figure 1C ) . These data indicate that reducing nhr-31 gene expression strongly impairs the ability to survive acute osmotic stress . Three different nhr-31 deletion strains have been isolated , and all of these strains are inviable ( www . wormbase . org ) . Using one of these strains ( nhr-31 ( tm1547 ) ) , we found that nhr-31 deletion leads to early embryonic lethality ( data not shown ) . Additionally , application of nhr-31 RNAi throughout growth and development results in significant embryonic lethality in the F1 generation ( data not shown ) . Thus , NHR-31 , like its mammalian homolog HNF4α , plays an essential role in early embryonic development . Because we found that the nhr-31 gene is primarily expressed in the excretory cell during larval and adult stages , however , we investigated the participation of nhr-31 in EC development and morphology using an RNAi feeding strategy that specifically reduced nhr-31 expression during larval development and adulthood . In postembryonic animals , the EC is an H-shaped cell , with four canals emanating from a main cell body located near the terminal bulb of the pharynx [17] . Two canals project along each side of the animal towards the posterior end , and two canals project forward towards the anterior end ( Figure 2A ) . To monitor EC morphology , WT animals were injected with the Pnhr-31::gfp reporter . In WT adult animals , GFP localization revealed that the outer diameter of the excretory cell was relatively uniform through the entire length of the canal , measuring ∼3 . 5 µm in proximal sections of the posterior canal , and tapering to ∼2 . 4 µm in distal sections of the posterior canal ( Figure 2B and 2C ) . When WT animals carrying the Pnhr-31::gfp construct were treated with nhr-31 RNAi from the L1 stage of larval development through adulthood , the morphology of the adult EC was dramatically altered ( Figure 2B and 2C ) . In particular , the excretory canals were not uniform in diameter; instead , they contained multiple enlarged varicosities , with diameters up to 10 µm ( Figure 2B and 2C ) . These varicosities showed considerable variability in size and shape and were located along the entire length of the EC , including the proximal , middle , and distal portions of the posterior arms , as well as in the anterior branches of the EC canal ( Figure 2B and 2C and data not shown ) . DIC images of nhr-31 ( +/− ) heterozygotes also revealed similar excretory cell abnormalities , providing support for the specificity of our nhr-31 RNAi construct ( Figure S1 ) . High magnification of the GFP images obtained in nhr-31 RNAi animals suggested that the varicosities consisted of dense cellular material with an abundance of vacuoles ( Figure 3A ) . This phenotype was different from previously reported EC abnormalities , which showed enlargement of the EC cell due to fluid accumulation or cyst formation [19] , [27] . To more closely examine the morphological defects in the EC of nhr-31 RNAi animals , we employed high pressure freezing transmission electron microscopy ( HP-TEM ) . Table 1 shows quantitative analysis of sections obtained from the middle region of the EC in 5 different control RNAi animals and 5 different nhr-31 RNAi animals . Cross sections of the EC of a WT animal showed a single circular lumen with an average diameter of 1 . 6 µm ( Table 1 ) . Additionally , an abundance of well-formed canaliculi were clearly visible in WT animals ( Figure 3C and Table 1 ) . Canaliculi are smaller “mini-canals” surrounding the canal lumen; these canals are thought to greatly increase the apical surface area of the EC lumen ( Figure 3B ) [17] . Canaliculi were visible in the wild type excretory canal cross section as small , round , circular shapes and were regular in size and consistent ( ∼70/section ) in number from section to section ( Figure 3D and Table 1 ) . According to our EM measurements , the average diameter of the EC was ∼2 . 8 µm , which agreed nicely with our GFP measurements ( Figure 2C and Table 1 ) . HP-TEM imaging revealed multiple morphological defects in the excretory canals of nhr-31 RNAi animals , particularly in the varicosities ( Figure 3D and Table 1 ) . First , the average canal diameter increased to 5 . 8 µm , with larger varicosities displaying diameters of up to 8 µm , and the narrow regions showing diameters from 2–3 µm . Second , the average diameter of the lumen in nhr-31 RNAi animals was increased by 26% to 1 . 95 µm , and the lumen often appeared multi-lobed . The diameter of the lumen correlated strongly with the outer cell diameter , as the largest lumen diameter measurements were found within large varicosities ( Table 1 ) . Third , we found that the canaliculi were uncharacteristically irregular in size and present at much higher numbers ( ∼126/cell ) in nhr-31 RNAi animals ( Figure 3D and Table 1 ) . Finally , the varicosities of nhr-31 RNAi animals possessed an unusually high number of large vesicles , elevated endoplasmic reticulum abundance , and a considerable increase in mitochondria ( Figure 3E and Table 1 ) . Importantly , the TEM cross sections showed that the varicosities were not a result of an EC canal lumen that was folded back on itself or bent away from the normal lateral alignment , or due to osmotic “swelling” , both of which have been previously reported for mutants that affected EC structure [19] , [27] . Consequently , the EC phenotypes resulting from loss of nhr-31 function are different from previous observations and suggest that nhr-31 defects are distinctive in their mechanism of origin . In summary , both fluorescence confocal microscopy and TEM showed that loss of nhr-31 function leads to significant defects in EC canal size , shape , and microstructure . The abundance of cellular material and organelles , along with significant structural abnormalities , implies that the abnormal varicosities observed in adult nhr-31 RNAi animals are likely to result from regions of uncontrolled cellular growth . We next applied gene expression profiling to establish downstream regulatory targets of nhr-31 . Gene expression was measured using C . elegans oligomer based microarrays . We carried out this study in L4 larvae , as this is the larval stage at which the EC morphology differences between WT animals and nhr-31 RNAi animals first begin to show . Overall , we found that , in nhr-31 RNAi worms , the expression of 20 genes was suppressed by greater than 2-fold and the expression of 63 genes were enhanced by greater than 2-fold ( Table S1 ) . The most striking outcome of our microarray experiments was the discovery that RNAi of nhr-31 dramatically affected the expression of 15 genes that encode subunits of the vacuolar ATPase ( gene names are referred to as vha ) , and one gene predicted to code for a vATPase cofactor ( gene name , R03E1 . 2 ) . In fact , of the 30 genes most strongly reduced by inhibition of nhr-31 , 15 of these were vha genes ( Table S2 ) . The vacuolar ATPase ( vATPase ) is an ATP-dependent proton pump , which transports protons across cellular membranes ( Figure 4A ) . Each C . elegans vha gene encodes for one subunit of the holoenzyme , and there are 15 separate subunits that make up the holoenzyme . For several of the vATPase subunits , C . elegans possesses multiple gene isoforms; consequently there are 18 vha genes in total . As a secondary confirmation of the microarray data , we employed quantitative RT-PCR to specifically measure the mRNA levels of all 18 vATPase genes found in C . elegans . We found that the expression of 16 of these genes was reduced when nhr-31 was inhibited ( Figure 4A ) . Importantly , previously published data show that nearly all vha subunits are expressed in the excretory cell , indicating NHR-31 is likely to be mediating expression of these vha genes directly in the EC ( Table 2 ) [19] , [20] , [29]–[33] . Additionally , most vha genes are also expressed in the intestine , where NHR-31 also resides . Accordingly , the only two vha genes not regulated by NHR-31 , vha-7 and unc-32 , are not expressed in the excretory cell . In sum , our microarray and QRT-PCR convincingly demonstrate that a primary function of NHR-31 is to coordinately promote the expression of almost the entire complement of vacuolar ATPase genes . NHR-31 localization to the excretory cell , where nearly all vha genes are expressed , also argues that NHR-31 is regulating vha genes in this cell type . Because the vacuolar ATPase subunits are highly expressed in the EC , we suspected that the impact of nhr-31 on EC development might be a consequence of vacuolar ATPase regulation . To test this hypothesis , we used RNAi feeding to specifically reduce the expression of three different vacuolar ATPase subunits: vha-5 ( small a subunit ) , vha-8 ( catalytic E subunit ) and vha-12 ( B subunit ) . Because previous studies have shown that RNAi of the vacuolar ATPase subunits leads to larval lethality , we did not apply vha or nhr-31 RNAi until the L3 stage of development . Using this approach , we found that RNAi of each of these subunits was sufficient to cause excretory canal formation defects similar to those of nhr-31 RNAi animals ( Figure 4B ) . These results imply that the control of EC development by NHR-31 is mediated , at least in part , by its stimulation of vATPase expression . We also note that this experiment shows that knockdown of nhr-31 or vATPase expression specifically in late larval development is sufficient to cause irregular EC growth and adult varicosities . Although the large , irregular , varicosities observed in nhr-31 RNAi animals were never observed in WT adults , we did notice varicosity-like structures early in WT larval development , residing at regular intervals along the EC canal in L1 and early L2 animals ( Figure 5A and Table 3 ) . These varicosities differed from those present in nhr-31 RNAi adults in that they displayed a consistently symmetrical oval shape ( Figure 5A ) . In L1 larvae , ∼10 of these varicosities were observed in each EC canal branch , but as worms developed the regions of the excretory cell between varicosities grew wider and the varicosities consequently decreased in prominence such that , by the late L3 stage of development , the entire length of the excretory cell possessed a diameter similar to the varicosities observed in L1 animals ( Figure 5B and 5E ) . The presence of these growth varicosities in WT L1 larvae was confirmed by hp-TEM ( Figure 5C and 5D ) . According to these TEM measurements , L1 varicosities displayed a diameter that was 2 . 8 times that of narrow regions , and a lumen diameter that was about 2-fold larger than the narrow regions ( Table 3 ) . Additionally , the varicosity regions harbored many more canaliculi ( Figure 5C and 5D and Table 3 ) . This data implies that the varicosities may form in L1 animals and spread horizontally along the excretory cell to help increase cellular diameter , and perhaps also length . Thus , we suspected that nhr-31 RNAi animals might improperly maintain these structures such that they continue to enlarged and become irregularly shaped as animals developed into adults . However , examination of nhr-31 RNAi animals revealed no obvious signs of varicosities in the L3 stage of development , implying that knockdown of nhr-31 did not interfere with the normal dissipation of these structures during mid-larval development ( Figure 5E ) . The varicosities that arise in nhr-31 RNAi animals first appear in the late L4 stage of development and continue to grow larger as animals grow older ( Figure 5E ) . Consequently , the varicosities observed in adult nhr-31 RNAi animals must either occur from growth of new structures , or the reactivation and renewed growth of these original varicosities . We also note that the varicosities caused by nhr-31 loss of function continue to grow larger during adulthood , such that by day 2 of adulthood they are nearly twice as large as varicosities in early adults ( Figure 5E ) . Nuclear receptors typically associate with complex binding motifs comprised of two hexameric half-sites [2] . These half sites may be paired in multiple orientations with various amounts of spacing , and this architecture helps determine the type of NHR that binds . To identify NREs in the promoters of the C . elegans vacuolar ATPase genes , we used the NHR-computational analysis program “NHR-scan” [34] . This program identified strong NRE candidates in nearly all of the vha promoters; 15 out of 18 vha genes harbored candidate NREs in close proximity to their transcription start site . If a vha gene was expressed as part of an operon , NREs were found near the transcription start site of the first gene in the operon . Analysis of predicted NREs showed a strong presence of an AGTTCA consensus half site ( Figure 6A and Table 4 ) . The most common repeats were an ER6 ( 40% of all binding sites ) , which is an everted repeat separated by 6 base pairs and an ER8 ( 27% of all binding sites ) , an everted repeat separated by 8 base pairs . In fact , 13 of 19 vATPase genes had at least one highly conserved ER6 or ER8 site in their promoters , while several other types of AGTTCA repeats were also found once or twice in vATPase promoters . Interestingly , we also found a consensus ER6 site in the nhr-31 promoter , implying that nhr-31 may regulate its own expression through a feedback or feed-forward mechanism . This putative regulation did not manifest in our GFP reporter studies , however , implying that self-regulation in the excretory cell is not very significant during development . The most common spacing for mammalian HNF4α receptors is a DR1 or DR2 , however it would not be surprising if NHR-31 adopted a different NRE specificity , as nematode NR binding sites have likely evolved to generate NREs to help distinguish between all of the different HNF4 paralogs in C . elegans . Consistent with this notion , the NHR-31 LBD does not retain two conserved amino acids that help direct HNF4α homodimerization on DR1 and DR2 sites [35] . It is also possible , however , that everted repeats have not yet been widely characterized as HNF4α sites in other organisms . The presence of so many binding sites that closely match a consensus site is quit remarkable , especially since NHR response elements are notoriously degenerate [36] . Furthermore , nuclear receptor regulated genes often contain several conserved and cryptic NREs that are necessary for modulating expression level , consequently , there are likely to be important cryptic NREs in these promoters as well [37] . Analysis of the vATPase gene promoters from mice ( Mus musculus ) showed an astonishing enrichment of HNF4α binding sites ( Table 4 ) . In fact , we found highly conserved HNF4α binding sites in 10 vacuolar ATPase genes , and most of these genes harbored at least two independent HNF4α binding sites . The repeats were almost always in DR1 or DR2 configuration and the consensus half-site sequence for these sites was AG ( G/T ) TCA ( Figure 6B ) , which matches the consensus site previously reported for HNF4α binding sites [38] . As with the C . elegans NREs , the enrichment of these binding sites is highly significant . Taken together , these data strongly argue that coordinate regulation of vacuolar ATPase genes by the HNF4 nuclear receptor is conserved in mammals . We should note , however , that the DR1 and DR2 elements can also bind other mammalian nuclear receptors; therefore , even though NHR-31 is most closely related to HNF4α , and expressed along with vATPases in the excretory system , the participation of other mammalian nuclear receptors in coordinate regulation of vATPase genes cannot be ruled out . Similarly , we cannot rule out the involvement of additional C . elegans NRs in regulation of nematode vATPase genes .
We have identified a new pathway involved in the development of the C . elegans renal system . In summary , we have shown that the NHR-31 nuclear receptor , through promotion of vacuolar ATPase gene expression , is essential for the appropriate growth , morphology , and function of the C . elegans excretory cell . This study not only identifies a new transcriptional regulator necessary for EC development , but also establishes the specific regulatory targets that mediate its effects , and highlights potential nuclear receptor response elements . The regulatory or developmental activities carried out by NHR-31 have not yet been observed for a nuclear receptor; consequently our findings expand the physiological repertoire of the NR superfamily . A primary function of NHR-31 is to maintain the structure of the EC canal during the transition from larval development into adulthood . When exposed to nhr-31 RNAi throughout larval development , or specifically in late larval development , we observed numerous large and irregular varicosities all along the length of the posterior and anterior EC canals , these varicosities first manifested in L4 development and continued to amplify and grow several days into adulthood . As the excretory cell is involved in the regulation of ion transport and osmolarity , we considered that these varicosities might have been due to accumulation of fluid within the EC cytoplasm to create “cyst-like” structures . However , HP-TEM revealed numerous sub-cellular abnormalities within the varicosities that could not be explained by an abnormal accumulation of fluid . For example , nhr-31 RNAi dependent varicosities generally contained abnormally shaped lumens , significant increases in the number of canaliculi , ER and mitochondria , and abnormally large numbers of ectopic vesicles . These data imply that the EC varicosities are not fluid filled , but rather overdeveloped . In contrast , in the narrow regions of the nhr-31 RNAi EC , we found normal numbers of mitochondria , ER , and canaliculi , implying the majority of EC irregularities that occur in nhr-31 RNAi animals are localized to the enlarged varicosities . This excessive growth phenotype significantly differs from previously characterized excretory cell phenotypes [19] , [27] . Another intriguing finding of our study is that NHR-31 has a surprisingly specific and strong impact on the expression of v-ATPase encoding genes ( vha genes ) . The vacuolar ATPase ( v-ATPase ) is an ATP-dependent proton pump that is organized into a peripheral domain ( V1 ) , which is responsible for ATP hydrolysis , and an integral domain ( V0 ) , responsible for proton transport . Although it is referred to as the vacuolar ATPase , this enzyme is found in multiple intracellular membranes , including endosomes , lysosomes , Golgi-derived vesicles , clathrin coated vesicles , secretory vesicles , as well as the plasma membrane [39] , [40] . vATPases are important for numerous cellular functions , including ion transport , substrate transport , acidification of vesicles and other organelles . Additionally , recent studies have shown that vATPases also play a predominant role in vesicular trafficking of the endocytic and exocytic pathways , participating directly in membrane fusion by not only providing the proper acidic environment , but also by directly forming protein complexes during the fusion process [40] . Given the diversity of vATPase functions , it seems likely that the transcription of vATPase would be precisely regulated both spatially and temporally in order to facilitate the development and function of different cell types . Although numerous factors have been shown to regulate the vATPase at the enzymatic level , our study has identified a transcription factor with a specific role in regulating vATPase expression in a tissue specific manner . In C . elegans it has been shown that vATPase subunits of either the V0 sector or the V1 sector , are important in excretory cell development and morphology [19] . In this previous study , several distinct vha subunits were knocked down early in development resulting in several defects in the hypodermis , cuticle , and excretory cell . Specifically , abnormal structures were observed in the ECs that were described as “whorls” . Because RNAi of nhr-31 leads to the reduced expression of 17 out of the 19 genes that encode vha subunits , we suspected that the role of nhr-31 in EC development may be due , at least in part , to regulation of vATPase gene expression . In support of this hypothesis , we found that larval specific knockdown of NHR-31 target genes encoding either an a subunit , an E subunit , or a B subunit of the vATPase , led to excretory cell phenotypes nearly identical to those observed in nhr-31 RNAi animals . Although varicosities found in our experiments may be related , in some fashion , to the “whorls” observed in the previous study ( [19] , it should be noted that the previous study focused on reduction of vATPase expression much earlier in development . In contrast , in our study , vha expression was knocked down specifically in late L3 development through early adulthood . Thus , our findings show that regulation of vATPase expression is a prominent factor in NHR-31 function . The phenotypic abnormalities observed in nhr-31 RNAi animals , combined with the predicted function of nhr-31 regulatory targets , provide several clues into how this nuclear receptor may impact the generation of a healthy EC ( Figure 7 ) . A critical component of EC development is the outgrowth of the excretory canals . During larval development , four excretory canals must grow out of the main cell body and migrate towards the posterior and anterior ends of the animal and then continue to grow as the animal increases in length . We observed that , during early larval development , the EC migrates along the length of the animal and is periodically punctuated with small oval shaped varicosities . By the time a worm reaches later larval stages , these varicosities are no longer present and adult EC canals are exquisitely uniform in diameter . We suggest that the growth varicosities that form during early larval development may be regions of high cellular growth activity , where robust protein , organelle , and membrane synthesis occur , these areas of growth then serve to supply material to the cytosol , as well as the basal and apical membranes of the EC , thus enabling the EC canal to elongate in a bidirectional manner . TEM images of the EC in L1 larvae , which show periodic varicosities with a more dense supply of membrane and organelles , support this hypothesis ( Figure 5C and 5D ) . As the EC reaches its full-length , precise regulation of new cellular synthesis and cellular elongation reaches equilibrium such that regions of high EC cellular mass become evenly distributed and the EC adopts a fully mature and uniform shape . Many of defects observed in nhr-31 RNAi animals are consistent with an inability to properly regulate the coordination between EC cell outgrowth and new synthesis of cellular material . Thus , the NHR-31 nuclear receptor may play an important role in regulating the growth and elongation of the EC cell , and , in nhr-31 RNAi animals , excess lipid synthesis and other factors involved in cellular growth proceed unchecked leading to the production of new EC cellular material , even as this cell is no longer growing lengthwise . In this scenario , actively growing regions of the excretory cell could not expand laterally in either direction; consequently , excess cellular material would accumulate in varicosities that continue to grow larger even after animals reach adulthood . It is astonishing that NHR-31 controls such a small and specific set of target genes , and that nearly all of its targets comprise subunits or cofactors of the vATPase . While the fundamental conclusions of this study are not dependent upon the mechanism by which NHR-31 regulates gene transcription , NHR-31 is a transcription factor of the nuclear receptor type , and therefore it is tempting to propose that NHR-31 regulates the vATPases in response to a ligand signal by directly binding to the vATPase promoters . Consistent with this hypothesis , our binding site analyses of the vATPase promoters revealed a significant enrichment of nuclear receptor response elements in the form of ER6 or ER8 everted repeats with an AGTTCA consensus half site ( Figure 6A and Table 4 ) . The fact that this response element does not perfectly match the preferred response element architecture of the mammalian HNF4α is not surprising , as C . elegans contains dozens of HNF4-like receptors , and it is likely that NREs have evolved in nematodes in order to distinguish between NHR paralogs . We did , however , find strong enrichment of classical HNF4α binding sites ( DR1 and DR2 ) in the promoters of the mouse vATPase genes , suggesting that coordinate regulation of the vATPase by HNF4 type receptors may be well conserved in mammals , even though the exact response element architecture may have changed . The physiological functions and target genes of nhr-31 have not been previously linked to an HNF4-type receptor , or any other nuclear receptor . NHR-31 shares a high degree of homology with mammalian HNF4 receptors , including nearly perfect conservation of key DNA binding elements and a strongly conserved ligand-binding domain ( LBD ) . Interestingly , it has been proposed that the mammalian HNF4 receptors interact with free fatty acids and fatty acyl-CoA molecules [41] , [42] . An ability of NHR-31 to bind to the acyl chain of a fatty acid or lipid molecule would provide a provocative explanation for how NHR-31 may be coordinating membrane synthesis and cellular elongation in the EC , which is likely to be occurring at sites distant from the nucleus . Because intensive membrane synthesis , transport , and fusion must take place in order to meet the needs of a growing excretory cell , such processes may release lipid based signals that activate or repress NHR-31 control of vacuolar ATPases and other genes associated with membrane biogenesis . Whether or not the functions of NHR-31 are conserved in mammals remains to be determined; however , the fact that both HNF4α and vacuolar ATPases are expressed at high levels in the proximal tubules of the mammalian kidney , combined with our demonstration that the mammalian vATPase genes contain a high density of HNF4α binding sites , implies that a functional role for HNF4 receptors in coordinate regulation of the vATPase in the renal system may indeed be a conserved process [16] , [39] .
The N2 Bristol strain of C . elegans was used for all experiments . Worms were maintained by standard techniques at 20–22°C . nhr-31 RNAi constructs were created by introducing the full-length NHR-31 cDNA into the L4440 RNAi feeding vector ( Andy Fire , Stanford University ) . RNAi constructs for vha-5 , vha-8 , and vha-12 were obtained from the Ahringer RNAi library ( University of Cambridge , Cambridge , UK ) . All RNAi constructs were transformed into the HT115 strain of E . coli and RNAi was introduced to N2 worms by RNAi feeding . RNAi expression was induced in the feeding bacteria by growing bacteria on NGM plates containing 3 mM IPTG and 100 µg/ml carbenicillin . Bacteria containing an empty L4440 RNAi vector were used for the RNAi control . Although NHR-31 is part of a large family of related nuclear receptors , these receptors have extensively diverged from one another during evolution , such that the closest paralog of NHR-31 shares only 55% homology in cDNA sequence; therefore , it is highly unlikely that there will be cross reactivity of the RNAi . Furthermore , C . elegans RNAi prediction programs do not indicate any cross reactivity ( www . wormbase . org ) [28] . Finally , the fact that nhr-31 ( +/− ) heterozygotes displayed similar EC defects further supports the specificity of this RNAi construct ( Figure S1 ) . An nhr-31 promoter/gfp reporter construct ( Pnhr-31::gfp ) was generated by fusing ∼3 kb of upstream regulatory sequence and 17 base pairs of the first nhr-31 exon to the gfp gene , primers were created using the nhr-31a . 1 predicted isoform . Promoter DNA was amplified from genomic DNA using the following primers: NR-31UPGF ( 5′-TAA CTC GAG GAC GCA GGA AAG TCG GCA GTA GG-3′ ) , as the 5′ upstream primer and NR-31-ExonI ( 5′-TCA CCC GGG TAC TCC CAA TCT TCG A-3′ ) as the 3′ downstream primer . Amplified DNA was inserted into the L3691 GFP reporter vector ( from Andy Fire , Stanford University ) . The Pnhr-31::gfp reporter vector was introduced into N2 worms by microinjection at a concentration of 50 ng/µl , worms were selected by EC fluorescence and no co-injection marker was used . Worms harboring the Pnhr-31::gfp transgene were examined by both standard fluorescence microscopy and confocal microscopy . Images were taken using AxioVision 4 . 6 software in multi-channel acquisition mode with an AxioCam MRU camera ( Carl Zeiss Microimaging ) . For observation , larval or adult worms were mounted on glass slides with 2% agarose pads containing azide . Stack images of animals treated with nhr-31 , vha-5 , vha-8 and vha-12 RNAi were taken in both the FITC channel ( 488 nm ) and DIC channels . To measure excretory cell diameter , control and nhr-31 RNAi animals expressing Pnhr-31::gfp ( 5–6 animals ) were analyzed by taking images which captured 50–100 µm of the proximal , middle , or distal regions of the posterior excretory cell tube . Diameter measurements were taken every 4–5 µm within the imaged regions using Zeiss measurement software . Data were plotted using Origen 5 . 0 ( OrigenLab , Northampton , MA ) software , and data displayed in dot plots reflected values from each independent measurement , along with the mean , and standard deviation from the mean . Day 2 adults or L1 larvae were placed into a 20% BSA/PBS buffer solution and prepared in a Leica-Impact-2 high-pressure freezer according to the following protocol: 1 ) 60 hours in 100% acetone and uranyl acetate at −90°C . 2 ) Temperature was ramped from −90°C to −25°C over the course of 32 . 5 hours . 3 ) Next , sample was incubated at −25°C for 13 hours . 4 ) Next , the temperature change was brought from −25°C to 27°C in a 13 hour temperature ramp . Serial sections were post-stained in uranyl acetate followed by lead citrate . Thin cross sections were taken from resin-embedded clusters of young adults or L1 larvae . Sections for nhr-31 RNAi and control RNAi adult animals were obtained from 5 different animals , and sections for L1 larvae were also taken from 5 independent animals . Synchronized L1 populations were prepared by hypochlorite bleaching of gravid N2 adults according to established protocols [6] . Synchronized L1 larvae were grown on control RNAi bacteria or nhr-31 RNAi until animals reached the early L4 stage of development . Worms were then harvested in M9 , washed five times and immediately frozen in liquid nitrogen . RNA was extracted using a TRIZOL based method as described [6] . RNA was then labeled with Cy3 or Cy5 and hybridized to Washington University manufactured C . elegans microarrays ( http://genome . wustl . edu ) . Data were obtained from three independent biological replicates and analyzed using GenePix Pro 6 . 0 software ( Molecular Devices , Sunnyvale , CA ) . Ratios were calculated using background corrected , and normalized data ( global mean ) . For QRT-PCR , RNA was extracted and cDNA was prepared using our previously published protocol [6] with the following exception: RNA was separated from genomic DNA with a Turbo DNA free prep kit from Ambion ( Austin , TX ) . qPCR was performed using a BioRad iCycler ( MyiQ Single Color , Bio-Rad Laboratories , Hercules , CA ) . The data were analyzed as previously described [6] . QRT-PCR primers amplified ∼100 base pair regions of NHR-31 target genes . Primers were designed using Primer3 software and calibrated by serial dilution of cDNA and genomic DNA . Primer sequences are available upon request . Worms treated with control RNAi or nhr-31 RNAi from the L1 to L4 stage of development were plated on high salt ( 500 mM NaCl ) NGM-Lite plates seeded with E . coli . After various periods of high salt exposure , worms were scored for the ability to survive when rescued to a standard salt plate . Data for each time point was obtained from 250 animals . For rescue , worms were collected from the salt plates using M9 buffer+300 mM NaCl and transferred to standard NGM plates containing 50 mM NaCl . Worms were scored for survival after 12 hours of recovery [28] . To identify putative nuclear receptor response elements ( NREs ) , we use the online computer program NHR-scan ( http://nhrscan . genereg . net ) , which was first presented in a study by Sandelin and Wasserman [34] . The promoters of C . elegans vATPase genes were defined as the sequence between the ATG translational start site of the vha gene of interest and the beginning or end of the next upstream gene in the C . elegans genome . For vATPase genes expressed in operons , the promoter was chosen using the ATG translational start site of the first gene in the operon . For mouse promoters , 2000 nucleotides of upstream sequence were extracted from each vATPase gene . This sequence included 1950 nucleotides upstream of the translational start site +50 nucleotides of coding sequence . In all cases , the isoform with the most 5′ translational start site was selected for promoter sequence extraction . To calculate and display the consensus half sites shown in Figure 6 , all half site sequences were analyzed using the WebLogo online program ( http://weblogo . berkeley . edu/logo . cgi ) [43] . | The function of many important biological structures requires the construction of very complex cellular shapes . For example , mammalian kidneys or related renal systems in other animals rely on the formation of elongated tubes that maximize surface area to facilitate the exchange of ions between the body and excreted fluid . Defects in kidney development or function may lead to kidney failure or polycystic kidney disease . Mechanisms involved in orchestrating the formation and function of the elaborate tube structures in renal systems are still poorly characterized . Here , we show a novel transcription factor involved in the growth and elongation of an excretory tube in C . elegans . This factor helps manage tube development by regulating genes involved in ion transport and membrane fusion , likely helping to balance the growth of the inner and outer portions of the excretory tube as this structure elongates . This transcription factor shares significant homology with a mammalian protein that participates in hormone signaling and is present in the kidney tubules , suggesting that elongation and growth of tube structures may rely on a new kind of hormonal communication that occurs between distant parts of the cell; this signaling mechanism may be important for appropriate kidney development in humans . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology/molecular",
"development",
"molecular",
"biology/translational",
"regulation",
"developmental",
"biology/pattern",
"formation",
"developmental",
"biology/morphogenesis",
"and",
"cell",
"biology"
] | 2009 | The Caenorhabditis elegans HNF4α Homolog, NHR-31, Mediates Excretory Tube Growth and Function through Coordinate Regulation of the Vacuolar ATPase |
Although effective rabies virus vaccines have been existing for decades , each year , rabies virus infections still cause around 50 . 000 fatalities worldwide . Most of these cases occur in developing countries , where these vaccines are not available . The reasons for this are the prohibitive high costs of cell culture or egg grown rabies virus vaccines and the lack of a functional cold chain in many regions in which rabies virus is endemic . Here , we describe the excellent temperature resistance of a non-replicating mRNA based rabies virus vaccine encoding the rabies virus glycoprotein ( RABV-G ) . Prolonged storage of the vaccine from -80°C to up to +70°C for several months did not impact the protective capacity of the mRNA vaccine . Efficacy after storage was demonstrated by the induction of rabies specific virus neutralizing antibodies and protection in mice against lethal rabies infection . Moreover , storing the vaccine at oscillating temperatures between +4° and +56°C for 20 cycles in order to simulate interruptions of the cold chain during vaccine transport , did not affect the vaccine’s immunogenicity and protective characteristics , indicating that maintenance of a cold chain is not essential for this vaccine .
Vaccines are effective means for protecting exposed individuals against infectious diseases , thereby improving people’s quality of life and public health . Current vaccines require a reliable cold chain during transport and storage that is technically complex , costly , and potentially prone to disruption [1] . This limits the global use of already existing effective vaccines in many areas of the world . Vaccines may accidentally be exposed to freezing temperatures or to heat [2 , 3] either of which , depending on the nature of the vaccine , may reduce vaccine efficacy [2 , 4] . In general , the stability of different vaccines is given at temperatures between 2° to 8°C and between 25° to 37°C and can range in duration from weeks to up to 2 years [5] . While thermostable vaccines promise to improve access in subtropical and tropical climates and in settings with limited resources , development of today’s vaccines is greatly complicated by the need for laborious and product-specific optimization in order to facilitate thermostability . Thus per se thermostable vaccines would present a major step forward in medical use in general and in supplying regions with extreme climates in particular . Here , we demonstrate that synthetic messenger RNA ( mRNA ) may serve as a technology platform for simplified generation of thermostable vaccines . RNA vaccines exhibit immunological versatility by inducing cellular and humoral immune responses , defined composition , and allow for a rapid and standardized manufacturing process . Recently , anti-infective effects have been demonstrated for synthetic RNA vaccines [6] , including our reports on mRNA vaccines against influenza and rabies [7 , 8 , 12] . We previously showed induction of humoral and cell-mediated immune responses , efficacy for newborns and elderly and long-lasting immunity in animal models . Extending initial reports demonstrating RNA was stable under heat stress after lyophilization with trehalose [9] we have previously shown protective efficacy of an mRNA vaccine encoding influenza virus HA upon storage at 37°C for 3 weeks[8] . In the present report , we demonstrate stability of an mRNA based vaccine in a comprehensive study of thermostability in the stringent rabies virus challenge model . We used a proprietary mRNA vaccine technology ( RNActive ) that involves optimization of the mRNA sequence , formulation , and production at high purity , as described elsewhere [10 , 11] . To validate and extend our previous findings , an mRNA vaccine encoding the rabies virus glycoprotein ( RABV-G ) was used as an execution example of a prophylactic mRNA vaccine . The used mRNA vaccine was stored in lyophilized form and reconstituted in buffer before injection . Since thermostability refers to resilience to both low and high temperatures and is usually tested at 2–8°C , 25°C , 37°C and ≥45°C [1 , 5] , we conducted a series of experiments to explore protective efficacy subsequent to both low and high temperature storage for up to twelve months . We show that the candidate mRNA vaccine encoding the rabies virus glycoprotein retains immunogenicity and protective effects upon exposure to temperatures as high as 70°C and prolonged storage for several months . Importantly , results of phase I first-in-human clinical trial with the mRNA based rabies vaccine ( CV7201 ) tested in the study here for thermostability have demonstrated that 32 of 45 ( 71% ) subjects given 80 or 160μg intradermally and 6 of 13 ( 46% ) with 200 or 400μg intramuscularly via needle-free device injection induced VNTs of 0 . 5 IU/ml or more across dose levels and schedules [12] .
The rabies virus CVS-11 was received from the archive of Lyssaviruses of the OIE Reference Laboratory for Rabies and WHO Collaborating Centre for Rabies Surveillance and Research , Friedrich-Loeffler-Institut , Insel Riems , Germany . CVS-11 was grown on baby hamster kidney cells ( BHK-21 from the collection of Cell Lines in Veterinary Medicine , Friedrich-Loeffler-Institut , Isles of Riems , Germany ) and used throughout the experiments . Virus culture and all infectious experiments in animals were performed at the Friedrich-Loeffler-Institute , Federal Research Institute for Animal Health ( Isles of Riems , Germany ) . The mRNA vaccine was based on CureVac's RNActive technology ( claimed and described in patents EP1392341 , EP1857122 and application WO2012019780A1 ) coding for the full-length rabies virus glycoprotein . mRNA vector structure and optimization was performed as described elsewhere containing a 5’ cap structure , 5’ UTR , open reading frame , 3’ UTR , and poly-A tail [7 , 13] . The complete nucleotide sequences and the complexation with the cationic peptide protamine of the used mRNA vaccines have been published before [7] . The licensed vaccines Rabipur ( Novartis ) and HDC ( human diploid cell vaccine; Sanofi Pasteur MSD ) are commercially available and were purchased from a local pharmacy . For thermostability testing , the mRNA vaccines in Fig 1 were stored at +5°±3°C ( uncontrolled humidity ) , 25°±3°C ( 60% ± 5% rel . humidity ) and 40°C ±3°C ( 75% ± 5% rel . humidity ) . mRNA in later trials , HDC and Rabipur were stored at indicated temperatures ( uncontrolled humidity ) . Vaccines were stored as lyophilisates or reconstituted , respectively , as indicated . mRNA lyophilisates were reconstituted using ringer lactate solution prior to injection . Borosilicate glass vials ( type I ) were half-closed with rubber stoppers prior to loading of the freeze-dryer . Vials were placed on the shelf of a state-of-the-art freeze- dryer ( Lyoflex 04 , BOC Edwards ) . The cycle included the lowering of the temperature to -40°C and freezing of the samples at this temperature for 2 h . The chamber pressure was reduced to 160 μbar , primary drying takes place at -10°C within 17 h , secondary drying was performed at 20°C for 10 h and 68 μbar . After lyophilization , vials were closed by lowering the upper shelf under nitrogen and sealed with an aluminum cap . Six to eight weeks old inbred BALB/c were purchased from Janvier Laboratories ( Le Genest-Saint-Isle , France ) . All animal experiments , including the rabies challenge infections using CVS-11 , were conducted according to German laws and guidelines for animal protection and approved by the regional council Tübingen under reference numbers CUR 4/13 . Mice were anesthetized by intraperitoneal application of ketamine ( Sanofi-Aventis , Frankfurt , Germany ) and Rompun ( Bayer , Leverkusen , Germany ) . Thereafter , intradermal injection of 2 x 50 μl of ringer-lactate-solution alone or containing 80 μg ( study in Fig 1 ) or 40μg of mRNA ( subsequent studies ) was performed using syringe and forceps for creating a skin fold . HDC and Rabipur were applied intramuscularly with 100 μl ( 0 . 1 human dose ) distributed to four injection sites . All mice were immunized twice with a time interval of three weeks ( day 0 and day 21 ) . Blood samples were taken by retro-orbital bleeding . Anti-rabies serum antibodies were analyzed by FAVN test by Eurovir Hygiene-Institut , Luckenwalde , Germany according to WHO protocol [14] . All samples were anonymized prior to shipment to Eurovir . After immunization procedure , animals were infected with rabies virus ( for time interval between immunization and infection , see respective figure legend ) . The rabies challenge virus CVS-11 was applied by intracerebral ( i . c . ) injection with a volume of 20 μl and an infectious dose of 40 median lethal virus doses 50 ( MLD50 ) . Body weight and clinical signs of infected mice were assessed daily over a period of two weeks after infection . Mice with less than 80% of initial body weight or that were scored accordingly were sacrificed . Statistical analysis was performed using GraphPad Prism software , Version 6 . 00 . Statistical differences between groups were assessed by Mann Whitney test .
In a first experiment , we tested the induction of virus neutralizing ( VN ) antibodies in BALB/c mice immunized with 80 μg of RABV-G encoding mRNA that had been stored at varying temperatures , ranging from -80°C to +40°C , for 6 months ( Fig 1 ) . As a control , a licensed inactivated rabies virus vaccine ( HDC ) that had been stored at 4°C as recommended by the manufacturer was used at 1/10 of the recommended human dose ( 100 μl ) , comparable to previously published work [15] . As shown in Fig 1A , the RABV-G mRNA vaccine induced protective RABV-G specific titers in the fluorescent antibody virus neutralization ( FAVN ) assay upon storage at -80°C for 6 months . Likewise , vaccination after storage of mRNA vaccine at 5°C and at slightly ( 25°C ) or significantly ( 40°C ) elevated temperatures induced high neutralizing antibody titers all significantly higher compared to the buffer treated control group ( Fig 1A ) . Vaccinated mice were then challenged intracerebrally ( i . c . ) with 40-fold median lethal doses ( LD50 ) of the rabies virus strain CVS-11 . All immunized mice ( mRNA and HDC controls ) survived the challenge infection , whereas buffer treated control animals all had to be sacrificed at day eight due to drastic body weight loss of >20% ( Fig 1B , S1 Table ) . Next , we assessed the immunogenicity of vaccines stored at -80° , 5° or 25°C for 12 months in a challenge experiment in mice . The immunogenicity ( Fig 1C ) and protective capacity of the RABV-G mRNA vaccine was again not affected by storage . As before , none of the mRNA-immunized mice needed to be sacrificed upon i . c . challenge ( S1 Table ) , although mice that received mRNA vaccine stored at 5°C showed a transient drop in body weight ( Fig 1D ) . In contrast , two out of five mice immunized with the licensed vaccine HDC stored at 4°C for 12 months had to be sacrificed due to critical weight loss and one of the three surviving mice had a body weight only slightly above the critical threshold , indicating compromised vaccine efficacy for the HDC vaccine , potentially reflecting the reported lower potency of HDC compared to Rabipur [16] . We then further extended the temperature range by testing mRNA vaccine storage at 60°C . RABV-G mRNA and a second licensed inactivated rabies virus vaccine , Rabipur , both remained immunogenic ( Fig 2A ) and induced 100% survival upon rabies challenge if stored at 60°C for 4 weeks , unlike HDC that had lost its protective potency almost completely when stored under the same conditions ( Fig 2B and 2C ) . In an extension of this experiment , we tested storage at 70°C for 1 month ( Fig 2D–2F ) and 60°C , 70°C and 80°C for 3 months ( Fig 2G–2I ) . Mice that had been vaccinated with RABV-G mRNA stored at 70°C for 1 month were again completely protected against lethal challenge infection ( Fig 2D–2F ) . In contrast , a licensed vaccine ( Rabipur ) only protected 60% of vaccinated mice against lethal infection ( Fig 2E ) . Constant body weights of mice vaccinated with mRNA vaccines clearly reflect the robust protective efficacy of the vaccine after storage at this elevated temperature , while mice immunized with the control vaccine ( Rabipur ) showed a mean weight loss of up to 10% when the first mouse had to be sacrificed ( Fig 2F ) . Since incubation at 70°C for 1 month did not result in a detectable decrease of immunogenicity and protective capacity of the mRNA vaccine , thermal stress was further increased to 3 months storage at 60°C , 70°C and 80°C . As shown in Fig 2G , 2H and 2I , immunogenicity was retained and protection was complete upon storage at 60°C and 70°C . However , at 80°C RABV-G mRNA lost some efficacy and was not completely protective , with 50% mortality upon challenge infection in this group ( Fig 2H ) . Finally , we conducted experiments to mimic handling and storage errors before application . First , we tested the stability of mRNA vaccines at 40°C for one week upon reconstitution in buffer and compared to the licensed benchmarks Rabipur and HDC . As shown in Fig 3A all RABV-G mRNA ( 40 μg/mouse ) and Rabipur-vaccinated mice exhibited high titers of virus neutralizing antibodies and survived the challenge infection ( Fig 3B ) without weight loss ( Fig 3C ) , whereas only 75% of mice in the HDC-vaccinated group survived ( Fig 3B ) . Second , we assessed the immunogenicity of mRNA vaccine stored at changing temperatures alternating between 4°C and 56°C for one month ( twenty cycles of 56°C for 8–9 h and 4°C for 15–16 h ) in comparison to vaccines stored at -20°C . Induction of VN titers ( Fig 3D ) and survival upon challenge infection ( Fig 3E ) without weight loss ( Fig 3F ) clearly demonstrated that storage at alternating temperatures did not compromise the efficacy of the mRNA vaccine . In this study , we demonstrated thermostability of a rabies-specific mRNA vaccine in the stringent i . c . rabies virus challenge model . To systematically assess thermostability of mRNA vaccines , we tested immunogenicity of a RABV-G mRNA vaccine stored at low ( -80°C , 5°C ) and at elevated temperatures ( 25° , 40° , 60° , 70° , 80°C ) ( S1 Table ) . Since thermal stress is a function of both absolute temperature and duration of exposure , various exposure times were tested ( three , six and twelve months ) . Under all conditions , except for extreme heat of 80°C for 3 months , tested , the RABV-G mRNA vaccine reliably induced high levels of VN antibodies ( ≥0 . 5 IU/ml ) , and also allowed the survival of immunized mice after lethal challenge with rabies virus . In contrast , licensed inactivated rabies virus vaccines used as benchmarks at least partially lost their protective capacity under the same experimental conditions . As outlined above , most licensed vaccines are stable at temperatures between 2 and 8°C , which includes lyophilisates such as the yellow fever vaccine 17D . In contrast , data presented here provide evidence for the exceptional temperature stability of an mRNA based vaccine that ranges from -80°C to +70°C . While a certain range of temperatures tolerated by different rabies vaccine preparations has been reported [17–19] no vaccine format featuring a comparable temperature stability-range has been described as yet . Importantly , previous studies have shown , that conventional rabies vaccines can exhibit significantly decreased efficacy upon storage at elevated temperatures [17] . Hence , data presented here support a scenario , in which an mRNA-based rabies vaccine could circumvent the need for a cold chain , allowing vaccine transport to areas in which a rabies vaccine is needed but its supply remains a logistical challenge . Moreover , we expect that our findings can be extended to other mRNA vaccines based on RNActive technology , given that these vaccines only differ in nucleotide sequence while the chemical nature of the active pharmaceutical ingredient , namely mRNA , remain the same . While a difference in nucleotide sequence may influence secondary structure , it seems unlikely to have any major impact on the response to thermal stress . Thermostability contributes to an extended shelf life of vaccines even in challenging environments , and also allows for more economical stockpiling of vaccines , for example , for global preparedness against epidemic threats . Subsequent studies are needed to confirm that the exceptional temperature stability observed here can be implemented into a final recommendation for transport outside the cold chain for a licensed product in the end . Those studies will most likely need to include clinical testing and alignment of the clinical data to a potency release test . In summary , mRNA represents a highly thermostable and efficacious vaccine platform that may contribute to simplifying global access to vaccines in the future . | Conventional prophylactic vaccines require transport and storage under controlled temperatures in an unbroken cold chain . Therefore , distribution of many vaccines is restricted to areas where the cold chain can be maintained which excludes especially rural areas in many countries from continues vaccine supply . Unfortunately , some diseases that can be prevented by vaccination , like the rabies virus infection , are still endemic in such areas . Therefore , logistic reasons often prevent delivery of life saving vaccines to areas in which they are most needed . Here , we describe an mRNA vaccine encoding the rabies virus glycoprotein ( RABV-G ) that remains protective in a mouse challenge model upon storage at highly variable temperatures . These results suggest that such a vaccine allows storage outside the cold chain and can therefore reach all areas of the world where rabies virus is endemic . Since mRNA vaccines consist of the same biochemical components , irrespective of the encoded protein , it is reasonable to assume that the thermostability observed for the rabies vaccine is a general characteristic of mRNA based vaccines . | [
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] | [
"viral",
"vaccines",
"medicine",
"and",
"health",
"sciences",
"body",
"weight",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"european",
"union",
"pathogens",
"immunology",
"geographical",
"locations",
"microbiology",
"vaccines",
"viruses",
"physiological",
"parameters",
"rna",
"viruses",
"infectious",
"disease",
"control",
"antibodies",
"glycoproteins",
"rabies",
"virus",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"proteins",
"medical",
"microbiology",
"antigens",
"microbial",
"pathogens",
"people",
"and",
"places",
"biochemistry",
"lyssavirus",
"germany",
"virology",
"viral",
"pathogens",
"physiology",
"virus",
"glycoproteins",
"biology",
"and",
"life",
"sciences",
"europe",
"glycobiology",
"organisms"
] | 2017 | A thermostable messenger RNA based vaccine against rabies |
We previously reported that foetuses congenitally infected with Trypanosoma cruzi , the agent of Chagas disease , mount an adult-like parasite-specific CD8+ T-cell response , producing IFN-g , and present an altered NK cell phenotype , possibly reflecting a post-activation state supported by the ability of the parasite to trigger IFN-g synthesis by NK cells in vitro . We here extended our knowledge on NK cell activation by the parasite . We compared the ability of T . cruzi to activate cord blood and adult NK cells from healthy individuals . Twenty-four hours co-culture of cord blood mononuclear cells with T . cruzi trypomastigotes and IL-15 induced high accumulation of IFN-g transcripts and IFN-g release . TNF-a , but not IL-10 , was also produced . This was associated with up-regulation of CD69 and CD54 , and down-regulation of CD62L on NK cells . The CD56bright NK cell subset was the major IFN-g responding subset ( up to 70% IFN-g-positive cells ) , while CD56dim NK cells produced IFN-g to a lesser extent . The response points to a synergy between parasites and IL-15 . The neonatal response , observed in all newborns , remained however slightly inferior to that of adults . Activation of IL-15-sensitized cord blood NK cells by the parasite required contacts with live/intact parasites . In addition , it depended on the engagement of TLR-2 and 4 and involved IL-12 and cross-talk with monocytes but not with myeloid dendritic cells , as shown by the use of neutralizing antibodies and cell depletion . This work highlights the ability of T . cruzi to trigger a robust IFN-g response by IL-15-sensitized human neonatal NK cells and the important role of monocytes in it , which might perhaps partially compensate for the neonatal defects of DCs . It suggests that monocyte- and IL-12- dependent IFN-g release by NK cells is a potentially important innate immune response pathway allowing T . cruzi to favour a type 1 immune response in neonates .
Chagas disease , caused by the protozoa Trypanosoma cruzi , is a major cause of cardiac failure in Latin America where it infects 8–10 million people [1] . Parasites are mainly transmitted by blood-sucking vector bugs , transfusion of infected blood , or from mothers to their foetuses . Our previous studies in infants from chagasic mothers showed T . cruzi being a potent activator of both innate and adaptive immune responses in early life . Indeed , neonates congenitally-infected with T . cruzi mount a mature parasite-specific CD8+ T lymphocyte response producing IFN-g [2] , whereas uninfected infants from T . cruzi-infected mothers ( probably by receiving circulating parasite molecules from their mothers ) display a pro-inflammatory environment associated with activated monocytes [3] . In addition , both congenitally infected and uninfected infants from chagasic mothers develop boosted type 1 immune responses to vaccines routinely administered in early life [4] . These data point out the ability of T . cruzi to overcome the immune deficiency associated with early life [5] , [6] . NK cells mediate protection against pathogens through secretion of IFN-g that activates phagocytes and shape Th1-dependent adaptive immune response , as well as through destruction of infected cells by their natural cytotoxic properties . These functions are associated with distinct human NK cell sub-populations identified by their differential expression of CD56 and CD16: the CD56dimCD16+ subset is preferentially cytotoxic , while the CD56brightCD16−/low subset is specialized in cytokine production . NK cells express a repertoire of inhibiting and activating receptors recognizing self-ligands or microbial molecules expressed on infected and tumour cells . The balance between signals delivered by these receptors tightly regulates their responses [7] , [8] . Cytokines ( IL-2 , IL-15 , IL-12 , IL-18 and type 1 IFNs ) and contact-dependent signals provided by dendritic cells ( DCs ) , monocytes/macrophages and CD4+ T cells also contribute to NK cell activation [9]–[12] . Though neonatal NK cells display some functional defects [13] , their intrinsic ability to produce IFN-g seems comparable to adults [14]–[16] . Nevertheless , the reduced ability of neonatal mononuclear cells to produce NK cell-activating cytokines likely hinders their IFN-g response [14] . Information on in vivo NK cell responses to pathogens in early life is scarce owing to the difficulty to perform such studies [13] . By investigating the functional properties of NK cells from T . cruzi-congenitally infected newborns , we previously showed they display a defective ability to produce IFN-g in response to cytokines and a reduced cytotoxic capacity at birth . These alterations might however reflect a down-regulated state of NK cells after activation having occurred in utero when the parasite was transmitted . This possibility is sustained by our observation that T . cruzi was able to trigger in vitro IFN-g synthesis by cord blood NK cells [13] , [17] . This is also in line with results reported by Sathler-Avelar et al suggesting that NK cells are activated during acute T . cruzi infection in infants [18] . We here confirm the ability of T . cruzi to induce IFN-g production by blood NK cells from a large cohort of healthy newborns , compared its effect to that on adult cells , and investigated the mechanism of activation of neonatal NK cells .
The ethical committee of U . L . B . has approved this study ( protocol # P2011/254 ) , and we obtained informed written consent from volunteers and mothers . Umbilical cord blood ( CB ) samples from full-term healthy newborns , born from healthy mothers , were harvested in heparinized tubes at the maternity ward of the Erasmus Hospital ( Brussels ) . Adult peripheral blood ( PB ) samples were obtained from healthy volunteers who have been tested to be negative in T . cruzi serology . Blood samples were used within 8 hours of collection . Live T . cruzi trypomastigotes and lysate ( Tulahuen strain , genotype VI [19] ) were obtained as previously described [3] . Absence of Mycoplasma was verified by PCR ( Lucron Bioproducts ) . CB and PB mononuclear cells ( MC ) were isolated by Nycoprep density gradient centrifugation . Their viability was ≥98% as determined by trypan blue exclusion staining . Cells ( 5×105 ) were distributed in polypropylene tubes in RPMI 1640 ( 1 mL ) containing 10% heat-inactivated fetal calf serum , penicillin G and streptomycin . They were incubated with recombinant human interleukin-15 ( 20 ng/mL ) ( R&D Systems ) and/or live or lysed T . cruzi trypomastigotes in a 1∶1 parasite-to-cell ratio for 24 h at 37°C ( 5% CO2 ) . Cells incubated in medium alone were used as control . In cultures designed to detect intracellular IFN-g , brefeldin A ( 10 µg/mL , Sigma-Aldrich ) was added for the last 4 hours of culture . For IL-12p70 blocking experiments , CBMC were incubated with anti-human IL-12 monoclonal antibody that does not cross-react with IL-23 ( clone 24910 , R&D Systems ) , or control unrelated IgG ( Sigma-Aldrich ) . Magnetically depletion of CD1c+ myeloid ( m ) DCs or CD14+ monocytes were performed using anti-CD1c or -CD14 microbeads , LD columns and MidiMACS equipment ( all from Miltenyi Biotec ) as described by the manufacturer . This led to depletion of 97 . 6±1 . 2% mDCs and 93 . 8±1 . 8% monocytes . mDCs or monocytes-depleted CBMC and reconstituted CBMC ( purified mDC or monocyte fraction added in CBMC depleted fraction ) were cultured as described above . To determine if cell-cell contacts were involved in activation of NK cells , an insert with a semi-permeable membrane ( pore size of 0 . 4 µM , Greiner Bio-One ) was used . Monocyte-depleted CBMC were cultured in the lower part of the transwell , purified autologous monocytes were added into the upper well , while parasites were added at both sides . Controls used reconstituted CBMC in both sides . Transwell experiments were also performed to determine the need for contact between parasites ( upper side of the membrane ) and CBMC ( lower side ) . After stimulation , cell cultures were centrifuged at 750 g for 5 min at room temperature and supernatants were kept at −70°C for cytokine assays . Cells were further processed for flow cytometry analyses or quantitative RT-PCR . Viability of NK cells ( ≥98% ) and monocytes ( ≥92% ) was verified by flow cytometry using the LIVE/DEAD Viability Assays ( Invitrogen ) and was not modified whatever the conditions of stimulation . IFN-g , TNF-a and IL-10 levels in culture supernatants were detected by ELISA using antibody pairs and standards from Biosource ( Invitrogen ) . IL-18 was detected using the anti-IL-18 clones 125-2H and 159-12B ( R&D Systems ) for coating and detection , whereas IL-12p70 was detected using READY-SET-GO ! IL-12p70 kit ( eBioscience ) . Assays were performed in duplicate following the manufacturer's instructions . Detection limits were 2 pg/mL for all cytokines . Extracellular and intracellular stainings were performed as previously described [2] , using the following mAb and their matched control isotypes in various combinations : anti-human ( h ) CD3-peridinin chlorophyll protein ( PercP ) , anti-hCD11c-allophycocyanin ( APC ) , anti-CD14-fluorescein isothiocyanate ( FITC ) , anti-hCD16-phycoerythrin ( PE ) , anti-hCD19-PE , anti-hCD34-FITC , anti-hCD54-PE , anti-hCD62L-PE , anti-hCD69-FITC , anti-hCD123-PE , anti-hIFN-g-FITC , anti-hIFN-g- PE , Lin1-FITC ( BD Biosciences ) , anti-hCD56-APC , anti-hHLA-DR-PerCP , anti-hIL-12p35-PE ( Miltenyi Biotec ) , anti-TLR-2-PE and anti-TLR-4-PE ( e-Biosciences ) . Data acquisition was stopped when 1000 events was reached for the CD56bright NK cell subset or 5000 events for the CD14+ monocytes . Data acquisition and analyzes were performed using a four-colour FACSCalibur flow cytometer and CELLQuest 6 . 0 software ( BD ) . NK cell analyzes were made on CD56brightCD16−/low and CD56dimCD16+ cells , targeted in CD3− cells present in a large lymphocyte gate determined in the SSC-FSC plot . The relative proportions of NK cell subsets were concordant with previous reports [13] . Proportions of the CD56bright subset were similar in cord and adult samples ( 13 . 2±1 . 3% and 10 . 1±1 . 6% respectively ) and slightly increased ( by 1 . 1–1 . 3 fold ) in the presence of parasites and/or IL-15 . Monocyte analyzes were made on CD14+ cells . Limits for the quadrant markers were set on negative populations and isotype controls . Results are presented as percentages of cells expressing the analyzed marker or as geometric mean fluorescence intensity ( MFI ) of the total cell population . Depletion of monocytes and mDCs was verified by analyzing population of untouched and depleted CBMC ( identifying monocytes as CD14+ cells and mDCs as CD11c+CD123−HLA-DR+Lin1−CD34− cells ) . Total RNA was isolated using High Pure RNA Isolation Kit ( Roche Applied Science ) as recommended by the manufacturer . The amount and purity of RNA were determined by spectrophotometry . Four hundred ng of each sample of RNA have been used for subsequent RT-PCR process on Mastercycler ep gradient ( Eppendorf ) using the Transcriptor First Strand cDNA Synthesis Kit ( Roche ) with oligo-dT primers following the manufacturer's instructions . Reverse-transcripted RNA samples were half-diluted and processed by real-time PCR on the Lightcycler 480 ( Roche ) using SYBR Green Supermix ( Quanta Biosciences/VWR ) and the following primers ( InVitrogen ) : IFN-g ( 5′-ACTGACTTGAATGTCCAACGCA-3′ and 5′-ATCTGACTCCTTTTTCGCTTCC-3′ [20] ) , IL-12p35 ( 5′-TTCACCACTCCCAAAACCTGC-3′ and 5′-GAGGCCAGGCAACTCCCATTAG-3′ [21] ) , IL-18 ( 5′-CAGACCTTCCAGATCGCTTC-3′ and 5′-GGGTGCATTATCTCTACAGTCAGAA-3′ [22] ) and GAPDH ( 5′-AACAGCCTCAAGATCATCAGC-3′ and 5′-GGATGATGTTCTGGAGAGCC-3′ [23] ) . PCR protocol consisted in a denaturation phase at 95°C for 5′ and 50 cycles of amplification [95°C 3″ , 60°C 1′] . Fluorescence emission was measured at the end of the elongation step . The cycle number at which fluorescence emission crossed the determined threshold value was determined . Melting curve analysis was used to assess the specificity of the assay . Fold changes were calculated using the 2−ΔΔCT method with GAPDH as house-keeping gene and unstimulated cells cultured for the same time as control . Each sample was tested in duplicate . Data are expressed as means ± SEM or Box and Whisker plots ( showing medians , quartiles and minimum and maximum values ) . Differences between unstimulated and stimulated cells were tested for significance using Wilcoxon matched-paired test . Comparisons between neonates and adults were performed using Mann Whitney U test . Statistical significance was accepted if P<0 . 05 . Statistical analyzes were performed with GraphPad Prism 5 . 02 .
CBMC co-cultured for 24 h with T . cruzi live trypomastigotes ( ratio parasite to cell of 1 ) associated with IL-15 ( 20 ng/mL ) produced large amounts of IFN-g , low levels of TNF-a and no IL-10 in response to T . cruzi associated with IL-15 , whereas parasites or IL-15 alone were markedly less effective , suggesting they synergize to trigger the release of these cytokines ( Table 1 ) . The synergy between parasites and IL-15 was also noticeable at the level of IFN-g transcript accumulation ( Figure 1A ) . A similar profile of response was observed in adult PBMC , though IFN-g transcript level and cytokine production in response to parasites and IL-15 were around two fold higher than in CB cells ( Table 1 and Figure 1B ) . Flow cytometry analysis of IFN-g producing cells in response to T . cruzi and IL-15 allowed to identify NK cells as major responding ones , since less than 0 . 15 ( cord ) to 0 . 6 ( adult ) % of T cells and no other cells contained IFN-g in the tested conditions ( data not shown ) . Parasites or IL-15 alone weakly triggered IFN-g synthesis by a low proportion of CD56bright NK cells in some CB samples ( Figure 2A ) . Strikingly , both signals strongly synergized to boost the IFN-g response by around 6 times , leading to production of IFN-g by meanly 20% of CD56bright NK cells ( up to 70% in some individuals ) . It is to notice that 100% of newborns respond to parasites and IL-15 . In adult cells , T . cruzi alone did not significantly trigger IFN-g production and the proportion of CD56bright NK cells producing IFN-g in response to IL-15 alone was comparable to that found in cord blood cells ( medians 7% vs . 3% , p>0 . 05 ) . Combination of parasites and IL-15 similarly synergized to increase IFN-g production by adult CD56bright NK cells ( Figure 2B ) . Yet , the mean proportion of CD56bright NK cells producing IFN-g after such activation was higher in adult than in cord blood cells ( Figures 2B vs . 2A , 36 vs . 20% , p = 0 . 017 ) . CD56dim CB and PB NK cells also produced IFN-g ( Figures 2CD ) though , as expected from the literature [7] , [24] , [25] , the proportion of IFN-g-producing cells remained largely inferior to that observed in CD56bright NK cells . These results indicate that T . cruzi synergizes with IL-15 in triggering IFN-g production by neonatal and adult CD56bright NK cells , and that , despite the response of CB cells was inferior to that of adult cells , IFN-g production can be obtained in all newborns . After 24 h of stimulation , IL-15 strongly induced the expression of CD69 and CD54 on around 30% of both cord and adult blood NK cell subsets and down-regulated dramatically CD62L expression , indicating their activation . T . cruzi used alone barely activated NK cells , only weakly inducing CD69 expression . Parasites enhanced by 1 . 5 to 2 fold the IL-15 effect for all the activation markers . The CD56dim NK cells were also activated by IL-15 and further by the combination of IL-15 and parasites . Expression of activation markers by both NK cell subsets from adult blood in response to parasites and/or IL-15 was very comparable to that of cord blood NK cells ( Table 2 ) . Most IFN-g positive CD56bright NK cells co-expressed CD69 and CD54 and had down-regulated CD62L ( Figure 3 ) . Since IL-12 and IL-18 are important cytokines for NK cell activation [9] , [26] and are induced during T . cruzi infection [27] , [28] , we sought for their potential involvement in activation of CB NK cells . We did not detect significant levels of IL-12 or IL-18 in supernatants of CBMC cultured with T . cruzi and IL-15 , neither at 24 h ( Table 1 ) nor at different time points between 2 h and 24 h of culture ( data not shown ) . On another hand , IL-12p35 ( Figure 4A ) but not IL-18 ( data not shown ) transcripts accumulated in CB cells . Indeed , IL-12p35 mRNA levels increased by 3 times after 12–24 h of IL-15 stimulation and earlier by 6 times when parasites were added . In line with this , the use of neutralizing anti-IL-12p70 mAb almost totally inhibited IFN-g synthesis by CD56bright NK cells while unrelated control IgG had no effect ( Figure 4B ) . Amongst blood MC , monocytes and mDCs are susceptible to produce IL-12 [26] . We thus looked at the production of IL-12 by these two populations and studied the effect of depleting these cell types on CB NK cell response . After 8 h of stimulation with IL-15 and T . cruzi , meanly 25% of monocytes contained IL-12 ( Figure 4C ) while we did not find any IL-12 in mDCs at this timing ( data not shown ) . According with this , depletion of mDCs did not affect IFN-g production by NK cells while monocyte depletion led to a drastic decrease in response to parasites and IL-15 ( Figure 5A ) . This decrease was not observed with control reconstituted cells , indicating that the absence of IFN-g synthesis after monocyte depletion was not due to alteration of cells that might have occurred during the depletion procedure . Cross-talk between NK cells and other cells may involve surface interactions or soluble factors like cytokines [9] , [29] . To investigate if NK cell activation was dependent on contact with monocytes , we separated monocytes from monocyte-depleted CBMC by a semi-permeable membrane . Such separation totally abrogated NK cell activation by parasites and IL-15 ( Figure 5B ) . On the other hand , trypomastigote lysate or trypomastigotes separated from CBMC by a semi-permeable membrane did not induce synergistic production of IFN-g by IL-15-primed NK cells ( Figure 5CD ) . TLR2 and TLR4 are known to recognize T . cruzi PAMPs [30] . We found TLR2 and TLR4 expression on 98 . 9±0 . 3% and 76 . 0±6 . 8% of CB unstimulated monocytes and 7 . 6±2 . 1% and 6 . 4±1 . 7% of CB unstimulated NK cells respectively ( n = 4–7 ) . Blockage of TLR2 or TLR4 by neutralizing Abs reduced the proportion of IFN-g producing CD56bright NK cells in response to parasites and IL-15 by 59 . 7±6 . 5% and 71 . 8±8 . 9% respectively . Simultaneous neutralization of both TLRs did not further inhibit IFN-g release ( Figure 5E ) . Altogether , these results indicate that contact-dependent signals between monocytes and CBMC , as well as live parasites and TLR-2 and 4 engagements are needed for NK cell activation .
Our work confirms that Trypanosoma cruzi strongly increases the production of IFN-g in response to IL-15 by cord blood NK cells from healthy newborns . The CD56bright NK cell subset is the main responding population . Their activation is associated with up-regulation of surface expression of CD69 and CD54 and down-regulation of CD62L . A low response from CD56dim was also observed . As this NK cell subset outnumbers CD56bright NK cells by meanly 8 fold in peripheral blood [24] its contribution to the final amount of IFN-g detected in supernatants may however be important . The stimulating action of T . cruzi on IL-15-sensitized CB NK cells requires the integrity of parasites as well as contacts between parasites , monocytes and other cells . Moreover , TLR2 and TLR4 engagements and IL-12 produced by monocytes played an important role in the IFN-g response . It is now accepted that NK cells have to be primed by IL-15 , after which various other signals delivered by a large variety of receptors may come into play to activate them [31] , [32] . In infections , IFN-g production by NK cells can be potentiated by direct contact with pathogens and/or indirectly by cross-talk with myeloid cells that deliver contact-dependant signals and cytokines . Our data support a preferential indirect pathway of NK cell activation by T . cruzi , as it is the case in other infections with protozoa like Leishmania , Toxoplasma and Plasmodium [9] , [33] , [34] . Indeed , TLR2 and TLR4 , which are involved in NK cell activation in our conditions and recognize T . cruzi molecules [30] , [35] , were poorly expressed by neonatal NK cells . On the contrary , most monocytes expressed TLR2 and TLR4 , sustaining their involvement in an indirect pathway of NK activation by monocytes . The involvement of monocytes in our system is in line with another recent study underlining the ability of neonatal macrophages to activate NK cells [36] . The role of monocytes could rely to contact-dependent and soluble signals delivered to NK cells . We here show that they synthesize IL-12 and that this cytokine is mandatory for the IFN-g NK cell response to T . cruzi and IL-15 . It does not exclude the involvement of other signals delivered by monocytes , especially since monocytes also upregulated surface expression of CD40 , CD80 and CD83 after T . cruzi stimulation ( unpublished data ) that might also contribute to NK cell activation [9] . IL-12 induction by T . cruzi in monocytes is in line with a study of Souza et al . showing the presence of IL-12-positive monocytes in chagasic adult patients [37] . Interestingly , even if we clearly show the induction of IL-12p35 mRNA and of intracellular production of IL-12 by monocytes and the drastic need for IL-12 in our system , we couldn't detect any substantial levels of IL-12 in supernatants by ELISA . This can be due to the fact that too low amounts of IL-12 are present in supernatants to be detected or that IL-12 production is polarized and released in an immunological synapse between monocytes and NK cells in order to be directly used , as reported by Borg et al . [10] . This latter hypothesis is sustained by the observation that contact-dependant signals between monocytes and other cells were needed to induce IFN-g release by neonatal NK cells in response to T . cruzi and IL-15 . We may however not rule out the existence of additional direct effects of T . cruzi on NK cells . Indeed , some pathogens directly drive IFN-g production by NK cells through TLR-2 , TLR-4 or other receptors [36] , [38] , [39] . We showed that these TLRs were expressed by a low proportion of cord blood NK cells and were involved in the NK cell response . Arguing for a potential direct effect of parasites on NK cells , we also observed that T . cruzi trypomastigotes induced CD69 expression on purified NK cells ( unpublished data ) . The fact that mDCs were not involved is quite surprising . Indeed , this cell type is thought to be pivotal for NK cell activation [32] and we recently showed that T . cruzi up-regulates the expression , on CB mDCs , of co-activation molecules [40] such as CD80 and CD40 , which are able to co-activate NK cells [9] . The differential contribution of monocytes vs . mDCs to activation of CB NK cells may relate to a delayed or insufficient IL-12 production by neonatal mDCs [41] , whereas neonatal monocytes might not present same deficiencies to produce IL-12 [42] . Differences in expression of innate receptors might also account for this [43] . Indeed , our data showing that live parasites ( able to invade cells ) but not lysed ones drive the NK cell response suggest that intracellular receptors need to be engaged in addition to surface TLR2 and TLR4 . Human monocytes but not mDCs express the endosomal TLR9 , known to recognize T . cruzi DNA and to drive IL-12 synthesis [30] , [33] , [35] , [44] . IFN-g production is regulated at multiple transcriptional and post-transcriptional levels [45] , [46] . IL-15 drives IFN-g expression by acting mainly at the transcriptional level , triggering the binding of STAT proteins to the regulatory sites of Ifng gene promoter [47] , [48] . We indeed observed accumulation of IFN-g transcripts in IL-15-primed neonatal NK cells , which was strongly increased when parasites were added . Preliminary studies of actinomycin D chase experiments suggest that the parasite would favor transcription of Ifng rather than mRNA stabilization ( unpublished data ) . This is in line with the known ability of IL-12 to induce transcription of Ifng [49] , the known synergy between IL-12 and IL-15 to induce IFN-g mRNA and protein production by NK cells [25] , and the key involvement of IL-12 in the activating effect of T . cruzi . The fact that T . cruzi would not stabilize IFN-g transcripts is also consistent with the absence of IL-18 production in our conditions , a cytokine shown to increase the half life of IFN-g mRNA [50] . Though substantial , the IFN-g synthesis of IL-15-sensitized CB CD56bright NK cells induced by T . cruzi remained slightly inferior to that of adult cells . We observed differences between neonates and adults both at levels of IFN-g transcript accumulation and protein production . We can however notice that the higher response of adult cells seems to preferentially rely on the action of T . cruzi rather than on the response to IL-15 , which was not that much different between neonates and adults . It is tempting to speculate that the better response of adults reflects differences in accessory cells involved in NK cell activation , such as impaired TLR signalling in early life [41] , [51] , [52] . Despite of this , it is worth noting that NK cells from all newborns synthesized IFN-g . As NK cells have to be primed by IL-15 to respond to T . cruzi , the source of this cytokine in vivo during infection may be questioned . No data are currently available on the ability of T . cruzi to induce IL-15 production , except that this cytokine is found in situ in hearth tissue from chagasic patients [53] . We could not detect IL-15 in supernatants of CBMC cultured in the presence of trypomastigotes . However , IL-15 is known to be mainly expressed at the surface of myeloid cells , linked to its alpha-chain receptor , which cross-presents it to cells bearing the IL2/IL-15Rβγ [9] , [54] . Membrane-bound IL-15 is constitutively produced at low levels in lymph nodes and in the spleen , and our results indicate that the synergistic effect between parasites and IL-15 required only low concentrations of IL-15 that could otherwise not or faintly induce IFN-g . Based on this information , we may raise the following hypothesis about IFN-g NK cell response in congenitally-infected foetuses [17] . T . cruzi trypomastigotes pass through placental tissues and directly enters the fetal blood [55] . Blood being filtered by the spleen , parasites might there infect myeloid cells and encounter NK cells , known to be numerous in this secondary lymphoid organ [8] , [56] . A mutual cross-talk between parasites , monocytes/macrophages and CD56bright NK cells might therefore occur in the spleen of infected foetuses , where IL-15 is constitutively produced at low levels [57] , leading to rapid IFN-g release [12] . Since NK cells can play an important role in induction of primary CD8+ T-cell immunity in the absence of CD4+ T cells [58] , we may presume that the here reported CD56bright NK cell-derived IFN-g is involved in vivo to endow myeloid cells to initiate the strong CD8+ T cell response observed in newborns congenitally infected with T . cruzi [2] . Our work highlights the ability of T . cruzi to trigger a robust IFN-g response by IL-15-sensitized NK cells in all neonates , as well as the important role played by IL-12-producing-monocytes , which might partially compensate for the neonatal defects of DCs . Strong activation of NK cells and monocytes may constitutes a way allowing the parasite to overcome the immaturity of the neonatal immune system and favour a type 1 immune response . Our results encourage identifying T . cruzi molecules which could play an interesting adjuvant role to improve the efficacy of vaccines , which is necessary to reduce the important morbi-mortality of infectious diseases in early life [5] . They also emphasize the need for complementary studies on CB NK cells activation pathways . | IFN-g release by NK cells is essential in early control of infections with intracellular pathogens by driving protective type 1 immune response . NK cell activation requires integration of signals delivered by cytokines , dendritic cells , monocytes/macrophages and/or pathogens . Little information is available about this topic in neonates , known to be deficient in mounting type 1 immune response . We show that Trypanosoma cruzi , the protozoa agent of Chagas disease , rapidly and strongly up-regulates the production of IFN-g by IL-15-primed cord blood NK cells to a level close to that produced by adult NK cells . This neonatal NK cell response was dependent on cross-talk with monocytes and engagement of TLR2 and TLR4 by the parasite . Importantly , IL-12 synthesis by monocytes , but not by dendritic cells , was central in driving NK cell IFN-g release . This study suggests that monocytes may compensate for the known defects of neonatal DCs to produce IL-12 . This innate pathway may allow a pathogen to circumvent the defect to mount type 1 immune response in early life . This observation may be relevant in vivo in T . cruzi congenital infection , since such newborns have previously been shown to mount an adult like type 1 immune response . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"immunology",
"parasitology",
"biology",
"microbiology"
] | 2013 | Monocytes Play an IL-12-Dependent Crucial Role in Driving Cord Blood NK Cells to Produce IFN-g in Response to Trypanosoma cruzi |
Nipah virus ( NiV ) is a paramyxovirus ( genus Henipavirus ) that emerged in the late 1990s in Malaysia and has since been identified as the cause of sporadic outbreaks of severe febrile disease in Bangladesh and India . NiV infection is frequently associated with severe respiratory or neurological disease in infected humans with transmission to humans through inhalation , contact or consumption of NiV contaminated foods . In the work presented here , the development of disease was investigated in the African Green Monkey ( AGM ) model following intratracheal ( IT ) and , for the first time , small-particle aerosol administration of NiV . This study utilized computed tomography ( CT ) and magnetic resonance imaging ( MRI ) to temporally assess disease progression . The host immune response and changes in immune cell populations over the course of disease were also evaluated . This study found that IT and small-particle administration of NiV caused similar disease progression , but that IT inoculation induced significant congestion in the lungs while disease following small-particle aerosol inoculation was largely confined to the lower respiratory tract . Quantitative assessment of changes in lung volume found up to a 45% loss in IT inoculated animals . None of the subjects in this study developed overt neurological disease , a finding that was supported by MRI analysis . The development of neutralizing antibodies was not apparent over the 8–10 day course of disease , but changes in cytokine response in all animals and activated CD8+ T cell numbers suggest the onset of cell-mediated immunity . These studies demonstrate that IT and small-particle aerosol infection with NiV in the AGM model leads to a severe respiratory disease devoid of neurological indications . This work also suggests that extending the disease course or minimizing the impact of the respiratory component is critical to developing a model that has a neurological component and more accurately reflects the human condition .
Nipah virus ( NiV ) ( Family Paramyxovirus , genus Henipavirus ) emerged in Malaysia in the late 1990s where it caused a significant outbreak of respiratory and neurological disease in people working closely with pigs in both Malaysia and Singapore [1] . This outbreak resulted in the death of at least 105 humans and the culling of more than one million pigs [1] . In 2001 , NiV re-emerged in Bangladesh [2] and has become a regular cause of severe disease in parts of rural Bangladesh and India where over 240 people have died from the infection [3] . Transmission of NiV to human populations in Bangladesh and India results from contact with the carcasses or excreta of Pteropus spp . bats , also known as flying foxes . In Bangladesh , there is a strong correlation in human cases of NiV infection with consumption of unpasteurized , and possibly fermented , date palm sap containing bat excreta [4–6] . In addition to transmission from bats and pigs , there has also been evidence of human-to-human transmission , including nosocomial infections [7 , 8] . NiV infection causes an acute febrile disease with rapid onset that is typically characterized by areflexia , seizures , muscle spasms , hypertension , cough , vomiting and development of atypical pneumonia and severe respiratory disease [3 , 9] . In a number of cases , development of neurological symptoms has been documented with instances of neurological relapse or late onset encephalitis in 5–10% of the cases [10] , with occurrence up to 11 years after the initial virus exposure [11] . NiV infection has a case fatality rate of around 54% with fatality rates ranging from 40–100% , depending upon the outbreak [12] . NiV infection in humans takes the form of an acute respiratory disease ( ARD ) or atypical pneumonia with development of neurological disease , or , in some cases , only a neurological disease [3 , 9 , 11] . Many survivors of NiV infection develop long-term neurological sequelae including myoclonus , cognitive dysfunction , personality changes and persistent abnormalities on brain magnetic resonance imaging ( MRI ) examinations [13 , 14] . The use of x-ray imaging in a Bangladesh outbreak described bilateral “ground glass” opacities indicative of acute respiratory distress syndrome [15] . In the case of neurological disease , MRI identified focal hyperintensities in the white matter of the parietal lobes of one patient from Malaysia that may have been evidence of microinfarctions [11 , 16 , 17] . Some of these lesions cleared over time , while others persisted [18] . Other studies of neurological disease in humans have described confluent cortical involvement rather than discrete lesions [19] . Previous work has identified the African green monkey ( AGM ) as a model for NiV-induced disease . Infection of AGM by intratracheal ( IT ) inoculation results in a largely lethal disease with evidence of respiratory infection including enlarged lungs , hemorrhage and consolidation and blood in the pleural fluid [20–22] . Congestion within the meninges , meningeal hemorrhage and edema in the brain has also been reported [23] . Histological evaluation identified systemic vasculitis and evidence of syncytia in the spleen , kidney and pancreas [20] . Intraperitoneal inoculation of NiV in AGM resulted in similar disease with pulmonary consolidations and evidence of developing encephalitis [24] . The objective of the studies described here was to determine if small particle aerosol infection resulted in a markedly different disease than IT inoculation . In order to address this objective we utilized computed tomography ( CT ) and MRI to characterize and quantify disease progression . We also evaluated changes in peripheral and infiltrating immune cell populations in order to determine if immunopathogenesis was a critical component of the disease process . In these studies we found that IT inoculation induced largely focal consolidation within the lung with a rapid decrease in lung volume , while aerosol inoculation caused a more diffuse change , but with similar outcome . Gross observation of animals indicated that all succumbed to an acute respiratory disease , a finding that was supported by CT . MRI found evidence of venous abnormalities in the brain of only two animals . Histological evaluation of tissues identified widespread vasculitis , as previously described , in both aerosol and IT exposed animals . Evaluation of the immune response provided evidence of a systemic inflammatory response that was highlighted in lung lesions where there was a significant increase in pro-inflammatory cytokines . Changes in peripheral T cell populations also indicated expansion of CD8+ T cells , but little change in CD4+ T cells over the 8–10 day course of infection . These data demonstrate that small particle aerosol inoculation of NiV causes a disease that is more broadly disseminated in the lung than IT inoculation , but that is equally lethal in the AGM model with a rapid inflammatory response and pulmonary infiltration with minimal evidence of neurological disease .
The Nipah-Malaysia virus that was used in this study was isolated from a human case in 1996 and initially provided to USAMRIID from an existing collection by the Special Pathogens Branch , Centers for Disease Control , Atlanta , GA . The IRF received the virus from the USAMRIID collection . Work with non-human primates was conducted in accordance with an Animal Study Protocol approved by the NIAID Division of Clinical Research Animal Care and Use Committee ( Protocol #IRF-034E ) following recommendations in the Guide for the Care and Use of Laboratory Animals . This institution also accepts as mandatory the Public Health Service policy on Humane Care and Use of Laboratory Animals . All animal work at NIAID was performed in a facility accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care International ( AAALACI ) . Non-human primates were housed either singly or in pairs in an ABSL-2 facility prior to introduction into the BSL-4 facility . Animals were singly housed during BSL-4 acclimatization and during the course of the study . At all times animals were provided with appropriate enrichment including , but not limited to , polished steel mirrors and durable toys . Animals were anesthetized in accordance with BSL-4 standard protocols prior to all procedures including inoculation , imaging and collection of blood to minimize stress to the animals . Animals were observed following anesthesia to ensure complete recovery . All work with non-human primates was done in accordance with the recommendations of the Weatherall Report . These studies utilized the Malaysian strain of Nipah virus ( NiV ) . The challenge stock was cultivated in VeroE6 cells following receipt from USAMRIID . USAMRIID passage history included three passages in VeroE6 cells and one passage in Vero cells followed by two subsequent passages in our facility . VeroE6 cells ( BEI #NR596 ) were maintained in α-MEM w/GlutaMAX and incubated at 37°C/5% CO2 . All work with viable NiV was performed in the BSL-4 facility at the NIAID Integrated Research Facility in Frederick , MD . Wild-caught Caribbean origin African green monkeys ( AGM ) were purchased from PrimGen ( Hines , IL ) . Animals were group housed prior to being assigned to study and singly housed during the course of the study . Two males and one female were included in each study group . Animals were identified for inclusion based on similarity of size . All work in this study was reviewed and approved by the NIAID Division of Clinical Research ( DCR ) Animal Care and Use Committee ( ACUC ) ( see Ethics Statement below ) . Animals in the first group ( n = 3 ) were each inoculated via the intratracheal ( IT ) route with a target dose of 1x104 pfu in a total volume of 1 . 0 ml of virus diluted in serum-free α-MEM using a bronchoscope to deposit 0 . 5 ml in each mainstem bronchus . Back calculation of the inoculum indicated a dose of 1 . 3x104 pfu was delivered to each animal . The animals in the second group ( n = 3 ) were exposed to a small particle aerosol challenge using a 16 Liter , head-only aerosol exposure chamber and an aerosol management platform ( AeroMP , Biaera Technologies , USA ) within a Class III biosafety cabinet ( Germfree , FL , USA ) . The animals were anesthetized and received a single , time calculated aerosol challenge with a target dose of 1x104 pfu . Respiration values for the AGM were obtained using Guyton’s formula , MV = 2 . 1 ( BW^0 . 75 ) , with an anesthesia adjustment , where MV is minute volume and BW is the body weight in grams [25] . Aerosol particles were generated by a 3-jet Collison nebulizer ( BGI by Mesa Labs , NJ , USA ) operating at 7 . 5 LPM ( 25–30 PSIG ) , which produced small particles ranging from 0 . 5–3 μm in size targeting the lower respiratory tract and alveolar region . An aerodynamic particle-sizing device ( Aerodynamic Particle Sizer , TSI , MN , USA ) verified particle size with real-time measurements ( 1 . 5 μm Mass Median Aerodynamic Diameter; 2 . 1 Geometric Standard Deviation ) . SKC biosamplers ( SKC Inc . , PA , USA ) containing Dulbecco’s Modified Eagle Medium ( Lonza , MD , USA ) , bovine serum albumin ( Sigma-Aldrich , MO , USA ) , and antifoam SE-15 ( Sigma-Aldrich , MO , USA ) operated at a continuous flow rate of 13 . 0 LPM and collected aerosol challenge material to determine the aerosol concentration within the exposure chamber . An air wash period of 5 minutes between each challenge allowed the particles within the exposure chamber to decay [26] . A presented dose was calculated using the simplified formula D = R x Cexp x Texp , where D is the presented or estimated inhaled dose ( PFU ) , R is the respiratory minute volume ( L/min ) , Cexp is the aerosol concentration ( PFU/L ) , and Texp is the duration of the exposure ( min ) . These formulae have been outlined previously [27] . The three animals received an average presented dose of 4 . 03x104 pfu NiV . The dose per animal ranged from 3 . 04x104 pfu to 4 . 06x104 pfu based on back titration of the samples collected in biosamplers . One animal ( #8095 ) had nasal discharge from one nostril during the aerosol exposure that potentially reduced the total dose delivered . Virus stocks and tissue samples were titrated in VeroE6 cells by plaque assay . Test samples were serially diluted 10-fold and inoculated onto multiwell plates containing nearly confluent VeroE6 cells . The virus was allowed to adhere and infect for 1h at 37°C/5% CO2 . Following infection , cells were overlaid with semi-solid 1 . 25% Avicel ( f/c ) ( FMC Biopolymer ) diluted in EMEM . The cells were then incubated for 5 days at 37°C/5% CO2 . Following incubation , the Avicel overlay was removed by aspiration and the cells were fixed and stained with neutral buffered formalin ( NBF ) containing 0 . 4% crystal violet for 30 min at room temperature . The plates were then washed with water and plaques enumerated . Subjects were sedated and an intravenous catheter was placed in the cephalic vein prior to being intubated and taken to the imaging suite where they were immobilized using isoflurane , and positioned on the scanner bed in a supine fashion . All subjects underwent imaging on an Achieva 3 Tesla clinical MR scanner and a Precedence single photon emission computed tomography ( SPECT ) /CT unit ( Philips Healthcare , Cleveland , OH , USA ) . A brief , high resolution CT scan of the AGM torso was acquired in the trans-axial plane during a breath hold . CT parameters used for the acquisition included 140 kVp ( kiloVolts to peak ) , 300 mAs ( milliAmperes * second ) , and a slice thickness of 0 . 8 mm with a 0 . 40 mm increment , a matrix size of 512 x 512 . Images were reconstructed to a 160 mm field of view resulting in a pixel size of 0 . 3 mm x 0 . 3 mm . To quantify CT data , lung regions were extracted in three steps: first , lung delineation was applied using a previously tested method [28]; then , each baseline lung mask was mapped to post-infection images via registration for refinement; and finally to further ensure the accuracy , the resulting lung segmentations were manually examined and adjusted . Within each segmented lung region , volumetric quantifications were then calculated based on Hounsfield unit thresholds determined from a histogram based approach [29] . Brain MR images were obtained using a pediatric head/neck coil ( head element selection only ) . During each imaging session , a series of sequences were obtained to examine the brain for indications of inflammation or lesions due to Nipah virus infection . These included a magnetization-prepared rapid gradient echo ( MPRAGE ) and a fluid attenuated inversion recovery ( FLAIR ) in addition to T2- weighted and T1-weighted fast field echo ( FFE ) sequences . Using a modification of an approach developed by Moonen et al . , [30] , alterations in vasculature were monitored using the principle of echo shifting ( PRES ) technique to produce a heavily weighted T2* image in order to examine changes in susceptibility contrast . The FLAIR and T1-weighted FFE were performed before and after injection with Magnevist ( 0 . 1 ml/kg ) contrast agent to determine if there was disruption of the blood brain barrier . A pre-charged line with Magnevist was attached to the IV catheter and used for manual injection followed by a saline bolus to flush the system . Serum chemistries were run on an Abaxis Piccolo using the General Chemistry 13 standardized analysis panel ( Abaxis ) . Complete blood counts ( CBC ) in whole blood were run with a five-part differential and reticulocytes on a Sysmex XT2000iV ( Sysmex ) . All data were analyzed and graphed using Prism ( GraphPad ) . Complete necropsies were performed on each subject with tissues samples collected from the same region of individual tissues from each animal . Tissues collected for histopathology were fixed with 10% neutral buffered formalin ( NBF ) prior to removal from the biocontainment space . The tissues were embedded in paraffin , sectioned , mounted and stained with hematoxylin & eosin ( H&E ) prior to microscopic assessment . Brain and lung tissue samples collected for immune cell analysis ( see below ) were collected from the cerebral cortex and left caudal lobe , respectively . PBMCs were isolated from whole blood using gradient purification within 4 hours of collection . Briefly , whole blood was overlain on Histopaque-1077 and then centrifuged ( 20 min , 800xg ) with low brake . The cells at the interphase were collected and suspended in Hank’s Buffered Salt Solution ( HBSS ) +2% FBS . The cells were pelleted ( 10 min , 250xg ) and re-suspended in HBSS+2% FBS prior to counting . Following isolation , PBMCs were aliquoted to flow tubes at 1x106 cells per tube and then were stained with fluorophore-conjugated antibodies using two individual panels , one focused on T cells and the second on “other” cell populations with unstained controls in parallel . The T cell and “other” cell panels are described in S1 Fig . Cells in the T cell panel were re-suspended in PBS+2% FBS and incubated at 37°C for 1 hour prior to staining . All cells were then pelleted ( 5 min , 250-300xg ) prior to adding the antibody cocktail . Cells were mixed and incubated for 20 min at room temperature in the dark . The cells were washed once with PBS+2% FBS and pelleted ( 5 min , 300xg ) and the supernatant removed . The cells were fixed by addition of BD Cytofix/Cytoperm ( BD Biosciences ) and incubating cells in the dark for 30 min at room temperature . The cells were pelleted ( 1 min , 500xg ) and re-suspended in Perm Wash ( BD Biosciences ) twice to wash the cells . The cells were re-suspended in Perm Wash prior to acquisition . Data were acquired on an LSR Fortessa ( BD Biosciences ) housed within the BSL-4 facility . All samples were compensated using the appropriate IgG1k or ArC reactive beads . Cells from lung and brain collected at necropsy were isolated by collagenase digestion . Tissues were minced in a solution of collagenase D ( 100U/mL ) and DNase I ( 100U/mL ) and incubated at 37°C for 30 min . The digestion media was strained through a 100 μm cell strainer and collected . Residual tissue in the strainer was dissociated using the flat end of a syringe plunger and collected in the digestion media . Cells were pelleted ( 10 min , 280xg ) and the supernatant removed . Lung cells were re-suspended in ACK Lyse ( Quality Biologics ) and incubated for 5 min at room temperature to lyse residual red blood cells . The cells were then pelleted ( 10 min , 280xg ) and the supernatant discarded . Brain cell pellets were re-suspended in Percoll stock solution ( 1 . 13 g/L ) . The brain cell suspension was then underlain with Percoll working stock solution ( 1 . 008 g/L ) . Tubes were centrifuged ( 20 min , 1200xg ) with minimal brake . The interface was collected , the cells pelleted ( 4 min , 1000xg ) and the supernatant discarded . Cells were re-suspended in PBS and counted . The cells were then stained as described above with fluorophore-conjugated antibodies using two separate panels , one primarily focused on T and NK cells and the second on myeloid cells . The myeloid and TNK panels are described in S1 Fig . Data was collected on an LSR Fortessa ( BD Biosciences ) and analyzed with FlowJo analysis software . Gating strategies for these analyses are provided in S2–S5 Figs . Cytokines from serum or supernatants from clarified tissue homogenates were tested using a standard 23-plex NHP cytokine assay panel ( Millipore ) and were stained following the manufacturer’s instructions . All samples were tested in duplicate wells . The panel was run on a FlexMap analysis system running xPonent software . Data was analyzed and graphed using Microsoft Excel . PCR analysis was completed using an in-house assay that targets only the viral genomic RNA and not replication intermediates by using primers and probe that bind only in the intergenic region between the viral fusion and glycoprotein genes . The forward primer used for this assay is 5’-CCGTGAATATGTAATTGATAATTTCCC-3’ ( Integrated DNA Technologies ( IDT ) , Coralville , IA ) and the reverse primer 5’-GCTTAGAAAGATACAGTTAAGTATCCAATGA-3’ ( IDT ) . The probe is FAM-5’-CTTAGGACCCAGGTCCATAA-3’ Applied Biosystems , Inc . ( ABI ) , Life Technologies , Grand Island NY ) . The assays were run on Trizol extracted RNA , isolated following manufacturer’s instructions , using either an ABI7500 or a Light Cycler real-time PCR instrument . Statistical analysis was not completed in this study . The small group sizes and out-bred nature of the animals used in this study limited the value of any statistical analyses . Where indicated , group means are presented and individual animals are identified to correlate specific findings with individual animals .
Animals infected by either the IT or small particle aerosol route developed apparently similar diseases with the primary clinical manifestations including lethargy , cough , difficulty breathing and decreased fluid and food consumption . There were no overt clinical signs of neurological disease or hemorrhage and no indication of a “bloody froth” from the mouth as has been previously reported in this model [23] . Disease onset was clearly evident beginning on day 6 post infection ( Fig 1A ) . The survival time for both IT and aerosol challenge groups was the same ( ~ 8 days ) ( Fig 1B ) , with the only animal surviving longer potentially having a reduced infection dose during aerosol exposure due to the presence of a nasal discharge from one nostril . There was no significant weight loss in any of the animals and only three animals had a body temperature that was moderately elevated over the course of disease ( Fig 1C and 1D ) . Changes in blood chemistry were largely unremarkable while all animals developed lymphopenia , neutrophilia and monocytosis ( Fig 2A–2C ) . In addition , five of six animals had a marked decrease in platelet counts at the terminal phase of disease ( Fig 2D ) . All animals had evidence of viral RNA in whole blood and plasma by day 6–8 post infection , except for one animal that had evidence of viral RNA in the plasma only at day 10 ( Fig 3A and 3B ) . There was little evidence of viable NiV in either the plasma or whole blood as the virus was undetectable by plaque assay . Previous studies with NiV animal models have only reported viral RNA levels [20 , 21 , 23 , 31–35] so viable NiV may be largely cell associated in the blood . Viral RNA was found in all tissues evaluated with genome copies per mg tissue generally consistent between animals ( Fig 3C ) . The presence of viable virus , as determined by plaque assay , was not as broadly distributed and varied considerably between animals ( Fig 3D ) . Only two animals had evidence of viable virus in the brain despite all animals having viral RNA in the brain at euthanasia . Animals evaluated during these studies were not perfused prior to tissue collection , an approach that may impact the determination of tissue associated virus or viral RNA versus that found in the blood . Previous studies developing the AGM model for NiV infection utilized IT inoculation to mimic aspirated virus . To understand the potential impact of inhaled virus , we utilized small particle ( ~1 . 5 μm ) aerosol inoculation in comparison with IT inoculation to determine if there were significant differences in the disease process between these two exposure routes . The use of small particle aerosol for infection was intended to seed the virus deep within the lungs rather than in the bronchial tree as occurs in IT inoculation . In both IT and aerosol exposure groups , CT images indicated minimal changes in the lungs through 4 days after inoculation ( Table 1 ) . As disease progressed in animals infected by the IT route , images showed a vascular pattern of infiltration extending to adjacent alveoli , manifested as radiating ground glass opacities with interstitial thickening progressing by day 8 to diffuse lobar consolidation that tended to extend from cranial to caudal in severity ( Fig 4A ) . In animals infected by small particle aerosol , diffuse progressive thickening of airway walls and adjacent interstitium was typically observable within 6 days of infection , ultimately extending into the alveoli , uniformly throughout the lungs ( Fig 4A ) . Where IT inoculation tended to induce some amount of consolidation , typically in caudal lobes , infection by small particle aerosol appeared to cause disseminated vascular and perivascular congestion changes throughout all lobes ( Table 1 ) . Quantification of changes in lung volume over the course of disease found that all of the IT inoculated animals had a loss of at least 20% of their lung volume , based on an increase in hyperdense regions quantified in CT images . One animal in the IT inoculation group had a loss of more than 40% of its lung volume ( Fig 4B ) . Only one animal in the aerosol exposure group had a marked decrease in lung volume with a loss of over 25% . Unlike previously described observations in MR images of acutely ill NiV infected patients [11 , 16 , 17 , 19] , neither the IT nor aerosol infection model for NiV induced abnormal signal intensity lesions or changes suggestive of encephalitis within the brain parenchyma . MPRAGE , FLAIR , T2-weighted images and T1-weighted images before and after contrast injection did not exhibit structural changes or contrast enhancement to suggest disruption of the blood brain barrier over the course of the disease . There was also no clear evidence of meningeal enhancement . However , in 2 of the 6 AGMs studied , susceptibility weighted images showed increased prominence of the deep veins ( e . g . internal cerebral , thalamostriate and basal veins ) and superficial cortical venous structures as early as 2 days after infection , probably reflecting venous dilation and stasis . However , there were no intraparenchymal hemorrhagic foci ( Fig 5 ) . One of these two animals , #8206 , also had evidence of viable virus in the brain ( Fig 3B ) . Due to the rapid nature of disease progression in these aerosol and IT exposure models , it is suspected that pulmonary disease progresses too rapidly to allow for brain pathology , such as encephalitis , meningitis or vasculitic ischemic/hemorrhagic changes , to be observed . Gross pathologic assessment was in agreement with CT images , indicating severity was often greater in the caudodorsal lung lobes of both exposure models ( Table 2 ) . Extensive lung congestion , hyperemia and multifocal areas of consolidation were more consistently observed with the IT route of infection , while the aerosol model typically exhibited mildly congested areas with multifocal areas of hemorrhage ( Fig 6 ) . In addition , tracheal mucosa was typically found to be congested , edematous and often hemorrhagic and there was evidence of edema in the mediastinum and pericardial connective tissue in all of the animals . In five of six animals the spleen was noted as turgid and friable while minimal congestion was identified in peripheral lymph nodes ( Table 2 ) . Hemorrhage was also noted in the mucosa of the urinary bladder , as has been noted previously [23] , and in mucosal surfaces of the intestine in two of the animals exposed by the IT route . All of the animals exposed by the IT route had a significant build-up of gas in the intestines while this was not noted in the aerosol-exposed animals . Similar to previously reported work , microscopic evaluation of tissue sections identified extensive vasculitis in nearly all tissues examined from each of the animals . Prominent in all of the animals was the presence of syncytia in the spleen , necrohemorrhagic lymphadenitis in the tracheobronchial lymph node , necrohemorrhagic tracheitis and hepatocellular degeneration in the liver . Alterations in the lung observed in CT could be attributed to vasculitis , hemorrhage , edema and bronchointerstitial pneumonia in a number of the animals . None of the animals had evidence of encephalitis , meningitis , ischemia or hemorrhage in the brain . In general , the pathology data gathered from this study suggests a “hemorrhagic” type disease with broadly distributed vasculitis likely the predominant contributor to vascular leakage . In order to understand the immune response to acute NiV infection , plasma cytokine and peripheral blood cell populations were measured over the course of disease . There were minimal changes in most of the cytokines and chemokines measured , however several cytokines were elevated and were indicative of a systemic proinflammatory response with stimulation of cell-mediated immunity . There were no clear differences in the response between the aerosol and IT exposure groups with any of the measured serum cytokine levels . The pro-inflammatory cytokine IL-6 was moderately elevated in several animals in both the aerosol and IT infection groups , but was particularly enhanced in the one animal that had nasal discharge during the aerosol exposure ( #8095 ) ( Fig 7 ) . The anti-inflammatory cytokines IL-1RA and IL-10 were also elevated in most of the animals , again with animal #8095 having significantly elevated response with both cytokines ( Fig 7 ) . Animal #8204 also had very high IL-1RA and IL10 levels . Cytokines associated with T cell stimulation became elevated in most of the infected animals as disease progressed , particularly in the late stages of disease ( Fig 7 ) . These cytokines included IL-2 and IL-15 , which serve biologically related functions associated with development of Th1 immunity . The levels of IFN-γ were also increased in all animals late in the disease process , again supporting the development of a Th1 biased cell-mediated response ( Fig 7 ) . Interestingly , animal #8095 was again one of the higher responders , perhaps suggesting an underlying infection that was not apparent prior to study initiation . Evaluation of peripheral T cell populations found that there was not a significant expansion of either CD4+ or CD8+ T cell populations as might be expected based on cytokine release data ( Fig 8A and 8B ) . However , when CD8+ T cell populations were evaluated for expression of the activation marker HLA-DR , there was a moderate increase in the population of activated CD8+ T cells in two of the aerosol exposed animals , but not in the remaining four animals ( Fig 8D ) . These data , in addition to systemic increases in IL-2 production , suggest expansion of CD8+ effector T cells . These data demonstrate that NiV infection stimulates a Th1-biased cell-mediated response that was detectable beginning about 6 days post-infection . Development of a marked cell-mediated response to NiV infection in this model may take more than the 8–10 days that was the duration of the disease course in this study . Tissues were collected at necropsy and analyzed for the presence of cytokines , chemokines and for populations of immune cells that could indicate changes in immune status or infiltration of immunoreactive cells . Cytokine and chemokine expression was largely consistent between the IT and aerosol inoculated groups . One animal , #8095 , had elevated IL-17A and MIP-1α in the lungs relative to other animals in either the IT or aerosol groups ( S6 and S7 Figs ) . Animal #8095 also had elevated IL-4 levels in the brain compared to other animals in the aerosol group , but that were comparable to animals in the IT inoculation group ( S6 and S7 Figs ) . As previously indicated , this animal survived until 10 dpi , which may have allowed the immune response to progress . In order to determine if the presence of specific immunoreactive cells could be correlated with disease severity we collected sections of lungs and brain at necropsy from two IT and three aerosol inoculated animals and measured the populations of B cells , T cells , granulocytes , monocytes , macrophages , myeloid dendritic cells ( mDC ) and natural killer ( NK ) cells by flow cytometry . Microglia populations were also evaluated in the brain . One of the animals ( #8213 ) in the IT inoculated group succumbed to infection precluding timely collection of viable tissues . As there is little literature available regarding the specific markers on immune cells in AGMs , data from human and rhesus macaque populations were utilized . Markers for cell populations in the lung and , particularly , the brain were based on largely on markers expected for circulating cell populations . In the lungs of IT inoculated animals hemorrhagic lesions were readily apparent throughout the tissue ( Fig 6 ) . In aerosol inoculated animals , lesions were not visibly apparent so tissues indicated as “lesion” or “non-lesion” should be considered largely equivocal . Evaluation of lymphocyte populations suggested that B cell populations were similar between the IT and aerosol infected groups ( Fig 9 ) . A larger percentage of the T cells in the aerosol group were CD4+ T cells compared to the IT group , whereas the IT inoculated group had a larger percentage of CD8+ T cells ( Fig 9 ) . Granulocyte populations appeared to be equivocal between the exposure groups . However , in the IT exposure group the granulocyte population had a higher proportion of activated ( HLA-DR+ ) granulocytes ( Fig 9 ) . Populations of monocytes , macrophages ( Fig 9 ) and myeloid dendritic cells were similar in both exposure groups . In this study we also found that there were a higher proportion of NK cells in the IT exposure group relative to aerosol exposure and that three distinct populations of NK cells could be differentiated in these animals while using CD16 and NKG2 as markers for NK cells ( Fig 9 ) . Animal # 8095 , which survived longer than other animals , had elevated numbers of proliferating ( Ki67+ ) CD4+ T cells and NK cells in their lungs relative to other animals . As indicated previously , this animal also had elevated IL-17A and MIP-1α in the lungs relative to other animals ( S7 Fig ) . Lesions were not grossly apparent in the brains of any of the NiV infected AGM . Two samples were collected from the cerebral cortex of all animals , except #8213 , for analysis by flow cytometry . There appeared to be an elevation in the total number of CD3+ T cells in the aerosol group compared to the IT inoculated group ( Fig 10B ) with a correlating proportion of 2–6% Ki67+ indicating cell expansion ( Fig 10C ) . Of the T cells found in the brain , around 90% were CD8+ T cells ( Fig 10F ) . There was a slightly increased number of NK cells and macrophages in the brain of aerosol inoculated animals ( Fig 10 ) , but all other cell types evaluated were equivalent between the two groups . Animal #8095 ( indicated by a half-filled square ) had elevated T cells in the brain relative to other animals ( Fig 10B ) . As indicated previously , this animal also had slightly elevated IL-4 in the brain relative to other aerosol inoculated animals ( S7 Fig ) . IL-4 stimulates B cell differentiation and also T cell proliferation .
In the study described here , the focus was on expanding our understanding of the AGM model for NiV disease by evaluating the impact of small particle aerosol exposure as compared to the previously established IT inoculation model . Disease progression was monitored , in part , by CT and MR imaging to visualize pulmonary and neurological changes without the need for serial sacrifice . In order to gain a broad understanding of the immune response to NiV infection in the AGM model , quantification of cytokine and immune cell populations were measured in blood over the course of the infection and in tissue and blood collected at necropsy . These studies found that the outcome of NiV infection by the IT or aerosol route was the same , but that disease development was markedly different between the two inoculation routes . In both infection models , severe ARD was the prominent manifestation of disease with no overt signs of neurological involvement , likely due to the rapidly progressing respiratory disease . The use of CT imaging allowed for near real time evaluation of disease progression and for quantification of changes in lung volume over the course of disease . Animals infected by the IT route tended to have a more significant loss of lung volume , based on measurement of hyperdensity within the CT images . The IT inoculated animals also had large focal hyperdensities with edema and consolidation whereas animals exposed by aerosol had more diffuse changes on the CT . While there was evidence of edema in the lungs suggesting a significant local inflammatory immune response , the primary impact of infection in both models was development of systemic vasculitis due to NiV infection and development of virus-induced syncytia , as has been described previously in both the AGM and hamster models for NiV infection [23 , 36] . The development of neurological disease has been previously described in humans following NiV infection , particularly during the initial outbreak in Malaysia [7 , 15 , 37] . Examination of human cases using MRI provided examples of discrete focal lesion within the brain or diffuse cortical involvement [16 , 19] . In the studies reported here , there was no overt indication of neurological disease in any of the infected animals . Minimal changes involving venous structures in the brain in two of the animals , as seen by MR imaging , suggested vasodilation and venous stasis , possibly a reflection of systemic vascular compromise . In order to visualize neurological disease in the AGM model , we believe a more protracted disease course is required and that neither the small-particle aerosol nor IT inoculation routes will consistently allow for a longer disease course due to severe pulmonary involvement . Evaluation of the peripheral immune response through evaluation of changes of cytokines and immune cell populations over the course of infection suggested the initiation of cell-mediated immunity through production of Th1-associated cytokines . However , there were no consistent changes in peripheral CD4+ T cell populations over the course of disease and limited activation of CD8+ T cells in only two animals . The changes in T cell populations correlated somewhat with the Th1 cytokine response in only one animal ( #8095 ) ; unlike other animals in the study , this animal survived until day 10 and allowed the cell-mediated response to progress . The short eight-day course of disease limited potential development of an adaptive immune response , as would be expected . NiV has been shown to inhibit components of the immune response that would lead to more robust development of adaptive immunity , including interferon signaling and components of the proinflammatory response [38–43] . To date , there is little known about the development of adaptive immunity in vivo following NiV infection , clearly demonstrating that further fundamental studies are needed . An aspect of this study that was particularly challenging was determination of immune cell populations in tissues . Specific markers for atypical immune cells ( e . g . NK cells , microglia ) are poorly defined in the AGM model making the development of gating strategies in complex flow panels an arduous task . Using the strategy that we developed , we found there were few notable differences between animals inoculated by aerosol or by the IT route , and none could be afforded statistical support given the small number of animals included in this study and inter-animal variation . However , some differences were notable . In particular , the populations of CD4+ and CD8+ T cells in the lung and the blood were comparable . CD4+ T cell populations were high in both the blood and the lung in the IT inoculation group while CD8+ T cells were high in sampled tissues in the aerosol inoculation group . Furthermore , when looking at activation/proliferation markers , activated CD8+ T cells were elevated in the blood and lung of the same two animals in the aerosol group . While there is little statistical strength to these observations , the data suggest that CD8+ T cells are being activated in the periphery and that they are extravasating into the infected lung tissue . Analysis of granulocyte and NK cells in the lungs suggested increased activation of granulocytes and total populations of NK cells in IT inoculated animals . While not defined in these studies , the granulocytes are most likely neutrophils responding to infected cells and contributing to the congestion seen in the lungs of these animals . Similarly , NK cells may be in higher proportions in the lungs of IT inoculated animals as a component of the large local inflammatory response . The decreased number of granulocytes and NK cells in the lungs of aerosol inoculated animals may reflect the broadly disseminated nature of the lung infection in these animals . In neither the IT nor the small particle aerosol inoculation model is the neurological model of human disease recapitulated . Anecdotal evidence and published reports have suggested that ARDS is the typical disease seen following NiV infection of humans in Bangladesh , with neurological disease common during the outbreak in Malaysia [1] . Studies in the hamster model have provided unequivocal results with one study suggesting that NiV isolated from Malaysia causes an accelerated infection [34] while another study indicated there was no difference between the two viruses [31] . Recently , Mire et al suggested that a NiV isolated in Bangladesh is more virulent than that isolated in Malaysia [22] . However , the limited size of the Mire et al study makes interpretation of the finding difficult , particularly given the 100% lethality described here at a much lower challenge dose of the Malaysia isolate . It is likely then , that the route of exposure , environmental conditions , medical support or underlying differences in the human populations may make significant contributions to disease development and outcome . NiV disease has historically been considered “encephalitis” based on indications of neurological disease in Malaysian patients . Given the findings here and in previous work , it appears more appropriate to refer to NiV infection as a “hemorrhagic” type disease in the AGM model given the extent of vasculitis , the hemorrhage seen in the terminal phase of disease , the lack of overt neurological signs and minimal evidence of neurological involvement on MR . In this model , NiV does not appear to be a true neurotropic virus where the brain is the primary target for viral infection , but rather , likely causes neurological manifestations as a result of endothelial barrier breakdown and vascular leakage that allows the virus to access the brain . Extending the course of disease will be critical for understanding the interaction between NiV and the primate brain and for building a model that truly recapitulates the human condition . Further studies to develop a neurological model for NiV infection and to evaluate potential routes of exposure in this model are required before appropriate approaches to therapeutic intervention can be adequately addressed . In summary , the use of small particle aerosol results in an acute diffuse pulmonary disease of the lower lung that results in a disease course similar to the previously described IT inoculation model . In this study , there was not significant evidence of neurological disease either through cage-side observations , MRI or pathology in either aerosol or IT inoculation groups . The development of overt neurological disease likely requires a more protracted disease course , as would more robust development of a systemic immune response against NiV infection . These studies provide the first evaluation of the host immune response to NiV infection in the AGM model and also demonstrate the value of advanced medical imaging in evaluating and quantifying disease progression without the need for serial sacrifice studies . | Nipah virus ( NiV ) was identified in the late 1990s as the causative agent of severe respiratory and neurological disease in Malaysia and Bangladesh . The virus is transmitted by inhalation , contact or consumption of contaminated material . In this study , our objective was to characterize NiV-induced disease progression in the African Green Monkey model utilizing clinical imaging capabilities . In this work , we also provide the first temporal evaluation of the immune response to infection following NiV infection and the first characterization of disease following aerosol exposure . Here , we found that NiV infection following intratracheal and aerosol exposure lead to a severe respiratory disease and rapid disease course with no overt clinical evidence of neurological disease . Despite the rapid course of disease , changes in the cytokine response and peripheral immune cell populations suggest development of a cell-mediated immune response in the latter stage of disease . While the current model for evaluating NiV infection is useful for testing of medical countermeasures , further work is required to understand how this model can represent human disease . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"blood",
"cells",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"respiratory",
"infections",
"cytokines",
"pathology",
"and",
"laboratory",
"medicine",
"diagnostic",
"radiology",
"immunology",
"pulmonology",
"magnetic",
"resonance",
"imaging",
"developmental",
"biology",
"signs",
"and",
"symptoms",
"materials",
"science",
"molecular",
"development",
"materials",
"by",
"structure",
"research",
"and",
"analysis",
"methods",
"white",
"blood",
"cells",
"imaging",
"techniques",
"animal",
"cells",
"aerosols",
"t",
"cells",
"immune",
"response",
"immune",
"system",
"radiology",
"and",
"imaging",
"diagnostic",
"medicine",
"pulmonary",
"imaging",
"cell",
"biology",
"physiology",
"hemorrhage",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"vascular",
"medicine",
"mixtures"
] | 2017 | Loss in lung volume and changes in the immune response demonstrate disease progression in African green monkeys infected by small-particle aerosol and intratracheal exposure to Nipah virus |
Active matter systems , and in particular the cell cytoskeleton , exhibit complex mechanochemical dynamics that are still not well understood . While prior computational models of cytoskeletal dynamics have lead to many conceptual insights , an important niche still needs to be filled with a high-resolution structural modeling framework , which includes a minimally-complete set of cytoskeletal chemistries , stochastically treats reaction and diffusion processes in three spatial dimensions , accurately and efficiently describes mechanical deformations of the filamentous network under stresses generated by molecular motors , and deeply couples mechanics and chemistry at high spatial resolution . To address this need , we propose a novel reactive coarse-grained force field , as well as a publicly available software package , named the Mechanochemical Dynamics of Active Networks ( MEDYAN ) , for simulating active network evolution and dynamics ( available at www . medyan . org ) . This model can be used to study the non-linear , far from equilibrium processes in active matter systems , in particular , comprised of interacting semi-flexible polymers embedded in a solution with complex reaction-diffusion processes . In this work , we applied MEDYAN to investigate a contractile actomyosin network consisting of actin filaments , alpha-actinin cross-linking proteins , and non-muscle myosin IIA mini-filaments . We found that these systems undergo a switch-like transition in simulations from a random network to ordered , bundled structures when cross-linker concentration is increased above a threshold value , inducing contraction driven by myosin II mini-filaments . Our simulations also show how myosin II mini-filaments , in tandem with cross-linkers , can produce a range of actin filament polarity distributions and alignment , which is crucially dependent on the rate of actin filament turnover and the actin filament’s resulting super-diffusive behavior in the actomyosin-cross-linker system . We discuss the biological implications of these findings for the arc formation in lamellipodium-to-lamellum architectural remodeling . Lastly , our simulations produce force-dependent accumulation of myosin II , which is thought to be responsible for their mechanosensation ability , also spontaneously generating myosin II concentration gradients in the solution phase of the simulation volume .
The study of active matter , a far from equilibrium , self-driven system that consumes energy from the environment to generate directed motion , is at the center of many interdisciplinary fields such as physical chemistry , biophysics , and non-linear physics [1] . Active matter systems demonstrate distinctly complex dynamics and self-organization , by way of intricate energy storage and transduction , that is not observed in ordinary systems and is far from being fully understood . For example , as reviewed by Ramaswany [2] , fundamentally different active matter systems , including flocks of birds and bacteria , exhibit interesting responses and order that are surprisingly similar across their varying length scales . Similarly , non-biological active matter systems , including Pt-silica particles in hydrogen peroxide solutions , become motorized and can hydrodynamically interact , displaying complex dynamics and self assembly [3 , 4] . A particularly interesting example of an active matter system is the cell cytoskeleton . Being a highly dynamic polymeric network of actin filaments , microtubules , and intermediate filaments that are controlled by a diverse collection of regulatory proteins , the cytoskeleton is essential for many large-scale biological processes , including embryonic development , wound healing , and immune response [5] . The dynamic nature of the cytoskeleton allows the cell to respond to both chemical and mechanical cues , providing complex feedback mechanisms for growth and remodeling . Using molecular motors , the cytoskeleton can harness energy from ATP hydrolysis , converting it into mechanical work that can drive the system into configurations not possible with thermal motion alone . Along with the inherent nature of cytoskeletal filaments , which can assemble or disassemble rapidly due to chemical species gradients or regulatory signaling cascades , this energy consumption allows the cytoskeleton to dynamically respond to a range of extracellular stimuli on varying timescales . Much progress has been made in recent years in modeling active networks , and in particular the cell cytoskeleton . Chemical models ranging from deterministic , ordinary differential equation as well as partial differential equation approaches describing reaction-diffusion processes [6–9] , to Monte Carlo approaches that rely on spatially resolved stochastic simulation [10–13] have been used to reproduce the spatial concentration distributions and chemical dynamics of cytoskeletal networks in in vivo and in vitro . Separately , multi-scale , coarse-grained mechanical models of the cytoskeleton with limited chemical detail have been created to study its viscoelastic properties [14–16] , growth and remodeling [17–19] , as well as interactions with a cell membrane and surfaces [20–22] . Recently , models have been developed to investigate the active nature of cytoskeletal networks , and can reproduce many of the dynamic mechanisms involved in actomyosin contractility [23–28] . Some hybrid models have begun to incorporate multiple aspects of cytoskeletal chemistry and molecular transport with network mechanics [29–32] , providing insight to the importance of this coupling in modeling and simulation . We believe that a desirable platform for mechanochemical simulations of cytoskeletal dynamics at high structural resolution should contain the following capabilities: A ) Spatially-resolved stochastic chemistry within the cytosol , the filamentous network , and between them , which would allow the establishment of global and local chemical gradients and heterogeneities , taking the fundamentally stochastic nature of chemical reactions into account . B ) A sufficiently rich set of filament chemical reactions that includes ( de ) polymerization processes , ( de ) branching , formin-based nucleation and capping , monomer aging via ATP or GTP hydrolysis , severing , cross-linker and molecular motor ( un ) binding , and molecular motor walking , which would enable the simulation of minimally complete cytoskeletal chemistries . C ) An accurate , yet computationally efficient mechanical force field , which would allow computing the deformations of a connected filamentous network that is being continuously deformed by force-generating proteins , such as myosins , as well as other chemical reaction events . D ) A deep coupling between chemistry and mechanics , where , for example , the chemical heterogeneity of individual monomers in a filament due to aging leads to the corresponding spatial modulation of bending stiffness along the chain , hence , correctly localizing buckling transitions . In S1 Table , we have compiled a salient selection of current agent-based approaches for modeling cytoskeletal dynamics . To the best of our knowledge , most of the individual capabilities listed above ( A-D ) , needed to enable next generation of structural modeling , are absent in the currently existing or prior methods [14 , 16 , 17 , 24 , 25 , 27 , 30–40] . Furthermore , it would be most useful to the community if the source codes for these modeling frameworks were publicly available , which is again not the case for most , but not all [30 , 32] , modeling frameworks listed in S1 Table . In yet another challenge , apart from the computational complexity in combining these cytoskeletal aspects , there is a need to achieve computational efficiency of scaling up simulations to micron length scales , where most interesting cytoskeletal phenomena take place , while still retaining locally high structural resolution at nanometer scale . With the above considerations , we introduce the Mechanochemical Dynamics of Active Networks ( MEDYAN ) model which contains all of the aforementioned capabilities . While explicitly accounting for the complex chemical dynamics of polymers and the molecular transport of chemical species in an active network using a stochastic reaction-diffusion scheme , based on a spatially resolved Gillespie algorithm , a new coarse-grained representation and set of force fields for semi-flexible polymers has been developed , including complementary force fields for polymer branching molecules , cross-linking molecules , and molecular motors . The model also allows for mechanochemical coupling of any of these molecules , producing a full treatment of active network mechanochemistry where mechanical stresses influence chemical rate constants , allowing the modeling of Brownian ratchets , slip-bonds , catch-bonds , or more complex biphasic mechanochemical feedbacks . With this model , the complex and non-linear mechanochemical properties of active networks can be studied in full detail with efficiency , and can give insight to many active networks , including the cell cytoskeleton and other biological and artificial polymer ensembles . Although the stochastic reaction-diffusion scheme of MEDYAN follows prior efforts from out laboratory [29 , 41–45] , in this work we have added significant new capabilities , including several new chemical reactions and their related mechanical elements , as well as a greatly accelerated stochastic reaction-diffusion algorithm for sparse reaction networks . But , perhaps a larger problem in cytoskeletal modeling has been the rigorous yet computationally efficient modeling of polymer mechanics in network at micron scales or above . This fundamental problem goes beyond cytoskeletal simulations and concerns many other semi-rigid polymeric melts or assembles , where there is a large discrepancy between the polymer’s persistent length and its diameter . A coarse-grained approach , based on representing polymer segments as cylinders which contain a number of monomeric units , is a natural way to address this problem . However , the difficulty is in enforcing the non-crossing constrain among the chains , where prior steric potentials were conceptually simple , but are non-analytic [16] , or analytic but computationally highly inefficient in the case of large aspect ratio of polymer chain segments [46] , raising serious concerns in many practical situations . In this work , we introduce a rigorous , fully analytic and computationally efficient excluded volume potential that solves this problem , enabling efficient simulations of melts of networks comprised of semi-flexible polymer chains with large aspect ratios at micron scales . In this paper , we first introduce both the chemical reaction-diffusion and mechanical models used in MEDYAN , while also highlighting the coupling of both parts and how they work together to provide a full mechanochemical treatment of an active network . Then , to explore the capabilities of this model and its publicly available software implementation ( available at www . medyan . org ) , we investigate a contractile actomyosin network containing actin filaments , α-actinin cross-linking proteins , and non-muscle myosin IIA mini-filaments , demonstrating the propensity for rich dynamical remodeling of these networks , as their mechanochemistry is tuned by varying myosin II and cross-linker concentrations . Our simulations indicate a clear threshold of cross-linker concentration which induces contractile behavior of actin filaments by myosin II mini-filaments in a smaller 1 × 1 × 1 μm3 actomyosin system , as well as other distinct network morphology changes . In particular , our analyses clearly indicate that in all simulated systems actin filaments tend not only geometrically align , but , surprisingly , this alignment is unipolar ( emerging from an initially random , disordered network ) . We further found that both this polarity alignment and contractile behavior are tightly regulated by the extent of actin filament turnover , producing biphasic super-diffusive motions of individual actin fibers driven by myosin II mini-filament force generating activity . We also discuss myosin II mini-filament force-dependent accumulation in these systems , as all simulated concentration configurations and system sizes produce this accumulation in areas of high network stress , spontaneously generating concentration gradients in the solution phase . In a larger 3 × 3 × 3 μm3 actomyosin system , we observed a distinct alignment , contraction and polarity sorting , reminiscent of arc formation in the rear of a lamellipodium .
The cell cytoskeleton , as well as other active networks , takes advantage of distinct chemical phenomena which allows the network to grow and remodel based on extracellular signaling and other chemical cues [47 , 48] . In order to model the complex chemical interactions that occur in these dynamic networks at a microscopic resolution , the MEDYAN model uses a stochastic reaction-diffusion scheme based on a three dimensional , spatially resolved Gillespie algorithm [49 , 50] as in previous works [29 , 41–45] . With simulation space divided into compartments , with compartment size chosen based on the so-called “Kuramoto length” of the reaction-diffusion system of interest [51 , 52] ( see Section B of S2 Text for an example determination of a Kuramoto length ) , diffusion and other transport events of chemical species , which could include active transport via molecular motors or convective transport such as retrograde flow , are modeled as stochastic jumps between compartments that can be directionally biased or unbiased in order to model various transport mechanisms . This allows for a discrete and spatially resolved treatment of small copy numbers and non-uniform concentration gradients , which could produce substantial and important fluctuations in chemical dynamics at the nanoscale . In particular , recent works have studied the significant effects of these stochastic fluctuations on filopodial growth [41 , 45] as well as the effects of active transport phenomena and its significance in both lamellipodia and filopodia formation and sustainability [29 , 43 , 44] . In these systems , the concentration of G-actin monomers as well as other cytosolic molecules fluctuates greatly across the spatial domain of the protrusion due to both diffusion and active transport mechanisms , producing non-linear chemical response and signaling . These important effects could not be captured with deterministic approaches , which ignore the cytoskeleton’s biologically inherent stochasticity . In the MEDYAN model , we have developed the stochastic reaction-diffusion scheme further such that one can use varying types of stochastic simulation algorithms in order to optimize a simulation based on the chemical properties of the simulated network . While the original Gillespie algorithm is an efficient and exact alternative to solving a chemical master equation [49 , 50] , with the chemical master equation being nearly impossible to solve for the complexity of systems we are considering , optimized methods have been developed for the original Gillespie direct method to decrease computational complexity for loosely-coupled chemical reaction networks , as reviewed by Cao et al . [53] . In particular , the next reaction method , developed by Gibson and Bruck [54] , makes use of clever data structures to optimize the propensity updating process after each reaction is executed , producing massive speed-ups for sparse reaction-diffusion networks compared to the original algorithm . The MEDYAN model can make use of either of these algorithms depending on the type of chemical system to be simulated . In most cases , the latter is more suitable for simulating most active networks , where the chemical reactions across the system are sparse and spatially localized by compartments . With these algorithm optimizations , the computational complexity for stochastically simulating active network evolution is greatly reduced , allowing the model to surpass timescales accessible with the original Gillespie schemes . The MEDYAN software implementation , which is discussed in Section D of S1 Text , is also designed such that such that new stochastic simulation algorithms can easily be included in the existing reaction-diffusion framework , including the optimized direct method [53] and partial propensity methods [55 , 56] . For a detailed benchmarking of the currently implemented optimizations in systems similar to the ones simulated in the Results section , see Section A of S4 Text . In order to account for the chemical heterogeneity of active network polymers , we represent them in the model as a distinct arrangement of chemical monomers that are overlayed onto the existing reaction-diffusion compartment grid , which allows them to undergo spatially resolved reactions with diffusing chemical species besides typical polymerization and depolymerization events . This can be of importance to network dynamics in the case of actin filaments , where polymerized actin hydrolyzes ATP , giving rise to a substantial change in polymerization kinetics at both ends of the filament [57 , 58] . In conjunction with hydrolysis , the cytoskeletal regulatory protein ADF/Cofilin can sever actin filaments preferentially where ATP has been hydrolyzed [59 , 60] . Together , and along with other chemical interactions in the cytoskeleton , these reactions are responsible for the actin filament turnover process observed in most types of cellular protrusions [61] . With the MEDYAN polymer representation , these important molecular processes can be included in the reaction-diffusion master equation ( RDME ) and simulated in full detail . We have also included detailed cross-linker chemical dynamics to the model . It has been well known that cross-linking molecules are important for producing the observed morphology of the actin cytoskeleton in vivo [62 , 63] , but most existing cytoskeletal models do not include the stochastic binding and unbinding of cross-linkers to actin filaments in the simulation space . In the MEDYAN model , cross-linker binding reactions with neighboring polymers are dynamically added; if two separate polymer binding sites are within a specified range in a given compartment , an unbound cross-linker species in that compartment can bind to them . An unbinding reaction is also associated with that molecule once bound , which can then release it from both polymers . This dynamic addition of reactions allows for computational efficiency as well as an exact , spatially resolved treatment of cross-linking molecules , which can be essential for active network evolution . See the Mechanical model section for a more detailed description of the mechanical interactions of cross-linking molecules . In order to make a simulated network active , we have introduced molecular motors in the model—molecules which utilize energy released due to chemical reactions in the system and transfer it into mechanical work . For example , in cytoskeletal networks , energy from ATP hydrolysis is used by number of protein species to generate forces . In particular , the non-muscle myosin II ( NMII ) motor family plays a significant role in cytoskeletal remodeling and cell motility [64 , 65] , where individual NMII motors assemble into larger bipolar filaments that can reach hundreds of nanometers in length [66] . The MEDYAN model can include bipolar NMII filaments that , in a similar manner to cross-linking molecules , can bind onto two neighboring actin filaments . The slow diffusion of these larger molecules may produce some spatial diffusion error on a compartment grid , and hybrid combinations of Brownian dynamics and stochastic reaction-diffusion models have been introduced in recent years [67] as a way to solve this error , which could be included in the MEDYAN model in the future . But , we believe for grids used in the Results section which are 500 nm in length , this is still a good estimate of true diffusive behavior . When bound , the head ensembles can make stochastic directional steps towards the barbed end of either filament , which generates “sliding” forces in the network , promoting reorganization and contractility . See the Mechanical model section for a more detailed description of the mechanical interactions of NMII filaments . In a MEDYAN simulation , a transport event or polymer-related reaction is chosen to occur by the stochastic simulation algorithm based on its reaction propensity . This process repeats , advancing the chemical reaction-diffusion system in time . Bulk reactions can also be included between diffusing species , allowing for even more complex chemical evolution . See Section A in S1 Text for a more detailed description of the entire set of chemical reactions that can be simulated . With the stochastic reaction-diffusion scheme and polymer representation described , complex active networks can be simulated with explicit and detailed chemical interactions and molecular transport . Fig 1 shows a cartoon depiction of a cytoskeletal network that could be simulated with the MEDYAN model . All molecules can diffuse throughout the simulation space according to their specified diffusion rate and the chosen compartment size . Actin filaments can grow and shrink due to the polymerization and depolymerization of G-actin monomers , as well as the binding and unbinding of capping proteins and formins , and Arp2/3 can nucleate new actin filaments on existing filaments at a 70° angle [68] . Lastly , cross-linking proteins can bind and unbind to actin filaments , and NMII mini-filaments can bind , unbind , and walk along actin filaments . To complement the detailed stochastic reaction-diffusion scheme described above , we have developed a new set of force fields in the MEDYAN model to account for the mechanical properties of an active network . In previous work [29 , 42] , a simple bead-spring model was used to describe actin filament mechanics , where a single filament was regarded as a composition of hard-core beads . These beads represented individual monomers which were then connected by either a harmonic or more complex potential . This method , while being a detailed and robust description , required the calculation of a large number of interactions between neighboring beads during a mechanical equilibration of the system . Considering that a cubic micron of a cytoskeletal network could contain on the order of 106 actin monomers , mechanical equilibration of a system with this simple model would severely limit simulation timescales that could be accessed . In order to overcome these computational limitations , we are introducing in this work a polymer model based on elongated cylindrical monomer segments for simulating semi-flexible polymers with a persistence length , denoted as lp , that is much larger than its diameter σ0 ( i . e . very large aspect ratio , lp > >σ0 ) . Cylinders have been previously introduced in various coarse-grained computational models for the description of systems containing elongated objects , including the modeling of viscoelastic actin networks [16] and hydrodynamics of suspensions [69 , 70] . Here we would like to emphasize that cylinders in the MEDYAN description are not considered as collections of beads , but rather as stiff weightless springs of diameter σ0 , connecting its end points . This fact , as it will be seen later , will help us to build up a rather intuitive mathematical formalism to describe polymer mechanics . Fig 2 represents the scheme of using cylinders as monomer units in a polymer chain . This assumption makes the model applicable for the description of most biopolymers ( in the case of actin filaments , lp/σ0 ≈ 103 ) , and while force-generating molecular motors could significantly change the correlation between two points along the polymer chain , these correlation lengths will still be significantly larger than the distance between two neighboring monomers in previously used bead-spring model . Moreover , the new model can describe flexible molecules as well , as a standard bead-spring model can be considered as a limit with lp → σ0 . For a detailed benchmarking of this coarse-graining scheme in systems similar to the ones simulated in the Results section , see Section B of S4 Text . We now introduce the interaction potentials used in the MEDYAN model . We note that the MEDYAN software implementation can easily be modified to include different types of potentials for the interactions presented below; see Section D of S1 Text for a more detailed discussion on the implemented software’s flexibility for the addition of various interaction potentials . For example , a finitely extensible nonlinear elastic ( FENE ) potential could be easily added to the existing code for less elastic semi-flexible polymers , molecular motors , and cross-linkers [71] . Other forms of polymer excluded volume effects could also be included . We assume that every coarse-grained monomer segment is represented by a cylinder with a finite thickness σ0 and equilibrium length l0 , as shown in Fig 2 . To account for filament bending , we use an angular potential between consecutive cylinders in the polymer chain , written as U i bend = ε bend 1 - cos ( θ i , i + 1 ) , ( 1 ) where θi , i+1 is the angle between two consecutive cylinders i and i + 1 along the polymer chain , and εbend is the bending energy , which can be chosen based on the persistence length of the simulated polymer . Cylinders also can be slightly stretched or compressed along their main axis , while radial deformations within the cylinder are not allowed . To illustrate this fact we draw springs inside of the cylinders in Fig 2 . The stretching energy corresponding to deformations of the ith cylinder can be represented as U i str = 1 2 K str | l → i | - l 0 2 , ( 2 ) where l → i = x → i 2 - x → i 1 is the vector connecting the endpoints of the ith cylinder , and Kstr is the stretching constant . As in the bending potential , this constant , along with the equilibrium length , l0 , can be chosen depending on the elastic modulus of the simulated polymer . In the case of actin filaments , these bending and stretching potentials allow the model to capture non-linear deformations reported by various studies [72–74]; with U i bend accounting for the thermal elasticity of the chain , U i str describes elastic deformations of the chain stretched beyond its entropically driven elastic limit [75] . These deformations are considered to have high energy penalties , which is reflected in high values of Kstr , therefore , can occur only under very large global deformations of the system . There are several common approaches usually used to calculate excluded volume interactions between two aspherical elongated particles , which are cylinders in our case . The most obvious approach is to represent the elongated particles as a collection of spheres; with this representation , interactions are simply calculated as a sum of pairwise hardcore repulsions between the spheres forming each cylinder . While this is a very simple and straightforward method , it defeats all purpose and efficiency of the initial cylindrical coarse-graining . Another widely used approach is to use the Gay-Berne potential to describe excluded volume interactions between interacting cylinders [46 , 76] , which can be used as a part of the LAMMPS [77] package . This potential , however , has limited applicability and lower computational efficiency when lp > > σ0 as in the case of most biopolymers . On top of that , computational complexity of this potential is also increased greatly due to constantly finding the distance of closest approach between the two cylinders , which is a very costly calculation . Finally , another method was used in the model of Kim et al . [16] which calculates cylindrical repulsive interactions using the closest distance between two interacting segments . This force is then transferred to the end points of the segments , based on the lever rule as well as the position of the point of closest approach . Despite the elegance of this method , we found several drawbacks for using this approach in the MEDYAN model: from a computational point of view , algorithms for calculating the point and the distance of closest approach between neighboring cylinders contains costly control flow as mentioned previously , increasing computational complexity for this approach greatly . From a mathematical and physical point of view , a lack of a continuous and analytical function for this closest distance puts limitations on the resulting force calculations , which might lead to oscillations and divergence during mechanical equilibration of the system . In order to overcome these issues , we introduce a novel approach for calculating excluded volume interactions between two cylinders . This approach is conceptually similar to early mentioned devision the cylinders into small point-like subunits and calculating interactions between them . However , instead of an actual representation of the cylinders as a collection of subparticles , we solve this analytically by writing a pair potential between two infinitely small fragments on both cylinders and then integrating this pair potential over the length of both cylinders . The potential of excluded volume interactions between two cylindrical units on neighboring polymers , denoted as i and j , can be given by: U i j vol = ∫ ∫ l i , l j δ U ( | r → i - r → j | ) d l i d l j . ( 3 ) Here , δ U ( | r → i - r → j | ) is the above mentioned pair potential between two points located on the two interacting cylinders i and j as shown in Fig 3A . For pure excluded volume repulsion , we have chosen δ U ( | r → i - r → j | ) = 1 / | r → i - r → j | 4 . This provides a steep enough function to mimic cylindrical hard core repulsion , while allowing the integrals in Eq 3 to be evaluated analytically . This allows us to derive analytical expression for the forces acting on the end points of the cylinders i and j . For every arbitrary point on a given cylinder i we can write the parametric equation r → i = x → i 1 + t ( x → i 2 - x → i 1 ) , where x → i 1 and x → i 2 are coordinates of the beginning and the end of cylinder i , respectively , and t ∈ [0 , 1] is a parameter . Taking this into account , and writing a similar parametric equation for cylinder j , Eq 3 can be written as U i j vol = K vol ∫ 0 1 ∫ 0 1 d s d t | r → i ( x → i 1 , x → i 2 , t ) - r → j ( x → j 1 , x → j 2 , s ) | 4 , ( 4 ) where Kvol is a constant determining the strength of repulsion . The MEDYAN model accounts for the process of polymer nucleation by branching . See the Chemical model section for a more detailed description of branching nucleation events . We introduce the following potential to describe the mechanical interactions of branched polymers , as seen in Fig 3B: U i j branch = U i j branch , str + U i j branch , ang + U i j branch , dihed , ( 5 ) where this interaction regards cylinder i as being on the “mother” polymer and cylinder j as the “daughter” or branched polymer . The first term in Eq 5 , which is a potential securing cylinder j to a branching point on cylinder i , can be written as U i j branch , str = K branch , str | d i j | - d 0 2 , ( 6 ) where d → i j = x → j 1 - x → i b is the distance between branching point on the cylinder i , x → i b , and the end point of the cylinder j , x → j 1 , d0 is the equilibrium value for this distance , and Kbranch , str is the stretching constant that can be chosen depending on the stiffness of the simulated branching molecule . As it was noted previously , we assume that axial deformation of the cylinders are small , and radial deformations are prohibited . In this case we can describe position of any branching point on the cylinder in terms of a scalar value γ ∈ [0 , 1] , which represents a fractional position of the branching point x → i b with respect to end points of the cylinder x → i 1 and x → i 2 , x → i b = ( 1 - γ ) x → i 1 + γ x → i 2 . In other words , γ will be generated as the result of a chemical branching event and will not depend on the stress generated in the branching junction; see the Chemical model section for more details on this chemical event and its effects . The second term in Eq 5 describes an angular potential at the chosen branching point between cylinders i and j: U i j branch , ang = ε branch , ang 1 - cos ( θ i , j - θ 0 ) , ( 7 ) where θ0 is the equilibrium value of the branching angle , θi , j is the angle between cylinders i and j , and εbranch , ang is the angular bending energy , which can be chosen based on the flexural rigidity of the branching molecule . In case of actin filaments , Arp2/3 grows nucleated filaments at an equilibrium angle θ0 ≈ 70° to the mother filament [68] . Finally , the last term in Eq 5 describes a dihedral potential between cylinders i and j , which uses the dihedral angle between two planes , formed by the points ( x i 2 , x i b , x j 1 ) and ( x i b , x j 1 , x j 2 ) : U i j branch , dihed = ε branch , dihed 1 - cos ( ϕ i , j - ϕ 0 ) , ( 8 ) where ϕ i , j = arccos ( n → i · n → j ) , n → i = ( x → i 2 - x → i b ) × d → i j | ( x → i 2 - x → i b ) | | d → i j | , and n → j = l → j × d → i j | l → j | | d → i j | . The symbols ( ⋅ ) and [×] stand for scalar and vector product , respectively , and εbranch , dihed represents the dihedral bending energy between the two cylinders , which can be chosen in a similar manner to εbranch , ang . The MEDYAN model incorporates molecular motors into the network as a dynamic object which can bind onto neighboring cylinders i and j at the positions x → i m and x → j m , and create a mechanical bond , as shown in Fig 3C . See the Chemical model section for more description of motor binding and walking events . For computational efficiency in studies of global deformations in large active networks under the force generation of small molecular motor ensembles , an implicit representation for molecular motors has been developed in which the motor is represented as a single potential acting on two neighboring cylinders . In the case of modeling myosin II mini-filaments , which are small ensembles of 10–30 myosin heads aligned in a bipolar fashion [78] , this is an excellent approximation . In future studies , a more explicit implementation of molecular motors , comprised of connected monomer units , can be implemented to allow a more detailed and accurate description of myosin II filaments at the cost of computational efficiency . This explicit representation may be of importance in studies including myosin II thick filaments , which can contain on the order of 100–800 motor heads [79] , thus allowing the ensemble to bind to a large number of actin filaments simultaneously . To describe the stretching energy of a bond created by an implicit motor , we introduce the following harmonic potential: U i j motor = 1 2 K motor | l → i j m | - l 0 m 2 , ( 9 ) where l → i j m = x → j m - x → i m is the instantaneous length of the motor , l 0 m is the equilibrium length of the particular motor , and Kmotor is the stretching constant , which can be chosen based on the stiffness of molecular motor to be simulated . The binding position of the motor head x → i m on cylinder i can be expressed as x → i m = ( 1 - α ) x → i 1 + α x → i 2 where α ∈ [0 , 1] . Here , similar to the case of the branching potential in Eq 6 , we assume that α is a scalar parameter , which does not change during mechanical minimization and is determined by a stochastic chemical event . Using this representation along with a similar expression for the binding position on cylinder j , we can write l → i j m as l → i j m = ( 1 - β ) x → j 1 + β x → j 2 - ( 1 - α ) x → i 1 - α x → i 2 . ( 10 ) where β ∈ [0 , 1] represents the fractional position on cylinder j . As the result of chemical reactions , α and β can stochastically change , which results in motor head relocation and the generation of new mechanical stresses in the system . Similarly , passive cross-linkers are represented using the potential in Eq 9 , but with time-independent values of α and β . Again , see the Chemical model section for more description of cross-linker binding and unbinding events . In a similar manner to molecular motors , by not explicitly introducing new classes of interactions for these molecules , but instead using analytically computed energies and forces between neighboring cylinders connected by passive cross-linkers ( i . e . relying on an implicit mechanical representation ) , the MEDYAN model can achieve much higher computational efficiency in the simulation of large active networks with these molecules . System boundaries in MEDYAN are modeled as non-deformable shells with a number of possible shapes , including cubic , spherical , and capsule geometries . These boundaries sterically repel approaching polymer segments , keeping the simulated network confined in the chosen domain . One of the possible potentials used to describe the interaction between the ith cylinder and the boundary can be written as: U i boundary = ε boundary e - d i → / λ , ( 11 ) where λ is the screening length and d i → is the distance between the boundary and the closest endpoint of the ith cylinder , x → i 2 or x → i 1 . εboundary represents the repulsive energy provided by the boundary . The total energy of the system Utot , assuming all corresponding species were chemically generated , is equal to a sum of the above contributions . This energy is then used in the MEDYAN model to mechanically equilibrate the system after a number of stochastic chemical reaction-diffusion steps . In order to perform this equilibration efficiently , most methods require analytical expressions for the derivatives of the energy with respect to cylinder position , e . g . forces in Langevin dynamics or gradient directions in conjugate gradient methods . Note that all terms in Utot ( Eqs 1–11 ) but Eqs 6 to 9 are initially written in terms of the end points of the cylinders; so , derivatives of those terms can be taken with respect to x → 1 and x → 2 such that , if using “force” terminology , will give forces acting on these end points of the cylinders . Eqs 6 to 9 also include coordinates of points located on the cylinders somewhere in between its end points: branching position on the “mother” filament in Eqs 6 to 8 and motor or cross-linker head positions in Eq 9 . However , as it was discussed before , for every point m along cylinder i we can write x → i m = ( 1 - α ) x → i 1 + α x → i 2 , where α ∈ [0 , 1] does not depend on the coordinates of the cylinder end points or stresses in the system during during a mechanical equilibration ( see Mechanochemical coupling ) . Taking this into account , Eqs 6 to 9 can be rewritten only in terms of positions of cylinder end points . Therefore , these potentials can be differentiated with respect to only x → 1 and x → 2 . This assumption follows under the condition of small axial deformations of the cylinders and the absence of radial deformations within each cylinder ( see Fig 2 ) , appropriate for relatively stiff filaments , such as F-actin and many other biological and artificial polymers . Very soft polymers , on the other hand , would be more profitably modeled as comprising of spherical beads and not cylinders . Mathematically speaking , this is equivalent to a simple chain rule: ∂ U i ∂ x → i m = ( 1 - α ) ∂ U i ∂ x → i 1 + α ∂ U i ∂ x → i 2 . From a mechanical point of view , this is equivalent to transferring of a force applied at a point x → i m to cylinder end points according to a lever rule , which was also used in [16] . Hence , to compute instantaneous forces needed for mechanical energy minimization in a system with the interaction potentials introduced in this section , we only need to calculate ∂ U tot ∂ x → i 1 , and ∂ U tot ∂ x → i 2 , ( 12 ) where the notation ∂ ∂ x → = { ∂ ∂ x x , ∂ ∂ x y , ∂ ∂ x z } represents the gradient in the direction of x → . This formalism allows us to calculate not only point-like interactions that can be described by a lever rule , but also more complex interactions , where the level cannot be applied , as in the case of our newly introduced cylindrical excluded volume potential ( Eq 3 ) . With these forces , an energy minimization is performed using a conjugate gradient method in the current MEDYAN software implementation , and is designed such that optimized minimization methods can be easily added to the existing code; see Section D of S1 Text for more description . In a MEDYAN simulation , the chemical and mechanical models work in tandem to evolve an active network in time . Fig 4 shows the general flow of the entire MEDYAN trajectory , where timescale separation of slower chemically-driven force generation and faster local force relaxation in a simulated active network allows for an iterative switching between stochastic chemical simulation and mechanical equilibration . After the stochastic simulation algorithm executes a set number of chemical steps to evolve the network in time , some of which have mechanical effects that drive the network slightly out of mechanical equilibrium , the energy of the network will be minimized according to the force fields specified in the simulation . For a detailed description of mechanical equilibration , as well as a list of mechanic effects of various chemical reactions , see Sections B and C in S1 Text . By performing highly efficient chemical stochastic simulation coupled with coarse-grained semi-flexible polymer chain mechanics , active network simulations with the MEDYAN model can reach time and length scales not accessible by its preceding models [29 , 42] with this high level of resolution in both aspects of stochastic reaction-diffusion and coarse-grained polymer chain mechanics; see Section C of S4 Text for a note on time and length scales attainable with MEDYAN compared to previous work . The above-stated iterative simulation protocol assumes that the mechanical subsystem is always near equilibrium , adiabatically following the slow chemical dynamics at all incremental time points during a simulation of an active network evolution . This is a valid approximation in the case of typical actin cytoskeletal networks undergoing small , localized force deformations , as evidenced by the recent microrheology experiment of Falzone et al . [80] . Their measurements of the relaxation time of various mesh-sized deformations in an actin filament network indicated an upper limit of approximately 10 milliseconds , which is significantly faster compared to the the walking rate of non-muscle myosin II motors [81] or actin filament growth rates [57] under physiological concentrations . While this timescale separation holds for most cytoskeletal networks undergoing typical molecular motor or filament growth-induced deformations , ones with slower network stress relaxation , possibly due to larger-scale network deformations or very fast reaction-diffusion processes , or if thermal motions need to be studied for other reasons , may be better served with a Langevin thermal dynamics approach at the cost of significantly reduced computational efficiency . In the latter case , the mechanical subsystem will evolve under constant time step Langevin dynamics , where it may be then more convenient to evolve the reaction-diffusion subsystem employing one of the multiparticle RDME methods [82 , 83] instead of a variable time-step algorithm such as the next subvolume approach used in the current work . The MEDYAN model also allows for the explicit coupling of both separate chemical and mechanical entities such that one can simulate the mechanochemical feedbacks of an active network . Many molecules in active networks , and in particular the cell cytoskeleton , have distinct mechanochemical properties that can greatly affect overall network dynamics and morphology [84–86] . MEDYAN allows for a detailed treatment of these relationships by dynamically updating reaction rates based on a reacting molecule’s evolving stresses , and any form of mechanochemical effect can be included in the model . Once the system is mechanically equilibrated following a number of chemical steps , reaction rates are updated based on newly formed mechanical deformations as shown in Fig 4 . With chemical , mechanical , and molecular transport properties of an active network being treated on equal footing , as well as their coupling being explicitly accounted for , the MEDYAN model allows simulations of various active networks with great mechanochemical detail and efficiency . The MEDYAN model has been implemented in a C++ software package which uses efficient data structures and object-oriented programming paradigms to simulate active networks with the scheme described in the earlier sections . The package has the capability for the user to specify the geometric , chemical , and mechanical properties of the simulated active network in a number of system input files , making the code robust and flexible enough to perform simulations for a range of active matter systems . This package , along with documentation on usage and compilation , as well as a visualization tool , is publicly available for use , modification , and addition of new patches ( www . medyan . org ) . See Section D of S1 Text for a more detailed description of the software implementation .
Fig 5 shows a single trajectory snapshot of an actomyosin system simulation containing actin filaments , NMIIA mini-filaments , α-actinin , and the constituent diffusing species within the simulation boundary . To define a quantitative measure of overall contractile behavior of the various actomyosin systems , we define an actomyosin network radius of gyration using all coarse-grained actin filament cylinder segments in the network: R g = 1 n ∑ i = 0 n ( r i - r GC ) 2 , ( 13 ) where rGC is the geometric center of the ensemble of cylinders , ri is the position of the ith cylinder , and n denotes the number of cylinders in the network . This is a more useful measure for our system than other more macroscopic measurements , including contractile velocity and minimum enclosing spherical volume . This is due to the fact that the dynamics of most networks studied do not show an obvious volume contraction , but do reorganize rapidly into contractile structures . See S1 Fig for a set of visual examples describing the relationship between Rg and contractile network morphology . Fig 6 shows a heat map of actomyosin network Rg for the various systems after 2000 s of network evolution . We see some very obvious patterns , including a decreased Rg for increasing NMIIA concentration , which implies more contractile behavior with this increase . We also observe the same effect of decreasing Rg for increasing α-actinin concentration . These relationships make physical sense , as more motors can provide more contractile force and linkers aid this contraction by increasing the transmitted force length scale . Fig 7A–7I show the network morphology for various values of Rα:a as Rm:a is also varied . We observe that for the lowest value of Rα:a = 0 . 01 , there is very little reorganization and contractile structure formation from the original randomly oriented network . But , with Rα:a values of 0 . 1 and 0 . 5 , there is very apparent actin filament bundle formation . Increases in Rm:a throughout the systems tends to slightly increase the network’s ability to contract into more tightly packed structures , as was also indicated by the values of Rg in Fig 6 . Since the final network Rg does depend on the initial configuration of the randomly oriented network , especially for non-contracted networks , it is useful to look at the ratio of final Rg after 2000 s to initial configuration Rg for the various systems , denoted as Rg , f/Rg , i . Fig 8 shows this value for a range of systems , holding Rm:a fixed , over the 2000 s of simulation time . We see that there is a clear divergence in time evolution for the lowest Rα:a values compared to the other higher values . This may imply , coupled with morphology observations of the various trajectories as in Fig 7A–7I , that there is not a continuous distribution of achievable contractile structures accessible with a given Rα:a and Rm:a as implied by Fig 6 , but only at a certain minimum α-actinin concentration , actin filament bundle formation is possible . This also seems likely due to the fact that the systems with Rα:a values of 0 . 1 , 0 . 2 , and 0 . 5 converge to a similar Rg value after the entire simulation . Comparing this observation to other systems with different Rm:a , we see that as motor concentration is increased , the minimum α-actinin concentration for actin filament bundle formation decreases , possibly due to the increased contractile strength of adding more NMIIA mini-filaments to the system . From these observations , we deduce that in these systems , actomyosin system cross-linker concentration is a switch-like mechanism that controls a transition between disordered and bundled networks , with system motor concentration widening this contractile structure formation regime , thus decreasing the minimum cross-linker concentration needed for bundle formation . This result is in agreement with the predictions of cross-linker percolation theory in larger scale actomyosin networks [28 , 95] . The formation of bundles agrees with the zippering mechanism of actin filament alignment and myosin II aggregation as proposed by Verkhovsky et al . [78 , 94] . But , our results show that their is a more active role of cross-linkers in the process than was previously suggested: if cross-linker concentration is decreased below a certain threshold value , this cross-linker binding propensity decrease hinders actin filaments from being adequately zippered onto the existing bundle before another contractile myosin II can pull in another direction , or filament turnover drifts it away from that position , disallowing global bundle formation . Other interesting morphological properties of the contractile actomyosin networks were observed . S1 Video shows a single trajectory for a smaller actomyosin system with Rm:a = 0 . 01 and Rα:a = 0 . 1 . When we color the plus and minus ends of these filaments with black and white beads in the same trajectory , respectively ( see S2 Video ) , we see that the actin filaments within the bundle are globally aligned in polarity . This is an interesting result , especially since the actin filament network started with random orientation within the uniform spatial boundary condition , and has not been predicted by previous models of bundle formation by way of zippering [78 , 94] , which describe the resulting apolar alignment of actin filaments , but not polarity alignment . The physical origins of this global polarity alignment by NMIIA mini-filaments is unclear from the trajectory videos , but has been observed and analyzed in two-dimensional motility assays [109–111] , as well as been predicted and modeled in one-dimensional actomyosin bundles undergoing polarity sorting [112 , 113] . Constant turnover in the plus end direction of actin filaments most likely causes anti-parallel orientations to be unstable , thus in the long-time limit of our simulations , only parallel bundles survive . But , the observed global contraction implies that NMIIA mini-filaments are driving network dynamics to a globally aligned , contractile state . It is reasonable to assume that combination of these two factors attributes to the observed behavior . To further quantify actin filament alignment in the simulated actomyosin networks , we define an orientational order parameter S of the system of actin filaments , which is the largest eigenvalue of the ordering tensor Q [114] which is constructed from Q α β = 3 2 ( 1 N ∑ i = 0 N u i α u i β - 1 3 δ α β ) , where α , β = x , y , z . ( 14 ) The vector ui represents the normalized direction of filament i over the N filaments in the system . When S is equal to zero , the filaments in the system are all randomly aligned , and when S equals 1 , the filaments are all perfectly aligned , regardless of polarity . To numerically capture the alignment of bent actin filaments , we chose to use a direction vector from minus to plus end of the entire filament , as opposed to a calculation of S using cylindrical filament segments , which may give values corresponding to unaligned networks if an actin filament bundle is aligned but significantly bent in any direction . Fig 9 shows S for the various systems after 2000 s of network evolution , as S correlates directly with trends in Rg over the concentration ratios of Rm:a and Rα:a , showing that all actomyosin systems produce alignment in tandem with contractile structure formation . We also confirmed qualitatively that in all actomyosin systems simulated , regardless of whether the systems eventually produced a single contractile bundle , all actin filament bundles formed consist of uniformly polar filaments , showing that all alignment observed is in fact polarity alignment . To probe the origins of this polarity alignment behavior which has been shown to be dependent on actin filament turnover in various systems [112 , 113 , 115] , we vary the reaction constants used for polymerization and depolymerization of actin filaments kactin , poly and kactin , depoly at both the plus and minus ends of the filament by a constant factor χ while keeping Rm:a = 0 . 02 and Rα:a = 0 . 1 and holding all other parameter values constant . When this turnover factor χ is varied from 0 . 125 to 8 , as shown in Figs 10 and 11 , resulting in actin filament turnover rates of 0 . 07 to 4 . 4 monomers per s , distinct changes in network morphology result . At low χ , which corresponds to a very slow actin filament turnover rate , highly contracted networks are formed with little to no overall polarity alignment . In the case of high χ , no global contraction of the networks is observed , but local polarity alignment in small bundles is seen over the trajectories . Interestingly enough , the original parameters ( χ = 1 ) which corresponded to physiological values of actin filament turnover , is the only parameter set to produce both global polarity alignment and contraction . Fig 11 shows the resulting network morphologies—low χ values corresponded to a dense , disordered clump with no polarity alignment , where high χ corresponded to local polarity alignment but overall disorder . To investigate further the contraction and alignment dependencies found by varying the turnover factor χ , we look at the displacement of actin filaments over the time of the actomyosin system simulations , and compare different actin filament turnover rates to the resulting filament diffusivity . It is important to note that in this simulation context , actin filaments are not diffusing via Brownian motion in the simulation volume , but are actively moving via actin turnover and NMIIA mini-filament force generation , thus causing a relative displacement of the filament midpoint . Fig 12A shows the mean squared displacement ( MSD ) of actin filament geometric centers , denoted as 〈Δx2〉 , with respect to simulation time over various χ values . To describe the motion of filaments under varying turnover rates , we linearly fit the first 1000 s of the MSD ( choosing this cutoff due to kinetic arrest and sub-diffusion occurring after this time point , as shown in the sharp turns in MSD plotted against time for all χ in Fig 12A ) on a log-log scale to obtain diffusion exponents , following the relation of general , anomalous diffusion: 〈 Δ x 2 〉 ∼ t ν . ( 15 ) The exponents ν corresponding to each turnover factor χ are shown in Fig 12B . All systems exhibited super-diffusion ( ν > 1 ) in the 1000 s interval , which is physically reasonable due to the active nature of the many constituents . Surprisingly , the variation of χ resulted in a biphasic distribution with a maximum ν value centered around χ = 1 , displaying the same χ dependence as the S distribution shown in Fig 10B . This relationship does make some intuitive sense; in a randomly oriented filament network , a higher-order filament diffusion relationship in any direction would cause anti-parallel filament bundle orientations to be less stable , thus producing a higher fraction of parallel bundles . But , it is counter-intuitive how the turnover factor χ affects the overall diffusive behavior in a biphasic manner . A physical explanation for the upper regime may be that when χ > 1 , actin filament turnover can out-run displacements via NMIIA mini-filament walking , thus not allowing NMIIA remodeling at all , and producing locally aligned but globally disordered actin networks . But , as χ is increased while remaining under the threshold χ = 1 , there is not a clear physical explanation for increased super-diffusive behavior and polarity alignment; a few studies have suggested actin filament turnover in the same direction of myosin II movement allows myosin II to walk farther on actin filaments , producing more contractile force [26 , 116] , but this is highly unlikely in our simulations due to the very short binding lifetime of NMIIA mini-filaments compared to actin filament turnover ( about a 5 s unloaded NMIIA mini-filament attachment time compared to an average 0 . 5 monomers per s turnover rate ) , and furthermore , does not explain polarity alignment behavior . In fact , the actomyosin systems with the lowest χ values contracted more than ones with higher χ , as shown in Fig 10A . Nevertheless , our results suggest that filament movement in these systems , and thus polarity alignment , is a cooperative effect depending on the synergy of actin filament turnover and NMIIA mini-filament walking . We also notice the mechanochemical phenomena of NMIIA mini-filament force-dependent accumulation in both the smaller and larger system simulations where contractile behavior was observed . As shown in a smaller actomyosin system in S3 Video , after a stable actin filament bundle is formed , NMIIA mini-filaments accumulate at the base of the bundle where it is pushing forward into the boundary via actin filament turnover . This phenomena of the force-dependent accumulation of various cytoskeletal proteins was observed experimentally for cellular aspiration and further quantified by Luo et al . [84] , and is responsible for many aspects of cellular mechanosensing . In particular , NMIIA has been shown to accumulate in areas of cytoskeletal stress , helping to maintain cytoskeletal integrity . In this case , the force-dependent accumulation of the NMIIA mini-filaments , being high-affinity cross-linkers when under stress , allows the bundle to maintain its structure when the pushing of the constituent actin filaments into the boundary may have disassembled the bundle . This spontaneous concentration gradient of NMIIA formed is a direct result of the coupling of diffusion of NMIIA mini-filaments and their force-dependent attachment affinity , which produces non-uniform compartmentalization within the simulation boundary . To test whether a larger , biologically relevant-sized system with longer actin filaments would undergo the same polarity sorting mechanisms as observed in the smaller 1 × 1 × 1 μm3 systems , we ran another set of 16 trajectories , for 500 s , of a 3 × 3 × 3 μm3 sized actomyosin network with an overall actin concentration of 12 μM and concentration ratios Rm:a = 0 . 02 and Rα:a = 0 . 1 . 400 filaments were nucleated in the system , resulting in a mean actin filament length of 1 . 4 μm when reaching a steady-state actin concentration . A video of a single trajectory is shown in S4 Video . All trajectories did in fact undergo polarity alignment of sub-domains and overall sorting; Fig 13 shows a single trajectory snapshot of the actomyosin network with actin filament cylindrical segments colored by their directional angle with respect to the x-y plane . We see uniformly polar domains , with connections between those domains that span the entire simulation volume and appear to have similar polarity structure to sarcomeric bundle patterning observed in vivo [117] . Unfortunately , a detailed analysis of these larger systems is out of the scope of this paper and will be investigated in a future study . We note that at the highest values of Rα:a , mainly at and above values of 0 . 5 , which may be much higher than α-actinin concentrations seen at physiological conditions , network dynamics may be affected by our modeling assumption that α-actinin and NMIIA head ensembles can occupy the same binding site on an actin filament . At these high concentrations , one might assume that some kinetic arrest and motor jamming may occur , causing the NMIIA head ensembles to stall and not deform the network as freely as our results predict , possibly producing the biphasic regime of cross-linker concentration where actomyosin contractility is inhibited by further increasing it [93] . But , since these studies are on much larger systems that have actin networks which form multiple connected and bundled structures , it is unclear whether inhibitory cross-linker concentrations would appear at all on our smaller length scale . In order to begin to study the effects of our binding occupancy assumption , we ran another set of simulations for all concentration configurations in a 1 × 1 × 1 μm3 system that did not allow α-actinin and NMIIA head ensembles to occupy the same binding site on an actin filament cylinder . This resulted in an overall decrease in contraction for each concentration configuration , but produced slightly different trends in contractile behavior across values of Rα:a which seem to be slightly biphasic in nature; see S2 Fig for a heat map of network Rg after 2000 s of simulation , as well as Rg , f/Rg , i over time for the various configurations . While this change in contractility trends across Rα:a did not seem to be statistically significant , we can hypothesize that the true competitive binding effects of cross-linkers and molecular motors lies somewhere between our two simulation extremes . A more detailed model of the steric exclusion of these molecules may give rise to a different contractile dependence on both Rα:a and Rm:a .
Active matter is a growing field of study at the interface of chemistry , mechanics and non-linear physics . In order to model active networks with complete realism , a model must take into account not only chemical processes and the molecular transport that occurs , but also the mechanical response of the network as well as complex mechanochemical feedbacks that result . With the MEDYAN model , one is able to , in a flexible manner , simulate these entities with precision , while also explicitly accounting for their coupling . Having the powerful capability to simulate active networks with this amount of flexibility and detail in aspects of stochastic reaction-diffusion and coarse-grained polymer chain mechanics , this model could be used to provide additional insights on the mechanochemical dynamics of many active networks , including the cell cytoskeleton . To compare the MEDYAN model to other recent agent-based cytoskeletal modeling approaches , an extensive list of models in recent literature is given in S1 Table , with notes on the essential mechanochemical capabilities of each model as outlined in the Introduction . Our public software implementation of MEDYAN ( available at www . medyan . org ) is also versatile enough such that other active networks , biological or artificial , could be simulated with a similar level of detail in comparison to its cytoskeletal applications , including self-organizing polymeric micelles [118] , ParM polymerization mechanisms in bacterial mitosis [119] , and many types of synthetic polymer gels . With these possibilities , the MEDYAN model is able to simulate a range of systems not previously achievable by other cytoskeletal models . Beyond the currently included chemical reaction set and mechanical force fields , the flexibility of the current software implementation also allows for the further development of the model to include new types of chemical and mechanical interactions as well as new classes of molecules , allowing for a completely customizable simulation framework . As shown in the example application , simple actomyosin network simulations using the MEDYAN model can already capture the dynamics and shed light on the underlying mechanisms of actomyosin contraction and remodeling . Our results show that in a system consisting of actin filaments , myosin II mini-filaments , and cross-linkers , actin filament turnover and cross-linker concentration are both powerful tools to control actomyosin network reorganization and polarity alignment . These results have interesting implications for transverse arc assembly , which has been shown to be critically dependent on myosin II [78 , 120 , 121]: by way tightly regulating actin filament turnover as well as localized cross-linker concentration via biochemical regulators , a dynamic transition area between the lamellipodium and lamellum could form , where sharp changes in these parameters could result in dynamic network reorganization and bundle assembly in the lamellar region . The polarity alignment as well as network contraction via myosin II and actin filament turnover we have observed in our simulations suggests that a reorganization mechanism is occuring that is more complex than the previously proposed actomyosin zippering [78 , 94] , which predicts the apolar alignment of actin filaments but not polarity alignment . It is possible that the observed polarity alignment behavior in these simulations via myosin II and actin filament turnover could drive the sarcomeric polarity pattern formation seen in transverse arcs [122] when developing from an initially disordered , lamellipodia-like actin filament network . But , more studies on larger actomyosin networks with multiple bundled structures should be investigated in the future to test this polarity alignment and contraction mechanism . While we observed contractile behavior in these systems as well as its dependence on cross-linker concentration , the exact contractile symmetry breaking mechanisms invoked in bundle formation , as well as the exact cooperation of actin filament turnover and myosin II mini-filament walking that results in actin filament polarity alignment , being difficult problems to analyze due to the many dynamic components of our simulation , remain unclear and will be further investigated in a future study . However , we hypothesize that cross-linkers may have an active role beyond increasing force transmission in overall contractile behavior due to the observed dependencies , and could break contractile-extensile symmetry by freezing contractile configurations into place by binding actin filament segments when they approach each other . We also propose that actin filament turnover may be a mechanism which allows actin filaments to flip and align in polarity more easily in the actomyosin-cross-linker system . Overall , our results show that in contractile systems where relevant timescales of motor movement are comparable to the timescale of network turnover , i . e . cross-linker ( un ) binding and actin filament turnover , interesting critical behavior can result , as shown in recent experiments [95 , 123] . Determining the exact relationships between these timescales of importance at the observed critical points , as well as the resulting dynamic behavior and network reorganization in these systems , will be an interesting endeavor for cytoskeletal researchers in the future . While the exclusivity of binding sites on actin filaments seems to alter the trends of contractile dependence of cross-linker concentration , more systems , as well as possible improvements to our model , should be studied in the future to probe these exclusion effects on network dynamics . In particular , we plan to include , in an explicit manner , a more realistic competition of cross-linkers and myosin II to binding sites on actin filaments . Also , the excluded volume effects of both molecules will be developed such that they cannot pass through actin filaments while network dynamics occur . These developments will help to study the dynamics of these actomyosin networks in a more realistic manner , and will provide additional insights to the problem of contractility emergence and mechanisms . The effect on the accumulation and kinetic trapping of myosin II mini-filaments when this steric exclusion is added will then be investigated , as it has interesting implications for myosin II compartmentalization within the cytoskeleton . We note that the imposed spatial boundary conditions could play a role in the actin filament polarity organization observed , and , in tandem with NMIIA mini-filaments , might be a contributing factor to the observed uniform bundle polarities in the actomyosin systems . Future works could examine the role of spatial boundary conditions on these organized structure formations , as there have been interesting in vitro investigations of the effect of boundaries on actomyosin network assembly as reviewed by Vignaud et al . [124] . In particular , the effect of pre-defined actin network microarchitectures on myosin II dynamics could be further investigated [125] . | Active matter systems have the distinct ability to convert energy from their surroundings into mechanical work , which gives rise to them having highly dynamic properties . Modeling active matter systems and capturing their complex behavior has been a great challenge in past years due to the many coupled interactions between their constituent parts , including not only distinct chemical and mechanical properties , but also feedback between them . One of the most intriguing biological active matter systems is the cell cytoskeleton , which can dynamically respond to chemical and mechanical cues to control cell structure and shape , playing a central role in many higher-order cellular processes . To model these systems and reproduce their behavior , we present a new modeling approach which combines the chemical , mechanical , and molecular transport aspects of active matter systems , all represented with equivalent complexity , while also allowing for various forms of mechanochemical feedback . This modeling approach , named MEDYAN , and software implementation is flexible so that a wide range of active matter systems can be simulated with a high level of detail , and ultimately can help to describe active matter phenomena , and in particular , the dynamics of the cell cytoskeleton . In this work , we have used MEDYAN to simulate a cytoskeletal network consisting of actin filaments , cross-linking proteins , and myosin II molecular motors . We found that these systems show rich dynamical behaviors , undergoing alignment and bundling transitions , with an emergent contractility , as the concentrations of myosin II and cross-linking proteins , as well as actin filament turnover rates , are varied . | [
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
] | [
"cell",
"motility",
"actin",
"filaments",
"simulation",
"and",
"modeling",
"molecular",
"motors",
"actin",
"motors",
"materials",
"science",
"cellular",
"structures",
"and",
"organelles",
"macromolecules",
"cytoskeleton",
"motor",
"proteins",
"materials",
"by",
"structure",
"research",
"and",
"analysis",
"methods",
"physical",
"chemistry",
"polymers",
"contractile",
"proteins",
"polymer",
"chemistry",
"proteins",
"chemistry",
"biophysics",
"physics",
"biochemistry",
"cytoskeletal",
"proteins",
"cell",
"biology",
"chemical",
"dynamics",
"myosins",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology",
"biophysical",
"simulations"
] | 2016 | MEDYAN: Mechanochemical Simulations of Contraction and Polarity Alignment in Actomyosin Networks |
To identify parameters of Leishmania infection within a population of infected sand flies that reliably predict subsequent transmission to the mammalian host , we sampled groups of infected flies and compared infection intensity and degree of metacyclogenesis with the frequency of transmission . The percentage of parasites within the midgut that were metacyclic promastigotes had the highest correlation with the frequency of transmission . Meta-analysis of multiple transmission experiments allowed us to establish a percent-metacyclic “cutoff” value that predicted transmission competence . Sand fly infections initiated with variable doses of parasites resulted in correspondingly altered percentages of metacyclic promastigotes , resulting in altered transmission frequency and disease severity . Lastly , alteration of sand fly oviposition status and environmental conditions at the time of transmission also influenced transmission frequency . These observations have implications for transmission of Leishmania by the sand fly vector in both the laboratory and in nature , including how the number of organisms acquired by the sand fly from an infection reservoir may influence the clinical outcome of infection following transmission by bite .
Experimental transmission of the etiological agents of vector-borne , parasitic diseases such as malaria , filariasis , trypanosomiasis and the leishmaniases , by the natural vector is the most relevant biological means to study the initiation and outcome of infection in experimental hosts . In the case of the protozoan parasite Leishmania , sand flies become infected when they obtain a blood meal from an infected mammallian host . Once inside the sand fly gut , parasites transform from the intracellular amastigote stage to the extracellular promastigote stage . Parasites then undergo a maturation process over the course of 1–2 weeks that involves escape through the peritrophic membrane surrounding the bloodmeal , attachment to the midgut wall , and migration to the anterior midgut and foregut . Anterior migration is accompanied by differentiation of the parasite into the non-dividing metacyclic promastigote , which is the infectious form that is deposited in the skin during a second or subsequent feeding attempt [1]–[3] . Experimental infection of vertebrate hosts with Leishmania have only rarely been initiated using natural sand fly transmission . These few experiences have nonetheless revealed significant differences in disease outcome and host response to sand fly versus needle inoculation of parasites [4]–[6] . Most critically , mice vaccinated with a killed Leishmania vaccine are protected against needle challenge but not against parasites that are transmitted by sand fly bite . [5] , [6] . These findings reinforce a series of studies demonstrating that needle injection of parasites with components of sand fly saliva or with promastigote secretory gel , both of which may be egested by infected sand flies , enhances disease [4] , [7]–[11] . Experimental transmission of Leishmania by infected sand flies presents several challenges that seriously undermine the practicality and physiologic relevance of experiments intended to test infection outcomes following “natural” exposure to the bite or bites of a single infected sand fly . The development of transmissible infections can vary enormously both within and between populations of infected flies [4] , [12]–[15] . Thus a large number of animal replicates and/or infected sand flies per animal are typically used to insure that a sufficient number of animals receive an infectious challenge , and to account for the wide variation in parasite dose delivered by individual flies [6] , [16] . The goal of the studies reported here is to identify parameters of Leishmania infections within the sand fly vector that correlate with successful experimental transmission of parasites to the mouse dermis . This information will not only improve our understanding of host-vector-pathogen interactions , but will permit predictions as to the degree of transmission competence within a group of experimentally infected flies so that experiments relying on sand fly challenge will become more manageable and better reflect the conditions of natural exposure .
Female BALB/c and C57BL/6 mice were purchased from Taconic Farms . Mice were 6–10 weeks in age at the time of exposure to sand flies . All mice were maintained in the National Institute of Allergy and Infectious Diseases animal care facility under specific pathogen-free conditions . Leishmania major RYN Strain ( L . m . RYN ) was isolated from a lesion biopsy of a laboratory worker accidentally exposed to Lutzomia longipalpis sand flies that were experimentally infected with a strain of L . major ( WR2885 ) originating in Iraq and isolated at the Walter Reed Army Institute of Research . A clone was obtained by limiting dilution and used to infect P . duboscqi sand flies . The L . major FV1 ( Friedlin ) strain is from the Jordan Valley , NIH/FV1 ( MHOM/IL/80/FN ) . A stable transfected line of L . m . RYN promastigotes expressing a red fluorescent protein was generated as described previously [12] . The resulting parasite is referred to as L . m . RYN-RFP . All parasites were grown in-vitro at 26°C in medium 199 supplemented with 20% heat-inactivated FCS ( Gemini Bio-Products ) , 100 U/ml penicillin , 100 µg/ml streptomycin , 2 mM L-glutamine , 40 mM Hepes , 0 . 1 mM adenine ( in 50 mM Hepes ) , 5 mg/ml hemin ( in 50% triethanolamine ) , and 1 mg/ml 6-biotin . The L . m . RYN-RFP line was grown in the presence of 50 µg/ml Geneticin ( G418 ) ( Sigma ) . Two-to-four day old Phlebotomus duboscqi females were obtained from a colony initiated from field specimens collected in Mali . Flies were infected by artificial feeding through a chick skin membrane on heparinized mouse blood ( drawn intracardially from BALB/c mice ) , penicillin ( 100 U/ml ) , streptomycin ( 100 ug/ml ) and L . major promastigotes . Sand fly infection ‘dose’ refers to the concentration of L . major . promastigotes per ml of blood upon which flies were allowed to feed . A fully engorged female P . duboscqi sand fly takes a blood meal of approximately 0 . 2–0 . 3 ul . Blood engorged flies were separated and maintained at 26°C and 75% humidity and were provided 30% sucrose ad libitum . After 13–14 days , 9–10 flies per experimental group were anesthetized with CO2 , killed in 5% soap solution , and whole midguts , including the crop , were dissected and transferred into tubes containing 25 µl 1× PBS . The guts were macerated briefly using a plastic pestle , then spun twice at 800 rpm for 1 minute to remove the debris . A 10-µl sample of the supernatant was counted under a hemocytometer and the numbers of metacyclic promastigotes , non-metacyclic forms , and total parasite number , as determined by morphology and movement , were counted . Leishmania infections were allowed to mature for 14–16 days within the sand fly midgut . One day before transmission the sucrose diet was removed . On the day of transmission , 4–5 flies were transferred to small plastic vials ( volume 12 . 2 cm2 , height 4 . 8 cm , diameter 1 . 8 cm ) covered at one end with a 0 . 25-mm nylon mesh . Mice were anesthetized by intraperitoneal injection of 30 ul of ketamine/xylazine ( 100 mg/ml ) . Specially designed clamps were used to bring the mesh end of each vial flat against the ear , allowing flies to feed on exposed skin for a period of 2–3 hours in the dark at 23°C and 50% humidity . In some experiments , sand flies were first induced to oviposit by placing blood-fed flies in a plaster-lined pot on day 5 after infection . On day 8–9 following infection flies were returned to paper cups and a diet of 30% sucrose . In some experiments , transmission occurred at 23°C and 30% humidity or 26°C and 75% humidity . Following exposure to the ear , the number of flies per vial with a blood meal was determined using a dissecting microscope . The average number of flies per vial with or without a blood meal was used to determine the potential influence of feeding intensity on transmission frequency and parasite load . Feeding intensity among different groups of infected flies was the same unless noted otherwise . Following exposure to infected sand flies , ear lesion diameters were measured ( in mm ) weekly for 5–6 weeks . Ears with more then one lesion are reported as total lesion diameter per ear . 5–7 weeks following transmission , mice were euthanized , ears removed and each ear was washed in 70% ethanol , separated into two sheets , and incubated at 37°C for 90 minutes in 1 ml of DMEM with 40 mM Hepes and 0 . 2 mg/ml Liberase . The ear sheets were then ground in a Medimachine ( Becton Dickenson ) . The tissue homogenate was added to 10 ml RPMI media containing 0 . 05% DNAse , filtered using a 70 um-pore-size cell strainer , spun-down for 10 mins at 1500 rpm , re-suspended in parasite growth medium and serially diluted in a 96-well flat-bottom microtiter plate , overlaying 100 ul onto 50 ul of NNN medium containing 20% defibrinated rabbit blood . The number of viable parasites in each ear was determined from the highest dilution at which promastigotes could be grown after 7 to 10 days of incubation at 26°C . To compare two groups with continuous responses ( e . g . , lesion diameters , parasite loads ) , we used the Mann-Whitney test , stratified by experiment in order to allow pooling of data where appropriate . To compare two groups with binary responses ( e . g . , presence of infection , presence of blood meal ) we used either Fisher's exact test for single experiments or the Mantel-Haenszel chi-squared test with continuity correction for combined experiments , with effects measured by odds ratios , for example see Figure 1B . We used Spearman's correlation ( reported as rs ) to compare pairs of continuous responses . The Meng , Rosenthal , Rubin ( MRR ) method [17] with Holm's adjustment for multiple comparisons [18] was employed to test for significant differences between Spearman correlations . Linear regression was used in Figures 2 E–H . To model percent transmission in Figure 3 , we used logistic regression with a quasi-likelihood model that allows over dispersion in the variance estimate that may be caused by lack-of-fit of the model . In Figure 3D we used a nonlinear least squares fit of a logistic model . All p-values are two-sided . Statistical calculations were done in Graphpad PRISM 5 . 0c ( www . graphpad . com ) or R 2 . 12 . 0 ( www . r-project . org ) with the coin package [19] . All animal experiments were performed under an Animal Study Protocol approved by the NIAID Animal Care and Use Committee under guidelines established by the Animal Welfare Act and the PHS Policy on Humane Care and Use of Laboratory Animals .
For the purpose of employing experimental sand fly challenge to study the host response to Leishmania infection under conditions that best reflect those of natural transmission , the ideal exposure would be to a single infected fly . We previously reported , however , that of the 301 L . m . FV1 infected P . duboscqi sand flies exposed singly to the mouse ear dermis , only 58 or 19% transmitted parasites . This frequency increased to only 25% ( 18/72 ) when the analysis was confined to infected flies that successfully acquired a second blood meal from an exposed mouse [12] . In order to achieve an acceptable rate of transmission to exposed animals , while at the same time trying to avoid an unnaturally high exposure to the immunomodulatory effects of sand fly bites , in our recent studies we have typically exposed the ear dermis to 4–5 infected sand flies [6] , [16] . Employing 4–5 infected flies as a constant , we have nonetheless noted a wide range in the frequency of successful transmissions , from 0% to 100% , varying as a function of the strain of parasite used , its culture history , and the concentration of organisms used to establish the infective blood meal . Figure 1A–C shows the outcome of L . major infections in BALB/c mice transmitted by flies with midgut infections initiated experimentally with varying doses of L . m . RYN , a recent primary clinical isolate of L . major . We approximate the initial dose of parasites acquired by individual sand flies in our experimental system to be 2–3 , 40–60 , or 800–1200 parasites/fly depending upon whether the flies are fed on blood containing 1×104 , 2×105 or 4×106 parasites/ml , respectively . BALB/c mouse ears exposed to flies infected with increasing doses of parasites developed larger lesions when compared with ears exposed to flies infected with lower doses ( Fig . 1A ) . Employing the infection status of each exposed ear to determine transmission success revealed that flies infected with either 2×105 or 1×104 per ml of blood were significantly less likely to transmit than those flies infected with the 4×106 dose ( Fig . 1B ) . Exposed ears that do not have lesions include both uninfected ears and infected ears that have yet to present with lesions . Analysis of only those ears that were successfully infected revealed that ears exposed to flies infected with 4×106 parasites per ml of blood had significantly larger lesions starting at 3 weeks post-transmission ( Fig . 1C ) . The difference in lesion sizes in Figure 1A versus 1C illustrates the need to confirm the presence or absence of parasites when interpreting lesion data following infected sand fly challenge . The observed differences in lesion size could not be accounted for by the inability of flies infected with lower doses of parasites to feed . The total number of flies with or without a blood meal following exposure to mouse ears revealed no significant differences between the groups of flies infected with different doses of parasites ( Fig . 1D ) . In order to identify parameters of Leishmania infections within the sand fly that correlate with experimental transmission , on day 13–14 of the infection , 9–10 flies from each group used in the transmission experiments described above were dissected and the viable promastigotes representing the various developmental stages were discriminated and counted on a hemocytometer . Infections initiated with 2×105 or 1×104 parasites/ml of blood generated Leishmania infections with lower numbers of total parasites ( Fig . 2A ) , lower numbers of metacyclic promastigotes per fly ( Fig . 2B ) , and significantly decreased percentages of metacyclic promastigotes ( Fig . 2C ) . Sand flies become infected in nature with tissue- or blood-derived amastigotes . In contrast , we employed culture-derived promastigotes mixed with mouse blood to initiate our sand fly infections . This was done to minimize the manipulation of the parasite following isolation from the original clinical biopsy . Sand fly infections initiated with L . m . RYN promastigotes , or amastigotes generated following passage through mice , revealed no significant difference in the percentage of metacyclic promastigotes , 91 . 2±6 versus 85 . 0±15 , respectively , p = 0 . 67 , n = 9 flies per group , suggesting the high frequency of metacyclics observed was not an artifact of employing promastigotes for sand fly infection . Comparison of the GeoM of the total number of parasites per fly with percent transmission ( n = 10–20 ears per dose per experiment , total n = 148 ears ) revealed a weak linear trend ( R2 0 . 5754 p = 0 . 018 ) but a non-significant correlation ( Spearman r 0 . 617; p = 0 . 086 ) ( Fig . 2E ) . In contrast , an increase in the number of metacyclic promastigotes or the percentage of metacyclic promastigotes returned higher rates of correlation with percent transmission ( Spearman r 0 . 80; p = 0 . 014 and 0 . 91; p = 0 . 001 , respectively ) ( Fig . 2 , F and G ) . The percentage of metacyclic promastigotes returned the highest Spearman r value for correlation ( 0 . 91 versus 0 . 80 ( total met-pro/fly ) and 0 . 61 ( total parasites/fly ) ) , and this was also reflected in the correlation between percent metacyclics and the geometric mean of the parasite loads detected in ears exposed to the different groups of flies , ( Spearman r 0 . 933; p = 0 . 0007 ) ( Fig . 2H ) . These results suggest that by determining the total number and percentage of metacyclic promastigotes from a sample of a larger group of experimentally infected flies , the frequency of transmission can be accurately predicted . We analyzed data in which mice were exposed to the bites of 24 different groups of experimentally infected flies in 16 different experiments comprising 314 exposed ears , including those experiments in which flies were infected with different doses of L . m . RYN parasites shown in Figures 1 and 2 , as well as flies infected with a poorly transmitted , culture-adapted , line of L . m . FV1 . The correlation between percent transmission and mean percent metacyclics ( Spearman r 0 . 85; 95% C . I . 0 . 681–0 . 937 ) ( Fig . 3C ) was significantly stronger than percent transmission and total parasite load ( Spearman r 0 . 56; 95% C . I . 0 . 191–0 . 791 ) ( Fig . 3A ) , pH = 0 . 0065 , or percent transmission and total number of metacyclic promastigotes ( Spearman r 0 . 74; 95% C . I . 0 . 478–0 . 886 ) ( Fig . 3B ) , pH = 0 . 023 . In addition , percent metacyclics returned the lowest p-value ( p<0 . 0001 ) as compared to total parasite load ( p = 0 . 013 ) or total number of metacyclic promastigotes ( p = 0 . 0005 ) when the different parameters were tested for their ability to predict transmission using a logistic regression model . Thus , the proportion of midgut promastigotes that have differentiated to metacyclic forms is the best predictor of transmission success based on the meta-analysis of multiple data sets in which ears were exposed to 5 infected flies . Applying a logical regression model to the data in Figure 3C , it can be predicted that in order to achieve ≥70% transmission as an arbitrary frequency sufficient to conduct experiments ( dashed horizontal line in Figure 3C ) , the percent metacyclics would need to be 71 . 7% ( 95% confidence interval 67 . 3–75 ) . Groups of flies with frequencies of metacyclic promastigotes above 71 . 7% transmitted parasites to an average of 82% ( SD±14 ) of exposed ears , while those groups with below 71 . 7% metacyclic promastigotes transmitted parasites to an average of 34% ( SD±26 ) of exposed ears . In addition , applying a cut-off of 71 . 7% metacyclic promastigotes would have excluded the use of 11 of the 13 groups of sand flies that transmitted to <70% of exposed ears . Therefore , a cutoff of 71 . 7% metacyclic-promastigotes can be employed in future experiments to predict if a group of experimentally infected flies , employing 5 flies per animal , should be used for transmission . Our meta-analysis also revealed a correlation between total parasite numbers and mean percent metacyclics ( Spearman r 0 . 64; p = 0 . 007; 95% C . I . 0 . 314–0 . 835 ) ( Fig . 3D ) . These results add to our previous findings in which an increase in the number of parasites in a single fly was associated with an increase in the frequency of metacyclic promastigotes [12] . We also explored the influence of environmental conditions and sand fly oviposition status on the transmission rate . Ears exposed to infected sand flies , with retained eggs , at 26°C and 75% humidity presented with significantly larger lesions , greater numbers of parasites , and were more likely to be infected ( Fig . 4A–C ) compared to ears exposed at 23°C and 30–50% humidity . Allowing infected flies to oviposit also resulted in increased lesion size , greater overall parasite loads , and a significantly increased rate of transmission following exposure to mouse ears at 23°C and 30% humidity ( Fig . 4E–G ) . In order to assess if oviposition status and increased humidity and temperature have an additive effect on transmission we directly compared each of these conditions alone or in combination in one experiment ( Fig . 4I–L ) . Exposure of ears to infected , oviposited , flies at 26°C and 75% humidity trended towards an additive effect over ears exposed to oviposited flies at 23°C and 30% humidity or flies that retained eggs at 26°C and 75% humidity . In addition , the combination of conditions resulted in significantly greater lesion sizes , larger parasite loads , and a higher frequency of transmission compared to infections initiated by infected flies that retained eggs at 23°C and 30% humidity . Flies that had oviposited were more likely to take a blood meal compared to flies with retained eggs ( Odds ratio , 0 . 274 , 95% C . I . 0 . 162–0 . 455 , p<0 . 00001 ) ( Fig . 4H ) . This result suggests flies feed more efficiently or pursue a second blood meal more aggressively after they have oviposited , and this may partially explain the enhanced rate of transmission by these flies ( Fig . 4E–G ) . In contrast , enhanced transmission by infected flies with retained eggs under conditions of higher temperature and humidity ( Fig . 4A–C ) was not associated with an increase in their ability to acquire a blood meal ( Odds ratio , 0 . 658; 95% C . I . 0 . 412–1 . 05 , p = 0 . 078 ) ( Fig . 4D ) . Therefore , oviposition status and environmental conditions at the time of exposure may influence the transmission of Leishmania by infected sand flies .
The use of sand flies to initiate experimental infection with Leishmania has revealed several important biological differences in the host response to infection and disease outcome as compared with needle inoculation [4]–[6] . However , the number of reports employing experimental transmission of Leishmania by bite , including evidence of viable transmitted organisms , is scant . This is due , at least in part , to the unpredictability of parasite transmission by sand fly bite , compounded by a shortage of information on the parameters of sand fly infection that influence transmission . In the studies reported here we identified a parameter of Leishmania infection within the sand fly , the percentage of metacyclic promastigotes , which best correlates with the subsequent frequency of parasite transmission to the mammalian host in an experimental setting . Meta-analysis of our extensive experience involving transmission attempts by 4–5 L . major-infected flies exposed to the mouse ear dermis permitted us to establish a threshold of metacyclic percentage in infected flies ( 71 . 7% ) that reliably predicts an acceptable level of transmission success , arbitrarily defined as ≥70% . The predictive value of the infection parameters reported here should help make experiments relying on sand fly challenge more manageable . In particular , it should prevent the use of populations of infected flies that are unlikely to transmit to a sufficient number of animals to interpret subsequent experimental results . This is of critical importance in experiments where the animals used for challenge , e . g . vaccinated mice , monkeys , or dogs , are valuable and limited . While increasing the number of flies per animal could be used to compensate for flies harboring sub-optimal numbers of metacyclics , we would argue that over-exposure of the inoculation site to sand fly bites begins to substitute one form of non-physiologic exposure , i . e . needle challenge , for another . This raises the question as to whether the numbers of parasites per midgut and frequencies of metacylics achieved in our experimental system reflect what is found in naturally infected flies . Published data on the intensity and composition of Leishmania infections within wild-caught sand flies are rare . One study revealed parasite loads of between 101 to 106 parasites per fly that appeared to vary according to oviposition status [20] , although this was no doubt also influenced by the stage of infection in the fly . Groups of laboratory-reared P . duboscqi flies with mature experimental L . major infections average between 102 to 105 parasites per midgut depending upon the dose of parasites used for infection [12] , [15] . Similar results have been reported following experimental infection of Lutzomyia longipalpus with L . infantum ( Lutz and Neiva ) [13] and Lu . Longipalpus with L . mexicana [4] . As far as we are aware , there is no published data on the frequency of metacyclic promastigotes within infected wild caught flies . Therefore , we can only speculate that flies infected with L . m . RYN , which contain in the range of 103–106 total parasites per fly and high frequencies of metacyclic promastigotes , approximate the infection status of transmitting flies in the wild . In nature , sand flies likely become infected with varying doses of parasites depending upon their feeding behavior and the concentration of parasites in the lesion or blood upon which they feed . We observed that groups of flies infected with larger initial doses of parasites had heavier infections , were more likely to transmit parasites during a second feeding attempt , and caused more severe disease when transmission occurred . This increased disease severity was likely due to a larger inoculum transmitted by the more heavily infected flies . In the single fly transmissions analyzed by Kimblin et al . [12] , there was a direct correlation between the intensity of midgut infections and transmitted dose . These results also suggest that the number of organisms picked up from an infection reservoir may have direct bearing on the severity of disease resulting from the bite of that infected fly . Thus , reservoir hosts , including humans for the anthroponotic forms of Leishmaniasis , with active infections containing large numbers of parasites may be more likely to give rise to heavily infected flies that will transmit more severe infections . The influence of transmitted dose on infection outcome has been difficult to study because it is impossible to control the number of parasites an individual sand fly deposits in the skin . Our results suggest that groups of flies infected with varying doses of parasites will deposit corresponding doses of parasites upon exposure to the dermis , leading to more or less severe disease . Finally , we demonstrated a role for infected sand fly oviposition status and environmental conditions at the time of transmission in subsequent transmission frequency . These observations add to those of Rogers et al . [21] in which maturation of L . mexicana infections within Lu . longipalpus sand flies resulted in more persistent sand fly feeding behavior and enhanced transmission . Together , these experiences should facilitate studies of the host response to Leishmania infection under conditions of experimental sand fly challenge , already severely limited by the few laboratories that have sand flies available for study . | Many infectious diseases are initiated when pathogenic organisms are deposited into the skin of the human host by the bite of an insect . In the case of the parasite Leishmania , the causative agent of Leishmaniasis , factors associated with the bite of the infected sand fly vector influence infection outcome , suggesting that this is the most relevant means to initiate disease in experimental hosts . However , transmission frequency , and the dose of parasites delivered when transmission occurs , can vary enormously both within and between populations of experimentally infected flies . This variability represents a major obstacle to the widespread use of sand flies to study Leishmaniasis . We identified a parameter of Leishmania infection in the sand fly vector that predicts the degree of transmission competence within a group of experimentally infected flies . We also identified environmental and biological factors that influence transmission frequency . This information will make experiments relying on infected sand fly challenge more manageable , thereby increasing the likelihood that infected sand fly challenge , rather than needle challenge , will be used in future experimentation . Lastly , we demonstrated that the number of organisms acquired by the sand fly can influence subsequent sand fly infection intensity , and that this infection intensity has implications for disease outcome . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"emerging",
"infectious",
"diseases",
"vector",
"biology",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"parasitology"
] | 2011 | Infection Parameters in the Sand Fly Vector That Predict Transmission of Leishmania major |
Respiratory syncytial virus ( RSV ) is the most common cause of infant hospitalizations and severe RSV infections are a significant risk factor for childhood asthma . The pathogenic mechanisms responsible for RSV induced immunopathophysiology remain elusive . Using an age-appropriate mouse model of RSV , we show that IL-33 plays a critical role in the immunopathogenesis of severe RSV , which is associated with higher group 2 innate lymphoid cells ( ILC2s ) specifically in neonates . Infection with RSV induced rapid IL-33 expression and an increase in ILC2 numbers in the lungs of neonatal mice; this was not observed in adult mice . Blocking IL-33 with antibodies or using an IL-33 receptor knockout mouse during infection was sufficient to inhibit RSV immunopathogenesis ( i . e . , airway hyperresponsiveness , Th2 inflammation , eosinophilia , and mucus hyperproduction ) ; whereas administration of IL-33 to adult mice during RSV infection was sufficient to induce RSV disease . Additionally , elevated IL-33 and IL-13 were observed in nasal aspirates from infants hospitalized with RSV; these cytokines declined during convalescence . In summary , IL-33 is necessary , either directly or indirectly , to induce ILC2s and the Th2 biased immunopathophysiology observed following neonatal RSV infection . This study provides a mechanism involving IL-33 and ILC2s in RSV mediated human asthma .
Respiratory syncytial virus ( RSV ) is the most common cause of lower respiratory tract infections in infants [1 , 2] , and is globally responsible for an estimated 64 million cases and 160 , 000 deaths each year [2] . In infants , severe RSV infection is characterized by bronchiolitis , interstitial pneumonitis , alveolitis [3] , and a T helper 2 ( Th2 ) -biased immune response in the lungs ( i . e . , Th2 cells , eosinophilia , mucus ) . One important correlate of severe RSV infection is age; most severe disease occurs in children <1 yr of age [4] , with highest hospitalization rates occurring in those <6 months of age [5] . More recently , our understanding of RSV has been aided by the use of an age-appropriate mouse model in which neonatal mice infected with RSV exhibit an immunological ( Th2 biased ) and pathophysiological ( pulmonary inflammation , eosinophilia , mucus hyperproduction , and long-term airways dysfunction ) phenotype typically seen in human infants with severe RSV [6 , 7] . We previously observed an early increase in IL-13 in the lungs of neonatal , but not adult mice , infected with RSV , which could not be explained by Th2 cells . Group 2 innate lymphoid cells ( ILC2s ) are a recently identified cell population naturally resident to the lungs that rapidly respond to IL-33 via its receptor ST2 to produce high levels of IL-13 [8] . It has been demonstrated that ILC2s play a critical role in the induction of Th2 immune responses [8–10] . Though ILC2s and IL-33 have both been closely associated with Th2 immunity , there are no data discerning either’s roles in the initiation and/or perpetuation of Th2 responses observed in infant RSV infection . This comes despite the fact that numerous studies show high correlation between genetic variation in the IL33 or ST2 genes and risk for asthma or severe RSV disease [11–13]–including a recent meta-analysis of GWAS studies which identifies ST2 as one of the top ten loci that influence allergic sensitization [14] . This information , combined with the fact that ILC2s appear to be essential for early production of IL-13 during viral infections [15] , makes ILC2s and IL-33 prime targets for the study of Th2-biased infant RSV disease . In the present study , we show that IL-33 is rapidly secreted in the lungs of neonatal mice infected with RSV , which is accompanied by an increase in lung ILC2s . This response is age-specific , as RSV infection in adult mice does not induce increases in IL-33 or ILC2s . We further demonstrate that Th2-biased immunopathophysiology that occurs upon reinfection with RSV is IL-33-dependent .
We previously observed an increase in pulmonary IL-13 immediately following RSV infection of neonatal , but not , adult mice . Since this increase was prior to the induction of Th2 cells and responses , it suggested a role for ILC2s . To determine if there are age-dependent differences in the induction of ILC2s in response to RSV , we infected neonatal mice ( 5 d of age; NR ) and adult mice ( 6–8 weeks of age; AR ) then kinetically measured IL-33 and IL-13 cytokine levels in whole lung homogenates ( Fig 1a ) . Highly elevated IL-33 levels were detected in neonates as early as 0 . 25 days post-infection ( dpi ) and peaking at 0 . 5 dpi . Although IL-33 could be detected in the lungs of adult mice , there was no significant change in expression following RSV infection . A similar trend was observed with IL-13 levels—increased expression in the lungs of mice infected as neonates , which peaked at 1 dpi , and no significant change in the lungs of mice infected as adults . An additional significant increase in IL-13 was observed at 6 dpi in mice infected as neonates . Significantly elevated levels of IL-33 were also observed in bronchoalveolar lavage ( BAL ) from infected neonates but not adults ( Fig 1b ) . Investigation into the cellular sources of IL-33 at 1 dpi revealed significant expression in airway epithelial cells ( CD45- EpCam+ ) of infected neonates ( Fig 1c ) . A similar trend was observed with detection of IL-33 mRNA by in situ hybridization ( Fig 1d ) . To determine if this rapid IL-33 increase in neonates requires live replicating virus , we “infected” neonates with UV-inactivated RSV ( UV-RSV ) and measured IL-33 in lung homogenates at 1 dpi ( Fig 1e ) . IL-33 levels in the lungs following administration of UV-RSV was statistically no different than sham infected neonatal control mice and significantly lower than RSV infected neonatal mice . Of note , neonatal mice displayed 3–4 fold more lung ILC2s ( lineage- CD45+ ICOS+ ST2+; gating strategy in S1 Fig; raw numbers in S3 Fig ) at baseline ( i . e . prior to infection ) compared to adults ( Fig 2a–2c ) . After infection , lung ILC2 percentages at 1 dpi were significantly higher in RSV-infected neonates compared to adults . Furthermore , neonatal ILC2s expressed more ST2 ( Fig 2d ) . There was no change in the frequency of ILC2s at baseline or at 1dpi ( Fig 2c ) or in ST2 levels on those ILC2s at baseline or at 1dpi ( Fig 2d ) in adults . Because our data showed increased IL-33 in the lungs of RSV-infected neonates , but not adults , we pretreated neonates with anti-IL-33 antibody ( α-IL-33+NR ) and pretreated adults with recombinant IL-33 ( rIL-33+AR ) prior to RSV infection ( see methods ) . When IL-33 was neutralized during primary RSV infection in neonates , the number of ILC2s and ST2 expression by those ILC2s was decreased compared to controls , while the opposite effect was seen in adults given rIL-33 ( Fig 3a ) . Additionally , lung levels of IL-13 were statistically decreased/increased depending upon IL-33 neutralization/augmentation , respectively ( Fig 3b ) . To determine if IL-33 modulation had an impact on overall viral load , we compared the amount of infectious virus in the lungs of treated and control mice at the peak of infection ( i . e . 4 dpi ) ( Fig 3c ) . Interestingly , IL-33 neutralization had no effect on RSV burden in neonates , but we observed a significant decrease in viral load in adult mice pre-treated with rIL-33 . In infants , severe RSV often causes bronchiolitis , recruitment of inflammatory cells ( i . e . , Th2 cells , eosinophils ) to the lungs , and increased airway mucus , resulting in significant airway obstruction and airway hyperresponsiveness ( AHR ) . A similar disease phenotype is readily observed ( following reinfection ) in mice initially infected with RSV as neonates , but not adults . To phenotypically recapitulate severe RSV infection seen clinically in human infants , mice from the same treatment groups as in Fig 3 were reinfected with RSV at 4 weeks post-primary infection ( established neonatal mouse RSV infection + reinfection protocol [6 , 7 , 16] ) and immune responses determined at 6 dpi ( Fig 4 ) . Neutralizing IL-33 during primary infection in neonates resulted in significant reductions in Th2 and multifunctional Th ( mTh; IFNγ+ , IL-4+ ) cells upon reinfection ( α-IL-33+NRR ) ( Fig 4a ) . Conversely , administration of rIL-33 to adult mice during primary infection resulted in significant increases in Th2 and mTh cells upon reinfection ( rIL-33+ARR ) , which were similar to levels induced in NRR mice . Similar trends were observed in other markers of disease severity; when IL-33 was neutralized during primary RSV infection in neonates , development of AHR was abolished ( Fig 4b ) . Interestingly , IL-33 administration to adult mice during primary infection resulted in exacerbated AHR , significantly above that of the NRR group . In the absence of RSV infection , neither IL-33 neutralization in neonates nor rIL-33 administration in adults resulted in AHR ( S2 Fig ) . Cellularity in BAL from these same treatment groups displayed decreases in total cells , lymphocytes , and eosinophils in α-IL-33+NRR mice , and an increase in eosinophils in rIL-33+ARR mice ( Fig 4c ) . Lung sections were stained with periodic acid-Schiff ( PAS ) to visualize mucus-producing cells in the airways . Airway mucus production was significantly decreased in α-IL-33+NRR mice compared to controls , while mucus was significantly increased in rIL-33+ARR mice compared to controls ( Fig 5a and 5b ) . To further demonstrate the role of IL-33 in promoting enhanced RSV disease in infected neonates , ST2 ( IL-33 receptor , Il1rl1-/- ) -deficient neonatal mice were infected with RSV as in Figs 4 , 5 and 6 . Compared to similarly-infected wild type mice ( WT NRR ) , Il1rl1-/- NRR mice failed to develop significant Th2 or mTh adaptive responses ( Fig 6a ) , did not exhibit AHR ( Fig 6b ) , and had decreased total cells , lymphocytes , and eosinophils in BAL ( Fig 6c ) . Nasal aspirates were obtained from RSV-infected human infants ( n = 81 ) on their initial day of clinical presentation ( d1 ) at Le Bonheur Children’s Hospital . A follow-up nasal aspirate sample was obtained from some patients ( n = 19 ) four weeks later ( d28 ) . Cytokine concentrations were analyzed in cell and mucus-free supernatants ( Fig 7 ) . In the paired samples , IL-33 and IL-13 levels were significantly elevated in d1 aspirates compared to d28 ( Fig 7a ) . For all d1 samples , IL-33 levels significantly correlated with IL-13 ( Fig 7b ) .
Infant susceptibility to severe RSV infection remains a significant global health burden for which no protective vaccine or adequate therapeutic is yet available . In recent years , it has become increasingly clear that age-dependent inherent differences in immunity play a vital role in both the magnitude [17 , 18] and type [7] of responses to RSV . Interestingly , while infants are capable of mounting robust type-1 responses to other respiratory viruses such as influenza [19] , they uniquely favor type-2 responses and the development of Th2 adaptive immunity ( i . e . elevated IL-13 compared to IFN-γ ) to RSV . Age at initial infection is critical in determining initial and subsequent immune responses to RSV [20] and in predicting subsequent wheeze/asthma [6 , 7 , 21 , 22] . The role of age in dictating the response to RSV remains incompletely understood with respect to mechanism . In the present study , we observed higher levels of lung ILC2s in neonates and an age dependent induction of ILC2s following RSV infection ( i . e . ILC2s were only induced if the initial infection with RSV occurred in neonates ) . Our data resembles the seminal data demonstrating a role for ILC2s in the induction of AHR following influenza infection [15] with the exception that adult mice were infected with influenza and no data were presented on the Th subtypes following that infection or a subsequent infection . However , our data with RSV are in-line with recent data demonstrating an age dependent ILC2 response to rhinovirus in mice ( i . e . increases in ILC2s in neonates but a minimal effect in adults ) [23] . A potential difference between our results and those with neonatal rhinovirus is the cytokine responsible for driving ILC2 induction; with rhinovirus , ILC2s were IL-25 dependent whereas ILC2 induction with RSV appears to be IL-33 dependent and not IL-25 dependent . This is intriguing , although the mechanism at this time is unclear , since both RSV and rhinovirus cause severe infection and type-2 inflammation during infancy , and it is those with severe infection that are at greatest risk for long term airways disease [21] . Combined , the data from these studies expand the mechanistic knowledge of age-dependent type-2 immunity “bias” observed clinically in infants . Severe RSV infection during infancy is heavily associated with the development of wheeze and/or allergic asthma during childhood and early adult life [21 , 22 , 24–26] , and various genetic studies in humans have identified both IL-33 and ST2 as being key regulators in the development of asthma [11–14] . Our results demonstrate , for the first time , the vital age-dependent role of IL-33 in RSV-mediated Th2 immunopathophysiology . Levels of IL-33 during infection was influenced by age at the time of initial infection; and this , in turn , correlated with an increase in the numbers of lung ILC2s coinciding with enhanced IL-13 expression and increased lung dysfunction ( i . e . AHR ) and pathology . In this study , IL-33 neutralization during initial RSV infection in the neonate was sufficient to completely abolish the severe RSV phenotype ( i . e . , Th2 inflammation , AHR , eosinophilia , airway mucus ) , while administration of rIL-33 during initial infection in adult mice was sufficient to induce the severe RSV phenotype . Notably , these IL-33 effects during neonatal RSV infection appeared to be through changes in innate ( i . e . ILC2 ) and adaptive ( i . e . Th2 ) immunity and not via changes in viral load . The effects of rIL-33 during adult RSV also appeared to be mediated through innate and adaptive immunity; however , the counterintuitive decrease in viral load in rIL-33 treated mice suggests that its influence is more complex . The strong Th2-promoting ability of IL-33-activated ILC2s has been largely demonstrated in animal models of Th2 disease such as asthma [10 , 27] and helminth infection [8 , 28] . It has been shown that ILC2s influence adaptive T cell responses both directly through MHCII-mediated crosstalk [29] and indirectly through Th2 cytokine production . Despite the severe immunopathophysiology we observed in rIL-33 treated adult mice infected with RSV , these mice displayed a significant yet relatively moderate increase in ILC2s during infection . Given the disparity in ILC2s between rIL-33 treated adult mice and neonatal mice , yet similar severity of RSV disease , it is probable that lung dendritic cells ( DCs ) are playing a large role in this model . In addition to activating ILC2s , IL-33 activates lung DCs via OX40L upregulation [30] to promote Th2 differentiation of naïve lymphocytes [31] and exacerbates eosinophilia [32] . Furthermore , blocking OX40L abrogates severe RSV in neonatal mice [33] . DCs are also important for anti-RSV responses , namely production of type-I interferons ( i . e . , IFNα , IFNβ ) , which are also impaired in RSV-infected infants [18 , 34] . We have observed IL-13-mediated Th2 promotion by DCs in RSV ( manuscript in preparation ) , and the rapidity of IL-13 expression following RSV infection suggests ILC2s as the source . We are currently investigating the ability of ILC2s , IL-33 , and IL-13 to negatively influence type-I interferon production by pDCs . A role for DCs is currently being explored . Though not as robust as earlier timepoints , a second , additional increase in IL-13 was observed at 6 dpi of initial infection . The significant increase in Th2 cells that has also been observed at 6 dpi suggests that they are at least partly responsible for elevated IL-13 levels at this time point . The receptor for IL-33 , ST2 , also exists in a soluble secreted form ( sST2 ) , which scavenges “free” IL-33 [35] . One would expect that increased IL-33 scavenging ability would correlate with decreased disease severity , as mice that overexpress sST2 display resistance to IL-33-induced inflammation [36] . However , IL-33 has been shown to upregulate sST2 levels , which subsequently reduces IL-33 levels [37] . Interestingly , sST2 has been detected in higher concentrations in nasal aspirates of severely RSV-infected infants requiring mechanical ventilation compared to mild/moderately-infected , non-ventilated infants [13] . This same study also identified SNPs in the sST2-encoding gene in these individuals; one of which correlated with disease severity . While it is unclear if this SNP is responsible for inefficient binding/scavenging of IL-33 resulting in enhanced disease severity , it is also plausible that elevated sST2 levels reflect earlier high levels of IL-33 in infants . If the latter is true , then the timecourse of infection ( i . e . days post-infection ) at the time of sampling will be an important factor as future data is interpreted . In either case , these data cumulatively demonstrate a role for sST2 in RSV disease . The exact role of sST2 is our neonatal mouse RSV model was not tested and therefore it is difficult to reconcile this human data with our data demonstrating increased cell bound ST2 and IL-33 on ILC2s . Surprisingly , early changes in RSV-induced IL-33 and ILC2s were age-dependent , as neonatal mice responded with robust IL-33 expression and a significant increase in ILC2s; while adult mice failed to induce IL-33 or ILC2s . Importantly , we observed elevated ( and positively correlated ) IL-33 and IL-13 in nasal aspirates of infants hospitalized with RSV infection . A limitation of our human data is the lack of samples from uninfected infants or infants infected with RSV but with mild ( no medical intervention ) or moderate ( office visit ) disease . With such samples , it is possible that these data would yield more robust conclusions ( i . e . IL-33 correlates to RSV disease severity ) . The increase in IL-33 in nasal aspirates from infants along with our mouse data cumulatively suggest that IL-33 and ILC2s are critical for the age-dependent development of asthma following severe RSV infection during infancy and that they may be a plausible therapeutic target . Infants that develop severe RSV bronchiolitis prior to 4 months of age are at increased risk to develop childhood asthma [38] . Differential regulation of either IL-33 or ILC2s as a factor of age is a previously unexplored area of developmental immunology that may further explain the Th2-biased immunity observed during early life . IL-33 has been reported to be constitutively produced ( but not secreted ) , predominantly by non-hematopoietic ( i . e . , epithelial ) cells in the lung [39 , 40] . It is produced as a full-length form , which is biologically active but confined to the nucleus [41] where it functions as a transcriptional regulator [42]; this form can be released upon tissue damage or necrosis to act as an alarmin for the surrounding tissue [43] or be proteolytically cleaved into a highly active mature form by cathepsin G [44] . Furthermore , there is evidence that IL-33 is inactivated by caspase-1 [45 , 46] which is increased during inflammasome activation [47] . Our data suggest that the airway epithelium is a significant source of IL-33 during neonatal RSV infection . Indeed , given the propensity for RSV to primarily infect the airway epithelium and the sheer number of epithelial cells in the lung , it is likely that the bulk of IL-33 secretion/release is epithelium-derived . Though outside the scope of the present study , we are investigating epithelial subsets within this context to determine which types of cells within the epithelium are responsible . Another product of inflammasome activation , IL-1β , suppresses IL-33 and ILC2s during Th2-mediated disease [48] . Interestingly , we have previously demonstrated defective inflammasome activation in RSV-infected neonatal mice compared to adult mice and in human cord blood mononuclear cells ( MNCs ) compared to adult peripheral blood MNCs [49] . It is therefore possible that a lack of caspase-1 activity and/or IL-1β production results in “unchecked” IL-33 release in RSV-infected neonates/infants . Additionally , increased numbers of ILC2s have been observed in fetal human lungs compared to adult lungs [50] and in human cord blood MNCs compared to adult peripheral blood MNCs [51] . Despite these indications that IL-33 and ILC2s play a larger role in the immunity of neonates/infants , to date there have been no studies focused on when/how these factors change during early development and their subsequent influence on immune responses . The present study defines roles for ILC2s and IL-33 in a previously unrecognized disease , and illuminates developmental variables as an area worthy of future consideration . The absence of IL-33 ( or IL-33 signaling via knockout of its receptor ) completely abolished the severe RSV disease phenotype in neonatal mice; while the administration of the cytokine during infection in adult mice resulted in exacerbated Th2 inflammation , lung dysfunction , and airway mucus . Age being a factor in IL-33 regulation has important implications for RSV research and pediatric immunology as a whole due to the involvement of IL-33 in many diseases affecting infancy and early childhood .
Mice were maintained in a specific-pathogen-free facility in ventilated microisolator cages . BALB/c mice were purchased from Harlan Laboratories ( Indianapolis , IN ) . Il1rl1-/- mice [52] on a BALB/c background were generously provided by Dr . Hirohito Kita ( Mayo Clinic ) with approval from Dr . Andrew McKenzie ( Medical Research Council , UK ) . Breeders were time-mated , and pups born on the same date were used for experiments . All animal protocols were prepared in accordance with the Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee at the University of Tennessee Health Science Center . RSV strain A2 ( Advanced Biotechnologies; Columbia , MD ) was propagated in Vero cells grown in serum-free media ( HyClone; Thermo Fisher ) , harvested using standard protocol [16 , 17] , and stored at -80°C until use . Adult mice ( 6–8 week old ) or neonatal mice ( 5 d old ) were infected I . N . with RSV in serum-free media at a dose of 2 × 105 50% tissue culture infectious dose ( TCID50 ) per gram of body weight . Reinfection with RSV occurred 4 weeks after the initial infection . Viral load was assessed in homogenized whole lung at 4 dpi by titer assay using the Spearman and Karber TCID50 method [53] with a lower detection limit of 316 TCID50 ml-1 . For UV-RSV , virus from the same stock was inactivated by exposure to 1800 mJ UV light ( Gene Linker; Bio-Rad; Hercules , CA ) . Inactivation was confirmed via viral titer assay . Three hr prior to RSV infection , neonatal mice received IL-33 neutralizing antibody ( α-IL-33; 2 mg kg-1 in PBS per supplier’s recommendation; I . N . ) . Controls received Rat IgG isotype control . Recombinant IL-33 ( rIL-33; Peprotech ) was administered to adult mice ( 0 . 5 μg in PBS 0 . 1% BSA ) [27] I . N . at 24 hr and 3 hr before infection . The volumes of all neonatal and adult treatments were 10 μl and 50 μl , respectively . Controls received vehicle . IL-33 neutralizing antibody and isotype control were generously provided via MTA with Pfizer Inc . Previously frozen lung samples were homogenized in T-PER reagent ( Thermo Fisher; Waltham , MA ) in the presence of a protease inhibitor cocktail . Cytokine expression in whole lung homogenate was determined by Platinum ELISA ( eBioscience; San Diego , CA ) and corrected for total protein determined by BCA assay ( Thermo Fisher ) . Lung single-cell suspensions [54] were stained with a fixable viability marker , stained with an antibody panel ( below ) , and assayed on a FACS Canto II ( BD; Franklin Lakes , NJ ) . For CD4 T cell population identification and determination of IL-33 cell sources , cells were stimulated for 5 hr with phorbol-12-myristate-13-acetate ( PMA; 5 ng ml-1 ) and ionomycin ( 500 ng ml-1 ) in the presence of a protein transport inhibitor ( GolgiPlug; BD ) prior to intracellular staining with CD3—eFluor450 , CD4—PerCP , IFNγ—PE , and IL-4—PE-Cy7 or CD45—PerCP-Cy5 . 5 , EpCam—APC , CD31—PE-Cy7 ( all eBioscience ) , and IL-33—PE ( R&D Systems; Minneapolis , MN ) . For ILC2 staining , cells were labeled with antibodies to CD3—PacBlue , CD19—PacBlue , CD11c—PacBlue , CD11b—PacBlue , CD49b—PacBlue , F4/80—PacBlue , FcεRI—PacBlue , and Sca-1—PE-Cy7 , ( all BioLegend; San Diego , CA ) , ST2—FITC ( MD Bioproducts; St . Paul , MN ) , Thy1 . 2 ( CD90 ) —biotin ( Southern Biotech; Birmingham , AL ) , Gata3—PE-Cy7 ( BD ) , CD45—PerCP-Cy5 . 5 , CD25—APC-Cy7/PE , and ICOS—APC ( all eBioscience ) . Data were analyzed using FlowJo v10 ( S1 Fig ) . In anesthetized ( ketamine/xylazine , 180/10 mg kg-1 ) , tracheotomized mice , peak airway resistance values in response to increasing doses of inhaled methacholine was measured using the flexiVent system ( Scireq; Montreal , Canada ) . Mouse bronchoalveolar cells were isolated by lavage of the lungs with 1 ml PBS . Cells were then counted , spun onto slides , and stained with Hema-3 kit ( Thermo Fisher ) . Differential cell counts were conducted in a blinded manner using standard morphological criteria . Mouse lungs were retroperfused with PBS , then gravity-inflated with zinc-formalin . Following fixation and paraffin embedding , 4 μm sections were prepared using standard procedures . Slides were stained with periodic acid-Schiff ( PAS ) to observe mucus production . As previously performed for quantification of mucus-producing cells [54] , the number of PAS+ epithelial cells was divided by the total number of epithelial cells in a respective airway; this was repeated for at least 10 airways on each section , giving an average percentage . In situ hybridization of IL-33 mRNA was performed on deparaffinized sections with RNAscope technology ( Advanced Cell Diagnostics; Hayward , CA ) according to manufacturer’s instructions . All images were acquired using an EVOS FL Auto ( Life Technologies; Grand Island , NY ) microscope . During the winter seasons of 2013–2015 , previously healthy children ( <2 yr of age ) testing positive for RSV by antigen and/or PCR ( and negative for influenza ) at Le Bonheur Children’s Hospital were prospectively enrolled in a natural history study . Exclusion criteria included diagnosed immunodeficiency , chronic lung disease , congenital heart disease , or had received steroid treatment within the last month ( patient demographics provided in S1 Table ) . Nasal aspirates were obtained on the first day of clinical presentation ( median of 3 days after first symptom onset , IQR 2–5 days ) , and cell/mucus-free supernatants were stored at -80°C until analysis . A subset of patients had an additional nasal aspirate sample obtained four weeks later . Cytokine levels were determined by Milliplex human cytokine assay ( Millipore , St . Charles , MO ) according to manufacturer’s instructions . All returned values that were below the limit of detection ( LOD ) for the respective cytokine target were replaced with the LOD divided by the square root of 2 [55] . This study was conducted with approval of the Institutional Review Board of the University of Tennessee Health Science Center . Data were analyzed with Prism5 ( Graphpad; La Jolla , CA ) , and presented as means ± s . e . m . One-way or Two-way ANOVA with Bonferroni post-tests or student’s t-tests ( both paired and unpaired ) were used , where appropriate . P values <0 . 05 were considered significant . For nasal aspirate analyses , a Sign analysis was used for paired data and a Kendall’s tau correlation coefficient was computed to describe the correlation between IL-33 and IL-13 . All animal protocols were prepared in accordance with the Guide for the Care and Use of Laboratory Animals and approved ( #14–045 . 0 ) by the Institutional Animal Care and Use Committee at the University of Tennessee Health Science Center . | IL-33 is responsible for the immunopathophysiological response observed following neonatal RSV infection in mice . Its presence in nasal aspirates of human infants with severe RSV and suggests its role in disease severity and asthma . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Respiratory Syncytial Virus Disease Is Mediated by Age-Variable IL-33 |
Many enveloped viruses invade cells via endocytosis and use different environmental factors as triggers for virus-endosome fusion that delivers viral genome into cytosol . Intriguingly , dengue virus ( DEN ) , the most prevalent mosquito-borne virus that infects up to 100 million people each year , fuses only in late endosomes , while activation of DEN protein fusogen glycoprotein E is triggered already at pH characteristic for early endosomes . Are there any cofactors that time DEN fusion to virion entry into late endosomes ? Here we show that DEN utilizes bis ( monoacylglycero ) phosphate , a lipid specific to late endosomes , as a co-factor for its endosomal acidification-dependent fusion machinery . Effective virus fusion to plasma- and intracellular- membranes , as well as to protein-free liposomes , requires the target membrane to contain anionic lipids such as bis ( monoacylglycero ) phosphate and phosphatidylserine . Anionic lipids act downstream of low-pH-dependent fusion stages and promote the advance from the earliest hemifusion intermediates to the fusion pore opening . To reach anionic lipid-enriched late endosomes , DEN travels through acidified early endosomes , but we found that low pH-dependent loss of fusogenic properties of DEN is relatively slow in the presence of anionic lipid-free target membranes . We propose that anionic lipid-dependence of DEN fusion machinery protects it against premature irreversible restructuring and inactivation and ensures viral fusion in late endosomes , where the virus encounters anionic lipids for the first time during entry . Currently there are neither vaccines nor effective therapies for DEN , and the essential role of the newly identified DEN-bis ( monoacylglycero ) phosphate interactions in viral genome escape from the endosome suggests a novel target for drug design .
With almost half of the world's population at risk for dengue infections , including life-threatening dengue hemorrhagic fever and dengue shock syndrome [1] , the lack of vaccines and effective therapies lends urgency to the search for new targets for antiviral drugs . In this work we have focused on the membrane fusion stage of DEN infection . As many flaviviruses and alphaviruses , DEN enters mosquito and human cells by receptor-mediated endocytosis [2]–[5] . Fusion between the DEN envelope and the endosomal membrane is mediated by an envelope glycoprotein E [6]–[9] structurally similar to E protein of other flaviviruses and to E1 protein of alphaviruses such as Sindbis virus ( SIN ) [8] , [10] . Acidification of endosomal content triggers a fusogenic restructuring of protein fusogens and fusion in flaviviruses and alvaviruses . DEN E undergoes a major conformational change that starts with the dissociation of the homodimeric form of E . Separated monomers rise from their initial positions parallel to the viral envelope to positions perpendicular to the envelope [6]–[9] . E monomers interact with the target membrane via their hydrophobic fusion loops and assemble into homotrimers that bridge the viral and target membranes . Subsequent refolding of the E trimer into its final hairpin structure , with the transmembrane domains and fusion loops at the same end of the rodlike molecules , bends the target and viral membranes towards each other and primes them for fusion [6] , [8] , [11]–[12] . Several observations have suggested that DEN fusion , in addition to low pH dependence , may involve as-yet-unidentified cofactors . Early research , confirmed in our preliminary experiments , found that low pH application to adjacent cells with DEN virions bound to the cell surface resulted in cell fusion for mosquito cells [13]–[15] but not for mammalian cells [13] . Furthermore , during viral entry into mammalian cells most DEN particles fuse only when they get to late endosomes/lysosomes [3]–[5] , and , thus , they neither fuse nor become inactivated in early endosomes , which are expected to have pH low enough to trigger conformational changes in DEN E and induce DEN-mediated fusion between mosquito cells [15] . We suggested that in analogy to alphavirus fusion dependence on lipid cofactors ( namely , cholesterol and sphyngomyelin ) [16]–[19] , the apparent differences in fusogenic properties of DEN towards different target membranes may reflect differences in their lipid composition . In contrast to the outer leaflets of plasma membranes of mammalian cells and to the inner leaflets of membranes of early endosomes , the outer leaflets of the plasma membranes of mosquito cells and the membranes of late endosomes of mammalian cells have high concentrations of the anionic lipids ( AL ) phosphatidylserine ( PS ) [20] and bis ( monoacylglycero ) phosphate ( BMP ) [21] , respectively . In this work , we explored the dependence of fusion of DEN , serotype 2 , strain TH-36 ( below referred to as “DEN” ) on the lipid composition of the target membranes and found this fusion reaction to require the presence of AL . Liposomal , plasma , and intracellular membranes that did not support DEN fusion became fusion-competent upon addition of AL such as BMP and PS . The discovered dependence on AL was also observed for dengue virus serotype 2 , strain New Guinea C and for dengue virus , serotype 4 , strain H241 . AL acted downstream of low pH-dependent stages of DEN fusion and promoted an advance of the fusion process beyond the earliest hemifusion intermediates . Endocytosed viral particles are acidified , and thus the restructuring of DEN E towards its fusogenic conformations is expected to be triggered before DEN comes into contact with the AL-containing membranes of late endosomes . For many viruses , including influenza virus [22] and SIN [17] , premature activation of the viral fusion machinery by an acidic medium in the absence of a target membrane results in a quick inactivation of the machinery . In contrast , we found that DEN particles treated with a low-pH medium in the presence of AL-free target membranes retain their fusogenic properties for a relatively long time ( >30 min ) . This unusually slow inactivation may explain the preservation of fusogenic properties of DEN for the duration of viral trafficking from early to late endosomes . We propose that the AL dependence and the delayed inactivation of the fusion machinery of DEN play an important role in defining the timing and location of the fusion stage of viral entry .
The fusogenic activity of diverse enveloped viruses is often characterized by measuring fusion between adjacent cells that is mediated by viral particles associated with the cell surface either through non-specific binding or through virus–receptor docking . For viruses that fuse in acidified endosomes , activation of viral fusogens and cell-to-cell fusion yielding syncytia is triggered by a short-term application of acidic pH . In agreement with earlier reports [13] , DEN effectively fused mosquito cells C6/36 ( Fig . 1A , B ) . Fig . 1B , curve 1 represents the pH dependence of DEN-mediated fusion of these cells and , as expected [15] , already shows significant fusion at moderately acidic pH . In striking distinction from insect cells , DEN produced in either insect or mammalian cells did not induce syncytium formation for any mammalian cells we tested ( Vero , BHK21 , CHO-K1 , MA104 , NIH 3T3 , HAb2 , BS-C-1 , U967 ( monocytes ) , and Raw ( macrophages ) ) in the experiments in which cells pre-incubated with DEN were treated for 1 to 15 min with an acidic medium of pH values ranging from 4 . 9 to 6 . 5 . Syncytium formation involves both the fusogen-dependent opening of fusion pores and their cell-machinery-dependent expansion , yielding a cell-size lumen [23] . We found that the absence of DEN-induced syncytia for mammalian cells reflects the inability of DEN to form fusion pores large enough to pass GFP and mRedFP , as evidenced by the lack of double-labeled cells ( shown for Vero cells in Fig . 1C ) . In parallel experiments , we contrasted the fusogenic activity of DEN with the well-characterized fusogenic activity of SIN , a virus that utilizes similar fusion machinery [8] . As expected , SIN effectively fused both mammalian cells and mosquito cells C6/36 ( Fig . 1A; B , curve 2; and 1C; see also [24] ) . We explored whether the inability of DEN to fuse mammalian cells can be explained by inefficient virus–cell-surface binding . To evaluate the binding ( including non-specific receptor-independent binding ) , we incubated Vero cells ( Fig . 1D ) or CHO-K1 cells ( not shown ) with mildly biotinylated DEN or SIN particles , applied fluorescence-tagged streptavidin , and then carried out flow cytometry analysis ( Fig . 1D ) . We found virus–cell binding for DEN to be somewhat higher than for SIN , the virus that effectively fuses the cells indicating that cell fusion for DEN is blocked downstream of cell-surface binding . This conclusion was further substantiated by comparing DEN binding to C6/36 cells that were readily fusable by the virus with DEN binding to mammalian cells ( MA104 cells ) that did not fuse in the presence of DEN . In these experiments , we blocked internalization of DEN virions labeled with a fluorescent lipid DiD by low temperature ( 10°C ) and quantified viral binding by measuring DiD fluorescence associated with the cells ( Fig . S1 ) . To take into account the very different sizes of C6/36 and MA104 cells , we normalized the amount of DiD fluorescence associated with the plasma membrane of the cells to the fluorescence of membrane probe NBD-tagged phosphatidylcholine ( NBD-PC ) . This normalization is based on the assumption that NBD-PC similarly partitions into the outer leaflets of plasma membrane bilayers of C6/36 and MA104 cells and , thus , the cell-associated NBD fluorescence is proportional to the total area of plasma membranes . As shown in Fig . S1 , under the conditions of our cell-to-cell fusion experiments on C6/36 cells and on MA104 cells , the ratios of DiD- and NBD- fluorescences , and thus the surface densities of DEN virions at plasma membrane , were very close . This finding argues against the hypothesis that DEN fuses C6/36 cells but does not fuse mammalian cells because of a large difference in the amounts of cell-bound virions . To summarize , while DEN binds to and effectively infects both mosquito and mammalian cells , in our experiments it fused mosquito but not mammalian cells . The inability of DEN to fuse the plasma membranes of the virus-permissive mammalian cells indicated that the fusogenic activity of DEN depends on the target membrane . To identify the cofactors that have to be present to support DEN fusion , we concentrated on the unusual lipid composition of the late endosomal membranes that DEN fuses with to inject its RNA into cells . Using liposomes , we explored the dependence of DEN fusion on the composition of the target membrane . To study the role of membrane lipid composition in DEN fusion , we measured low pH-induced lipid mixing between DEN particles labeled with a self-quenching concentration of a fluorescent lipid , DiD , and unlabeled liposomes of different composition . Only slow and inefficient lipid mixing between DEN and liposomes was observed for liposomes formed from lipids characteristic of the outer leaflet of the plasma membranes of mammalian cells ( PM composition ) ( Fig . 2A , B ) . In contrast , we observed robust lipid mixing between DEN and liposomes containing phospholipids characteristic of late endosomal membranes ( LEM composition ) . A much higher efficiency of DEN fusion to LEM liposomes than to PM liposomes was also observed for dengue virus serotype 4 , strain H241 ( Fig . S2 ) and for dengue virus , serotype 2 , strain New Guinea C ( not shown ) . As expected [19] , including Chol into the LEM lipid mixture ( LEM-Chol composition ) did not significantly change the initial rate or the extent of lipid mixing detected at t = 10 min ( Fig . 2A , B ) . Inactivation of DEN particles by pre-incubation at pH 4 . 5 for 30 min ( 37°C ) or by a histidine-modifying reagent diethylpyrocarbonate ( DEPC ) [25] , resulted in a loss of DEN fusogenic properties ( Fig . 2A ) . We also found that lipid mixing between DEN and LEM liposomes was inhibited by DEN monoclonal antibody 4G2 [26] ( data not shown ) . The inefficient fusion between DEN and liposomes containing lipids characteristic of plasma membranes contrasted with the robust fusogenic activity of SIN ( Fig . S3 ) . At pH 5 . 3 , the initial rate and extent of lipid mixing with PM liposomes at t = 10 min for SIN were 10 fold and 3 fold higher than those for DEN . Strong lipid mixing between SIN and these liposomes is in agreement with earlier reports [17] and is also consistent with efficient fusion between alphaviruses and plasma membranes of mammalian cells ( see above and [27]–[29] ) . Lipid mixing between SIN virions and PM liposomes developed slower than that between SIN and LEM-cholesterol liposomes but reached similar extents . Note that the rate of SIN-liposome lipid mixing detected in our experiments using the DiD dequenching assay is notably lower than that reported in [17] , [30] and closer to the rates reported in [31]–[32] . This divergence most likely reflects the differences in assays used by different groups . We hypothesized that LEM liposomes support DEN fusion because these liposomes contain an AL BMP , a specific lipid marker of multivesicular late endosomes that is present in different membrane domains of these organelles in concentrations between 20 and 70 mol% [21] . To test this hypothesis , we used liposomes of simpler compositions . While DEN did not fuse with liposomes that were formed from phosphatidylcholine ( PC ) , liposomes that in addition to PC contained 30 mol % of either BMP or phosphatidylglycerol ( PG ) or PS supported lipid mixing ( Fig . 2C ) , indicating that DEN fusion to liposomes depends on the negative charge of the target membrane rather than on a specific polar group of AL . We then tested whether the inability of DEN to fuse mammalian cells can be explained by the lack of AL in the outer leaflet of the plasma membrane . We incubated CHO-K1 cells with the virus; then , after a wash , we placed the cells at 4°C and treated them with exogenous AL , PS . After a wash , we applied a low-pH medium at room temperature to trigger fusion . DEN effectively fused PS-treated cells but did not fuse untreated cells ( Fig . 3A , B ) . While PS added to the outer leaflet of plasma membranes of mammalian cells undergoes inward transmembrane redistribution mediated by ATP-dependent aminophospholipid translocases [33] , at the time of low pH application significant percentage of exogenous PS remained in the outer leaflet , as evidenced by its accessibility for extraction with bovine serum albumin , BSA ( data not shown ) . Furthermore , DEN fusion of mammalian cells was similarly supported by addition of PG , an AL that is not an aminophospholipid ( Fig . 3B ) . In control experiments , DEN did not mediate fusion between CHO-K1 cells if cell membranes were not supplemented with exogenous lipid or were supplemented with PC ( Fig . 3B ) . In similar experiments carried out with SIN , we found cell fusion mediated by this virus to be almost unaffected by addition of either PS or PG ( Fig . S4 ) . Since AL were applied only after removal of unbound DEN , the dramatic increase in fusion after PS treatment cannot be explained by a better DEN binding . We directly verified that cells treated and untreated with PS carried similar amounts of DEN particles in experiments with fluorescence-labeled viral particles . CHO-K1 cells were incubated with DiD-labeled virus at 10°C and washed . The temperature was lowered to 4°C and cells with associated virus were either treated or not treated with PS and then solubilized with 1% Triton X-100 at room temperature to dequench the DiD . The levels of fluorescence measured for samples from PS-treated and untreated cells were statistically indistinguishable . As for many other enveloped viruses , fusion of DEN to the plasma membrane can be indirectly evaluated by measuring cell infection caused by low pH-induced fusion at the cell surface under conditions where acidification of the endosomes , essential for the biologically relevant entry pathway , is blocked [34] . We found the application of this fusion-infection assay ( FIA ) to DEN and mammalian cells ( MA104 , Vero and BHK21 ) to require a very high concentration of virions: 300 infectious units/cell ( at least 10 times higher than the number of infectious viral particles per cell we had to use in FIA for mosquito C6/36 cells , data not shown ) . In this setting , FIA , while indirect , may be a very sensitive assay for fusion since fusion of a single virion out of hundreds may result in infection . As for DEN-mediated cell-to-cell fusion , the anionic lipids ( PS or PG ) -supplemented MA104 cells demonstrated much higher ( 15-fold ) levels of fusion in FIA ( Fig . 3C ) . Note that DEN-dependent fusion of PS-treated cells was still dependent on low pH . We observed a similar promotion of fusion-infection for Vero and BHK21 cells ( data not shown ) . The importance of AL for DEN-plasma membrane fusion was further confirmed in the experiments on mosquito cells . As mentioned above , C6/36 cells were reported to expose unusually high concentrations of AL at their surface [20] . In agreement with this study , we found a much higher cell-surface labeling with R-phycoerythrin -tagged annexin V for C6/36 than for MA104 cells ( Fig . S5 ) . Blocking PS and perhaps other AL [35] exposed at the surface of C6/36 cells with annexin V inhibited DEN-mediated fusion between these cells ( Fig . 3D ) . While SIN-mediated fusion between C6/36 cells was also inhibited by annexin V perhaps because of a steric hindrance , inhibition of the DEN-fusion was much stronger . These findings support the hypothesis that the known ability of DEN to fuse C6/36 cells reflects the elevated concentrations of externalized AL in their membranes . In brief , as with DEN-liposome fusion , the efficiency of DEN fusion to plasma membranes correlates with the accessibility of AL . The effects of AL on fusion events during viral infection were studied in MA104 cells and BS-C-1 cells with pre-bound DEN labeled with DiD in a self-quenching concentration . Viral fusion events along the endocytic pathway diluted DiD and , thus , significantly increased cell fluorescence ( Fig . 4A and S6 ) . While first fusion events were observed less than 5 min after the rise in temperature to 37°C , most viral particles fused only at later times , with a median waiting time of ∼15 min ( Fig . S6 ) , consistent with an earlier study [3] . As expected , no intracellular fusion was observed when endosomal acidification was blocked by chloroquine or when DEN was DEPC-inactivated ( Fig . 4A , B ) . In agreement with reports suggesting that DEN fusion reactions take place in late endosomes [3]–[5] , we found that microtubule-depolymerizing nocodazole known to disrupt endosomal trafficking from early to late endosomes/lysosomes [36] inhibits intracellular fusion of DEN . When cells with pre-bound DEN were treated with AL ( 5-min , 4°C ) before warming up to 37°C , we observed a dramatic increase in the average cell fluorescence ( Fig . 4A , B ) . As for untreated cells , the increase in fluorescence of PS-treated cells was inhibited by chloroquine . The increase in DiD fluorescence reflects a higher efficiency of DEN–endosome fusion and suggests that in the AL-treated cells DEN fuses in early endosomes that normally would not have AL . This conclusion was further substantiated by the finding that intracellular fusion of DEN in AL-treated cells was much less sensitive to inhibition of endosomal trafficking by nocodazole than in untreated cells ( Fig . 4B ) . Promotion of intracellular fusion for PS-supplemented cells was also observed for dengue virus of serotype 2 , strain New Guinea C ( not shown ) . One may expect the delivery of DEN virions to late endosomal compartments that support viral fusion to be blocked by a dominant negative Rab7 ( DN Rab7 ) that disrupts late endosomal/lysosomal biogenesis . Indeed , at least for some strains of DEN , DN Rab7 inhibits the infection of mammalian cells and DEN fusion [3] . As expected , we found that EGFP-tagged DN Rab7a S22N expression inhibits intracellular fusion of DiD-labeled DEN in MA104 cells , as evidenced by a much lower DiD fluorescence associated with DN Rab7-expressing cells detected by the EGFP fluorescence ( Fig . S7A ) . Treating the cells with PS alleviated the DN Rab7 inhibition , as evidenced by the appearance of cells displaying both DiD- and EGFP- fluorescence ( Fig . S7B ) . These findings further substantiate our hypothesis that DEN delays its fusion until entry into late endosomes because of the AL-enriched lipid composition of their membranes and does not fuse in early endosomes because these organelles normally do not have anionic lipids . Since fusion is an early stage of DEN infection , promotion of DEN fusion in endosomal compartments may promote DEN infection . We extended our work from fusion assays to infection analysis and explored the effects of AL on the physiologically-relevant pathway of infection via endocytosis . A strong ( ∼16-fold ) promotion of infection for cells supplemented with PS or PG ( Fig . 4C ) indicates that the AL dependence of DEN fusion results in a corresponding dependence of viral infection . Conformational changes of DEN E and DEN fusion with insect cells are already triggered at the moderately acidic pH ( ∼pH 6 . 0 ) ( Fig . 1B ) that the endocytosed virus is expected to encounter in early endosomes . While premature activation of most viral fusogens inactivates them [37] , inactivation of DEN has to be relatively slow to keep viral fusion machinery functional until the virus reaches late endosomes . Indeed , we found that more than half of DEN particles remained fusogenic towards LEM-liposomes after a 15-min pre-incubation at pH 5 . 5 either in the absence of the target membranes or in the presence of fusion-incompetent PC liposomes ( Fig . 5A ) . Low pH exposure of DEN at the surface of MA104 cells resulted in similarly slow inhibition of subsequent intracellular fusion ( Fig . 5B ) . Since most of the endocytotic cargo reaches late endosomes within 15 min [38] , we concluded that the inactivation of DEN at acidic pH is slow enough to preserve the virus's fusogenic properties during its trafficking from early to late endosomes . Which of the stages of the fusion pathway mediated by DEN E protein depends on the AL presence in the target membranes ? Diverse fusion processes start with a local merger of contacting leaflets of two membranes , a stage referred to as hemifusion [39] . However , lipid flow through the earliest hemifusion intermediates may be restricted by the proteins surrounding the fusion site [39] . These ‘restricted hemifusion’ intermediates ( RH ) can be transformed into complete fusion with chlorpromazine ( CPZ ) . Inverted-cone shaped CPZ preferentially partitions into the inner leaflets of cell membranes and breaks hemifusion structures composed solely of these leaflets [40] . To test whether DEN E forms RH intermediates , we used the experimental system of HAb2 cells with pre-bound human red blood cells ( RBC ) [41]–[42] utilized in earlier studies on RH [43] . HAb2 cells express HA0 , an uncleaved form of influenza hemagglutinin that mediates very tight binding between HAb2 and RBC but is fusion-incompetent [43] . We allowed DEN to bind to HAb2 cells for 30 min at 10°C , then added RBCs labeled with a fluorescent lipid PKH26 and 15 min later washed out unbound virus and RBCs . Low pH application to HAb2–DEN–RBC complexes yielded no lipid mixing , unless the cells were pre-treated with PS ( Fig . 6A ) . However , robust fusion was observed for cells not treated with PS when low pH pulse was followed by an immediate application of CPZ indicating that DEN-mediated HAb2-RBC fusion was blocked at the stage of RH intermediates . The extents of lipid mixing gradually decreased when we extended the time interval between the end of low pH application and the CPZ pulse , indicating that RH formed by DEN E dissociated with time ( Fig . 6B ) . This is similar to RH in fusion mediated by influenza and SIN viruses [43] . Likewise , 40% of cell complexes developed lipid mixing when PS was applied 5 min after the end of a 1-min pulse of pH 5 . 3 , with much lower lipid mixing observed when PS was applied 30-min after the end of the low pH pulse . These findings indicated that pre-lipid-mixing fusion intermediates formed by low-pH-conformations of DEN E advanced to yield lipid mixing in the presence of AL independently of low-pH but dissociated with time in the absence of AL . To summarize , DEN-AL interactions facilitate the transition from RH to more advanced fusion intermediates . The AL dependence of this transition distinguishes fusion machinery of DEN from that of SIN . As expected [43] , SIN effectively mediated lipid mixing between RBC and HAb2 cells not treated with PS and CPZ application strongly promoted SIN-mediated fusion only for suboptimal pH showing that for pH≤5 . 8 most of the RH intermediates advanced to yield lipid mixing without CPZ application ( Fig . S8 ) .
All membrane fusion reactions are expected , and in many cases shown , to depend on the lipid composition of the membranes . Some dependencies are conserved among fusion processes driven by very diverse fusogens [39] . In contrast , the essential role of AL for DEN fusion is not conserved even within the same class ( II ) of viral protein fusogens . In spite of similarities between the structures of the fusogens utilized , another flavivirus , TBE , and alphaviruses such as SIN effectively fuse with AL-free target membranes [44]–[45] . Interestingly , although it is AL-independent , the fusion of alphaviruses including SIN does require the specific lipids cholesterol and sphingomyelin as cofactors for the protein machinery [46]–[48] , and neither of these lipids is required for DEN fusion . The specific mechanisms by which DEN E interactions with AL control E restructuring and fusion remain to be clarified . To start with , the local pH near a membrane containing AL is lower than the pH in the bulk of the solution because of the accumulation of protons near the negatively charged lipid headgroups . The changes in ion concentration at a charged surface are described by the Gouy-Chapman theory that yields the relation between the surface charge density , the electrostatic potential , the ionic strength of the solution and ion distributions near charged membranes [49] . An estimate based on the Gouy Chapman theory suggests that for a bilayer containing 30 mol% AL in 100 mM NaCl buffer , the apparent pH dependence of fusion may be shifted to a less acidic pH by up to 0 . 7 units . However , while DEN fusion with liposomes containing 30 mol% PS was already observed at pH 6 . 8 , there was almost no lipid mixing for AL-free PC liposomes even at pH 4 . 5 ( not shown ) . Thus , the AL-induced shift of local pH does not fully explain the role of AL as a prerequisite for DEN fusion . Note that pH dependences of fusion observed for dengue virus in different assays somewhat differ ( see for instance , Fig . 1B and Fig . 2B ) . These differences likely reflect different numbers of activated fusion proteins required to reach detectable fusion stages for different target membranes . In the fusion pathway mediated by several viruses , RH is followed by more-energy intensive stages that culminate in opening of an expanding fusion pore [39] , [43] . While DEN forms the RH intermediates in the absence of AL , the later stages of DEN fusion require interactions between E protein and AL . In alphavirus fusion , the lipid cofactor cholesterol promotes the insertion of E1 fusion loops into the target membrane and formation of functional E1 homotrimers [47] . In analogy to this mechanism , we propose that the completion of DEN fusion requires E assembly into stable trimers dependent on interactions between the fusion loop of DEN E and AL in the target membrane ( Fig . 7 ) . Indeed , for a synthetic peptide representing the fusion loop region of DEN peptide , insertion into the lipid bilayer and peptide–peptide interactions at the bilayer surface are promoted in the presence of AL [50] . To facilitate the development of antivirals targeting the fusion stage of DEN entry it is important to have a reliable quantitative approach that may be used in a high throughput in vitro screening . In the most recent studies [11] , [51] , DEN fusion to liposomes has been detected by exposure of the viral core protein to liposome-encapsulated trypsin . A successful application of this approach ( first developed for another virus in [52] ) to DEN is an important advance . However this approach is difficult to quantify and still needs to be validated by excluding the possible role of leakages [52] that may accompany DEN-target membrane interactions at acidic pH [51] . For many viruses , including SIN and some flaviviruses , fusogenic activity can be conveniently monitored by measuring lipid mixing between viruses and liposomes [16]–[17] , [19] , [30]–[32] , [44] , [52]–[55] . However , development of a lipid mixing assay for DEN fusion has proved to be surprisingly challenging [56] . Our work explains the very low efficiency of lipid mixing between DEN virions and AL-free liposomes [11] and describes a simple fluorescence dequenching assay of fusogenic activity of DEN towards AL-containing liposomes . In addition to a lipid mixing assay that characterizes fusion between DiD-labeled DEN and liposomes by measuring DiD dequenching , we also used DiD-labeled virions to develop the intracellular DEN fusion assay . Our assay is based on earlier work [3] , [57] that elegantly explored the DEN entry pathway in living cells by following a single DiD-labeled DEN particle using real-time fluorescence microscopy . In our approach , instead of detailed characterization of the time course and localization of the DEN fusion events for ∼50–100 virions achieved in [3] , [57] , we have focused on developing a much simpler approach characterizing intracellular DEN fusion as averaged over thousands of virions and cells . We expect our quantitative assays of DEN fusion within cells and with liposomes to be of help in screening for potential anti-DEN drugs . Viruses have developed different strategies to prevent the premature release of the conformational energy of their protein fusogens that would result in irreversible “discharge” or in undesirable fusion . For many viruses , including DEN , which utilize low pH-dependent fusogen proteins , these proteins are synthesized in an inactive form and then are converted to a mature fusion-competent form by proteolytic cleavage of the fusogen itself or an accessory protein [6]–[7] , [10] . However , viral fusogens such as DEN E that can be activated at pH values close to neutral may need additional mechanisms for avoiding premature release of the conformational energy stored in the proteins . Our finding that DEN neither fuses nor rapidly inactivates in the absence of AL-containing target membranes suggests that AL dependence of DEN prevents functional inactivation of the virus until it reaches AL-enriched late endosomes ( Fig . 7 ) . RH connections provide an additional receptor-independent docking mechanism that holds viral and endosomal membranes in tight contact . Within multivesicular endosomes , the virus can fuse either to the limiting membrane or to the internal vesicles that contain the highest concentration of AL BMP [21] . In the latter case , viral RNA release requires an additional fusion event: BMP-dependent but likely DEN E-independent back-fusion between the internal vesicle and the limiting membrane . Back-fusion has been proposed as a mechanism for endosome-to-cytosol transport of RNA of vesicular stomatatis virus ( [58] , but see [59] ) . Our work concentrates on fusion mediated by DEN serotype 2 , strain TH-36 . While experiments on virus/liposome lipid mixing for dengue virus of serotype 4 , strain H241 ( Fig . S2 ) and experiments on virus/liposome lipid mixing and intracellular fusion for dengue virus of serotype 2 , strain New Guinea C suggest that these viruses have a similar dependence on AL , DEN virions of different serotypes and strains may use alternative pathways for their entry into mammalian cells [60]–[61] . Future research will clarify the applicability of the AL-dependent mechanism of timing viral fusion to the entry into late endosomes for different serotypes/strains of DEN . To summarize , the AL dependence of DEN fusion identified in our work suggests a novel mechanism allowing viruses to exploit cell-controlled changes in membrane lipid composition . In this mechanism , internalized virus uses the specific lipid composition of late endosomes highly enriched in AL as a way of timing fusion to deliver viral RNA to its translation-replication sites effectively . We hope the assays developed in this study to directly characterize DEN fusion will help in developing antivirals including those targeting DEN-AL interactions to block or prematurely activate DEN E refolding . Furthermore , interactions between DEN E and AL may have implications for the pathogenesis of dengue hemorrhagic fever , which is characterized by activation of endothelial cells and extracellular exposure of PS .
Vero , MA104 , BHK21 , BS-C-1 , CHO-K1 , RAW 264 . 7 , U967 , and NIH3T3 cells ( American Type Culture Collection ( ATCC ) , Manassas , VA ) , and HAb2 cells ( a kind gift of Dr . Judith White , University of Virginia ) , a line of NIH 3T3 cells stably expressing A/Japan/305/57 influenza hemagglutinin ( HA ) in the immature fusion-incompetent HA0 form [41] were grown in Advanced DMEM medium ( ADMEM ) supplemented with 10% fetal bovine serum , 25 mM HEPES , 2 mM glutamine , and antibiotics ( complete medium ) at 37°C and 5% CO2 . Aedes albopictus C6/36 ( American Type Culture Collection , Rockville , MD ) were cultured in the complete medium at 28°C and 5% CO2 . To facilitate detection of cell fusion events by coplating differently labeled cells , we developed Vero , CHO-K1 , and BHK21 cells stably expressing either EGFP or mRedFP according to the standard procedure using pEGFP and pmRFP plasmids ( kind gifts of Dr . Eugene Zaitsev , NIH , NICHD ) . Human red blood cells ( RBC ) were freshly isolated from whole blood obtained from the National Institutes of Health ( Bethesda , MD ) blood bank and labeled with a fluorescent lipid PKH26 ( Sigma , St . Louis , MO ) , as described in [62]–[63] . If not stated otherwise , we used dengue virus of serotype 2 , strain TH-36 . In some experiments we used dengue virus of serotype 2 , strain New Guinea C and dengue virus of serotype 4 , strain H241 . All viruses were purchased from ATCC . We propagated DEN by inoculating monolayers of C6/36 cells or Vero cells in complete medium in the presence of 0 . 005% Pluronic F-127 ( Invitrogen , Eugene , OR ) at a multiplicity of infection ( MOI ) of 0 . 1 . DEN particles released from the cells were harvested 5 days postinfection and cleared from cell debris by means of low-speed centrifugation . Working virus stocks with titers between 107 and 108 infectious units ( IU ) /ml were kept at 4°C for less than two weeks . SIN strain AR-339 ( ATCC ) was used for infection of Vero or C6/36 cells with a low multiplicity of infection to propagate the working virus stocks with titer between 108–109 IU/ml . Virus was collected 24-hours post-infection . DEN and SIN were titrated for Vero cells using a fluorescent focus assay . Viruses were concentrated by overnight centrifugation ( SW28 rotor , 21 K rpm , 4°C ) on a 55% cushion of Optiprep Density Gradient medium ( Sigma , St . Louis , MO ) buffered with 20 mM Tricine-HCl and 140 mM NaCl , pH 7 . 8 ( buffer TrN ) supplemented with 0 . 005% Pluronic F-127 . To label viral particles with a self-quenching concentration of a fluorescent lipid , we mixed 10 µl of a 1 mM DiD solution from a Vybrant cell-labeling kit ( Molecular Probes , Eugene , OR ) with 1 µl of 10% Pluronic F-127 and bath-sonicated the dispersion for 5 min . Freshly prepared DiD dispersion was injected into 1 ml of virus stock ( approximately 4×108 IU ) under intensive vortexing . The mix was incubated for 30 min at room temperature and then for 2 hr at 4°C . Labeled virus was purified from unincorporated dye and from non-viral membranes and proteins by centrifugation ( SW55 rotor , 1 . 5 h , 53 K rpm , 4°C ) on a 40%–25%–20%–15% step gradient of Optipep density medium in TrN supplemented with 0 . 005% Pluronic F-127 . We collected the band between 20% and 25% densities containing DiD-labeled virus . BSA ( final concentration 1% ) was added to stabilize the preparation . Labeled virus was used within 3 days . Before experiments , the viral suspension was passed through a PES Millipore 0 . 22 µm filter to remove viral aggregates . In fluorescence microscopy examination , the number of DiD-labeled spots practically coincided with the number of fluorescent spots observed when viral particles ( DEN or SIN ) were visualized by immunofluorescence with DEN antibody ( MAB 8705 ) or SIN antibody ( Sindbis Hyperimmune Ascetic Fluid ) . DiD-labeled virus retained infectivity , but with an approximately two-fold titer decrease . All lipids used: Chol , PC ( 1 , 2-dioleoyl-sn-glycero-3-phosphocholine ) ; PE ( 1 , 2-dioleoyl-sn-glycero-3-phosphoethanolamine ) ; SPM ( N-oleoyl-D-erythro-sphingosylphosphorylcholine ) ; PI ( L-α-phosphatidylinositol , Soy ) ; BMP ( bis ( monooleoylglycero ) phosphate , S , R Isomer ) ; PS ( 1 , 2-dioleoyl-sn-glycero-3-phospho-L-serine ) ; and PG ( 1 , 2-dioleoyl-sn-glycero-3-phospho- ( 1′-rac-glycerol ) ) were purchased from Avanti Polar Lipids , Alabaster , AL . Liposomes were formed from the following lipid mixtures: PM composition: PC , phosphatidylethanolamine ( PE ) , sphingomyelin ( SPM ) , cholesterol ( Chol ) in a molar ratio of 4/1/0 . 5/4 . 5; LEM composition , PC/PE/phosphatidylinositol ( PI ) /BMP in a molar ratio of 5/2/1/2; LEM-Chol composition , PC/PE/PI/BMP/Chol in a molar ratio of 4/1/1/2/2 ) and from binary mixtures of PC with either BMP , PG , or PS in a 7/3 molar ratio . To form large unilamellar liposomes , lipid mixtures dissolved in benzene/methanol ( 95:5 ) were frozen in liquid nitrogen and then freeze-dried under vacuum overnight . Dry lipid powder was re-suspended in TrN buffer by vigorous vortexing . We subjected the lipid suspension to 10 freeze-thaw cycles by alternating immersion into liquid nitrogen and hot water . Finally , we extruded the lipid suspension 10 times through double-stacked track-etched 100 nm-pore polycarbonate filters ( GE Osmonics ) using a LIPEX extruder . Liposome sizes were checked using dynamic light scattering on N4 plus submicron particle size analyzer ( Beckman Coulter , USA ) . Liposomes were kept on ice and used within a day . In all experiments that involved virus-to-cell binding we incubated cells with viruses at 10°C . We found this temperature to be optimal for DEN binding because at lower temperatures ( for instance , at 4°C ) viral binding was very inefficient , and at higher temperatures we observed internalization of viral particles by cells . To compare cell binding for DEN with that for SIN , we surface-biotinylated DEN and SIN particles by incubating viruses ( 106 IU ) with 10 mM EZ-Link Sulfo-NHS-SS-Biotin ( Pierce Biotechnology , Rockford , IL ) in 0 . 5 mL phosphate buffer solution ( PBS ) supplemented with 20 mM Tricine , pH 7 . 8 ( PBS-T buffer ) for 30 min at room temperature . The reaction was quenched with 100 mM glycine in PBS . Biotinylated virus ( 105 IU of DEN or SIN ) was allowed to bind to lifted CHO-K1 cells ( 104 cells ) at 10°C for 30 min . Cells were washed three times with cold serum-free ADMEM medium supplemented with 1% BSA by pelleting at 4°C . After removing the unbound virus , we incubated the cells with streptavidin Alexa Fluor 488 conjugate ( Molecular Probes , Eugene , OR ) as recommended by the manufacturer and carried out flow cytometry analysis using a FACSCanto fluorescence-activated cell sorter ( BD Biosciences ) . In another experimental approach , we incubated the C6/36 and MA104 cells with DiD-labeled virus in 35 mm plates at 10°C for 1 hr in the ADMEM medium . Unbound viral particles were removed by washing , treated with 2 . 5 µM NBD-PC at 4°C for 5 min . The cells were washed with cold PBS . Then the cells were lysed and DiD fluorescence dequenched by replacing PBS with 200 µl of PBC supplemented with 1% Triton X100 . Samples were cleared from debris by centrifugation . C6/36 cells are much smaller than MA104 cells . To compare surface densities of bound DEN particles at plasma membrane of C6/36 and MA104 cells , we normalized the DiD fluorescence that provides a measure of the amount of bound virus to the NBD fluorescence that provides a measure of the total area of plasma membranes accessible for NBD-PC insertion . This normalization is based on the assumption that NBD-PC similarly partitions into the outer leaflets of plasma membrane bilayers of different cells . The effects of AL on virus infection were quantified in MA104 , Vero and BHK21 cells by fluorescent focus assay . Briefly , serial dilutions of DEN in the ADMEM medium were incubated with confluent monolayers of the cells at 10°C for 60 min . After removing unbound viruses , the cells were treated with 2 . 5 µM of PS or PG in the ADMEM medium for 5 min at 4°C . The cells were incubated at 37°C for 1 hour in the ADMEM medium , then overlaid with the medium containing 2% FBS and supplemented with 0 . 75% carboxymethyl-cellulose . The cells were grown for 3 days at 37°C and fluorescent focus units were detected by immunostaining with the primary monoclonal antibody 4G2 and fluorescent secondary antibodies against mouse IgG . Data were normalized to control infection observed for the cells not treated with any exogenous lipids . Cells grown to high confluency were incubated with DEN or SIN in complete medium for 30 min at 10°C . Unbound virus was removed by three washes with cold PBS-T , and fusion was triggered at room temperature by application of serum-free ADMEM medium adjusted to different pH values with MES and acetic acid . In unsuccessful attempts to achieve DEN-mediated fusion of mammalian cells , we increased the MOI to 1 , 000 and extended the duration of the low-pH application to 15 min . To detect virus-mediated fusion between mammalian cells by redistribution of aqueous contents , we co–plated cells expressing either EGFP or mRedFP at a 1∶1 ratio . A day later , we incubated the cells with virions ( DEN , MOI of 300 or SIN , MOI of 40 ) as described above , applied a 5-min low-pH pulse , and incubated cells for 30 more minutes in the complete medium at 37°C . The average number of co-labeled cells per microscopic field was normalized to the average number of contacts between differently labeled cells in the control experiment , in which cells were not treated with low pH . For each condition , we carried out 3 independent experiments and analyzed at least 10 microscopic fields in each experiment . To score fusion between mosquito cells C6/36 with bound DEN ( MOI of 100 ) or SIN ( MOI of 40 ) virions we treated the cells with media of different pH for 15 min and then re-neutralized the cells . After a 2-hour incubation at 37°C in complete medium we quantified the efficiency of syncytium formation by measuring a decrease in the number of mononucleated cells . More than 10 fields of view were analyzed for each experimental condition . In the experiments on virus-mediated HAb2-RBC fusion , cells with associated virions were incubated with PKH26-labeled RBCs to achieve 0–2 bound RBC per cell [62] . After three washes with PBS-T to remove unbound RBC , HAB2–virion–RBC complexes were treated with PBS titrated with citrate to an acidic pH for 5 min and then re-neutralized with PBS-T . Fusion was quantified as the ratio of dye-redistributed bound RBC to the total number of bound RBC . While the inability of DEN to fuse mammalian cells is illustrated in the figures only for CHO-K1 cells and Vero cells and HAb2-RBC fusion , we carried out similar experiments and observed no DEN-mediated fusion for MA104 , BHK21 , BS-C-1 , RAW 264 . 7 , U967 , and NIH3T3 cells . Fusion of DEN virions to the plasma membrane of MA104 cells , Vero and BHK-21 cells was evaluated by a fusion-infection assay ( FIA ) [34] . This assay is based on measuring infection caused by low-pH-induced fusion between viral particles and plasma membrane under conditions when endocytotic entry of virus is blocked by inhibitors of endosomal acidification . We plated the cells on Lab-tek II 8-well chambered coverglass ( Nalge Nunc ) a day before experiment , then pre-treated them with 50 µM chloroquine ( Sigma , St . Louis , MO ) for 30 min at 37°C . Viral particles were allowed to bind to the cell surfaces at 10°C for 60 min ( MOI = 300 ) . After removal of unbound virus , the cells were treated ( or not treated ) with 2 . 5 µM of PS or PG in the ADMEM medium for 5 min at 4°C . Fusion was triggered at room temperature by replacing the medium with the ADMEM medium acidified to pH 5 . 3 . 5 min later the cells were neutralized , incubated at 37°C for 4 hours in the ADMEM medium supplemented with chloroquine . By that time , virions that did not infect the cells by low pH-induced fusion to plasma membrane , have been internalized and , because of the blocked endosomal acidification and , thus , fusion , have already passed the endosomal compartments allowing productive RNA release and infection . After this 4 hour incubation in the presence of chloroquine , we overlaid the cells with the ADMEM medium supplemented with 2% FBS and 0 . 75% carboxymethyl cellulose to prevent virus spread . Fluorescent focus units were detected 3 days later by immunostaining with the antibody 4G2 . Data were normalized to those obtained for the cells that were not treated with AL . We found FIA assay for DEN and mammalian cells to be very sensitive to the concentration of chloroquine ( and other inhibitors of endosomal acidification ) and the duration of the application of these inhibitors ( longer applications results in cytotoxity ) and to MOI used . MA104 and BS-C-1 cells were grown on the coverglass bottom of 35-mm tissue culture dishes ( MatTek , MA ) to high confluency . DiD-labeled virus was added to cells ( MOI of 100 ) and allowed to bind for 30 min at 10°C . Unbound virus was removed by three washes with cold PBS-T . The cells were warmed up to 37°C to allow virus internalization . After incubation of cells at 37°C for different times , the cells were fixed with 4% paraformaldehyde and analyzed by fluorescence microscopy . Fusion of DiD labeled virus within endosomes leads to dequenching of DiD and appearance of bright fluorescent spots throughout the whole cell but mostly in the perinuclear region . To quantify fusion efficiency , we imaged cells with an iXonEM+ 885 EMCCD Camera ( Andor Technology , CT ) on an AxiObserver inverted fluorescence microscope ( Zeiss , Germany ) using a Cy5-4040A filter set ( Semrock , NY ) . To capture signal from all fused viruses , we collected image stacks throughout the cell with 250 nm spacing between slices . We analyzed the acquired images using an ImageJ macro developed in-house to measure the total fluorescence signal from bright spots per imaging field ( 20 fields per experimental condition were analyzed in each independent experiment; each experiment was repeated at least three times ) . We assayed lipid mixing between DiD labeled viral particles and liposomes as DiD dequenching in four-clear-sided methacrylate cuvettes ( Fisher Scientific , Pittsburgh , PA ) . The medium in the cuvettes was continuously stirred with a magnetic stirring device and thermostatted at 37°C . We mixed 10 µl ( ∼105 IU ) of purified labeled virus with liposomes ( final concentration of lipid 30 µM ) in 2 ml of TrN buffer . We initiated the fusion reaction by adding a pre-titrated amount of MES/acetic acid buffer to reach the desired pH . We recorded fluorescence for at least 15 min at excitation and emission wavelengths of 620 and 665 nm , respectively , using an Aminco Bowman Series 2 luminescence spectrometer ( Rochester , NY ) . At the end of each recording , we added Triton X-100 to a final concentration of 0 . 1% to fully dequench DiD ( “100% lipid mixing” ) . We routinely verified that under our conditions the efficiency of lipid mixing ( rates and extents ) was not limited by the concentration of the liposomes used . DEN was inactivated by a 15-min incubation at room temperature in PBS-T supplemented with 2 mM DEPC ( Sigma , St . Louis , MO ) added from freshly prepared stock solution in cold ethanol . DEPC was reported to inhibit vesicular stomatatis virus by modifying histidine residues on viral protein fusogen [25] . To add exogenous lipids ( PS or PC or PG ) to plasma membranes of CHO-K1 , MA104 , BS-C-1 cells and HAb2–RBC pairs , we incubated the cells with associated virions in a PBS supplemented with 16:0-06:0 NBD PS ( 1-palmitoyl-2-{6-[ ( 7-nitro-2-1 , 3-benzoxadiazol-4-yl ) amino]hexanoyl}-sn-glycero-3-phosphoserine ) , 16:0-06:0 NBD PC ( 1-palmitoyl-2-{6-[ ( 7-nitro-2-1 , 3-benzoxadiazol-4-yl ) amino]hexanoyl}-sn-glycero-3-phosphocholine ) or 16:0-06:0 NBD PG ( 1-palmitoyl-2-{6-[ ( 7-nitro-2-1 , 3-benzoxadiazol-4-yl ) amino]hexanoyl}-sn-glycero-3-[phospho-rac- ( 1-glycerol ) ] ) , all lipids purchased from Avanti Polar Lipids , Alabaster , AL ) . A 2 . 5 mM stock solution ( 1 µl ) of PS , PC or PG in ethanol was injected into 1 ml of PBS-T under intensive vortexing . The cells were cooled down to 4°C and placed into this lipid-supplemented medium for 5 min , still at 4°C . We inferred from the levels of cell-associated NBD fluorescence observed with fluorescence microscopy that PS , PC and PG incorporated into cell membranes to similar concentrations ( see also [64] ) . For virus-mediated cell–cell fusion , the lipid-supplemented buffer was replaced with warm ( room temperature ) serum-free ADMEM medium adjusted to different pH values by titration with MES and acetic acid . After the end of low pH application the cells were incubated at 37°C for 30 min and fusion was quantified by fluorescence microscopy . We verified that at the time of low pH application a significant part of exogenous lipids remain extractable by delipidated BSA . For the intracellular fusion assay , the cells with bound DiD-labeled DEN or SIN virions were treated or not treated with exogenous lipids as described above . The temperature was raised and , after incubation at 37°C for different times , we fixed the cells and analyzed them by fluorescence microscopy . To reveal DEN-mediated RH between HAb2 cells and PKH26-labeled RBCs , a 5-min low pH pulse was followed by a 1-min application of a 0 . 5 mM solution of CPZ ( Sigma , St . Louis , MO ) in PBS-T . The percentage of HAb2–RBC pairs demonstrating lipid mixing ( PKH redistribution from RBC to HAb2 cell ) was assayed with fluorescence microscopy 20 min after the end of the low–pH pulse . Annexin V is widely used to evaluate the expression of PS on cell surfaces [65] . We used this protein in two different experimental approaches . To inhibit DEN interactions with PS in the outer leaflet of plasma membranes of C6/36 cells , we first incubated the cells in annexin-binding buffer ( BD Pharmingen , San Jose , CA ) at room temperature for 30 min and then allowed DEN or SIN virions to bind to the cells . After removal of unbound virions , we incubated the cells with 50 µg/ml recombinant annexin V ( BD Pharmingen , San Jose , CA ) for 30 min at 10°C , washed the cells from unbound annexin , and then triggered fusion by applying an acidic pH medium . We verified that annexin V treatment had no effect on virus–cell binding using DiD-labeled virions . To compare PS expression at the surfaces of different cells , we used R-phycoerythrin -tagged annexin V ( BD Pharmingen , San Jose , CA ) , as recommended by the manufacturer . In some experiments on intracellular fusion , to block endosomal acidification we treated the cells with the lysosomotropic agents chloroquine ( Sigma , St . Louis , MO; 50 µM , 30 min , 37°C ) or bafilomycin-A1 ( Sigma , St . Louis , MO; 2 µM , 30 min , 37°C ) prior to applying viral inoculum . Cells were transfected using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer's instructions with a EGFP-Rab7a S22N plasmid [66] , a kind gift from Julie Donaldson , NIH , on Lab-tek II 4-well chambered coverglass ( Nalge Nunc ) . 18 hours later , DiD-labeled viral particles were allowed to bind to the cell surfaces at 10°C for 60 min . After removal of unbound virus , cells were treated ( or not treated ) with 2 . 5 µM of PS in the ADMEM medium for 5 min at 4°C and then incubated in the ADMEM medium at 37°C for 40 min . Cells were fixed and nuclei were stained with DAPI ( Invitrogen ) . Analysis by fluorescence microscopy allowed us to identify the transfected cells by their EGFP fluorescence and to detect DEN fusion within endosomal pathway as intracellular structures displaying DiD fluorescence . MA104 cells were preincubated with nocodazole ( Sigma , St . Louis , MO; 60 µM in complete medium , 30 min , 37°C ) prior to application of DEN and exogenous lipids . Nocodazole was present throughout the entire experiment . | Dengue virus infection is a growing public health problem with up to 100 million cases annually , and neither vaccines nor effective therapies are available . To search for the ways of preventing and treating dengue infections we need to better understand their molecular mechanisms . As with many other viruses , dengue virus enters cells by fusion between the viral membrane and the membrane of intracellular vesicles ( endosomes ) . In this work we explored the fusion stage of dengue virus entry in different experimental systems ranging from virus fusion to artificial lipid membranes to fusion inside the cells . While earlier work on dengue virus entry has focused on viral protein that mediates fusion , we found that effective action of this protein requires specific lipid composition of the membrane the virus fuses to . In effect , this lipid dependence allows virus to control intracellular location of the fusion event and , thus , the place of its RNA release by exploiting cell-controlled differences between lipid compositions of different organelles the virus travels through . The essential role of the interactions between dengue virus and its lipid cofactors during viral entry suggests that these interactions may be targeted in drug design . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/host",
"invasion",
"and",
"cell",
"entry"
] | 2010 | Dengue Virus Ensures Its Fusion in Late Endosomes Using Compartment-Specific Lipids |
Zika virus ( ZIKV ) is an emerging flavivirus typically causing a dengue-like febrile illness , but neurological complications , such as microcephaly in newborns , have potentially been linked to this viral infection . We established a panel of in vitro assays to allow the identification of ZIKV inhibitors and demonstrate that the viral polymerase inhibitor 7-deaza-2’-C-methyladenosine ( 7DMA ) efficiently inhibits replication . Infection of AG129 ( IFN-α/β and IFN-γ receptor knock-out ) mice with ZIKV resulted in acute neutrophilic encephalitis with viral antigens accumulating in neurons of the brain and spinal cord . Additionally , high levels of viral RNA were detected in the spleen , liver and kidney , and levels of IFN-γ and IL-18 were systematically increased in serum of ZIKV-infected mice . Interestingly , the virus was also detected in testicles of infected mice . In line with its in vitro anti-ZIKV activity , 7DMA reduced viremia and delayed virus-induced morbidity and mortality in infected mice , which also validates this small animal model to assess the in vivo efficacy of novel ZIKV inhibitors . Since AG129 mice can generate an antibody response , and have been used in dengue vaccine studies , the model can also be used to assess the efficacy of ZIKV vaccines .
Zika virus ( ZIKV ) , a mosquito-borne flavivirus , was first isolated from a febrile Rhesus monkey in the Zika Forest in Uganda in 1947 [1] . During the last 5 decades sporadic ZIKV infections of humans were reported in Gabon , Nigeria , Senegal , Malaysia , Cambodia and Micronesia [2 , 3 , 4] , leading to a benign febrile disease called Zika fever . The latter is characterized by headache , maculopapular rash , fever , arthralgia , malaise , retro-orbital pain and vomiting [5 , 6] . In 2007 , an epidemic of fever and rash associated with ZIKV infection was reported in Micronesia . During this outbreak 185 cases of ZIKV infections were confirmed . The seroprevalence in the affected region was 73% [7] . During the more recent ZIKV outbreak in French Polynesia [FP] between October 2013 and February 2014 over 30 , 000 people sought medical care [8 , 9] . Since then , ZIKV has spread to new areas in the Pacific , including New Caledonia , the Cook Islands , and Chile’s Easter Island [7 , 10] . As of 2015 ZIKV is causing an epidemic in Central and South America with an increasing number of cases reported particularly in Brazil , Colombia and El Salvador [11–14] , demonstrating that this is a truly emerging human pathogen . Hundreds of cases of Guillain-Barré syndrome have been reported in the wake of ZIKV infections [15 , 16 , 17] . As a result of a marked increase in the number of cases of microcephaly among infants born to virus-infected women , Zika has been declared a public health emergency of national importance in Brazil [16 , 17 , 18] . In addition , an increasing number of travelers returning sick from endemic regions were diagnosed with ZIKV [19–24] . The Aedes aegypti mosquito , the primary vector for ZIKV transmission , is expanding in all ( sub- ) tropical regions of the world and was recently reported to be present in California , USA [25] . There is neither a vaccine nor a specific antiviral therapy for the prevention or treatment of infections by ZIKV . The increasing incidence of Zika fever stresses the need for both preventive and therapeutic measures . We here report on the establishment of ( i ) a panel of assays that allow to identify inhibitors of ZIKV replication as well as ( ii ) a robust animal model of ZIKV infection with brain involvement . The viral polymerase inhibitor 7-deaza-2’-C-methyladenosine ( 7DMA ) was identified as an inhibitor of in vitro ZIKV replication and was shown to reduce viremia and to delay the time to disease progression in virus-infected mice .
Ribavirin , 1- ( β-d-ribofuranosyl ) -1H-1 , 2 , 4-triazole-3-carboxamide ( Virazole; RBV ) was purchased from ICN Pharmaceuticals ( Costa Mesa , CA , USA ) . 2’-C-methylcytidine ( 2’CMC ) and 7-deaza-2'-C-methyl-D-adenosine ( 7DMA ) were purchased from Carbosynth ( Berkshire , UK ) . Favipiravir ( 6-fluoro-3-hydroxy-2-pyrazinecarboxamide; T-705 ) and its defluorinated analogue T-1105 ( 3-hydroxypyrazine-2-carboxamide ) were obtained as custom synthesis products from abcr GmbH ( Karlsruhe , Germany ) . ZIKV ( strain MR766 , passaged five times in the insect cell line C6/36 ) was obtained from the European Virus Archive ( EVA; http://www . european-virus-archive . com/viruses/zika-virus-strain-mr766 ) . Lyophilized virus was reconstituted in DMEM and virus stocks were generated on C6/36 mosquito cell cultures ( ATCC CRL-1660 ) grown in Leibowitz medium supplemented with 10% fetal calf serum ( FCS ) , 1% non-essential amino acids ( NEAA ) and 20 nM HEPES at 28°C , without CO2 . At the time ZIKV caused a complete cytopathic effect ( CPE ) [d5-d7 post infection; pi] the supernatant was harvested and viral titers were determined by endpoint titration on Vero cells ( African Green monkey kidney cells; ECACC ) , Vero E6 cells ( Vero C1008; ATCC CRL-1586 ) and BHK-J21 cells ( baby hamster kidney cells; ATCC CCL-10 ) . For end point titrations , cells were seeded in a 96-well plate at 5×103 or 104 cells/well in 100 μL assay medium and allowed to adhere overnight . The next day , 100 μL of ZIKV was added to each well , after which the virus was serially diluted ( 1:2 ) . Following 5 days of incubation , culture medium was discarded and replaced with ( 3- ( 4 , 5-dimethylthiazol-2-yl ) -5- ( 3-carboxymethoxyphenyl ) -2- ( 4-sulfophenyl ) -2H-tetrazolium; MTS ) and the absorbance was measured at 498 nm following a 1 . 5h-incubation period . Subsequently , cultures were fixed with ethanol and stained with 1% Giemsa staining solution ( solution of azure B/azure II-eosin/methylene blue 1:12:2 ( w/w/w ) in glycerol/methanol 5:24 ( v/v ) ; total dye content: 0 . 6% ( w/w ) Sigma-Aldrich ) . The different cell types as well as ZIKV tested negative for mycoplasma . Vero cells were grown in growth medium , consisting of MEM ( Life Technologies ) supplemented with 10% FCS , 2 mM L-glutamine and 0 . 075% sodium bicarbonate ( Life Technologies ) . Antiviral assays were performed using the same medium except that 10% FCS was replaced with 2% FCS , referred to as ‘assay medium’ . Vero cells were seeded at a density of 104 cells/well in a 96-well plate in 100 μL assay medium and allowed to adhere overnight . To each well 100 μl of culture medium containing 50% cell culture infectious doses ( i . e . , CCID50 ) of ZIKV was added , after which 2-fold serial dilutions of the compounds were added . Following 5 days of incubation CPE was determined by means of the MTS readout method and by microscopic evaluation of fixed and stained cells . In parallel , cell cultures were incubated in the presence of compound and absence of virus to evaluate a potential cytotoxic effect . The 50% effective concentration ( EC50 ) , which is defined as the compound concentration that is required to inhibit virus-induced CPE by 50% , and 50% cytotoxic concentration ( CC50 ) , which is defined as the compound concentration that is required to inhibit the cell growth by 50% , was visually determined . The Z’ factor was calculated by the following formula 1-[3× ( SDCC+SDVC ) / ( ODCC-ODVC ) ]; VC , virus control; CC , cell control . Vero cells were seeded at a density of 5×104 cells/well in 96-well plates in growth medium and allowed to adhere overnight . Cells were washed 3 times with PBS and incubated with 100 μL CCID50 ( MOI~0 . 2 ) of ZIKV in assay medium for 1 h at 37°C . Next , cells were washed 3 times with PBS and 2-fold serial dilutions of the compounds were added . Supernatant was harvested at day 4 pi and stored at -80°C until further use . The EC50 value , which is defined as the compound concentration that is required to inhibit viral RNA replication by 50% , was determined using logarithmic interpolation . Vero cells were seeded at a density of 2×105 cells/well in 24-well plate in growth medium and allowed to adhere overnight . Cells were washed twice with PBS and incubated with ZIKV at an MOI~1 in assay medium for 30 min at 37°C . After the incubation , cells were washed twice with PBS , after which assay medium was added to the cells . Cells were harvested at 0 , 4 , 6 , 8 , 10 , 12 , 14 , 16 , 18 , 20 , 22 and 24 hours pi and stored at -80°C until further use . For the time-of-drug addition studies , cells were seeded and infected as described above and 7DMA ( 178 μM ) or ribavirin ( 209 μM ) was added to the medium at different time points pi ( see above ) . Cells were harvested at 24 hours pi and stored at -80°C until further use . Vero cells were cultured in growth medium . Cells were incubated with ZIKV for 1 h , washed and overlaid with a mixture of 2% ( w/v ) carboxymethylcellullose ( Sigma Aldrich ) and MEM supplemented with 2% FCS , 4 mM L-glutamine and 0 . 15% sodium bicarbonate . Two-fold serial dilutions of compounds were made in the overlay medium . Cells were fixed and stained using a 10% v/v formaldehyde solution and a 1% methylene blue solution , respectively . Infectious virus titer ( PFU/mL ) was determined using the following formula: number of plaques × dilution factor × ( 1/inoculation volume ) . Vero cells were infected with ZIKV as described for the virus yield reduction assay . After removal of the virus , 2-fold serial dilution ( starting at 89 μM ) of 7DMA was added to the cells . At 72 h pi , cells were subsequently fixed with 2% paraformaldehyde in PBS and washed with PBS supplemented with 2% BSA . Anti-Flavivirus Group Antigen Antibody clone D1-4G2-4-15 ( Millipore ) and goat anti-mouse Alexa Fluor 488 ( Life Technologies ) were used to detect ZIKV antigens in infected cells . Cell nuclei were stained using DAPI ( 4' , 6-diamidino-2-fenylindool; Life Technologies ) and read out was performed using an ArrayScan XTI High Content Analysis Reader ( Thermo Scientific ) . The EC50 value , which is defined as the compound concentration that is required to inhibit viral antigen expression by 50% , was determined using logarithmic interpolation . RNA was isolated from 100–150 μl supernatant using the NucleoSpin RNA virus kit ( Filter Service , Germany ) according to the manufacturer’s protocol . RNA from infected cells was isolated using the RNeasy minikit ( Qiagen , The Netherlands ) , according to the manufacturer’s protocol , and eluted in 50 μL RNase-free water . During RT-qPCR the ZIKV NS1 region ( nucleotides 2472–2565 ) was amplified using primers 5’-TGA CTC CCC TCG TAG ACT G-3’ and 3’-CTC TCC TTC CAC TGA TTT CCA C-5’ and a Double-Quenched Probe 5’-6-FAM/AGA TCC CAC /ZEN/AAA TCC CCT CTT CCC/3’IABkFQ/ ( Integrated DNA Technologies , IDT ) . Viral RNA was quantified using serial dilutions of a standard curve consisting of a synthesized gene block containing 145 bp of ZIKV NS1 ( nucleotides 2456–2603 ) : 5'-GGT ACA AGT ACC ATC CTG ACT CCC CTC GTA GAC TGG CAG CAG CCG TTA AGC AAG CTT GGG AAG AGG GGA TTT GTG GGA TCT CCT CTG TTT CTA GAA TGG AAA ACA TAA TGT GGA AAT CAG TGG AAG GAG AGC TCA ATG CAA TCC TAG-3' ( Integrated DNA Technologies ) . All experiments were performed with approval of and under the guidelines of the Ethical Committee of the University of Leuven [P087-2014] . Virus stock was produced as described earlier and additionally concentrated by ultracentrifugation . Infectious virus titers ( PFU/ml ) were determined by performing plaque assays on Vero cells . 129/Sv mice deficient in both interferon ( IFN ) -α/β and IFN-γ receptors ( AG129 mice; male , 8–14 weeks of age ) were inoculated intraperitoneally ( ip; 200 μL ) with different inoculums ranging from 1×101–1×105 PFU/mL of ZIKV . Mice were observed daily for body weight change and the development of virus-induced disease . In case of a body weight loss of >20% and/or severe illness , mice were euthanized with pentobarbital ( Nembutal ) . Blood was collected by cardiac puncture and tissues ( spleen , kidney , liver and brain ) were collected in 2-mL tubes containing 2 . 8 mm zirconium oxide beads ( Precellys/Bertin Technologies ) after transcardial perfusion using PBS . Subsequently , RLT lysis buffer ( Qiagen ) was added to the Precellys tubes and tissue homogenates were prepared using an automated homogenizer ( Precellys24; Bertin Technologies ) . Homogenates were cleared by centrifugation and total RNA was extracted from the supernatant using the RNeasy minikit ( Qiagen ) , according to the manufacturer’s protocol . For serum samples , the NucleoSpin RNA virus kit ( Filter Service ) was used to isolate viral RNA . Viral copy numbers were quantified by RT-qPCR , as described earlier . For histological examination , tissues ( harvested at d13-15 pi ) were subsequently fixed in 4% formaldehyde , embedded in paraffin , sectioned , and stained with hematoxylin-eosin , essentially as described before [26] . Anti-Flavivirus Group Antigen Antibody , clone D1-4G2-4-15 ( Millipore ) was used to detect ZIKV antigens in tissue samples . Induction of pro-inflammatory cytokines and chemokines was analyzed in 20 μL serum using the mouse cytokine 20-plex antibody bead kit ( ProcartaPlex Mouse Th1/Th2 & Chemokine Panel I [EPX200-26090-901] ) , which measures the expression of TNF-α , IFN-γ , IL-6 , IL-18 , CCL2 ( MCP-1 ) , CCL3 ( MIP-1α ) , CCL4 ( MIP-1β ) , CCL5 ( RANTES ) , CCL7 ( MCP-3 ) , CCL11 ( Eotaxin ) , CXCL1 ( GRO-α ) , CXCL2 ( MIP-2 ) , CXCL10 ( IP-10 ) , GM-CSF , IL-1β , IL12p70 , IL-13 , IL-2 , IL-4 , and IL-5 . Measurements were performed using a Luminex 100 instrument ( Luminex Corp . , Austin , TX , USA ) and were analyzed using a standard curve for each molecule ( ProcartaPlex ) . Statistical analysis was performed using a one-way ANOVA . AG129 mice ( male , 8–14 weeks of age ) were treated with either 50 mg/kg/day 7DMA resuspended in 0 . 5% or 0 . 2% sodium carboxymethylcellulose ( CMC‐Na; n = 9 ) or vehicle ( 0 . 5% or 0 . 2% CMC‐Na; n = 9 ) once daily ( QD ) via oral gavage for 10 consecutive days . Since the bulk-forming agent CMC has dehydrating properties [27] , mice that received the drug ( or vehicle ) formulated with 0 . 5% CMC received ( on days 6–9 ) subcutaneous injections with 200 μL of saline . One hour after the first treatment , mice were infected via the intraperitoneal route with 200 μL of a 1×104 PFU/ml stock of ZIKV . Blood was withdrawn from the tails at different days pi . Viral RNA was extracted from 20 μL of serum using the RNA NucleoSpin RNA virus kit ( Filter Service ) followed by viral RNA quantification by means of RT-qPCR . Statistical analysis was performed using the Shapiro-Wilk normality test followed by the unpaired , two-tailed t-test in Graph Pad Prism6 . Inter-group survival was compared using the Log-rank ( Mantel-Cox ) test . The in vivo efficacy of 7DMA was determined in two independent experimental animal studies . Evaluation of cytokine induction was performed using the ProcartaPlex Mouse Simplex IP-10 ( CXCL10 ) , TNF-α , IL-6 and IL-18 kits . In an additional animal study , AG129 mice ( male , 8–14 weeks of age ) were treated with 50 mg/kg/day 7DMA resuspended in 0 . 2% sodium carboxymethylcellulose ( CMC‐Na; n = 6 ) or vehicle ( 0 . 2% CMC‐Na; n = 6 ) once daily ( QD ) via oral gavage for 5 successive days ( starting 2 days prior to infection ) and infected ip with 200 μL of a 1×104 PFU/ml stock of ZIKV . Animals were euthanized at day 5 pi and testicles were collected and stored until further use .
End point titrations in different cell lines revealed that Vero cells are highly permissive to ZIKV , hence , these cells were selected to establish antiviral assays . Infection with 100×TCID50 of ZIKV resulted in 100% cytopathic effect 5 days after infection ( S1B Fig ) , as assessed by microscopic evaluation as well as by the MTS readout method . The Z’ factor ( a measure of statistical effect size to assess the quality of assays to be used for high-throughput screening purposes; [28] ) of the CPE-reduction assay was 0 . 68 based on 64 samples ( from 8 independent experiments ) determined by the MTS readout method ( S1C Fig ) . The assay is thus sufficiently stringent and reproducible for high throughput screening purposes ( see also S2 Fig ) . The CPE-reduction assay was next employed to evaluate the potential anti-ZIKV activity of a selection of known ( + ) ssRNA virus inhibitors ( i . e . 2’-C-methylcytidine , 7-deaza-2'-C-methyladenosine , ribavirin , T-705 and its analogue T-1105 ) . All compounds resulted in a selective , dose-dependent inhibitory effect on ZIKV replication ( Table 1 ) . The antiviral effect of these compounds was confirmed in a virus yield reduction assay , a1 . 7log10 and 3 . 9log10 reduction in viral RNA load at a concentration of 22 μM and ≥45 μM , respectively , was noted ( Table 1 and Fig 1A ) . Since 7DMA resulted in the largest therapeutic window ( SI > 37; Table 1 ) , the antiviral activity of this compound was therefore next assessed in a plaque reduction assay and in an immunofluorescence assay to detect viral antigens . The inhibitory effect of the compound in both assays was in line with those of the CPE-reduction and virus yield reduction assay ( Table 1 , Fig 1A ) . At a concentration of 11 μM , 7DMA almost completely blocked viral antigen expression ( Fig 1B , left panel ) . 7DMA is , as its 5’-triphosphate metabolite , an inhibitor of viral RNA-dependent RNA polymerases . Addition of the compound to infected cells could be delayed until ~10 hours pi without much loss of antiviral potency; when first added at a later time point , the antiviral activity was gradually lost . This is line with the observation that onset of intracellular ZIKV RNA production was determined to occur at 10 to 12 hours pi ( Fig 2 ) . The reference compound ribavirin ( a triazole nucleoside with multiple proposed modes of action; [29] ) , in contrast , already lost part of the antiviral activity when added at time points later than 4 hours pi ( Fig 2 ) . Intraperitoneal inoculation of IFN-α/β and IFN-γ receptor knockout mice ( AG129 ) with as low as 200 μL of a 1×101 PFU/ml stock of ZIKV resulted in virus-induced disease ( see below ) and mice had to be euthanized at a MDE ( mean day of euthanasia ) of 18 . 5 days pi ( Fig 3A ) . Infection with higher inoculums ( 1×102–1×105 PFU/ml; 200 μL ) resulted in a faster progression of the disease ( MDE of 14 days pi ) with the first signs of disease appearing at day 10 pi . Surprisingly , inoculation of SCID mice with 200 μL of a 1×104 PFU/ml stock of ZIKV resulted in delayed disease; SCID mice had to be euthanized at day 40 . 0 ± 4 . 4 pi , roughly 26 days later than AG129 mice ( S3 Fig ) . Disease signs in AG129 mice included paralysis of the lower limbs , body weight loss , hunched back and conjunctivitis . High levels of viral RNA were detected in brain , spleen , liver and kidney from mice that were euthanized at day 13–15 pi ( Fig 3B ) . Histopathological analysis on tissues collected at day 13–15 pi revealed the accumulation of viral antigens in neurons of both the brain ( Fig 4A ) and the spinal cord ( Fig 4D ) as well as in hepatocytes ( Fig 4E ) . Acute neutrophilic encephalitis ( Fig 4C ) was observed at the time of onset of virus-induced morbidity . It was next assessed whether infection with ZIKV resulted in the induction of a panel of 20 cytokines and chemokines at different time points pi ( day 2 , 3 , 4 and 8; Figs 3C and 3D and S4A–S4G ) . In particular , levels of IFN-γ and IL-18 were increased systematically and significantly during the course of infection ( Fig 3C and 3D ) , whereas levels of IL-6 , CCL2 , CCL5 , CCL7 , CXCL1 , CXCL10 and TNF-α first increased , reaching a peak level at day 3 pi ( CCL2 , CXCL1 , TNF-α; S4A–S4C Fig ) or day 4 pi ( IL-6 , CCL7 , CXCL10; S4D–S4F Fig ) pi and then gradually declined . Levels of CCL5 subsequently increased at day 2 pi , dropped at day 3 pi , and gradually increased again at day 4 and 8 pi ( S4G Fig ) . AG129 mice were infected with 200 μL of a 1×104 PFU/ml stock of ZIKV and were treated once daily with 50 mg/kg/day of 7DMA or vehicle via oral gavage ( Fig 5 ) [data from the two independent experiments were not pooled since different amounts of CMC ( respectively 0 . 5% and 0 . 2% ) were used for formulation] . Vehicle-treated mice had to be euthanized two weeks after infection [MDE of 14 . 0 and 16 . 0 days , respectively] . 7DMA was well tolerated [no marked changes in body weight mass , fur , consistency of the stool or behavior during the treatment period] and markedly delayed virus-induced disease progression [MDE of 23 . 0 in the first study ( p = 0 . 003 as compared to the control ) and 24 . 0 in the second study ( p = 0 . 04 as compared to the control ) ] ( Fig 5A ) . 7DMA also reduced the viral RNA load in the serum of infected mice by 0 . 5log10 , 0 . 8log10 , 0 . 9log10 , 0 . 7log10 and 1 . 3log10 , respectively , at day 3 , 5 , 6 , 7 and 8 pi ( Fig 5B ) . Interestingly , at day 5 pi high levels of viral RNA ( 6 . 4log10 ) were found in the testicles of vehicle-treated mice ( Fig 5C ) . At day 8 pi ( shortly before the onset of disease in the vehicle controls ) , levels of IFN-γ in the serum were significantly higher in vehicle than in drug-treated mice ( Fig 5D ) .
The rapid geographical spread of ZIKV , particularly in Central and South America poses a serious public health concern given that infection with this virus is less benign than initially thought . Hundreds of patients have been reported with Guillain-Barré syndrome [16 , 17] . Most importantly , in Brazil a dramatic upsurge in the number of cases of microcephaly has been noted in children born to mothers infected with ZIKV . The annual rate of microcephaly in Brazil has increased from 5 . 7 per 100 000 live births in 2014 to 99 . 7 per 100 000 in 2015 [16 , 17 , 18] . There is , hence , an urgent need to develop preventive and counteractive measures against this truly neglected flavivirus member . We here report on the establishment of ( i ) in vitro assays that will allow to identify novel inhibitors of ZIKV replication and ( ii ) a ZIKV infection model in mice in which the potential efficacy of such inhibitors can be assessed . ZIKV was found to replicate efficiently in Vero cells and to produce full CPE within a couple of days . The Z’ factor that was calculated for a colorometric ( MTS method ) CPE-based screen indicated that this is a robust assay that is amenable for high-throughput screening purposes . A plaque reduction , an infectious virus yield and a viral RNA yield reduction assay as well as an immunofluorescent antigen detection assay were established that will allow to validate the in vitro activity of hits identified in CPE-based screenings . Productive infection of human dermal fibroblasts , epidermal keratinocytes and immature dendritic cells with the ZIKV has recently been reported [30] . However , Vero cells may be ideally suited for high throughput screening purposes , making these cells most useful to confirm the antiviral activity of interesting inhibitors of viral replication . We employed the assays that we established to assess the potential anti-ZIKV activity of a number of molecules with reported antiviral activity against other ssRNA viruses . In particular , the nucleoside analogue 7DMA was identified to inhibit ZIKV replication with a potency that was more or less comparable between the different in vitro assays . 7DMA was originally developed by Merck Research Laboratories as an inhibitor of hepatitis C virus replication [31] , but was also shown to inhibit the replication of multiple flaviviruses , [i . e . dengue virus , yellow fever virus as well as West Nile and tick-borne encephalitis virus] with EC50 values ranging between 5 and 15 μM , which is thus comparable to the EC50 values for inhibition of in vitro ZIKV replication [31 , 32 , 33] . In line with its presumed mechanism of action , i . e . inhibition as its 5’-triphosphate of the viral RNA-dependent RNA polymerase , time-of-drug-addition experiments revealed that the compound acts at a time point that coincides with the onset of intracellular viral RNA replication . To assess the in vivo efficacy of ZIKV inhibitors , we established a model of ZIKV infection in mice . AG129 mice proved highly susceptible to ZIKV infections; even an inoculum of ~10 PFU/ml resulted in virus induced-morbidity and mortality . Although ZIKV-infected SCID mice ( deficient in both T and B lymphocytes ) developed severe disease requiring euthanasia ( paralysis of the lower limbs , body weight loss , hunched back ) , these mice were more resistant to ZIKV infection than AG129 mice . SCID mice succumbed to infection roughly 26 days later than AG129 mice when inoculated with the same viral inoculum . Thus , ZIKV infections in mice are mostly controlled by the interferon response rather than by lymphocytes , indicating that the innate immune response to ZIKV is critical . AG129 mice have been shown to be highly susceptible to infection with other flaviviruses; in particular allowing the development of dengue virus infection models in mice [32 , 34 , 35] . At the time of virus-induced morbidity and mortality , ZIKV was detected in multiple organs such as kidney , liver and spleen , but also in the brain and spinal cord . The latter is in line with the observation that infected mice developed acute neutrophilic encephalitis with movement impairment and paralysis of the limbs . Brain involvement in ZIKV-infected mice may be relevant for brain-related pathologies in some ZIKV-infected humans [16 , 17] . Interestingly , the virus was also detected at high levels in the testicles of infected mice . A few cases of sexual transmission of the ZIKV in humans have been reported [36 , 37]; the observation that the virus replicates in the testicles in mice may suggest that the virus can also replicate in human testicle tissue thus explaining sexual transmission . Pro-inflammatory cytokines ( IFN-γ , IL-18 , IL-6 , TNF-α ) and chemokines ( CCL2 , CCL5 , CCL7 , CXCL1 , CXCL10 ) were found to be increased in sera of ZIKV-infected mice , indicating that infection causes systemic inflammation . In particular IFN-γ and IL-18 were continuously increased during the course of infection; both cytokines could therefore potentially function as predictive markers of disease progression and disease severity in this mouse model . Whether these cytokines are also upregulated during the acute phase of the infection in humans remains to be studied . Of note , the fact that ZIKV infection leads to the production of IL-18 suggests that the inflammasome is activated during the course of infection . Surprisingly , we could detect increased levels of IL-18 , but not of IL-1β , which is also produced upon activation of the inflammasome [38] . To our knowledge , the observation that the inflammasome could be implicated in ZIKV infection is unprecedented . Recently , a small study was reported involving 6 ZIKV-infected patients in which during the acute phase 11 cytokines/chemokines were found to be significantly increased , of which 7 were also increased during recovery [39] . Despite the fact that immunocompromised AG129 mice have an altered cytokine metabolism and were infected with the prototype ZIKV MR766 strain belonging to a different lineage than the one infecting the Latin American patients ( African versus Asian , respectively ) , similarities in cytokine expression were noted between both studies . IL-6 , CCL5 and CXCL10 were significantly increased in ZIKV-infected patients as well as in the infected mice . In the ZIKV-infected patients IFN-γ levels , which were markedly increased in ZIKV-infected mice , were also increased during both the acute and the reconvalescent phase of the infection , albeit non-significantly . Likewise , TNF-α levels , which were increased early in infection in mice , were ( non-significantly ) increased during the acute phase of infection in the patients . More studies are necessary to assess whether the cytokine profile in these 6 patients is representative for larger groups . Treatment of ZIKV-infected mice with 7DMA significantly reduced viremia ( between day 3 and 8 post infection ) and delayed virus-induced morbidity and mortality . The compound was very well tolerated in mice , which is in line with earlier reports [31] . The reduction in viremia and , hence , the delay of virus-induced disease was relatively modest , which is not surprising given the relatively weak in vitro activity of the compound as compared to , for example , the EC50 values ( sub μM or even nM range ) of most HCV inhibitors . Most importantly , the use of this compound allowed to validate the ZIKV mouse model to assess the efficacy of ZIKV inhibitors . Whether 7DMA ( or related analogues ) may have future in the control of ZIKV infections remains to be explored . AG129 mice have been used as well in the development of DENV vaccines , the DENV AG120 mouse models offer multiple disease parameters to evaluate protection by candidate vaccines [40] . Hence , the ZIKV mouse model presented here may also serve to study the efficacy of vaccine strategies against the ZIKV . In conclusion , we here report on a panel of in vitro cellular assays that will allow to run large-scale antiviral screening campaigns against ZIKV and to validate the antiviral activity of hit compounds . A number of molecules , including the viral polymerase inhibitor 7DMA , were found to inhibit the in vitro replication of ZIKV . Hence , 7DMA can be used as a reference compound/comparator in future studies . Moreover , a robust ZIKV mouse infection model was established; 7DMA delayed virus-induced mortality and , hence , validates this model for antiviral studies . Moreover , the model may be useful to study the efficacy of vaccination strategies against the ZIKV . | A robust cell-based antiviral assay was developed that allows to screen for and validate novel inhibitors of Zika virus ( ZIKV ) replication . The viral polymerase inhibitor 7-deaza-2’-C-methyladenosine ( 7DMA ) was identified as a potent ZIKV inhibitor . A mouse model for ZIKV infections , which was validated for antiviral studies , demonstrated that 7DMA markedly delays virus-induced disease in this model . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"vero",
"cells",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"cytokines",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"animal",
"models",
"of",
"disease",
"biological",
"cultures",
"rna",
"extraction",
"microbiology",
"immunology",
"animal",
"models",
"viruses",
"developmental",
"biology",
"model",
"organisms",
"rna",
"viruses",
"molecular",
"development",
"extraction",
"techniques",
"research",
"and",
"analysis",
"methods",
"animal",
"models",
"of",
"infection",
"animal",
"studies",
"medical",
"microbiology",
"microbial",
"pathogens",
"mouse",
"models",
"viral",
"replication",
"cell",
"lines",
"immune",
"system",
"flaviviruses",
"virology",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"organisms",
"zika",
"virus"
] | 2016 | The Viral Polymerase Inhibitor 7-Deaza-2’-C-Methyladenosine Is a Potent Inhibitor of In Vitro Zika Virus Replication and Delays Disease Progression in a Robust Mouse Infection Model |
Gammaherpesviruses chronically infect their host and are tightly associated with the development of lymphoproliferative diseases and lymphomas , as well as several other types of cancer . Mechanisms involved in maintaining chronic gammaherpesvirus infections are poorly understood and , in particular , little is known about the mechanisms involved in controlling gammaherpesvirus reactivation from latently infected B cells in vivo . Recent evidence has linked plasma cell differentiation with reactivation of the human gammaherpesviruses EBV and KSHV through induction of the immediate-early viral transcriptional activators by the plasma cell-specific transcription factor XBP-1s . We now extend those findings to document a role for a gammaherpesvirus gene product in regulating plasma cell differentiation and thus virus reactivation . We have previously shown that the murine gammaherpesvirus 68 ( MHV68 ) gene product M2 is dispensable for virus replication in permissive cells , but plays a critical role in virus reactivation from latently infected B cells . Here we show that in mice infected with wild type MHV68 , virus infected plasma cells ( ca . 8% of virus infected splenocytes at the peak of viral latency ) account for the majority of reactivation observed upon explant of splenocytes . In contrast , there is an absence of virus infected plasma cells at the peak of latency in mice infected with a M2 null MHV68 . Furthermore , we show that the M2 protein can drive plasma cell differentiation in a B lymphoma cell line in the absence of any other MHV68 gene products . Thus , the role of M2 in MHV68 reactivation can be attributed to its ability to manipulate plasma cell differentiation , providing a novel viral strategy to regulate gammaherpesvirus reactivation from latently infected B cells . We postulate that M2 represents a new class of herpesvirus gene products ( reactivation conditioners ) that do not directly participate in virus replication , but rather facilitate virus reactivation by manipulating the cellular milieu to provide a reactivation competent environment .
Plasma cells , which are the cellular factories that produce secreted antibody , play a critical role in mounting an effective immune response to many pathogens . Early plasma cell responses to foreign antigens can be divided into two phases: ( i ) differentiation of short-lived plasma cells arising from naïve marginal-zone and mature follicular B cells , which secrete low affinity antibodies that have not undergone somatic mutation and are thought to provide an initial rapid response to the invading pathogen; and ( ii ) differentiation of follicular B cells upon encountering antigen and receiving T cell help , leading to the formation of germinal centers , several rounds of B cell proliferation , affinity maturation , class-switching , and ultimately the development of memory B cells and plasma cells that serve to sustain humoral immune responses [1] . Although the signal ( s ) that initiate plasma cell differentiation remain controversial , recent progress has identified several critical transcriptional regulators of plasma cell differentiation - including B lymphocyte induced maturation protein 1 ( Blimp-1 ) , interferon regulatory factor-4 ( IRF-4 ) and XBP-1s [2] , [3] , [4] , [5] , [6] . Crawford and Ando [7] provided early evidence that Epstein-Barr virus ( EBV ) replication-associated antigens were present in Burkitt's lymphoma cells that exhibited a plasma cell phenotype – providing the first evidence that plasma cell differentiation is associated with virus reactivation from latency . More recently this observation has been extended to show that plasma cell differentiation is associated with reactivation of both EBV and Kaposi's sarcoma-associated herpesvirus ( KSHV ) . Induction of EBV and KSHV replication in latently infected B cells appears to be driven by the plasma cell-specific transcription factor XBP-1s , which activates transcription of the viral immediate-early genes encoding the critical transcriptional activators that trigger the EBV and KSHV replication cascades ( BZLF1 and BRLF1/gene 50 in the EBV genome and gene 50 in the KSHV genome ) [8] , [9] , [10] , [11] , [12] . Whether plasma cell differentiation leading to virus reactivation is a common strategy utilized by B cell-tropic gammaherpesviruses remains to be determined . Murine gammaherpesvirus 68 ( MHV68 ) infection of mice provides a tractable small animal model to investigate basic issues of gammaherpesvirus pathogenesis . Previous characterizations of MHV68 latency in mice have shown that B cells , as well as some populations of macrophages and dendritic cells , harbor latent virus - with B cells appearing to represent the major long term latency reservoir [13] , [14] , [15] , [16] , [17] . Analyses of virus latency in the spleen have shown that during the establishment of latency MHV68 is found in naive , germinal center and memory B cells [14] , [18] . However , latency in naive and germinal center B cells wanes with time and at late times post-infection MHV68 , like EBV , is predominantly found in memory B cells [14] , [18] . Recently , we have also identified MHV68 in plasma cells at the peak of virus latency in the spleen [19] . The latter observation raises the possibility that , like EBV and KSHV , plasma cell differentiation may be associated with MHV68 reactivation . Here we demonstrate that plasma cell differentiation is linked to MHV68 reactivation from latency . In addition , we provide evidence that a MHV68 encoded gene product , M2 , plays a seminal role in virus gaining access to plasma cells . We have previously shown that the MHV68 M2 protein , which is expressed in a subset of infected B cells at the peak of viral latency [20] , plays critical roles in both the establishment of latency as well as virus reactivation from latently infected B cells - phenotypes that are influenced by both route and dose of virus inoculation [18] , [21] . Additionally , efficient transition of latently-infected B cells from the germinal center reaction to the memory B cell reservoir appears to be stalled in the absence of M2 , suggesting M2 may manipulate B cell signaling or differentiation to facilitate establishment of long-term latency in the memory B cell pool [18] , [22] . Importantly , M2 is dispensable for virus replication in permissive fibroblasts in vitro or during acute virus replication in the lungs following intranasal inoculation [21] . Thus , M2 appears to have a specialized role during establishment of latency and reactivation . M2 contains several SH3 domain docking sites , along with 2 functionally important tyrosine residues that are targets for phosphorylation , and is thought to function as a molecular scaffold that modulates B cell signaling pathways [23] , [24] . Notably , M2 has been shown to target phosphorylation of the guanosine nucleotide exchange factors Vav1 and Vav2 , and more recently has been shown form a trimolecular complex with Vav1 and the Src family tyrosine kinase Fyn [24] - although the functional consequences of these interactions remain unclear . Here we focus on the role of M2 in virus reactivation from B cells .
To begin characterizing the role that M2 plays in virus reactivation from latently infected B cells , we initially determined whether a requirement for M2 in MHV68 reactivation from latently infected B cells could be recapitulated in a tissue culture B cell latency model . Utilizing the murine M12 B lymphoma cell line , we generated a number of clonal cell lines harboring either a recombinant wild type MHV68 containing a hygromycin-GFP fusion protein expression cassette , or a M2-null MHV68 mutant ( M2 . Stop ) on the same genetic background ( Fig . 1A ) . Stable cell lines were generated using hygromycin selection , and then analyzed for virus reactivation following stimulation with the phorbol ester TPA . Notably , TPA treatment induced significant virus reactivation in all the M12 clones infected with wild type MHV68 - as shown by both immunoblot analyses of expression of MHV68 replication-associated antigens ( Fig . 1B ) and increased titers of infectious virus in the tissue culture supernatants ( Fig . 1C ) . In contrast , TPA treatment of M12 cell lines infected with the M2 . Stop virus resulted in little increase in virus replication ( Fig . 1B and 1C ) . Similarly , treatment with either 5-azacytidine or trichostatin A was able to induce reactivation of the wild type MHV68 M12 cell lines , but not the M2 . Stop infected clones ( data not shown ) . Importantly , ectopic expression of M2 rescued virus reactivation from the M2 . Stop infected M12 cell lines ( Fig . 1D ) - indeed , expression of M2 alone in the absence of TPA induction resulted in a significant increase in the expression of replication-associated viral antigens from both wild type and M2 . Stop infected M12 cell lines . The ability of ectopic M2 expression to drive MHV68 reactivation was not limited to the latently infected M12 cell lines , but independently observed with latently infected murine A20 B lymphoma cell lines ( data not shown ) . The latter results suggest a direct link between M2 expression and MHV68 reactivation from latently infected B cells . Although M2 is completely dispensable for MHV68 replication in permissive cell lines [18] , [21] , we next assessed whether it might play a B cell-specific role in activating transcription from the immediate-early gene 50 promoter which encodes the essential lytic switch protein RTA . To assess whether M2 could directly activate the immediate-early gene 50 , we used reporter constructs in which the proximal gene 50 promoter was cloned upstream of a firefly luciferase reporter gene . We have previously shown that the proximal gene 50 promoter is required for reactivation of MHV68 from latently infected B cells [25] . Notably , M2 expression ( in the absence of other viral proteins ) could only weakly upregulated ( <3-fold ) gene 50 promoter activity ( data not shown ) , indicating that the role of M2 in virus reactivation from the latently infected M12 B cell lines is unlikely to be via direct targeting/regulation of gene 50 transcription . As such , we turned our attention to further investigating the impact of loss of M2 expression on virus infection in vivo . To track MHV68 latently infected B cell populations in vivo , we generated a recombinant MHV68 harboring an enhanced yellow fluorescent protein ( eYFP ) transgene under the control of the HCMV immediate-early promoter and enhancer ( MHV68-YFP ) [19] . We have extensively characterized the MHV68-YFP recombinant virus and have shown that it behaves like wild type virus and is able to efficiently mark latently infected cells at the peak of viral latency ( days 16–18 post-infection ) [19] . For our studies on M2 function we generated an M2 null MHV68 harboring the eYFP expression cassette ( M2 . Stop-YFP ) , which is described below , as well generating and characterizing a recombinant MHV68 with an AU1 epitope tag fused to the C-terminus of M2 [MHV68-YFP ( M2 . AU1 ) ] . To ensure that insertion of the eYFP expression cassette did not adversely impact the M2 null virus phenotype , we compared infection of mice with either the previously characterized M2 . Stop mutant virus [18] , the M2 . Stop-YFP virus , or MHV68-YFP virus , followed by analysis of establishment latency and reactivation at day 16 post-infection . In addition , since the analyses of M2 function discussed above used an M2 expression vector in which an AU1 epitope tag was inserted at the C-terminus of M2 , we also generated and characterized a recombinant MHV68 in harboring the AU1 epitope tagged M2 [MHV68-YFP ( M2 . AU1 ) ] . These analyses demonstrated that the phenotype of the eYFP expressing M2 null virus was indistinguishable from the well characterized M2 . Stop mutant virus ( Fig . 2A and 2B ) . In addition , the insertion of an AU1 epitope tag did not appear to have any impact on the phenotype of the MHV68-YFP virus ( Fig . 2A and 2B ) . We next sought to characterize and compare B cell populations harboring wild type or M2 null MHV68 ( M2 . Stop ) in infected mice . The infected spleens were harvested at day 16 post-infection and the presence of virus in distinct B cell populations was assessed by flow cytometry . Analyses of total splenoctyes revealed equivalent levels of marginal zone B cells ( CD21hiCD23lo ) in infected and naïve mice , but slightly lower levels of follicular B cells ( CD21hiCD23hi ) coupled with a slight increase in newly formed B cells ( CD21loCD23lo ) in mice infected with either wild type or M2 null MHV68 compared to naïve mice ( Fig . 2C ) . Analysis of the distribution of virus in these B cell populations revealed that M2 null virus infection , like wild type MHV68 , was present in each of these B cell populations ( Fig . 2D ) . However , the M2 null virus was diminished in marginal zone B cells and elevated in follicular B cells compared to wild type MHV68 ( Fig . 2D ) . As we have previously noted [19] , the pattern of CD21 and CD23 surface expression on MHV68 infected cells is somewhat distinct from that observed in naïve mice . Indeed , it has been shown that both CD21 and CD23 expression can be modulated by EBV , KSHV and MHV68 [26] , [27] ( C . M . Collins and S . H . Speck . , unpublished data ) . Thus , some caution must be taken in interpreting the distribution of wild type and M2 null MHV68 in these splenic B cell populations . Finally , although we observed decreased levels of activated B cells ( CD19+CD69hi ) in the spleens of mice infected with the M2 null virus compared to wild type MHV68-YFP infected mice ( which correlates with a less robust establishment of splenic latency in M2 . Stop infected mice ) , a similar percentage ( ca . 40% ) of virus infected B cells ( YFP+ ) exhibited an activated phenotype in M2 . Stop-YFP and wild type MHV68-YFP infected mice ( Fig . 2E ) . Comparing establishment of B cell latency in the spleen using wild type MHV68-YFP and M2 . Stop-YFP recombinant viruses , we observed a smaller germinal center response in mice infected with the M2 . Stop-YFP virus at day 16 post-infection ( Fig . 3A and 3B ) . However , even though there was a diminished germinal center response ( coupled with a lower frequency of M2 . Stop-YFP infected B cells compared to MHV68-YFP infected mice ) , when we examined the distribution of virus infected cells we observed that a very similar percentage of wild type and M2 . Stop virus infected B cells exhibited a germinal center phenotype ( GL7+/CD95+ ) ( Fig . 3C and 3D ) . This substantiates earlier analyses demonstrating the ability of M2 null viruses to form and expand within germinal centers [18] , [22] . Examination of immunoglobulin isotype class switching in the spleens of MHV68-YFP and M2 . Stop-YFP infected mice revealed a significant difference in the presence of B cells that had switched to IgG2a , the predominant IgG subtype observed following MHV68 infection ( unpublished data ) as well as many other viral infections [28] . While approximately 6% of splenic B cells were IgD−/IgG2a+ following MHV68-YFP infection , <1% had switched to IgG2a following infection with the M2 . Stop-YFP virus ( Fig . 3E ) . Furthermore , approximately 50% of wild type MHV68-YFP was found in IgG2a+ B cells while <10% of M2 . Stop-YFP virus was in IgG2a+ B cells ( Fig . 3F ) . These results are consistent with our previous analyses indicating that the M2 . Stop virus is impaired in gaining access to isotype switched B cells [18] . These results implicate a role for M2 in modulating immunoglobulin isotype class switching . Based on the recently established link between plasma cell differentiation and gammaherpesvirus reactivation [8] , [9] , [10] , [11] , [12] , we assessed the presence of MHV68 in plasma cells at the peak of splenic latency ( days 16–18 post-infection ) . We analyzed individual mice infected with wild type MHV68-YFP or M2 . Stop-YFP for the presence of virus infected plasma cells ( YFP+/B220lo/−/CD138+ ) . Consistently , in mice infected with MHV68-YFP , approximately 8% of virus infected B cells were plasma cells at day 16 post-infection ( Fig . 4A and 4C ) . In stark contrast , ≤1 . 5% of M2 . Stop-YFP virus infected B cells were plasma cells ( Fig . 4A and 4C ) . Notably , analysis of bulk splenocytes revealed that the frequency of plasma cells in the spleens of wild type and M2 null virus infected mice were relatively comparable ( approximately 1 . 5% ) , and slightly higher than the levels observed in naive mice ( about 0 . 5% ) ( Fig . 4B ) . Finally , we used a functional assay to directly assess the presence of wild type MHV68 in plasma cells . YFP+ splenocytes were isolated by flow cytometry and analyzed by ELISPOT to determine the frequency of antibody secreting cells ( ASC ) . As expected , the YFP+ cell population recovered from mice infected with the wild type MHV68-YFP virus was substantially enriched for plasma cells compared to unfractionated splenocytes ( Fig . 2D ) . Because we have previously shown that dose of M2 . Stop virus can impact the phenotype observed [18] , we assessed whether increasing the dose of M2 . Stop from 100 to 1 , 000 pfu would lead to detectable M2 . Stop virus infection of plasma cells . Notably , even following intranasal inoculation with 1 , 000 pfu of M2 . Stop-YFP we failed to observe any YFP+ plasma cells ( data not shown ) . Thus , we conclude that a significant percentage of MHV68 infection at the peak of viral latency is present in plasma cells , and that this population is largely absent in mice infected with the M2 null virus ( M2 . Stop-YFP ) , indicating a critical role for M2 in plasma cell differentiation during MHV68 chronic infection . We noted that the percentage of virus infected splenocytes that were plasma cells ( see Fig . 4C ) correlated closely with the percentage of infected splenocytes that spontaneously reactivate MHV68 upon explants [13] . To assess whether MHV68 reactivation is linked to plasma cell differentiation , we purified plasma cells from mice infected with wild type MHV68-YFP and simultaneously isolated plasma cell-depleted splenocytes ( Fig . 5A and 5B ) . Subsequent ELISPOT analyses of the purified populations confirmed appropriate enrichment or depletion of plasma cells ( Fig . 5D ) . To determine the frequency of cells in each population harboring viral genome ( a surrogate measure of the frequency of latently infected cells ) , the plasma cell enriched and depleted populations , along with unfractionated splenocytes , were analyzed for the presence of MHV68 infection using a limiting dilution nested PCR analysis [13] ( Fig . 5C , left panel ) . This analysis revealed that there was a slightly higher frequency of viral genome positive cells in the plasma cell population ( approximately 1 in 100 plasma cells ) than in either total splenocytes or plasma cell depleted splenocytes ( approximately 1 in 300 cells ) . Virus reactivation was examined using a limiting dilution analysis in which splenocyte populations were plated onto permissive monolayers of mouse embryo fibroblasts and virus reactivation scored 2 to 3 weeks post-plating by the appearance of viral cytopathic effect ( cpe ) [13] . Notably , this reactivation analysis revealed a profound difference between the plasma cell enriched and depleted populations ( Fig . 5C , right panel ) . Approximately 50% of MHV68 infected plasma cells spontaneously reactivated virus in this assay ( approximately 1 in 200 plasma cells reactivated virus compared to 1 in 100 plasma cells which harbor viral genome ) , while only approximately 1% of the virus infected non-plasma cell population reactivated virus ( approximately 1 in 25 , 000 non-plasma cells reactivated virus compared to 1 in 300 in this population that harbor viral genome ) . As expected , approximately 10% of unfractionated splenocytes spontaneously reactivated virus ( approximately 1 in 3 , 000 splenocytes reactivated virus compared to 1 in 300 harboring viral genome ) ( Fig . 5C , right panel ) . Thus , even though the frequency of infected cells is roughly the same in the plasma cell and non-plasma cell splenocyte populations , the frequency of plasma cells reactivating virus is ca . 100-fold higher than the non-plasma cell fraction . These results are consistent with recent studies characterizing EBV and KSHV reactivation that have linked plasma cell differentiation to virus reactivation [8] , [9] , [10] , [11] , [12] . In light of these results , the nearly complete absence of the M2 null mutant ( M2 . Stop-YFP virus ) in splenic plasma cells correlates with the B cell reactivation defect observed with this mutant virus . To determine whether M2 plays a direct role in driving plasma cell differentiation , we utilized the BCL-1 B lymphoma cell line which can be induced to differentiate into plasma cells by various stimuli [3] , [29] . Transient transfection of murine stem cell virus ( MSCV ) vectors containing either M2 or a negative control ( MSCV harboring the M2 . Stop expression cassette ) into the BCL-1 cell line resulted in an M2-dependent change in cell morphology ( Fig . 6A ) . M2 expression ( monitored by GFP expression ) led to acquisition of a plasmacyte morphology ( Fig . 6A ) , which could be detected by flow cytometry as an increase in both size and granularity of the cells ( Fig . 6B ) . We extended this analysis to examine changes in the expression of genes associated with the plasma cell differentiation program . Compared to M2 . Stop transfected cells , there was a significant induction in the levels of transcripts encoding several plasma cell-associated factors ( XBP-1s , Blimp-1 , J chain and IRF-4 ) ; changes that were also observed upon LPS stimulation of the BCL-1 cell line ( Fig . 6C ) . Notably , we repeatedly observed , following transfection with the M2 . Stop vector , lower levels of these plasma cell-associated transcripts than was observed in untreated BCL-1 cells – suggesting that the transfection protocol leads to selective loss of those cells in the starting BCL-1 cell population that have spontaneously differentiated to plasma cells during normal passage in culture ( Fig . 6C ) . Finally , analysis of the levels of secreted IgM in the tissue culture supernatants at 48 hours post-treatment or transfection demonstrated higher levels of secreted IgM in the M2 expressing and LPS treated cultures than in either the M2 . Stop ( vector ) or untreated cultures ( Fig . 6D ) . Thus , we conclude that M2 alone is able to drive plasma cell differentiation of the BCL-1 cell line .
We have shown here that MHV68 reactivation from splenic B cells is linked to M2-driven terminal differentiation of B cells to plasma cells in vivo , supporting previous data that terminal differentiation into plasma cells is linked to reactivation of the human gammaherpesviruses KSHV and EBV [8] , [9] , [10] , [11] , [12] . Our previous studies have demonstrated that M2 expression in primary murine B cells in tissue culture was able to drive B cell differentiation along a path toward plasma cells , although during their limited time survival in culture these cells only reached a phenotype referred to as pre-plasma memory B cells [30] . Previous studies on the function of M2 suggest a possible mechanism ( s ) to drive plasma cell differentiation ( Fig . 6E ) . Madureira et al . [31] and Rodrigues et al . [23] demonstrated that three PXXP motifs located in the C-terminal half of M2 play a role in binding Vav1 and Vav2 , and that M2 induces phosphorylation of Vav leading to downstream Rac1 stimulation . Additionally , the M2 protein harbors 2 tyrosine residues that are predicted to be potential phosphorylation sites and have been shown to be essential for the formation of a trimolecular complex with Vav1 and the Src family kinase Fyn [31] . Notably , it has been shown that Vav knockout mice have very low levels of serum immunoglobulin and a severe defect in the induction of Blimp-1 expression [32] . Thus , it seems likely that under some conditions M2 activation of Vav may lead to Blimp-1 expression and entry into the plasma cell program ( Fig . 6E ) . In addition , we have previously shown M2 expression in primary murine B cells leads to high level IL-10 expression [30] . We have proposed to M2-driven IL-10 expression plays a role in both driving expansion of latently infected B cells , as well as suppressing the host immune response during the establishment of latency [30] . However , it is also possible that M2-driven IL-10 expression plays a direct role in facilitating plasma cell differentiation . The latter is based on studies that have shown a role for IL-10 in plasma cell differentiation using model systems employing human B cells [33] , [34] , [35] , [36] . Finally , as we have shown here , M2 also upregulates the expression of IRF4 [we have observed both M2-driven upregulation of IRF4 transcripts ( Fig . 6C ) and IRF4 protein ( data not shown ) in BCL-1 cells] . Induction of IRF4 is of significant interest because it has been shown to play critical roles in both isotype switching during the germinal center reaction , and plasma cell differentiation [2] , [37] . We propose that entry into the plasma cell program results in the induction of the MHV68 immediate-early transcriptional activator RTA and subsequent activation of the virus replication cycle ( Fig . 6E ) . The latter steps are based on the observed ability of the plasma cell-associated factor XBP-1s to transactivate the critical viral promoters involved in driving expression of the EBV and KSHV lytic switch genes [8] , [9] , [10] , [11] , [12] . Does M2-driven plasma cell differentiation play a role in MHV68 reactivation from latently infected M12 B lymphoma cells ( see Fig . 1 ) ? We hypothesized that the role of M2 in facilitating TPA reactivation of the latently infected M12 B cell lines could reflect TPA induction of M2 expression , leading to sufficient levels of M2 protein to drive plasma cell differentiation and virus reactivation . This would be consistent with our observation that ectopic expression of M2 alone is sufficient to drive MHV68 reactivation from either wt or M2 . Stop infected M12 B cell lines ( see Fig . 1D ) . We have attempted to assess this following TPA stimulation of wild type MHV68 infected M12 cells and have been unable to document any hallmarks of plasma cell differentiation , with the exception of a modest increase in the levels of secreted IgG ( XL and SHS , unpublished data ) . This could reflect insufficient sensitivity of the assays employed – the increased levels of secreted IgG would be consistent with this interpretation , suggesting that a small percentage of cells in the culture differentiate to plasma cells , secrete IgG and then perhaps rapidly disappear due to virus induced cytopathic effect . Alternatively , these results may point to an independent function of M2 that is also involved in promoting virus reactivation from latently infected B cells under some conditions . With respect to the latter possibility , it is clear that plasma cell differentiation is not the only pathway for gammaherpesvirus reactivation from latently infected B cells – other stimuli such as DNA damage can also trigger MHV68 reactivation [38] . A role for M2 in these responses remains to be determined . It is notable that loss of M2 leads to alterations in immunoglobulin isotype switching in infected B cells ( see Fig . 3E and 3F ) . These results are consistent with our previous observation that M2 null mutants exhibited a significantly slower decay in naïve B cells ( CD19+/IgD+ ) compared to wild type MHV68 [18] , suggesting that M2 is involved in driving the differentiation of naïve B cells . Similarly , Simas and colleagues noted that the absence of M2 led to a prolonged persistence of virus infected B cells in germinal centers [22] . Taken together , these data implicate a role for M2 in facilitating B cell differentiation through the germinal center reaction and perhaps directly impacting immunoglobulin isotype switching . Among the genes that were upregulated upon expression of M2 in the BCL-1 B lymphoma cell line was the cellular transcription factor IRF4 ( Fig . 6C , and unpublished data ) , which has been shown to be required for both isotype switching as well as plasma cell differentiation [2] . As such , it is possible that M2 modulation of the levels of IRF4 may account for the apparently distinct roles of M2 in immunoglobulin isotype switching and plasma cell differentiation . This will require further investigation to identify the relevant cellular pathways that are manipulated by M2 . Previous studies have demonstrated that M2 is completely dispensable for virus replication in permissive cell lines , as well as in the lung following intranasal inoculation [18] , [39] . However , we have shown a more rapid clearance of M2 null virus replication in the spleen following high dose intraperitoneal inoculation ( equivalent replication of wild type and M2 null viruses at day 4 post-infection , and a >20-fold decrease in virus titer of M2 null mutants compared to wild type MHV68 at day 9 post-infection ) [21] . The basis for the acute replication defect in the spleen is unknown , but based on a requirement for B cells to seed splenic latency following intranasal virus inoculation ( but not following intraperitoneal inoculation ) [13] , [40] , we have postulated a role for virus reactivation from latently infected B cells playing a role in seeding acute virus replication in the spleen . Thus , while high dose intraperitoneal inoculation bypasses the requirement for B cells to seed initial acute MHV68 replication in the spleen at day 4 post-infection [13] , virus reactivation from MHV68 infected plasma cells may play a role in driving the sustained virus replication observed in the spleen at day 9 post-intraperitoneal inoculation ( levels of virus in the spleen at days 4 and 9 post-infection are equivalent ) . If so , then the B cell reactivation defect observed in M2 null virus infected mice would translate as a late stage acute virus replication defect in the spleen . Regardless , there is no evidence that M2 plays a direct role in virus replication . As such , M2 appears to play a specialized role to facilitate virus reactivation from latently infected B cells . An alternative model that is worthy of consideration relates to the impact of M2-driven IL-10 expression on virus-specific CD8+ T cell responses . We have previously shown that loss of M2 leads to increased levels of virus-specific CD8+ T cells , as assessed using tetramers to two distinct viral epitopes ( ORF6486–498 and ORF61524–531 ) expressed during MHV68 replication [30] . Thus , enhanced CD8+ T cell responses directed against MHV68 replication-associated antigens in M2 . Stop infected mice may result in the rapid clearance of virus infected plasma cells expressing replication-associated viral antigens - leading to the observed absence of infected plasma cells at the peak of viral infection in the spleen . As such , in this model , M2 would not be expected to be playing a direct role in driving plasma cell differentiation . We believe that this model is unlikely to account for the absence of MHV68 infected plasma cells based on the observed role of M2 in virus reactivation from the MHV68 M12 infected cell lines ( see Fig . 1B and 1D ) , as well as the ability of M2 to drive terminal differentiation of the BCL-1 cell line in culture ( Fig . 6 ) . However , we clearly cannot dismiss a role for enhanced antiviral CD8+ T cell responses in M2 . Stop infected mice in controlling the frequency of virus infected plasma cells . Based on the analysis of M2 function , we propose that M2 falls into a new class of herpesvirus genes that do not directly impact virus replication , but rather facilitate virus reactivation from latency by manipulating cellular differentiation/activation leading to a reactivation competent cellular environment . We have adopted the term reactivation conditioner for such genes . In the case of viruses that establish latency in memory lymphocytes , it is attractive to speculate that it may be necessary to encode functions that drive quiescent memory B or T cells into a state which is more conducive to virus replication . With respect to latency established in memory B cells , plasma cells would appear particularly well suited to support herpesvirus replication . As such , we hypothesize that manipulation of plasma cell differentiation leading to virus reactivation from latently infected memory B cells is relevant to reactivation of the human gammaherpesviruses . Although there is no obvious M2 homolog in either EBV or KSHV , there are several well documented examples of conserved functions encoded by gammaherpesvirus latency-associate gene products that lack obvious sequence homology [41] . Indeed , our previous observation that M2 expression in primary murine B cells triggers IL-6 and IL-10 expression [30] , recapitulates functions modulated by both EBV and KSHV [42] , [43] , and provides further evidence of pathogenic strategies that are conserved among this family of viruses . Importantly , our studies provide the impetus to identify viral gene products encoded by EBV and/or KSHV that manipulate plasma cell differentiation , which may ultimately provide new targets for the development of antiviral therapies against these chronic infections .
The recombinant viruses , generated as described below , were passaged and titered as previously described [44] . Murine NIH 3T12 fibroblast cells were cultured in Dulbecco's modified Eagle medium supplemented with 10% fetal cal serum , 100 U/ml penicillin , 100 µg/ml streptomycin , and 2 mM L-glutamine . The M12 B lymphoma cell line was generously provided by Dr . David Schatz ( Yale University School of Medicine , New Haven , CT ) , and was cultured in RPMI 1640 medium supplemented with 10% fetal cal serum , 100 U/ml penicillin , 100 µg/ml streptomycin and 50 µM 2-mercapto-ethanol . Female C57Bl/6 mice 6 to 8 weeks of age were purchased from the Jackson Laboratory . Mice were sterile housed and treated according to Emory University School of Medicine ( Atlanta , GA ) guidelines and all animal studies were approved by the Emory University Institutional Animal Care and Use Committee . Following sedation , mice were infected intranasally with 100 pfu of either MHV68-YFP or M2stop-YFP viruses in 20 µL of cMEM . Mice were allowed to recover from anesthesia before being returned to their cages . The recombinant hygromycin–EGFP-expressing MHV68M2 . Stop viruses ( M2 . Stop-HE ) were generated by allelic exchange using a previously described MHV68-BAC containing a hygromycin-EGFP fusion gene under the control of the HCMV immediate-early promoter in the ORF27/29b locus ( MHV68-HE BAC ) [38] and a targeting vector containing the M2 gene with a translation stop and frame shift incorporated at bp 4 , 559 of the viral genome ( pGS284/M2stop ) as previously described [45] . To establish MHV68 latently infected M12 cell lines , M12 cells were infected with WT-HE or M2stop-HE viruses by spinoculation at 1 , 800 rpm for 90 min and then subjected to hygromycin ( 400 µg/ml ) selection . The individual clonal cell lines were established by limiting dilution cloning , and the presence of episomal copies of the MHV68 genome confirmed by Gardella gel electrophoresis [46] . Cells were lysed with RIPA buffer ( 150 mM NaCl , 20 mM Tris . Cl , 2 mM EDTA , 1% NP-40 supplemented with EDTA-free protease inhibitor ) ( Roche ) . The whole cell lysates were resolved by SDS-PAGE gel electrophoresis , transferred to nitrocellulose membranes and immunoblotting with chicken anti-ORF59 or rabbit anti-MHV68 antiserum [38] . Plaque assays were performed in NIH3T12 fibroblast cells as previously described [38] . Briefly , cells and supernatants were collected at various times post-induction with TPA ( 20 ng/ml ) and frozen at −80°C . Samples were then subjected to two cycles of freezing and thawing , and virus titers were quantitated by plaque assay on NIH 3T12 fibroblasts . Total RNA was isolated from untreated or treated cells using TRIzol per manufacturer's protocol ( Invitrogen ) . 2 µg RNA was used for first-strand cDNA synthesis ( Invitrogen ) , followed by PCR amplification using the appropriate oligonucleotide primers as previously described [38] . The primers used for the detection of plasma cell-associated transcripts in BCL-1 cells were as follows; specific spliced XBP-1 ( s ) : 5′-GTAGCAGCGCAGACTGCTCGAGATAG-3′ and 5′-GAGGTGCACATAGTCTGCACCAGC-3′ , unspliced XBP-1 ( u ) : 5′-GTAGCAGCGCAGACTGC TCGAGATAG-3′ and 5′-AGTGCTGCGGACTCAGCAGACCCGGC-3′ , J chain: 5′-ATGAAGACC CACCTGCTTCTC-3′ and 5′-GTCAGGGTAGCAAGAATCGG G-3′ , IRF4: 5′-ATGAACTTGGA GACGGGCAGCCGGGGC-3′ and 5′-TCACTCTTGGA TGGAAGAATGACGGAGGGA-3′ , Blimp-1: 5′-GGAGGATCTGACCCGAATCA-3′ and 5′-CTCCACCATGGAGGTCACATC , M2: 5′-ATGG GCCCAACACCCCCACAAGGAAAG-3′ and 5′-TTACTCCTCGCCCCACTCCACAAAACC-3′ , actin: 5′-TAAGTGGTTACAGGAA G-3′ and 5′-AGCCTTCATACATCAAG-3′ . All primers employed were designed to amplify spliced gene products and , as such , any products arising from contaminating DNA would run at a larger size ( not detected ) . The frequency of MHV68 genome-positive cells was determined using a previously described nested PCR assay ( LD-PCR ) [13] . Briefly , cells were counted , resuspended in an isotonic solution , and diluted into a background of 104 uninfected NIH 3T12 cells . Following cell lysis with proteinase K , two rounds of nested PCR were performed on each sample to detect the presence of the MHV68 ORF50 . To ensure sufficient sensitivity of the nested PCR reaction , 10 , 1 , or 0 . 1 copies of a gene 50 containing plasmid ( pBamHI N ) were diluted into a background of 104 uninfected cells and analyzed in parallel with the experimental sample . The frequency of MHV68 reactivation from latency was also detected as previously described [13] . Briefly , cells were plated in a series of twofold dilutions onto MEF monolayers in 96-well tissue culture plates . After 21 days , wells were scored microscopically for the presence of viral cytopathic effect ( CPE ) . To detect preformed infectious virus , parallel samples were subjected to mechanical disruption as previously described [13] , a process that kills >99% of cells without affecting the preformed MHV68 virions [13] . Disrupted cells were plated in a similar series of twofold dilutions . Flow cytometry analyses for murine splenic cells were done as previously described [30] with the following antibodies: GL-7-Biotin , Stratavidin-APC , CD95-PE , CD138-PE , B220-Pacific Blue ( CALTAG Laboratories ) , CD19-FITC , CD3e-PerCP , IgG2a-Biotin ( BD Pharmingen , except where noted ) . The data were collected on a LSRII flow cytometer ( BD Biosciences ) . For purification of plasma cells , single-cell suspensions were isolated from infected spleens at day 16 post-infection with 100 pfu of the MHV68-YFP recombinant virus administered via intranasal inoculation . The cells were labeled with CD138-PE and B220-Pacific Blue on ice for 20 min , followed by washing with 1% BSA/PBS . The stained cell populations were then sorted on either a FACSVantage or FACSAria™ II flow cytometer ( BD Biosciences ) . For purification of YFP+ cell populations , the single cell suspensions were directly subjected to separation on a FACSVantage and FACSAria™ II flow cytometer ( BD Bioscience ) . ELISA for IgM secretion was performed as per manufacturer's protocol ( Bethyl Laboratory ) . To analyze antibody secretion from sorted YFP+ or plasma cell populations , mouse IgG capture antibodies were coated onto a PVDF-backed microplate . The sorted cell populations were plated onto blocked plates at serial dilution and incubated in a humidified 37°C CO2 incubator for overnight . The wells were washed and bound antibodies were detected with anti-mouse IgG antibodies . The spots were counted using automated ELISPOT reader . | Gammaherpesviruses are associated with the development of lymphomas , particularly in immunosuppressed individuals , as well as several other types of cancers . Like all herpesviruses , once a host is infected these viruses cannot be cleared and , as such , infected individuals harbor these viruses for life . One of the important strategies utilized by herpesviruses to chronically infect their host is their ability to establish a largely quiescent form of infection referred to as latency , in which no progeny virus is produced . Importantly , all herpesviruses have the capacity to emerge from latency and replicate , a process referred to as reactivation . Gammaherpesviruses largely persist in a population of white blood cells called B lymphocytes which , upon differentiation into plasma cells , produce antibodies in response to infection . Notably , it has been recently shown for the human gammaherpesviruses , Epstein-Barr virus and Kaposi's sarcoma-associated herpesvirus , that virus reactivation from latently infected B lymphocytes involves differentiation of the infected B lymphocytes to plasma cells . Here , using a small animal model of gammaherpesvirus infection , we show that plasma cell differentiation is also associated with reactivation of murine gammaherpesvirus 68 . Furthermore , we show that this requires a protein encoded by the virus which is able to drive plasma cell differentiation . Thus , our studies not only confirm the importance of plasma cell differentiation in gammaherpesvirus reactivation from B lymphocytes , but also provide evidence that this process is controlled by a viral protein . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/persistence",
"and",
"latency",
"infectious",
"diseases",
"virology/animal",
"models",
"of",
"infection",
"virology",
"immunology",
"microbiology"
] | 2009 | Gammaherpesvirus-Driven Plasma Cell Differentiation Regulates Virus Reactivation from Latently Infected B Lymphocytes |
The average human genome contains a small cohort of active L1 retrotransposons that encode two proteins ( ORF1p and ORF2p ) required for their mobility ( i . e . , retrotransposition ) . Prior studies demonstrated that human ORF1p , L1 RNA , and an ORF2p-encoded reverse transcriptase activity are present in ribonucleoprotein ( RNP ) complexes . However , the inability to physically detect ORF2p from engineered human L1 constructs has remained a technical challenge in the field . Here , we have employed an epitope/RNA tagging strategy with engineered human L1 retrotransposons to identify ORF1p , ORF2p , and L1 RNA in a RNP complex . We next used this system to assess how mutations in ORF1p and/or ORF2p impact RNP formation . Importantly , we demonstrate that mutations in the coiled-coil domain and RNA recognition motif of ORF1p , as well as the cysteine-rich domain of ORF2p , reduce the levels of ORF1p and/or ORF2p in L1 RNPs . Finally , we used this tagging strategy to localize the L1–encoded proteins and L1 RNA to cytoplasmic foci that often were associated with stress granules . Thus , we conclude that a precise interplay among ORF1p , ORF2p , and L1 RNA is critical for L1 RNP assembly , function , and L1 retrotransposition .
Long Interspersed Element-1 ( LINE-1 or L1 ) sequences comprise 17% of human DNA and represent the predominant class of autonomous retrotransposon-derived sequences in the genome [1] . Greater than 99 . 9% of L1 elements are molecular fossils that are no longer capable of mobilization ( i . e . , retrotransposition ) [1]–[3] . However , the average human genome still harbors a small cohort ( approximately 80–100 ) of retrotransposition-competent L1s ( RC-L1s ) [4] , [5] . A wealth of experimental evidence suggests that ongoing RC-L1 retrotransposition has the potential to impact the genome by a myriad of mechanisms ( reviewed in [6]–[8] ) . A human RC-L1 is approximately 6 kb in length; it begins with a ∼910 bp 5′ untranslated region ( UTR ) that harbors an internal RNA polymerase II promoter [9]–[11] , two non-overlapping open reading frames ( ORF1 and ORF2 ) , and ends with a 3′ UTR that is followed by either a polyadenylic acid ( poly A ) or A-rich sequence ( Figure 1A ) [12] , [13] . Genetic and biochemical evidence suggest that the ORF1 and ORF2-encoded proteins ( ORF1p and ORF2p , respectively ) preferentially associate with their encoding mRNA in cis to form a ribonucleoprotein particle ( RNP ) that probably is an intermediate in the retrotransposition process [14]–[19] . The resultant RNP then gains access to the nucleus , where L1 integration presumably occurs by target-site primed reverse transcription ( TPRT ) [20]–[23] . Studies conducted with mouse and human RC-L1s have uncovered a number of conserved domains within ORF1p that are important for retrotransposition . The amino acid sequence of the ORF1p amino-terminus is poorly conserved among mammalian L1s , but it is predicted to form a coiled-coil or α-helical domain that is important for ORF1p multimerization [15] , [24]–[27] . In human ORF1p , this region contains a putative leucine zipper ( LZ ) domain that is absent from other mammalian L1s , although a similar motif is present in the L1-like Swimmer element of teleosts [15] , [24] , [27]–[29] . The coiled-coil domain of ORF1p is followed by a RNA recognition motif ( RRM ) [30] , and experiments in cultured human cells have shown that mutations in conserved residues of the RRM domain ( e . g . , a N157A/R159A double mutant ) adversely affect L1 retrotransposition and the formation of cytoplasmic structures known as ORF1 cytoplasmic foci [31] . The carboxyl-terminus of ORF1p contains amino acid residues that are conserved among mammalian L1s [24] , [27] , [32] . Biochemical analyses have shown that mouse ORF1p homotrimers bind L1 RNA in a sequence independent manner [33] , [34] . Mutations of a conserved di-arginine motif ( RR261–262 in human L1 ) can decrease ORF1p RNA binding or mouse ORF1p nucleic acid chaperone activity [33] , [35] . Similarly , studies using human L1s revealed that alanine mutations in conserved amino acid residues in the carboxyl terminus of ORF1p ( RR261–262 , and YPAKLS282–287 , respectively ) both compromise the ability of ORF1p to localize to RNPs and severely reduce L1 retrotransposition efficiency [16] , [32] . Thus , ORF1p is postulated to have critical functions at discrete steps in the retrotransposition pathway . Biochemical and genetic studies have revealed that human and mouse ORF2 are translated by an unconventional mechanism [36]–[39] . It is hypothesized that as few as one or two molecules of ORF2p are translated per L1 RNA molecule , which could explain why it has been difficult to detect ORF2p produced from engineered L1s in cultured cells [37] . ORF2p contains endonuclease ( EN ) and reverse transcriptase ( RT ) activities that are critical for the target-site cleavage and reverse transcription steps of TPRT [22] , [23] , [32] , [40] . ORF2p also contains a conserved cysteine-rich ( C ) domain near its carboxyl-terminus [27] , [41] . Mutations in the C-domain adversely affect L1 retrotransposition [32]; however , the biochemical role of the C-domain in L1 retrotransposition remains poorly understood . Epitope-tagging systems and enzymatic assays have been developed to facilitate detection of L1 ORF1p and ORF2p RT activity from engineered wild-type and mutant human L1s [16] , [17] . However , the inability to reliably and directly detect ORF2p from engineered human L1s in transfected cultured human cells has hindered progress in the field [37] , [42] . Here , we have devised an epitope and/or RNA-tagging system to show that ORF1p , ORF2p , and L1 RNA form a ribonucleoprotein complex , which may represent a minimal RNP retrotransposition intermediate . Consistent with previous studies , transient transfection/immunofluorescence-based experiments revealed that the L1-encoded proteins and L1 mRNA often form discrete cytoplasmic foci , and that many of these foci associate with stress granules [31] . Finally , we have extended previous analyses [16] , [17] and demonstrate that mutations in conserved functional domains of ORF1p and/or ORF2p adversely affect L1 RNP formation , the reverse transcription of L1 RNA , and L1 cytoplasmic foci formation . Thus , we have developed a system that should allow a greater understanding of the L1 retrotransposition mechanism at the molecular level .
Previous studies have examined the co-localization of L1 ORF1p and L1 RNA in RNPs derived from cells transfected with epitope-tagged wild-type or mutant human L1 expression constructs [16] , [17] . To physically detect L1 ORF2p , we modified existing L1 expression vectors ( pJM101/L1 . 3 or pDK101 ) to contain either a 530 bp TAP tag or a 72 bp FLAG-HA tag on the carboxyl-terminus of ORF2p ( Figure 1A; pAD2TE1 and pES2TE1 ) [43] , [44] . To facilitate the identification of L1 RNA , we also introduced a 1312 bp DNA fragment that contains 24 copies of a stem-loop sequence that can bind the phage MS2 protein into the L1 3′UTR ( Figure 1A; pAD3TE1 ) [45] , [46] . As a control , we generated a plasmid that expresses TAP-tagged ORF2p from a monocistronic transcript ( Figure 1A; pAD500 ) . L1 constructs were equipped with a retrotransposition indicator cassette ( mneoI ) , subcloned into a pCEP4 episomal expression vector , and were assayed for retrotransposition in cultured human HeLa cells [32] , [47] , [48] . Inclusion of either the TAP or FLAG-HA epitope tag onto the carboxyl-terminus of ORF2p had little effect on the L1 retrotransposition efficiency when compared to a wild-type control construct lacking the tag ( Figure 1B; pADO2Tt , pAD2TE1 , and pES2TE1 vs . pJM101/L1 . 3 ) . Similarly , the inclusion of the MS2 stem loop sequences into the L1 3′UTR did not dramatically affect L1 retrotransposition efficiency ( Figure 1B; pADL1MT vs . pJM101/L1 . 3 ) , although we did observe an approximate 2 . 7 fold reduction in L1 retrotransposition efficiency from a construct containing both the protein and MS2 tags ( Figure 1B; pAD3TE1 vs . pJM101/L1 . 3 ) . As a negative control , we demonstrated that a construct containing a missense mutation in the putative L1 RT active site ( pAD135; D702A ) was defective for retrotransposition ( Figure 1B ) . Thus , engineering epitope and/or RNA tags into the L1 expression vectors is compatible with retrotransposition in cultured cells . To detect the L1-encoded proteins from the engineered plasmids , we transfected each construct into HeLa cells and selected for cells containing the respective L1 expression vectors by exploiting the hygromycin B selectable marker on the pCEP4 episome ( Figure 1A; see Materials and Methods ) . Consistent with previous studies [16] , [17] , [49] , western blot analyses of whole cell lysates using antibodies directed against the ORF1p T7-epitope tag revealed the presence of a ∼40 kDa protein from constructs containing the tag ( Figure 2A , middle panel ( αT7 ) ; pAD2TE1 , pAD3TE1 , and pES2TE1 ) , but not from controls lacking the tag ( Figure 2A; pJM101/L1 . 3 and pADO2Tt ) . We also could detect the ∼40 kDa protein with polyclonal antibodies against endogenous human ORF1p ( Figure 2B , αORF1 panels; pAD2TE1 , pJM101/L1 . 3 , and pDK101 ) [50] . Notably , we observed a slight difference in the mobility of T7-tagged and untagged ORF1p ( Figure 2B; right panel ( αORF1 ) , pJM101/L1 . 3 vs . pDK101 ) , which most likely is due to the additional amino acids imparted by the T7 epitope tag . Controls revealed that ORF1p was not detected from a construct that lacks ORF1 ( Figure 2A and 2B; pAD500 ) or from a construct that contains a premature stop codon in ORF1 ( Figure 2A; pADO1S ) . Qualitative reverse transcriptase-PCR ( RT-PCR ) experiments further confirmed that L1 RNA was expressed from each of the transfected constructs ( Figure 2C; see Oligonucleotides and RT-PCR sections in Materials and Methods for details ) . To detect ORF2p expression , we conducted western blot analyses on whole cell lysates derived from transfected cells using antibodies directed against either the TAP or FLAG-HA epitope tag ( Figure 2A ) . A ∼170 kDa protein was detected from L1 constructs containing TAP-tagged ORF2p , but not from an untagged wild-type control ( Figure 2A; left panel ( αTAP ) ; pADO2Tt , pAD2TE1 , pAD3TE1 , and pAD500 vs . pJM101/L1 . 3 ) . The ∼170 kDa product corresponds to the predicted size of ORF2p ( ∼150 kDa ) plus the predicted size of the TAP tag ( ∼19 kDa ) [12] , [43] . A ∼170 kDa protein also was detected using antibodies against endogenous ORF2p ( Figure 2B; left panel ( αORF2 ) ) [42] . Similarly , a ∼155 kDa protein was detected from L1 constructs containing FLAG-HA-tagged ORF2p , but not from the untagged wild-type control ( Figure 2A; right panel ( αHA ) : pES2TE1 vs . pJM101/L1 . 3 ) . Consistent with previous genetic studies , TAP-tagged ORF2p expression was greatly diminished by introducing a stop codon in ORF1 ( Figure 2A; pADO1S ) and was most abundant when expressed from an ORF2p monocistronic expression vector ( Figure 2A and 2B; pAD500 ) [37] . To test whether ORF2p localizes to ribonucleoprotein particles ( RNPs ) , we transfected HeLa cells with pAD2TE1 , selected for transfected cells , and isolated RNPs by ultracentrifugation ( see Materials and Methods ) [16] , [17] . Western blotting revealed that ORF1p and ORF2p were readily detected in the RNP fraction ( Figure 3A , top panel ) . We next used the L1 Element Amplification Protocol ( LEAP ) assay to determine whether the RNP preparations contained an L1-specific reverse transcriptase activity [17] . Consistent with previous studies , a diffuse set of LEAP products that ranged in size from ∼220 to ∼400 bp was detected in pAD2TE1-derived RNPs , but not from pAD135-derived ( D702A; RT mutant ) RNPs ( Figure 3A , lower panel ) . Cloning and sequencing of the pAD2TE1-derived LEAP products confirmed that L1 reverse transcription generally initiated at variable sites within the L1 poly ( A ) tail , which accounts for variably-sized LEAP products ( data not shown [17] ) . To further verify that ORF1p , ORF2p , and L1 mRNA form an RNP , HeLa cells were transfected with either pES2TE1 or pDK101 . Whole cell extracts then were subjected to immunoprecipitation using an anti-FLAG M2 antibody fused to agarose beads ( Figure 3B ) . Incubation of the beads with a FLAG peptide followed by western blot analysis revealed an enrichment of ORF1p and ORF2p in the pES2TE1 , but not in the pDK101 immunoprecipitated reactions ( Figure 3C ) . We sometimes detected a faint band of ∼40 kDa in the pDK101 immunoprecipitated reactions upon longer film exposures , suggesting that some T7-tagged ORF1p may bind non-specifically to the anti-FLAG M2 agarose beads ( data not shown ) . However , subsequent experiments/product characterization determined that the pES2TE1 immunoprecipitated fraction contained LEAP activity , whereas the pDK101 immunoprecipitated fraction lacked a readily detectable LEAP activity ( Figure 3D ) . Interestingly , we consistently observed less ORF1p associated with RNPs in immunoprecipitation experiments when compared to experiments conducted with whole cell lysates or crude RNPs ( Figure 2A and Figure 3A ) . These data suggest either that ORF1p is less tightly associated with L1 mRNA than ORF2p in RNPs ( which is consistent with previous observations [17] ) and/or that a fraction of ORF1p is dissociated from L1 RNA during the immunoprecipitation process . Regardless , whereas previous studies showed that ORF1p , ORF2p RT activity , and L1 RNA co-localize to RNPs [16] , [17] , we were able to demonstrate the physical association of these components in immunoprecipitation experiments . Previous studies identified activities associated with ORF1p and ORF2p that are critical for L1 retrotransposition [22] , [30] , [32] , [35] . Here , we expanded on these analyses to determine whether mutations in the L1-encoded proteins affect their ability to localize to RNPs and/or impact L1 reverse transcriptase activity in the LEAP assay . We first tested mutants in the following functional domains of ORF1p: 1 ) the putative leucine zipper domain ( pADLZC; L93 , 100 , 107 , 114V ) ; 2 ) the RNA-recognition motif ( pAD113; NLR157–159ALA ) ; 3 ) the carboxyl-terminal nucleic acid binding domain ( pAD105; RR261–262AA ) ; 4 ) an ORF1p mutation that affects mouse nucleic acid chaperone activity ( pAD106; RR261–262KK ) ; and 5 ) a double mutant in the putative leucine zipper domain and carboxyl-terminal nucleic acid binding domain ( pADL/R; L93 , 100 , 107 , 114V/RR261–262AA ) ( Figure 4A; see Materials and Methods ) [16] , [30]–[32] , [35] . Each of these mutations , including the LZC mutation ( pADLZC; L93 , 100 , 107 , 114V ) , severely compromise L1 retrotransposition efficiency in HeLa cells ( Figure S1A , S1B ) . The LZC mutant data are in agreement with a published report , which demonstrated a L93/100/114A triple mutation inactivates L1 retrotransposition [31] . Multiple independent RNP preparations derived from cells transfected with each of the respective mutants were analyzed by western blotting to examine the presence and abundance of both ORF1p and ORF2p ( Figure 4B ) . LEAP assays then were used to determine whether those RNPs contained an L1-specific reverse transcriptase activity ( Figure 4C ) . Once again , control MLV RT-PCR-based experiments , using the same oligonucleotide primers employed in the LEAP assay , indicated that L1 RNA was present at roughly comparable levels in the RNP fraction of HeLa cells transfected with the mutant constructs ( Figure 4C ) . Consistent with previous data [16] , a mutation in the ORF1p carboxyl-terminal domain ( pAD105; RR261–262AA ) led to a severe reduction in the ability of ORF1p , but not ORF2p , to localize to the RNP fraction ( Figure 4B ) . RNPs derived from pAD105-transfected cells had a readily detectable LEAP activity , although the constellation of LEAP products differed from those in the wild-type control , pAD2TE1 , because they frequently initiated reverse transcription from within the 3′ end of the L1 mRNA ( Figure 4B and 4C; Figure S2A , S2B , S2C and S2D; pDK105 RR261–262AA; data not shown ) . Similar data also were observed for an L1 containing a mutation in the carboxyl-terminal domain ( pDK116; YPAKLS282–287AAAALA ) as well as for pAD500 , a TAP-tagged ORF2p construct that lacks ORF1 ( Figure 4B and Figure S2A and S2B ) . These findings support the hypothesis that ORF2p can preferentially associate with its encoding RNA independent of ORF1p binding and that the resultant RNPs retain LEAP activity [17] . Indeed , the constellation of LEAP products observed in the RR261–262AA , YPAKLS282–287AAAALA , and pAD500 mutants support our previous hypothesis that ORF1p binding to L1 mRNA possibly may restrict hybridization of the LEAP primer to the L1 poly ( A ) tail [17] . Mutations that affect mouse nucleic acid chaperone activity ( pAD106; RR261–262KK ) had little effect on the ability of ORF1p and ORF2p to localize to RNPs or on LEAP activity ( Figure 4B; [16] , [35] ) . We occasionally observed a greater abundance of the lower molecular weight LEAP products , when compared to our wild-type control , pAD2TE1 ( Figure 4C ) . Indeed , closer inspection consistently revealed slightly higher levels of the major LEAP products ( ∼220 to ∼400 bp ) and a slightly lower level of the shorter LEAP products from the RR261–262KK mutant ( pAD106 and pDK106; Figure 4C , Figure S2B and S2C ) when compared to LEAP products derived from the RR261–262AA ( pAD105 and pDK105; Figure 4C , Figure S2B and S2C ) and YPAKLS282–287AAAALA mutants ( pDK116; Figure S2B ) . Thus , although the L1 RT activity detected in the LEAP assay does not appear to require ORF1p , it is clear that specific mutations in ORF1p can affect the constellation of products observed in these assays . Mutations in the putative ORF1p leucine zipper-binding domain ( pADLZC; L93 , 100 , 107 , 114V ) reduced ORF1p and ORF2p localization in the RNP fraction and consistently exhibited lower qualitative levels of LEAP activity when compared to the wild-type control , pAD2TE1 ( Figure 4B and 4C ) . Indeed , quantitative LEAP experiments conducted with pLZC-derived RNPs ( a L93 , 100 , 107 , 114V mutant that lacks an epitope tag on ORF2p ) revealed a five to seven-fold reduction in LEAP activity when compared to a corresponding wild-type control ( pDK101; Figure S2E ) . Subsequent data from LEAP experiments designed to detect variable length L1 cDNA products further suggest that the LZC mutation adversely affects early steps in the reverse transcription of L1 RNA and does not appear to affect L1 RT elongation ( Figure S2F ) . The putative leucine zipper domain-carboxyl terminal domain double mutant ( ADL/R; L93 , 100 , 107 , 114V/RR261–262AA ) shared biochemical characteristics of each single mutant . Similar to pAD105; RR261–262AA , ORF1p levels were severely reduced in pADL/R-derived RNPs . However , similar to the putative leucine zipper domain ( pADLZC; L93 , 100 , 107 , 114V ) mutant , ORF2p levels , as well as LEAP activity , were reduced in pADL/R-derived RNPs when compared to the wild-type control , pAD2TE1 . Moreover , the LEAP product profile in the double mutant resembled that in the pAD105 mutant ( Figure 4B and 4C; Figure S2 ) . Thus , the above data suggest that the LZC mutant adversely affects the accumulation and/or stability of L1 RNPs and that the reduction of ORF2p in RNPs likely contributes to the observed decrease in LEAP activity . Mutations in the ORF1p RRM domain ( pAD113; NLR157–159ALA ) also led to a severe reduction in the ability of ORF1p and ORF2p to localize to the RNP fraction of transfected cells ( Figure 4B ) . Indeed , ORF2p only was observed upon over-exposure of the resultant western blots ( data not shown ) . The reduced level of ORF2p in pAD113-derived RNPs also correlated with a decrease in LEAP activity when compared to the pAD2TE1 wild-type control ( Figure 4B and 4C ) . Notably , it is unlikely that the NLR157–159ALA mutation dramatically affects ORF2 translation because we can detect ORF2p from this mutant by immunofluorescence ( see below ) . Moreover , preliminary data ( n = 4 independent experiments ) indicates that the NLR157–159ALA mutant can serve as a “driver” in a genetic-based trans-complementation assay to mobilize a reporter gene ( ORF1mneoI; [19] ) at roughly 60 to 80% the level of the wild-type control , pAD2TE1-NT ( Doucet et al . , preliminary data ) . These data are consistent with previous genetic studies , which suggested that ORF1p binding to L1 RNA is not required for ORF2 translation [37] . Moreover , the data suggest that the NLR157–159ALA mutations severely compromise the accumulation and/or stability of L1 RNPs ( see Discussion ) . We next tested mutants in the following functional domains of ORF2p for their effect on L1 RNP formation and L1 reverse transcriptase activity: 1 ) the L1 endonuclease domain ( pAD136; H230A ) ; 2 ) the L1 reverse transcriptase domain ( pAD135; D702A ) ; 3 ) the cysteine-rich domain ( pAD162; CWWDC1143–1147SWWDS ) ( Figure 4A ) [19] , [22] , [32] . As expected , the L1 RT mutant ( pAD135; D702A ) did not dramatically affect the ability of ORF1p or ORF2p to localize to RNPs , although it did abolish LEAP activity ( Figure 4B and 4C ) [17] . These data are consistent with previous suppositions that the D702A mutant likely blocks the reverse transcription step in TPRT [17] , [32] , [40] . We repeatedly observed a slight reduction of ORF2p in RNPs derived from the tested endonuclease mutant , and this reduction correlates with a reproducible decrease in LEAP activity ( Figure 4A; pAD136; H230A ) . We also observed a severe reduction of ORF2p , as it was only detected upon longer film exposures ( data not shown ) , and a strong decrease of LEAP activity in RNPs derived from the tested cysteine-rich domain mutant ( pAD162; CWWDC1143–1147SWWDS ) . Finally , the leucine zipper/C-domain double mutant ( pADL/C; L93 , 100 , 107 , 114V/CWWDC1143–1147SWWDS ) displayed both a reduction of ORF1p in RNPs and a concomitant decrease in LEAP activity ( Figure 4B and 4C ) . As additional controls for the above experiments , we demonstrated that mutant constructs containing a T7-epitope tag on ORF1p , but lacking an ORF2p epitope tag exhibited similar qualitative LEAP activities as the pAD2TE1 mutant based constructs ( Figure S2 ) . We also demonstrated that the amount of T7-tagged ORF1p and TAP-tagged ORF2p in whole cell lysates is similar to that in the RNP fraction for each of the pAD2TE1 mutant constructs , and that these proteins were not enriched in insoluble aggregates in the pellet obtained after cell lysis ( data not shown ) . Thus , we conclude that mutations within discrete functional domains of ORF1p and ORF2p have differential effects on L1 RNP formation/function . Previous studies have shown that ORF1p often aggregates in cytoplasmic structures termed cytoplasmic foci [31] . Unlike the RNP assays described above ( which detect the steady state amount of ORF1p and ORF2p in the RNP fraction of hygromycin resistant cells ∼9 days post-transfection ) , the L1 cytoplasmic foci formation assays allows the opportunity to visually detect the L1-encoded proteins and/or L1 RNA when over-expressed ∼48 hours post-transfection . To test whether the ORF1p cytoplasmic foci also contain ORF2p and L1 RNA , we conducted immunofluorescence-based localization experiments in a U-2 OS human osteosarcoma cell line that can support the retrotransposition of engineered human L1 constructs ( Figure S3A ) . Initial experiments conducted with pAD2TE1 revealed that ORF1p and ORF2p generally co-localized to discrete cytoplasmic foci 48 hours post-transfection , and that many foci were located near the periphery of the nucleus ( Figure 5A ) . Time course analyses further demonstrated that cytoplasmic foci were apparent in ∼50% of transfected cells as early as 12 hours post-transfection , and that ∼90% of transfected cells displayed cytoplasmic foci 72 hours post-transfection ( Figure S3B ) . ORF1p/ORF2p-containing cytoplasmic foci also were observed in U-2 OS cells transiently transfected with pAD2TE1-NT , which lacks the mneoI retrotransposition indicator cassette ( Figure 5A ) and with a pAD2TE1 derivative lacking the heterologous cytomegalovirus immediate early ( CMV ) promoter , although foci appeared 24–48 hours later as compared to cells transfected with the wild type control , pAD2TE1 ( data not shown ) . ORF1p and ORF2p co-localization also was observed using an anti-HA antibody to detect ORF2p ( Figure 5A; pES2TE1 ) or antibodies against endogenous ORF1p or ORF2p ( Figure S3C; pES2TE1 ) . Qualitatively similar results were obtained when pAD2TE1 was transiently transfected into HeLa or 143Btk cells , which also support L1 retrotransposition [32] , [51] ( data not shown ) . To test whether L1 RNA co-localizes with ORF1p and ORF2p to cytoplasmic foci , we transiently transfected pAD3TE1 into U-2 OS cells . In situ hybridization experiments using a fluorescently-labeled probe complementary to the MS2 stem loop structures in the L1 3′UTR revealed the presence of L1 RNA in cytoplasmic foci as well as in nuclei of transfected cells ( Figure 5B and Figure S3D ) . The co-localization of ORF1p , ORF2p , and L1 RNA was confirmed by conducting co-transfection experiments with pAD3TE1 and a plasmid expressing a fluorescently labeled MS2 protein ( Figure S3D ) , and by staining with antibodies against ORF1p and ORF2p ( Figure S3C ) . As above , qualitatively similar results were obtained upon transient transfection of pAD3TE1 into HeLa or HEK293 cells , which also support L1 retrotransposition [32] , [51] ( data not shown ) . To determine whether L1 foci are associated with specific cytoplasmic substructures , we co-transfected U-2 OS cells with pAD2TE1 and plasmids that express GFP fusion proteins that can localize to processing bodies ( i . e . , P-bodies ) and/or stress granules . Consistent with previous analyses , ORF1p and ORF2p associated with an Ago2-GFP fusion protein that localizes both to P-bodies and stress granules ( Figure 5C; panel 1 ) [31] , [52] . Refining this analysis revealed that ORF1p and ORF2p co-localized with the stress granule marker G3BP-GFP [53] , but did not associate with the P-body marker DCP1α-GFP [54] ( Figure 5C; panel 2 and 3 ) . By comparison , experiments conducted with fluorescently labeled antibodies specific for eIF3 and G3BP [53] , [55] revealed that stress granules appear to closely associate with the L1 foci ( Figure 5C , panel 4 and 5 ) . Together , the above data demonstrate that ORF1p , ORF2p , and L1 mRNA co-localize to cytoplasmic foci when over-expressed from a variety of engineered L1 episomal expression constructs and that many of these cytoplasmic foci associate with stress granules . However , future experiments are needed to determine whether cytoplasmic foci represent accumulation depots for L1 RNPs or if they play an important role in L1 retrotransposition . We next examined if mutations in the L1-encoded proteins affect L1 cytoplasmic foci formation . Transient transfection of ORF1p mutant expression vectors into U-2 OS cells followed by immunofluorescence staining with anti-T7 and anti-TAP antibodies confirmed that ORF1p and ORF2p are expressed in these cells ( Figure 6A ) . Consistent with previous studies , mutations in the ORF1p RRM domain ( pAD113; NLR157–159ALA ) and carboxyl-terminal RNA binding domain ( pAD105; RR261–262AA ) led to a reduction in the number of L1 cytoplasmic foci ( Figure 6A and 6B ) [31] . A reduction in the number of L1 cytoplasmic foci also was observed for an RRM domain mutant ( pAD102; REKG235–238AAAA ) , an additional carboxyl-terminal domain mutant ( pAD116; YPAKLS282–287AAAALA ) , and the putative leucine zipper domain/carboxyl-terminal RNA binding domain double mutant ( pADL/R; L93 , 100 , 107 , 114V/RR261–262AA ) . By comparison , mutations in the putative ORF1p LZ domain ( pADLZC; L93 , 100 , 107 , 114V ) or mutations that affect the nucleic acid chaperone activity of mouse ORF1p ( pAD106; RR261–262KK and pAD107; R261K ) had little effect on L1 cytoplasmic foci formation ( Figure 6A and 6B ) , although we sometimes observed an apparent nucleolar localization of ORF1p in pADLZC transfected cells . None of the ORF2p mutations had a dramatic effect on L1 cytoplasmic foci formation ( Figure 6A and 6B ) , although , we observed a diffuse nuclear localization of TAP-tagged ORF2p in cells transfected with either pAD162 or the putative leucine zipper domain/cysteine-rich domain double mutant ( pADL/C; L93 , 100 , 107 , 114V/CWWDC1143–1147SWWDS ) ( Figure 6A ) . The above data suggest that the ability of ORF1p to bind L1 RNA is critical for L1 cytoplasmic foci formation ( Figure 6B ) . Consistent with this idea , we were able to detect L1 cytoplasmic foci , as well as diffuse ORF1p staining , in U-2 OS cells transiently transfected with a T7-tagged ORF1p expression vector ( Figure 6C; pDK500 ) . However , L1 cytoplasmic foci were not detected in U-2 OS cells transiently transfected with a TAP-tagged ORF2p expression vector ( Figure 5D; pAD500 ) . Thus , these data , as well as our previously published trans-complementation experiments [56] , suggest that ORF1p interacts with its encoding RNA in cis , and that this association allows L1 cytoplasmic foci formation in the absence of ORF2p .
ORF2p has been notoriously difficult to detect from engineered human L1s in cultured cells . It has been hypothesized that human ORF2p is translated at low levels when compared to ORF1p and/or may be an unstable protein , which might help explain why it has evaded detection [36]–[39] , [42] . Previous biochemical studies have identified human ORF2p from vascular endothelial cells in vivo [57] and have demonstrated that ORF2p RT activity co-localizes with ORF1p and L1 RNA in cytoplasmic RNPs derived from HeLa cells transfected with wild-type engineered human L1 expression constructs [16] , [17] . Here , we have built on these studies and have combined epitope and RNA tagging strategies to physically detect L1 ORF1p , ORF2p and L1 mRNA in cytoplasmic RNPs . Why our approach allows the ready detection of ORF2p expressed from engineered human L1 constructs requires further study . Experiments conducted with anti-TAP antibodies consistently yielded more robust detection of ORF2p when compared to anti-HA or anti-ORF2p antibodies . Thus , the inclusion of a large carboxyl-terminal tag , such as the TAP-tag , might stabilize ORF2p . However , since engineered L1 constructs containing either the TAP or HA epitope tags on the carboxyl-terminus of ORF2p remain retrotransposition-competent , the strategy described here allows a way to both directly study the expression of ORF1p and ORF2p from a bicistronic transcript and establishes an experimental platform to determine how each protein interacts with L1 RNA . Furthermore , this strategy now allows a comprehensive means to assess how mutations in ORF1p and/or ORF2p affect L1 RNP biogenesis and/or L1 retrotransposition . Biochemical methods allowed us to assess how mutations in the L1-encoded proteins affect RNP function . For example , in agreement with previous studies , a mutation in the carboxyl-terminal domain of ORF1p ( pAD105 , RR261–262AA ) markedly reduced ORF1p levels in RNPs , but did not noticeably affect ORF2p accumulation or LEAP activity ( Figure 6A; Figure S3 ) [16] , [17] . Similarly , we could detect ORF2p and LEAP activity in RNPs derived from cells transfected with a construct that lacks ORF1 ( Figure 6A and 6B ) . Thus , we conclude that ORF2p can preferentially associate in cis with its encoding transcript to form an RNP independently of ORF1p RNA binding ( Figure 7 ) . Our studies further suggest that an interplay exists between ORF1p , ORF2p and L1 RNA that is critical for proper L1 RNP formation/function ( Figure 7 ) . For example , mutations in the putative leucine zipper ( pADLZC; L93 , 100 , 107 , 114V ) or RRM ( pAD113; NLR157–159ALA ) domains led to a reduced amount of ORF2p in the RNP fraction , as well as a decrease in LEAP activity . The L93 , 100 , 107 , 114V mutations reside in the N-terminal coiled-coil domain of ORF1p and could potentially alter the structure of the protein . Similarly , the NLR157–159ALA mutations reside near coiled-coil domain/RRM junction and structural studies indicate that a hydrogen bond between N157 and D252 is important for correct folding of the RRM domain [30] . Thus , both of the above mutations may adversely affect the structural integrity of ORF1p , leading to the destabilization of the resultant L1 RNPs . Indeed , such a scenario could potentially account for the reduced levels of ORF2p in RNPs and/or L1 RT activity in these mutants ( Figure 7 ) . It is unlikely that the L93 , 100 , 107 , 114V , L93 , 100 , 107 , 114V/RR261–262AA , and NLR157–159ALA mutants significantly affect ORF2p translation , since our preliminary data indicate that each mutant can serve as a “driver” in a genetic-based trans-complementation assay ( Doucet , Hulme et al . , preliminary data ) . As expected , a mutation in the endonuclease domain of ORF2p ( pAD136; H230A ) had no discernable affect on the ability of ORF1p to accumulate in RNPs when compared to a wild-type control construct . However , this mutation consistently led to a slightly reduced amount of ORF2p in RNPs , which correlated with a lower LEAP activity [17] . These findings could potentially explain why the H230A mutant consistently exhibited lower levels of endonuclease-independent L1 retrotransposition in Chinese Hamster Ovary cells that are deficient in the non-homologous end-joining pathway of DNA repair when compared to a D205A endonuclease domain mutation [58] . Mutations in the cysteine-rich domain ( pAD162; CWWDC1143–1147SWWDS ) did not have a major effect on the ability of ORF1p to accumulate in the RNP fraction . However , these mutations led to a reduced amount of ORF2p in the RNP fraction and a concomitant decrease in LEAP activity when compared to a wild-type control construct . How mutations in the C-domain affect ORF2p accumulation in RNPs requires further study; however , it is possible that these mutations alter the ability of ORF2p to interact with L1 RNA and/or host factors that are important for the biogenesis of L1 RNPs ( Figure 7 ) . A second assay allowed us to determine how mutations in the L1-encoded proteins affect L1 protein expression and cytoplasmic foci formation shortly after transfection . First , we observed that ORF1p and ORF2p can be detected when transiently expressed from the wild-type and mutant L1 constructs used in the study . We next measured the ability of these proteins to form cytoplasmic foci . Consistent with previous studies , retrotransposition-defective L1s containing mutations in either the RRM ( pAD113; NLR157–159ALA , pAD102; REKG235–238AAAA ) or carboxyl-terminal domain of ORF1p ( pAD105 , RR261–262AA; pAD116 , YPAKLS282–287AAAALA ) reduced L1 cytoplasmic foci formation [31] , [42] , [59] . In contrast , mutations in the putative leucine zipper domain ( pADLZC; L93 , 100 , 107 , 114V ) or mutations analogous to those that adversely affect the nucleic acid chaperone activity of mouse ORF1p ( pAD106; RR261–262KK ) , which are not predicted to inhibit L1 RNA binding , or mutations in ORF2p had little effect on L1 cytoplasmic foci formation [16] , [35] . Thus , unlike our biochemical assays , the L1 cytoplasmic foci formation assay does not allow us to readily assess ORF2p function . Instead , it provides a valuable tool to screen for ORF1p mutations that affect RNA binding or perhaps protein stability ( Figure 7 ) . Consistent with previous studies , we found that L1 cytoplasmic foci are in close association with proteins that are components of stress granules ( Figure 7 ) [31] , [59] . Interestingly , recent studies have shown an important role for another cytoplasmic structure ( P-bodies ) for Ty3 and Ty1 retrotransposition in yeast [60]–[62] . Whether L1 cytoplasmic foci play an important role in L1 retrotransposition awaits further experimentation . In sum , we have developed a powerful system to physically detect the proteins and RNA encoded by both retrotransposition-competent and mutant L1 constructs in RNP complexes , which now augments previous studies that were based on inferring the presence of ORF2p from its enzymatic activity . It is noteworthy that RNPs derived from the RC-L1s characterized in this study exhibit the biochemical properties predicted of a “basal” L1 retrotransposition intermediate . Thus , we speculate that at least some of the L1 cytoplasmic foci identified here could serve as bona fide L1 retrotransposition intermediates . Finally , we predict that the use of the L1 expression constructs developed here will allow a powerful means to identify host factors that play a role in L1 retrotransposition and predict that adaptations of this system will prove useful in identifying RNPs encoded by other non-LTR retrotransposons .
Sequences of the oligonucleotides used in this study that have been published previously or are available upon request . 3′RACE adapter: 5′- GCGAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTTVN-3′ 3′RACE outer: 5′-GCGAGCACAGAATTAATACGACT-3′ GAPDH 3′ end: 5′-GACCCTCACTGCTGGGGAGTCC-3′ Neo Promoter Sens ( NPS ) : 5′-GGTTGCTGACTAATTGAGATGCATGC-3′ Neo8161S: 5′-CACATTCCACAGCTGATCGATACC-3′ L1 3′end: 5′-GGGTTCGAAATCGATAAGCTTGGATCCAGAC-3′ LEAP-86: 5′-CAAACCACAACTAGAATGCAGTG-3′ LEAP-46: 5′-GTGAAATTTGTGATGCTATTGC-3′ The following plasmids are based on the previously described pJM101/L1 . 3 and pDK101 constructs [4] , [16] . The amino acid and nucleotide numbers indicate the mutation position based on L1 . 3 accession number L19088 [63] . The constructs were cloned into the pCEP4 expression vector ( Invitrogen ) and contain the mneoI indicator cassette [32] , [47] in the L1 3′UTR unless otherwise indicated . PCR followed by subcloning was used to introduce the respective epitope tag sequences onto the 3′ end of ORF2 . As a result of this procedure , we deleted a portion of the L1 3′UTR ( nts 5818 to 5953 ) . Oligonucleotides used in our cloning strategies are available upon request . pADO2Tt contains a Tandem Affinity Purification epitope tag ( TAP tag ) [43] on ORF2p and was cloned from the pZome-1-C vector ( Euroscarf ) . pAD2TE1 is derived from pDK101 ( L1 . 3 ) [16] and contains both the T7 gene 10 epitope tag on the carboxyl-terminus of ORF1p and a TAP tag on the carboxyl-terminus of ORF2p . pAD2TE1-Δ2 is derived from pAD2TE1 , but lacks CMV promoter and SV40 polyadenylation signal present in the original pCEP4 vector . pAD2TE1-NT is identical to pAD2TE1 , but lacks the mneoI indicator cassette . pES2TE1 is identical to pAD2TE1 , but contains a tandem affinity FLAG-HA tag on the carboxyl-terminus ORF2p [44] . pAD500 is derived from L1 . 3ΔORF1NN [37] , and contains a TAP tag on the carboxyl-terminus of ORF2p . pADL1MT is derived from pJM101/L1 . 3 and contains 24 repeats of the MS2 stem-loop ( MS2 tag ) upstream of the mneoI indicator cassette in the L1 3′UTR . The MS2 repeats were subcloned from the pTRIP vector [64] . pAD3TE1 is identical to pAD2TE1 , but contains the MS2 tag in the 3′UTR ( at the same position as in pADL1MT ) . pADO1S is identical to pAD2TE1 , but contains three stop codons in ORF1 . The first two stop codons ( R7Stop; K8Stop ) were generated by introducing a thymidine at nucleotide position 928 to create a frameshift mutation and by mutating an A to a T at nucleotide position 930 . The third stop codon is from the construct pJM108/L1 . 3 carrying the mutation S119Stop [19] , [32] . pADLZC is identical to pAD2TE1 , but contains four leucine to valine mutations ( L93 , 100 , 107 , 114V ) in the ORF1p putative leucine zipper domain . pAD102 is identical to pAD2TE1 , but contains the REKG235–238AAAA mutations in the ORF1p RRM domain [16] , [32] . pAD105 is identical to pAD2TE1 , but contains the RR261–262AA mutations in the ORF1p C-terminal domain [16] , [19] , [32] . pAD106 is identical to pAD2TE1 , but contains the RR261–262KK mutations in the ORF1p C-terminal domain [16] . pAD107 is identical to pAD2TE1 , but contains the RR261–262KR mutation in the ORF1p C-terminal domain [16] . pAD113 is identical to pAD2TE1 , but contains the NLR157–159ALA mutations in the ORF1p RRM domain [31] . pAD116 is identical to pAD2TE1 , but contains the YPAKLS282–287AAAALA substitution in the ORF1p C-terminal domain [16] , [32] . pAD135 is identical to pAD2TE1 , but contains the D702A mutation in the putative ORF2p RT active site [19] . pAD136 is identical to pAD2TE1 , but contains the H230A mutation in the ORF2p EN domain [19] . pAD162 is identical to pAD2TE1 , but contains the CWWDC1143–1147SWWDS mutations in the ORF2p C-domain [32] . pADL/R is identical to pAD2TE1 , but contains a putative leucine zipper domain as well as a C-terminal domain mutant ( L93 , 100 , 107 , 114V; RR261–262AA ) in ORF1p . pADL/C is identical to pAD2TE1 , but contains a putative leucine zipper domain mutation ( L93 , 100 , 107 , 114V ) in ORF1p as well as a C-domain mutation ( CWWDC1143–1147SWWDS ) in ORF2p . LZC is derived from pDK101 and contains four leucine to valine mutations ( L93 , 100 , 107 , 114V ) in the ORF1p putative leucine zipper domain . LZ1/2 is derived from pDK101 and contains two leucine to valine mutations ( L93 , 100V ) in the ORF1p putative leucine zipper domain . LZ2/3 is derived from pDK101 and contains two leucine to valine mutations ( L100 , 107V ) in the ORF1p putative leucine zipper domain . LZ3/4 is derived from pDK101 and contains two leucine to valine mutations ( L107 , 114V ) in the ORF1p putative leucine zipper domain . pDK101 , pDK102 , pDK105 , pDK106 , pDK107 , pDK108 , pDK116 , pDK135 , and pDK500 were described previously [16] . pMS2-GFP-nls , pMS2-CFP , and pTRIP were generous gifts from Edouard Bertrand [64]–[66] . pAgo2-GFP and pDCP1α-GFP were generous gifts from Gregory Hannon [54] . pG3BP-GFP was a generous gift from Jamal Tazi [53] . Cell lines were maintained in a tissue culture incubator ( 37°C at a 7% CO2 level ) in high glucose Dulbecco's modified Eagle medium ( DMEM ) without pyruvate ( GIBCO ) , supplemented with 10% fetal bovine calf serum and 1X Penicillin-Streptomycin-Glutamine ( GIBCO ) as described previously [32] . The cultured cell retrotransposition assay was conducted as described previously [32] , [48] . Briefly , 2×104 HeLa cells/well were plated in 6 well dishes . Within 24 hours , each well was transfected with 1 µg of plasmid DNA ( prepared with a Midiprep Plasmid DNA Kit ( QIAGEN ) ) using FuGene-6 transfection reagent ( Roche ) . Three days post-transfection , cells were grown in the presence of G418 ( 400 µg/mL ) to select for retrotransposition events . The media was changed daily . After ∼12 days of selection , the resultant cells were washed with 1X Phosphate-Buffered Saline ( PBS ) , fixed , and stained with crystal violet to visualize colonies . In parallel , HeLa cells were plated in 6 well dishes and transfected with 0 . 5 µg of the same plasmids and hrGFP ( Stratagene ) . Three days post-transfection cells were subjected to flow cytometry and the transfection efficiency was determined based on the number of GFP positive cells by FACS . In some experiments , 2×105 HeLa cells/well were transfected to monitor L1 retrotransposition . HeLa cells were transfected with a given L1 expression construct in T-25 , T-75 , or T-175 tissue culture flasks . Whole cell lysates then were prepared after 9 days of hygromycin selection as described previously [16] . The cells were washed in 1X PBS , scraped from plates in 1X PBS , and spun at 3 , 000 g for 5 minutes at 4°C . One volume of pelleted cells was lysed using two volumes of the following buffer: 1 . 5 mM KCl , 2 . 5 mM MgCl2 , 5 mM Tris-HCl , pH 7 . 5 , 1% deoxycholic acid , 1% Triton X-100 , 1X Complete Mini EDTA-free Protease Inhibitor Cocktail ( Roche Applied Science ) . The cells were resuspended by gentle pipetting and incubated on ice for 10 minutes . The lysate was cleared by centrifugation at 3 , 000 g for 5 minutes at 4°C . Untransfected HeLa cell samples were obtained three days after plating . The Bradford reagent ( Bio-Rad ) was used to determine the protein concentrations [67] . The same amount of total protein was separated by SDS-PAGE . BenchMark Pre-Stained Protein Ladder ( Invitrogen ) was used as a molecular weight marker . The proteins were detected by western blot using the following primary antibodies: mouse anti-T7-Tag ( Novagen ) , rabbit anti-TAP ( Open Biosystems ) , rat anti-HA ( 3F10 clone , Roche ) , mouse anti-α-tubulin ( Sigma ) , rabbit anti-S6 ( Cell Signaling Technology ) , rabbit anti-ORF1p ( a generous gift from Thomas Fanning [50] ) and rabbit α-ORF2p-N ( a generous gift from John Goodier [42] . Goat anti-mouse , anti-rabbit and anti-rat HRP-conjugated secondary antibodies were purchased from GE/Amersham . Western blots were developed using either the pico or femto ECL substrate ( Pierce ) according to manufacturer's protocols . RNA isolation was performed with the RNeasy Kit ( QIAGEN ) coupled to an on-column DNase treatment ( QIAGEN ) . Whole cell lysates ( 10–50 µL ) were used as starting material . The isolated RNAs were resuspended in Ultrapure distilled water ( GIBCO ) and quantified using a Nanodrop spectrophotometer ( Thermo Scientific ) . For the LEAP assay controls , RNA was isolated from a 50 µL RNP sample ( 1 . 5 mg/mL ) . RT-PCR was performed on 0 . 5 µg total RNA , using the 3′RACE adapter primer ( 0 . 4 µM ) and M-MLV reverse transcriptase ( 200U ) ( Promega ) . The resultant cDNA products then were amplified by PCR using HotStart Pfu Turbo polymerase ( Stratagene ) with one primer specific to the transfected L1 constructs ( L1 3′ end ) or GAPDH ( GAPDH 3′ end ) and the 3′RACE outer primer , as described previously [17] . The PCR cycles were as follows: one cycle at 94°C for 3 minutes , then thirty five cycles of 94°C for 30 seconds , 58°C for 30 seconds and 72°C for 30 seconds . Then , a final extension was performed at 72°C for 10 minutes . The LEAP assay has been described previously [17] . Briefly , HeLa cells were plated at 6×106 cells/flask in T-175 flasks , and transfected within 24 hours with 30 µg plasmid DNA ( Midiprep Plasmid DNA Kit ( QIAGEN ) ) using FuGene-6 transfection reagent ( Roche ) . HeLa cells were grown in the presence of hygromycin from days 3 to 9 post-transfection ( 200 µg/mL ) to select for episome-containing cells . HeLa cells grown for three days in the absence of hygromycin served as an untransfected ( naïve ) control . On day 9 , transfected cells and naïve HeLa cells were harvested , lysed , and the cleared whole cell lysates were centrifuged through an 8 . 5%/17% ( w/v ) sucrose cushion at 178 , 000 g for 2 hours . The resultant pellet was resuspended with 100 µL dH2O +1X Complete EDTA-free protease inhibitor cocktail ( Roche ) . Bradford reagent ( Bio-Rad ) was used to determine protein concentration and this RNP sample was diluted to a final concentration of 1 . 5 mg/mL . An aliquot ( 1 . 5 µg ) of the RNP sample was added to 49 µL of LEAP assay master mix ( 50 mM Tris-HCL ( pH = 7 . 5 ) , 50 mM KCl , 5 mM MgCl2 , 10 mM DTT , 0 . 4 µM 3′RACE adapter primer , 20U RNasin ( Promega ) , 0 . 2 mM dNTPs , and 0 . 05% ( v/v ) Tween 20 ) and was incubated at 37°C for 1 hour . LEAP cDNA products ( 1 µL ) were amplified in a standard 50 µL PCR reaction containing 0 . 4 µM of the 3′RACE outer primer and 0 . 4 µM of one of the following forward primers: L1 3′ end; Neo promoter sense; Neo8161S; LEAP-86; LEAP-46 , using HotStart Pfu Turbo polymerase ( Stratagene ) according to the manufacturer's protocol ( see Figure S2 ) . The resultant products were visualized on 2% agarose gels . PCR products were isolated , cloned into the pCR-Blunt vector ( Invitrogen ) , and sequenced to confirm their identity . The diffuse profile of the amplification above 220 bp is explained by initiation of reverse transcription at many places on L1 poly ( A ) . Lower bands , below 200 bp , are due to an internal initiation of reverse transcription 5′ of the poly ( A ) tail [17] . The affinity purification procedure described in Figure 3 was adapted from a published protocol [68] . To prepare the samples , HeLa cells were plated in T-175 flasks and transfected as described in the previous paragraph . Hygromycin selection on days 3 to 9 post-transfection was used to select for cells expressing the respective constructs . Using these conditions , one T-175 flask per plasmid was sufficient to yield enough cellular material ( 3 mg ) for an experiment . Cells were washed , scraped from the flasks in 1X PBS , and centrifuged at 3 , 000 g for 5 minutes at 4°C . Cells were lysed by repeated pipetting with 3 volumes of IP FLAG buffer ( 0 . 1% NP-40 , 100 mM KCl , 20 mM Tris-HCl pH 8 , 1 mM DTT , 10% Glycerol , 1X complete EDTA free Protease inhibitor ( Roche ) ) and incubated for 15 minutes on ice . The cellular debris was removed by centrifugation at 3 , 000 g for 5 minutes at 4°C . The protein concentration of the supernatant was quantified by a Bradford assay ( Biorad Protein Assay ) . For immunoprecipitation reactions , anti-FLAG beads ( EZview Red ANTI-FLAG M2 Affinity Gel , Sigma ) were equilibrated in 0 . 1M Glycine ( pH 2 . 2 ) ( 5 µL for 100 µL of beads ) for 5 minutes at room temperature . After addition of Tris-HCl ( pH 8 . 0 ) ( 10 µL for 100 µL of beads ) , the beads were spun down for 3 minutes at 3000 rpm and then washed 3 times with IP FLAG buffer ( mentioned above ) . For each condition , 3 mg of protein extract ( input ) was then incubated on a rotating wheel overnight at 4°C with 20 µL of the pre-equilibrated anti-FLAG beads . The next day , the beads were washed 5 times with 1 mL of IP FLAG Buffer for 10 minutes at 4°C . The beads were incubated 1 hour ( at 4°C on the wheel ) with 200 µL of IP FLAG buffer containing 200 µg/mL of 3X FLAG peptide ( Sigma ) . The elution fraction then was collected and analyzed alongside the corresponding input fraction by western blotting ( as described above in the dedicated section ) . The femto ECL substrate ( Pierce ) was used in the detection of both T7-tagged ORF1p and FLAG-HA-tagged ORF2p in this experiment . An aliquot ( 1 µL ) of the input and elution samples then were used to perform the LEAP assay ( see previous section for detailed protocol ) . Quantitative PCR was performed on LEAP cDNA samples or M-MLV RT-PCR products using the 7300 Real Time PCR system ( Applied Biosystems ) . For analysis , 1 µL of LEAP or M-MLV RT products was added to 19 µL of master mix ( 1X SYBR Green PCR Master Mix ( Applied Biosystems ) , 500 nM L1 3′ end primer , and 500 nM L1 Reverse primer ) , and amplified in a standard Q-PCR run of 45 cycles . The average cycle threshold ( Ct ) value for each experimental or control sample was calculated from three independent reactions within a Q-PCR run . The ‘absolute quantitation by standard curve’ method was used to determine the number of cDNA molecules in each LEAP RNP or RNA sample . A standard curve was generated using dilutions of a L1 LEAP product cloned into a plasmid , and a best fit line ( log ( molecules ) versus average Ct value ) for these standards was generated by linear regression . For each wild-type or mutant L1 , RNA levels from three independent RNP samples were examined by at least one RT reaction and two Q-PCR runs . The level of LEAP activity in each wild-type or mutant L1 was determined from four independent RNP samples . These RNP samples were characterized by at least one and up to three independent LEAP RT reactions and one or two independent Q-PCR runs . For LEAP activity , the negative control RT- ( pDK135 ) gave a background amplification level of ∼15–30 molecules of cDNA due to the presence of the transfected L1 plasmid in the RNP sample . This RT- background control was included in each Q-PCR run and the background amount of molecules was subtracted from each experimental sample in Figure S2 and when calculating fold changes . U-2 OS cells were plated at 105 onto sterile glass cover slips in 6 well tissue culture dishes . The following day , cells were transfected using 1 µg of purified plasmid DNA ( Midiprep Plasmid DNA Kit , QIAGEN ) and 3 µL of FuGene-6 Transfection Reagent ( Roche Applied Science ) . The FISH protocol was adapted from the Robert Singer ( Albert Einstein College of Medicine , New York ) lab protocol ( available at http://www . singerlab . org/protocols ) and was modified to allow protein detection by immunofluorescence . Briefly , 48 h post-transfection , cells were washed twice with 1X PBS and fixed with 4% paraformaldehyde in 1X PBS for 10 minutes at room temperature . The fixed cells then were washed 2 additional times with 1X PBS . The fixed cells were permeabilized by treatment with 70% ethanol overnight at 4°C . The following day , cells were rehydrated with 1X saline-sodium citrate ( SSC ) and 10% formamide for 5 minutes at room temperature . To prepare the hybridization solution , a first mix containing 40 µg of E . coli tRNA ( Sigma ) , 1X SSC , 10% formamide , and 7 . 5 ng of MS2-Cy3 probe ( generous gift from Dr . Edouard Bertrand ) was boiled for 1 minute at 100°C in order to denaturize the probe . The quantities of probe and tRNA are indicated for hybridization of one slide . A second mix was prepared with 10% dextran sulfate , 2 mM vanadyl-ribonucleoside complex ( Sigma ) , and 0 . 02% RNase free BSA ( Roche Applied Science ) . After probe denaturation , mixes 1 and 2 were combined to form the final hybridization solution . The re-hydrated cells were hybridized overnight at 37°C in 30 µL of this hybridization solution . Cells were then washed twice for 30 minutes at 37°C with 1X SSC , 10% formamide and 3% BSA and then were incubated with primary antibodies for 1 hour at 37°C . The cells were washed three times with 1X PBS and were incubated with secondary antibodies and 0 . 2 µg/mL 4′ , 6′-diamidino-2-phenylindole ( DAPI , Molecular Probes ) for 30 minutes at 37°C and washed three times with 1X PBS . The primary and secondary antibodies were diluted in 1X PBS and 3% BSA and are as follows: anti-T7 ( Novagen ) , anti-TAP ( Open Biosystems ) , anti-HA ( Roche ) , rabbit anti-ORF1p ( a generous gift from Thomas Fanning ) [50] ) and rabbit α-ORF2p-N ( a generous gift from John Goodier ) [42] , anti-eIF3 ( Santa Cruz BioTechnology ) , anti-G3BP ( generous gift from Jamal Tazi ) , Alexa Fluor 488 anti-mouse and anti-rabbit ( Invitrogen ) , Alexa Fluor 546 anti-mouse and anti-rabbit ( Invitrogen ) , Cy3-conjugated anti-rat ( Jackson Immuno Research ) and Cy5-conjugated anti-mouse and anti-rabbit ( Jackson Immuno Research ) . Cells were rinsed with water and mounted on slides with Vectashield ( Vector Laboratories ) . Samples were then analyzed with appropriate fluorescent filters on DMRXA Leica microscope and images were captured using a Zeiss LSM510 META confocal microscope . The above protocol was used for both RNA and protein detection analyses . In experiments where we only sought to detect RNA , the protocol was stopped after hybridization with the MS2-Cy3 probe and subsequent washes . Cover slides were stained with DAPI and mounted on slides as described above . In experiments where we only sought to detect protein , fixed cells were permeabilized by treatment with anhydrous methanol for 1 minute . After three washes with 1X PBS , the cells were incubated with 3% BSA in 1X PBS for 30 minutes . Antibody incubation and DAPI staining were performed as described above . We verified that the protein A domain contained in the TAP tag of ORF2p did not react with the secondary antibodies ( data not shown ) . In general , L1 cytoplasmic foci formation was measured 48 hours post-transfection . At least two independent series of slides were analyzed . Each analysis corresponds to 100 transfected cells that were quantified in a blinded manner . Cells in which we were able to distinguish a concentrated cytoplasmic signal from a diffuse cytoplasmic signal using a 63x or 100x objective ( equivalent of 1 micrometer of diameter ) were considered as L1 cytoplasmic foci . | Long Interspersed Element-1 ( LINE-1 or L1 ) sequences are the predominant class of autonomous retrotransposons in the human genome and comprise an astounding 17% of human DNA . Although the majority of L1s are considered to be “dead , ” an average human genome contains ∼80–100 active L1s . Active L1s encode two proteins ( ORF1p and ORF2p ) that are required for mobility ( retrotransposition ) by a “copy and paste” mechanism termed target-site primed reverse transcription . Prior experiments suggested that ORF1p , ORF2p reverse transcriptase activity , and L1 mRNA associate in ribonucleoprotein ( RNP ) particles and that RNP formation is a necessary step in L1 retrotransposition . However , the difficulty in detecting ORF2p from engineered human L1s has prevented a thorough understanding of its role in L1 retrotransposition . Here , we have exploited epitope and/or RNA–tagging strategies to detect and characterize a “basal” RNP complex from engineered human L1s . We also expanded on previous studies and characterized how mutations in conserved functional domains of ORF1p and ORF2p can adversely affect L1 RNP formation/function . Finally , our strategy allowed us to detect the L1–encoded proteins and L1 RNA in cytoplasmic foci . Thus , we have developed and employed a system to gain greater understanding of LINE-1 retrotransposition at the molecular level . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"biology/rna-protein",
"interactions",
"genetics",
"and",
"genomics",
"biochemistry/macromolecular",
"assemblies",
"and",
"machines"
] | 2010 | Characterization of LINE-1 Ribonucleoprotein Particles |
Cortical firing rates frequently display elaborate and heterogeneous temporal structure . One often wishes to compute quantitative summaries of such structure—a basic example is the frequency spectrum—and compare with model-based predictions . The advent of large-scale population recordings affords the opportunity to do so in new ways , with the hope of distinguishing between potential explanations for why responses vary with time . We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons , conditions , and times , they are naturally expressed as a third-order tensor . We examined tensor structure for multiple datasets from primary visual cortex ( V1 ) and primary motor cortex ( M1 ) . All V1 datasets were ‘simplest’ ( there were relatively few degrees of freedom ) along the neuron mode , while all M1 datasets were simplest along the condition mode . These differences could not be inferred from surface-level response features . Formal considerations suggest why tensor structure might differ across modes . For idealized linear models , structure is simplest across the neuron mode when responses reflect external variables , and simplest across the condition mode when responses reflect population dynamics . This same pattern was present for existing models that seek to explain motor cortex responses . Critically , only dynamical models displayed tensor structure that agreed with the empirical M1 data . These results illustrate that tensor structure is a basic feature of the data . For M1 the tensor structure was compatible with only a subset of existing models .
Cortical neurons often display temporally complex firing rate patterns ( e . g . , [1 , 2] ) . Such temporal structure may have at least two non-exclusive sources . First , temporal structure may reflect external variables that drive or are being encoded by the population; e . g . , a time-varying stimulus or a time-varying parameter represented by the population [3 , 4] . Second , temporal structure may reflect internal population-level dynamics . For example , oscillatory responses are observed in isolated spinal populations [5] , and even sensory areas exhibit response transients due to cellular and network dynamics [6] . One often wishes to disentangle the contributions of external variables and internal dynamics . Yet without full knowledge of the relevant external variables , response patterns can in principle originate from either source [7] . For example , a sinusoidal response might reflect a sinusoidal external variable , oscillatory population dynamics , or both . Motor cortex ( M1 ) presents a paradigmatic example where temporal response complexity [1 , 8–10] has fed a long-standing debate [11–21] . Guided by one viewpoint , many studies have focused on the possibility that M1 responses reflect specific external behavioral variables , and have sought to determine their identity ( reach direction , velocity , joint torques , muscle forces , etc . [21] ) and reference frame [22–28] . Guided by another viewpoint , recent studies suggest that the temporal structure of M1 responses may largely reflect the evolution of internal population dynamics [29–33] . This second viewpoint is embodied in recurrent network models of pattern generation [34–36] , and is broadly compatible with control-theory models [37–39] where dynamics may involve both internal recurrence and feedback . While not necessarily opposed , the first and second viewpoints often make different predictions even when starting with shared assumptions . Suppose one began with the assumption that , during reaching , motor cortex controls muscle activity more-or-less directly [14] . The first viewpoint predicts that neural responses will be a function of ( will ‘encode’ ) the patterns of muscle activity . The first viewpoint does not predict that neural responses should obey dynamics: the future neural state would not be a consistent function of the present neural state . While muscle activity is ‘dynamic’ in the sense that it is time-varying , it is not typically true that the set of muscle activations obeys a single dynamical system ( i . e . a fixed flow field ) across different reaches . The second viewpoint , in contrast , predicts that the motor cortex population response should obey consistent dynamics . The second viewpoint , like the first , predicts that muscle activity will be a function of neural responses [40 , 41] . Yet because that function is presumably non-invertible , neural responses will not be a function of muscle activity , in opposition to the first viewpoint . The hypothesis that neural responses reflect external variables ( e . g . , muscle activity itself ) and the hypothesis that neural responses reflect internal dynamics ( e . g . , the dynamics that produce muscle activity ) could be readily distinguished were it known that muscle activity was the relevant external variable . However , that assumption is itself the subject of controversy [8 , 14 , 15 , 17 , 27 , 40 , 42–45] . It therefore remains debated whether M1 response structure originates from a representation of external movement variables or the unfolding of internal dynamics . Recent experimental studies [30 , 46] and reviews [19 , 32] have advanced both positions . Motor cortex thus illustrates a general need: the ability to infer the predominant origin of time-varying responses . We report here that a basic but previously unmeasured feature of neural population data is surprisingly informative to this need . We considered the population response as a third-order tensor ( a three-dimensional array ) indexed by neuron , condition and time . We were motivated by the idea that tuning for external variables constrains structure across neurons; if there are ten relevant external variables , responses are limited to ten degrees of freedom across neurons . We refer to this setting as ‘neuron-preferred . ’ Conversely , internal dynamics constrain structure across conditions; if a population obeys the same dynamics across conditions , responses will have limited degrees of freedom across conditions . We refer to this situation as ‘condition-preferred . ’ Neuron-preferred or condition-preferred structure is hidden at both the single-neuron level and in standard population-level analyses—i . e . this structure is hidden if the data is viewed only as a matrix . Intuitions regarding neuron-preferred versus condition-preferred structure can be gained by considering linear models . For example , the input-driven system x ( c , t ) =Bu ( c , t ) , ( 1 ) and the autonomous dynamical system x ( c , t+1 ) =Ax ( c , t ) , ( 2 ) can be viewed as two different generators of a data tensor X∈ℝN×C×T , with x ( c , t ) ∈ ℝN the vector of N neural responses at time t for condition c , u ( c , t ) ∈ ℝM the vector of M input variables , B ∈ ℝN×M , and A ∈ ℝN×N . Time-varying structure of X generated by the first equation is inherited from the time-varying structure of u ( c , t ) , while for the second it is inherited from the time-varying structure of At , since Eq ( 2 ) can be expressed as x ( c , t ) = Atx ( c , 0 ) . As will be formalized later , neuron-preferred tensor structure follows naturally from Eq ( 1 ) : each C × T ‘slice’ of the data tensor X ( i . e . , the data for a given neuron across all conditions and times ) is a linear combination of a bounded number of basis elements , each of size C × T . Condition-preferred structure follows naturally from Eq ( 2 ) : each N × T ‘slice’ of the data tensor X ( i . e . , the data for a given condition across all neurons and times ) is a linear combination of a bounded number of basis elements , each of size N × T . We choose the term ‘neuron-preferred’ to describe the case where there are fewer degrees of freedom across neurons , and the term ‘condition-preferred’ to describe the case where there are fewer degrees of freedom across conditions . Thus , the ‘preferred mode’ is the mode ( neuron or condition ) from which the data tensor can be most accurately reconstructed using the smallest number of basis elements . Our investigation of the preferred mode was guided by a three-part hypothesis . First , we hypothesized that empirical population responses may often have a clear preferred mode . Second , we hypothesized that the preferred mode likely differs between brain areas . To address these hypotheses , we assessed the preferred mode for three neural datasets recorded from primary visual cortex ( V1 ) and four neural datasets recorded from M1 . V1 datasets were strongly neuron-preferred , while M1 datasets were strongly condition-preferred . Third , we hypothesized that the preferred mode might be informative regarding the origin of population responses . We concentrated on models of M1 , and found that existing models based on tuning for external variables were neuron-preferred , in opposition to the M1 data . However , existing models with strong internal dynamics were condition-preferred , in agreement with the data . Model success or failure depended not on parameter choice or fit quality , but on model class . We conclude that tensor structure is informative regarding the predominant origin of time-varying activity , and can be used to test specific hypotheses . In the present case , the tensor structure of M1 datasets is consistent with only a subset of existing models .
We analyzed nine physiological datasets: three recorded from V1 during presentation of visual stimuli , four recorded from M1 during reaching tasks , and two recorded from muscle populations during the same reaching tasks . Each dataset employed multiple conditions: different stimuli/reaches . Each neuron’s response was averaged across trials within a condition and smoothed to produce a firing rate as a function of time . Some recordings were simultaneous and some were sequential , but in all cases the same set of conditions was employed for every neuron . Stimuli were never tailored to individual neurons ( e . g . , to their preferred direction or receptive field ) . This allows for analysis of the true population response , indexed by neuron , condition , and time . For the muscle populations , electromyographic ( EMG ) voltages were converted to a smooth function of intensity versus time via standard rectification and filtering . Muscle populations were then analyzed in the same way as neural populations , but individual elements were muscles rather than neurons . We analyzed ten further datasets simulated using existing models of M1 . We first focus on two datasets: one from V1 ( Fig 1A ) and one from M1 ( Fig 1B ) . The V1 dataset was recorded using a 96-electrode array from an anesthetized monkey viewing one-second movies of natural scenes ( 25 movies , 50 trials each ) . The M1 dataset was recorded using a pair of implanted 96-electrode arrays , spanning the arm representation of primary motor cortex and the immediately adjacent region of dorsal premotor cortex ( all results were similar if primary motor and premotor cortex were treated separately ) . Neural responses were recorded during a delayed reach task: the monkey touched a central spot on a screen , was presented with a target , then executed a reach following a go cue . We analyzed data for 72 conditions ( Fig 1B , insets ) , each involving a different reach distance and curvature ( average of 28 trials per condition ) [30] . Both V1 and M1 neurons displayed temporally complex response patterns ( Fig 1 ) . Each colored trace plots the trial-averaged firing rate over time for one condition: a particular movie ( Fig 1A ) or reach ( Fig 1B ) . V1 neurons exhibited multiphasic responses throughout the stimulus . M1 neurons exhibited multiphasic activity over a ~700 ms period that began shortly after the go cue . Tight standard error bars ( not displayed ) confirmed that temporal response structure was statistically reliable rather than the result of sampling noise . In M1 it has been debated whether such structure primarily reflects external factors such as reach kinematics or primarily reflects internal dynamics . Both hypotheses can claim support from surface-level features of the data . Responses vary strongly with reach kinematics ( insets show reach trajectories color-coded according to the response properties of the neuron in that panel ) as proposed by the first hypothesis . On the other hand , responses show some quasi-oscillatory features that could reflect underlying dynamics . Might a comparison with V1—where responses are known to be largely externally driven—be illuminating regarding the source of temporal response structure in M1 ? V1 and M1 responses differed in a number of straightforward ways including frequency content and the overall response envelope . Such differences are expected given the different pacing of the task and stimuli . We wondered whether V1 and M1 datasets might also differ in deeper ways that are hidden at the level of the single neuron but clear at the level of the population . In general , a population response can differ across neurons , conditions , and time . While structure across time can be partially appreciated via inspection of single neurons ( as in Fig 1 ) , the joint structure across neurons and conditions is less patent . Are some datasets more constrained across neurons ( ‘neuron preferred’ ) and others more constrained across conditions ( ‘condition preferred’ ) ? If so , might that carry implications ? Neural population data is often analyzed in matrix form , allowing a number of standard analyses . Such analyses include assessing covariance structure and applying principal component analysis to extract the most prevalent response patterns [47] . One can then quantify , for a given number of extracted response patterns , how well they reconstruct the original data . This can provide a rough estimate of the number of degrees of freedom in the data [48] . However , when recordings span multiple neurons , conditions and times , the data are naturally formulated not as a matrix but as a third-order tensor of size N × C × T , where N is the number of neurons , C is the number of conditions , and T is the number of times . Each of these three indices is referred to as a ‘mode’ [49] . One can consider an N × C × T tensor as a collection of N matrices , each of size C × T ( one per neuron ) , or as a collection of C matrices , each of size N × T ( one per condition ) ( Fig 2A ) . One can then reconstruct the population tensor in two ways . First , one can reconstruct the responses of each neuron as a linear combination of a small collection of ‘basis-neurons , ’ each of size C × T ( Fig 2B , red matrices ) . Second , one can reconstruct each condition as a linear combination of a small collection of ‘basis-conditions , ’ each of size N × T ( Fig 2B , blue matrices ) . Unlike in the matrix case , for tensors a ‘preferred mode’ can exist . To assess the preferred mode we applied the singular value decomposition ( SVD ) to the neuron and condition modes of the population tensor ( Methods ) , yielding a set of basis-neurons and a set of basis-conditions . Performing SVD along a mode of a tensor , X∈ℝN×C×T , equates to performing SVD on one of the tensor’s matrix ‘unfoldings . ’ We define the ‘mode-1’ and ‘mode-2’ unfolding of X as X ( 1 ) ≔[X ( 1 ) X ( 2 ) ⋯X ( T ) ]∈ℝN×CT , ( 3 ) X ( 2 ) ≔[X ( 1 ) ⊤X ( 2 ) ⊤⋯X ( T ) ⊤]∈ℝC×NT , where X ( t ) ∈ ℝN×C is the N × C matrix slice of X at time t . Each row of X ( 1 ) corresponds to one neuron , and each row of X ( 2 ) corresponds to one condition . The top k right singular vectors of X ( 1 ) are of dimension CT , thus can be reshaped to C × T matrices , corresponding to k basis-neurons . Similarly , the top k right singular vectors of X ( 2 ) are of dimension NT and can be reshaped to N × T matrices , corresponding to k basis-conditions . In this way each neuron ( i . e . , each row of X ( 1 ) and the corresponding C × T slice of X ) can be approximately reconstructed as a linear combination of k basis-neurons . Similarly , each condition ( i . e . , each row of X ( 2 ) and the corresponding N × T slice of X ) can be approximately reconstructed as a linear combination of k basis-conditions . To assess the preferred mode we reconstructed each population tensor twice: once using a fixed number ( k ) of basis-neurons , and once using the same fixed number ( k ) of basis-conditions . Reconstruction error was the normalized squared error between the reconstructed tensor and the original data tensor . If basis-neurons provided the better reconstruction , the neuron mode was considered preferred . If basis-conditions provided the better reconstruction , the condition mode was considered preferred . ( We explain later the algorithm for choosing the number of basis elements k , and explore robustness with respect to that choice ) . The above procedure is related to several tensor decomposition techniques , and the preferred mode is related to the tensor’s approximate multilinear rank [49] . Here , instead of decomposing a tensor across all modes we simply perform independent mode-1 and mode-2 decompositions and compare the quality of their corresponding reconstructions . For the V1 dataset illustrated in Fig 1 the neuron mode was preferred; it provided the least reconstruction error ( Fig 2C , left ) . In contrast , for the M1 dataset illustrated in Fig 1 the condition mode was preferred ( Fig 2C , right ) . This analysis considered all time points in the shaded regions of Fig 1 . Keeping in mind that reconstruction along either mode is expected to perform reasonably well ( data points are rarely uncorrelated along any mode ) the disparity between V1 and M1 is large: for V1 the basis-neuron reconstruction performed 33% better than the basis-condition reconstruction , while for M1 it performed 68% worse . A preferred mode exists because the population tensor spans multiple neurons , conditions , and times . Consider the population response at a single time , yielding an N × C × 1 subtensor ( a matrix ) . For this case neither mode is preferred—the row rank ( neuron mode ) of a matrix equals the column rank ( condition mode ) . How does the preferred mode emerge as more times are considered ? We assessed reconstruction error as a function of timespan ( Fig 3 ) beginning with a single time-point , halfway through the response . Using this time we chose bases of k elements such that there was a 5% reconstruction error of the N × C × 1 matrix ( this determined the choice of k = 12 and 25 for the V1 and M1 datasets ) . Keeping k fixed , we increased the tensor size , adding both an earlier and a later time point ( we considered time points sampled every 10 ms ) . Thus , reconstruction error was measured for subtensors of size N × C × Ti where Ti = 1 , 3 , 5 , … , T . The emergence of the preferred mode was often readily apparent even when reconstructing single-neuron responses ( note that the entire tensor was always reconstructed , but each neuron can nevertheless be viewed individually ) . Fig 3B shows the response of one V1 neuron for one condition ( black trace ) with reconstructions provided by the neuron basis ( red ) and condition basis ( blue ) . Each of the ( shortened ) light red and light blue traces show reconstructions for a particular timespan ( Ti ) . Dark red and dark blue traces show reconstructions for the full timespan ( Ti = T ) . Unsurprisingly , for short timespans ( short traces near the middle of the plot ) the two reconstructions performed similarly: blue and red traces both approximated the black trace fairly well . However , for longer timespans the condition-mode reconstruction became inaccurate; the longest blue trace provides a poor approximation of the black trace . In contrast , the neuron-mode reconstruction remained accurate across the full range of times; short and long red traces overlap to the point of being indistinguishable . Thus , the reason why the V1 data were neuron-preferred ( Fig 2C ) is that the neuron basis , but not the condition basis , continued to provide good reconstructions across long timespans . For the M1 dataset we observed the opposite effect ( Fig 3D ) . For very short timespans both the neuron and condition bases provided adequate approximations to the black trace . However , for longer timespans the neuron-mode reconstruction ( red ) was unable to provide an accurate approximation . In contrast , the condition mode reconstruction remained accurate across all times; short and long blue traces overlap to the point of being indistinguishable . The disparity in reconstruction error between the preferred and non-preferred mode was often clear at the single-neuron level , and was very clear at the population level . We computed overall reconstruction error for the population tensor as a function of timespan Ti ( Fig 3C and 3E ) . The profile of each trace reflects reconstruction ‘stability . ’ Reconstructions were never perfectly stable; error inevitably grew as more data had to be accounted for . However , stability was considerably better for the preferred mode: the neuron mode for V1 and the condition mode for M1 . As can be inferred from the standard errors of the mean ( shaded regions ) reconstruction error in V1 was significantly lower for the neuron mode for all but the shortest windows ( p = 0 . 007 for the longest window ) . Conversely , reconstruction error in M1 was significantly lower for the condition mode for all but the shortest windows ( p < 10−10 for the longest window ) . When a particular reconstruction fares poorly—e . g . , the failure of the condition mode to accurately capture the firing rate of the V1 neuron in Fig 3B—it is not trivial to interpret the exact manner in which reconstruction failed . However , the underlying reason for poor reconstruction is simple: the data have more degrees of freedom along that mode than can be accounted for by the corresponding basis set . For V1 , the data have more degrees of freedom across conditions than across neurons , while the opposite was true for M1 . Thus , different datasets can have strongly differing preferred modes , potentially suggesting difference sources of temporal response structure . Before considering this possibility , we ask whether the difference in preferred mode between V1 and M1 is robust , both in the sense of being reliable across datasets and in the sense of not being a trivial consequence of surface-level features of the data , such as frequency content , that differ between V1 and M1 recordings . To assess robustness we analyzed two additional V1 datasets recorded from cat V1 using 96-electrode arrays during presentation of high-contrast grating sequences[4 , 50] ( Fig 4B; top , 50 different sequences; bottom 90 different sequences; panel a reproduces the analysis from Fig 3C for comparison ) . For all V1 datasets the neuron mode was preferred: reconstruction error grew less quickly with time when using basis-neurons ( red below blue ) . We analyzed three additional M1 datasets ( Fig 4C and 4D; the top of panel c reproduces the analysis from Fig 3E for comparison ) , recorded from two monkeys performing variants of the delayed reach task . For all M1 datasets the condition mode was preferred: reconstruction error grew less quickly with time when using basis-conditions ( blue below red ) . Most datasets involved simultaneous recordings ( the three V1 datasets in Fig 4A and 4B and the two M1 datasets in Fig 4C ) . However , the preferred mode could also be readily inferred from populations built from sequential recordings ( the two M1 datasets in Fig 4D ) . Critically , we note that sequential recordings employed the same stimuli for every neuron ( stimuli were not tailored to individual neurons ) and behavior was stable and repeatable across the time-period over which recordings were made . To avoid the possibility that the preferred mode might be influenced by the relative number of recorded neurons versus conditions , all analyses were performed after down-selecting the data so that neuron count and condition count were matched ( Methods ) . Typically , there were more neurons than conditions . We thus down-selected the former to match the latter . The preferred mode was , within the sizeable range we explored , invariant with respect to condition count . The three V1 datasets employed a different number of conditions ( 25 , 90 , and 50 ) yet all showed a neuron mode preference . The four M1 datasets employed a similarly broad range ( 72 , 72 , 18 , and 18 conditions ) yet all showed a condition mode preference . We further explored the potential impact of condition count by taking the 72-condition datasets in Fig 4C and restricting the number of analyzed conditions . The preferred mode was robust to this manipulation ( see Methods ) across the range tested ( 10–72 conditions ) . We also performed this analysis for all V1 datasets , and again found that the preferred mode was robust ( not shown ) . Thus , even a modest number of conditions is sufficient to produce a clear preferred mode . That preferred mode then remains consistent as more conditions are added . Might the differing preferred modes in V1 and M1 be in some way due to differing surface-level features such as frequency content ? A priori this is unlikely: properties such as frequency content may have an overall impact on the number of basis-set elements required to achieve a given accuracy , but there is no reason they should create a bias towards a particular preferred mode . Such a bias is also unlikely for three empirical reasons . First , as will be shown below , some existing models of M1 yield a condition-mode preference while others yield a neuron-mode preference . This occurs despite the fact that the surface-level structure produced by all such models resembles that of the M1 data . Second , the preferred mode remained unchanged when surface-level features were altered via temporal filtering ( see Methods ) . In particular , V1 datasets remained neuron-preferred even when filtering yielded responses with lower frequency content than M1 responses . Third , it can be readily shown via construction that data with the surface-level features of V1 ( or of M1 ) can have either preferred mode . To illustrate this last point we constructed data with the surface-level of features of V1 but with a condition-mode preference . We began with the V1 dataset analyzed in Fig 4A and extracted a set of ‘basis-conditions’ that captured most of the data variance . This was necessarily a large set of basis conditions ( 24 ) given the true neuron-mode preference of the data . We artificially reduced that number of basis conditions by summing random sets of the original basis conditions . For example , the new first basis condition might be a sum of the original basis conditions 1 , 7 , 12 and 23 . Thus , the same patterns were present in the data ( no basis conditions were removed ) but the degrees of freedom were greatly reduced . We then constructed an artificial population response by replacing the original response of each neuron with the linear combination of modified basis conditions that best approximated the original response . This manipulation resulted in a control dataset with responses that are intentionally altered yet retain the surface-level features of the original data ( Fig 5A , original data; Fig 5B , control data ) . The manipulated V1 data had a strong condition-mode preference , ( blue lower than red ) in opposition to the true neuron-mode preference of the original data . Using the same procedure ( but reducing degrees of freedom within the neuron basis ) we constructed control M1 datasets where surface-level features were preserved but where the neuron mode became preferred ( Fig 5D , red lower than blue ) in opposition to the original data ( Fig 5C , top , blue lower than red ) . Thus , the preferred mode is not a consequence of surface-level features . We were interested in the possibility that the origin of temporal structure might influence the preferred mode . Specifically , tuning for external variables might constrain structure across neurons; if responses reflect a fixed number of external variables then neurons would be limited to that many degrees of freedom . Conversely , internal dynamics might constrain structure across conditions; if each condition evolves according to the same dynamics , conditions could differ along limited degrees of freedom . The above intuition agrees with the neuron-preferred tensor structure of the V1 datasets , for which the trial-averaged response is expected to be dominated by the stimulus-driven component . Does this intuition extend to , and perhaps help differentiate , models of M1 ? Many prior studies have modeled M1 responses in terms of tuning for of movement parameters ( target direction , reach kinematics , joint torques , etc . ) . Although the causality is assumed to be reversed relative to V1 ( with the M1 representation producing the downstream kinematics ) , such models formally treat neural responses as functions of time-varying external variables; in particular , responses differ across neurons because different neurons have different tuning for those external variables . M1 ‘tuning-based models’ are thus fundamentally similar to tuning models of V1 . On the other hand , some recent studies have modeled M1 responses as the outcome of internal population level dynamics that are similar across conditions . In such models , downstream quantities such as muscle activity are assumed to be a function of cortical activity but cortical activity is not a function of downstream quantities ( due to non-invertibility ) . These M1 ‘dynamics-based models’ are thus fundamentally dissimilar from tuning models of V1 . We analyzed simulated data from five published models of M1 , including two models based on tuning for kinematic variables [30] and three models that assumed strong population-level dynamics subserving the production of muscle activity [30 , 34 , 36] . All M1 models displayed surface-level features that resembled those of the recorded M1 responses , including a burst of multiphasic responses . Each simulated dataset had neuron and condition counts matched with a corresponding neural population . Each model was simulated twice ( top and bottom of the relevant panels in Fig 6A , 6B , 6D , 6E and 6F ) with each instance being based on the empirical kinematics or muscle activity for one of the neural datasets . The neuron mode was preferred for the two models that were based on tuning for kinematics ( Fig 6A and 6B red below blue ) . For the first tuning-based model ( Fig 6A ) , the relevant kinematic variables were hand velocity and speed ( the magnitude of velocity ) as in [51] . For the second tuning-based model ( Fig 6B ) , the kinematic variables also included hand position and acceleration [52] . Thus , the second tuning-based model reflects the possibility that neural responses are complex due to tuning for multiple movement-related parameters—a position which has recently been argued for based on the ability to decode such parameters [46] . The condition mode was preferred for the three models ( Fig 6D , 6E and 6F ) that employed strong population-level dynamics . The model in Fig 6D was based on a pair of simple oscillations that followed approximately linear dynamics and provided a basis for fitting empirical patterns of muscle activity [30] . The model in Fig 6E was a nonlinear recurrent neural network ( RNN ) trained to produce the empirical muscle activity patterns [34] . The model in Fig 6F was an RNN with ‘non-normal’ dynamics realized via separate excitatory and inhibitory populations[36] . Critically , these three dynamics-based models were not fit to neural responses; their responses reflect the dynamics necessary to produce the desired outputs . Each has been recently proposed as a possible model of M1 activity during reaches . Despite their substantial architectural differences , all dynamics-based models displayed a condition-mode preference ( blue below red ) . In a subsequent section we employ a formal approach to explore why different model classes produce different preferred modes . Presently , we simply stress that the preferred mode can be used to test model predictions . In particular , the tuning-based models displayed neuron-preferred tensor structure in opposition to the data . In contrast , the dynamics-based models displayed condition-preferred tensor structure in agreement with the data . Thus , although all models of M1 reproduced ( to some reasonable degree ) the basic surface-level features of M1 responses , only the dynamics-based models predicted the true condition-mode preference of the M1 population data . We also analyzed the tensor structure of populations of recorded muscles . Because muscle activity is in some sense an external movement parameter , one might expect the muscle population to be neuron-preferred , in agreement with the tuning-based models above . On the other hand , the dynamics-based models were trained so that a linear projection of the model population response replicated the empirical muscle population response . Given this tight link one might expect the muscle population be condition-preferred . Empirically , the muscle populations had no clear preferred mode: reconstruction error was similar and in some cases overlapping for the neuron and condition modes . There was an overall tendency for the muscle data to be neuron-preferred ( the blue trace tended to be above the red trace at many points ) but this was not statistically compelling ( p = 0 . 37 and p = 0 . 80 ) . This analysis of muscle populations again highlights that the preferred mode cannot be inferred from surface-level features . Muscle responses and neural responses share many similar features yet do not show the same tensor structure . The muscle data also highlight that a clear preferred mode need not exist for all datasets . Furthermore , the tensor structure of a system’s outputs need not reflect the tensor structure of the system itself . Dynamics-based models built to produce muscle activity showed robust condition-mode preferences ( Fig 6D , 6E and 6F ) . Yet the muscle populations themselves did not show a condition mode preference ( if anything they were weakly neuron-preferred ) . We return later to the point that the output of a dynamical system need not share the same preferred mode as the system itself . As a side note , a natural desire is to examine the bases themselves , which might be informative regarding the underlying model . For example , the first basis neuron is essentially the projection of the data onto the first principle component of the N × N covariance matrix that captures covariance between neurons . The first basis condition is the same , but for a C × C covariance matrix that captures covariance between conditions . It is indeed possible to make inferences from both such projections [29 , 30] , yet this typically requires specific hypotheses and tailored analysis methods . The fundamental hurdle is that , for any given basis set , there are infinitely many rotations of that basis set that provide equally good reconstruction . Thus , the details of any given projection can be difficult to interpret without bringing additional information to bear . We therefore focus in this study on the quality of the reconstruction , rather than the features of the basis set . We assessed whether the preferred mode analysis is robust to a key parameter: the number of basis-elements used when quantifying reconstruction error . This is important because it is not possible to directly measure the degrees of freedom ( i . e . , the number of basis elements that produces zero reconstruction error ) for each mode , given measurement noise and other practical considerations . For this reason , the analyses above compared not degrees of freedom per se , but rather the reconstruction error for a fixed number of degrees of freedom . Before concluding that data have fewer degrees of freedom across one mode versus another , one should assess whether the preferred mode is robust with respect to the choice of that fixed number . To assess robustness we focused on the difference in error between the condition-mode reconstruction and the neuron-mode reconstruction for the longest time window ( Ti = T ) . We swept the number of basis elements and plotted the normalized difference in reconstruction errors ( Fig 7 ) . Positive values indicate a neuron-mode preference and negative values indicate a condition-mode preference . We considered from 1–20 basis elements , stopping earlier if the dataset contained fewer than 20 total degrees of freedom ( e . g . , the M1 single-electrode data had 18 conditions and the muscle populations contained 8 and 12 recordings respectively ) . All datasets displayed a preferred mode that was robust with respect to the number of basis elements . In most cases the preferred mode was clearest when a modest number of basis elements was used . Indeed , there was often a peak ( for neuron-preferred datasets; data lying in the red shaded area ) or trough ( for condition-preferred datasets; data lying in the blue shaded area ) . Unsurprisingly , the difference in reconstruction error trended towards zero as the number of basis elements became large ( the difference is necessarily zero if the number of basis elements is equal to the number of neurons / conditions in the data itself ) . The analysis in Fig 7 supports the results in Figs 4 and 6 . All V1 datasets and all M1 tuning-model datasets were consistently neuron-preferred . All M1 datasets and all dynamical M1 models were consistently condition-preferred . The muscle populations , which had trended weakly towards being neuron-preferred in the analysis in Fig 6 , trended more strongly in that direction when examined across reconstructions based on different numbers of basis elements ( Fig 7E ) . Thus , if a dataset had a clear preference for our original choice of basis elements ( the number necessary to provide a reconstruction error <5% when using a single time-point ) then that preference was maintained across different choices , and could even become stronger . The analysis in Fig 7 also underscores the very different tensor structure displayed by different models of M1 . Dynamics-based models ( panels h , i , j ) exhibited negative peaks ( in agreement with the empirical M1 data ) while tuning-based models ( panels c , d ) and muscle activity itself ( panel e ) exhibited positive peaks . Why did tuning-based models display a neuron-mode preference while dynamics-based models displayed a condition-mode preference ? Is there formal justification for the motivating intuition that the origin of temporal response structure influences the preferred mode ? This issue is difficult to address in full generality: the space of relevant models is large and includes models that contain mixtures of tuning and dynamic elements . Nevertheless , given reasonable assumptions—in particular that the relevant external variables do not themselves obey a single dynamical system across conditions—we prove that the population response will indeed be neuron-preferred for models of the form: x ( t , c ) =Bu ( t , c ) , ( 4 ) where x ∈ ℝN is the response of a population of N neurons , u ∈ ℝM is a vector of M external variables , and B ∈ ℝN×M defines the mapping from external variables to neural responses . The nth row of B describes the dependence of neuron n on the external variables u . Thus , the rows of B are the tuning functions or receptive fields of each neuron . Both x and u may vary with time t and experimental condition c . A formal proof , along with sufficient conditions , is given in Methods . Briefly , under Eq ( 4 ) , neurons are different views of the same underlying M external variables . That is , each um ( t , c ) is a pattern of activity ( across times and conditions ) and each xn ( t , c ) is a linear combination of those patterns . The population tensor generated by Eq ( 4 ) can thus be built from a linear combination of M basis-neurons . Critically , this fact does not change as time is added to the population tensor . Eq ( 4 ) imposes no similar constraints across conditions; e . g . , u ( : , c1 ) need not bear any particular relationship to u ( : , c2 ) . Thus , a large number of basis-conditions may be required to approximate the population tensor . Furthermore , the number of basis-conditions required will typically increase with time; when more times are considered there are more ways in which conditions can differ . A linear tuning model therefore implies a neuron-mode reconstruction that is stable with time and a condition-mode reconstruction that is less accurate and less stable . Conversely , the population response will not be neuron-preferred ( and will typically be condition-preferred ) for models of the form: x ( t+1 , c ) =Ax ( t , c ) , ( 5 ) Where A ∈ ℝN×N defines the linear dynamics . This equation admits the solution x ( t , c ) = At−1x ( 1 , c ) . Thus , the matrix A and the initial state x ( 1 , c ) fully determine the firing rate of all N neurons for all T times . In particular , the linear dynamics captured by A define a set of N × T population-level patterns ( basis-conditions ) from which the response for any condition can be built via linear combination . Critically , this fact does not change as different timespans ( Ti ) are considered . Although the size of each N × Ti basis-condition increases as Ti increases , the number of basis-conditions does not . In contrast , the number of necessary basis-neurons may grow with time; neural activity evolves in some subspace of ℝN and as time increases activity may more thoroughly explore this space . Thus , a linear dynamical model implies a condition-mode reconstruction that is stable with time , and a neuron-mode reconstruction that is less accurate and less stable ( for proof see Methods ) . The above considerations likely explain why we found that tuning-based models were always neuron-preferred and dynamics-based models were always condition-preferred . While none of the tested models were linear and some included noise , their tensor structure was nevertheless shaped by the same factors that shape the tensor structure of more idealized models . Tuning-based models and dynamics-based models are extremes of a continuum: most real neural populations likely contain some contribution from both external variables and internal dynamics . We therefore explored the behavior of the preferred mode in simple linear models where responses were either fully determined by inputs , were fully determined by population dynamics , or were determined by a combination of the two according to: x ( t+1 , c ) =Ax ( t , c ) +Bu ( t , c ) . ( 6 ) The case where responses are fully determined by inputs is formally identical to a tuning model; inputs can be thought of either as sensory , or as higher-level variables that are being represented by the population . When A was set to 0 and responses were fully determined by inputs ( Fig 8A ) the neuron mode was preferred as expected given the formal considerations discussed above . Indeed , because the model is linear , neuron-mode reconstruction error was perfectly stable as times were added ( the red trace remains flat ) . When B was set to zero and responses were fully determined by internal dynamics acting on an initial state , the condition mode was preferred and condition-mode reconstruction error was perfectly stable ( Fig 8D ) , consistent with formal considerations . For models where tuning for inputs was strong relative to dynamics , the neuron mode was preferred ( Fig 8B ) . However , because dynamics exerted a modest influence , neuron-mode reconstruction error was not perfectly stable . When dynamics were strong relative to inputs , the condition mode was preferred ( Fig 8C ) . However , because inputs exerted a modest influence , condition-mode reconstruction error was not perfectly stable . Thus , simple simulations confirm the expected behavior . A neuron-mode preference is produced when temporal response structure is dominated by tuning for inputs , even if dynamics exert some influence . A condition-mode preference is produced when temporal response structure is dominated by dynamics , even if inputs exert some influence . Thus , the preferred-mode analysis can reveal the dominant source of structure , but does not rule out other contributions . A potentially confusing point of interpretation is that all neurons necessarily respond to inputs; each neuron is driven by the inputs it receives . How then can there be a difference in tensor structure between a population that is tuned for inputs versus a population that reflects dynamics ? The answer lies in how fully the population reflects dynamics . In the case of tuning for external variables , those variables typically do not fully reflect dynamics . Although the local environment is in some sense ‘dynamic , ’ those dynamics are incompletely observed via the sensory information available to the nervous system . Conversely , if dynamics are produced by the local population they may be fully observed provided that sufficient neurons are recorded . To illustrate this point we repeated the simulations with the model population either partially ( Fig 8E ) or completely ( Fig 8H ) reflecting an identical set of underlying dynamics . As expected , the case where dynamics are partially observed behaved like the case when the system is input driven: the neuron mode was preferred . As dynamics became more fully reflected , the population switched to being condition-preferred . Thus , condition-preferred structure results from a very particular circumstance: the neural population obeys dynamics that are consistent across conditions and are close to fully reflected in the neural population itself . In contrast , neuron-preferred structure is observed when the temporal structure is inherited from outside the system: from sensory inputs or from dynamics that may be unfolding elsewhere in the nervous system . This explains why there is no paradox in the fact that the muscle populations tended to show neuron-preferred structure ( Fig 6C and Fig 7E ) even though dynamical models that produce muscle activity show condition-preferred structure ( Fig 6D–6F , Fig 7H–7J ) as does M1 itself . More generally , these simulations illustrate that one may often expect a difference in preferred mode between a system that produces a motor output and a system that ‘listens’ to that output ( e . g . , a sensory system that provides feedback during movement ) . A key point illustrated by the simulations in Fig 8A–8D is that the preferred mode is independent of smoothness in the temporal domain . For example , the idealized models in Fig 8A and 8D have responses with closely matched temporal smoothness , yet yield opposing preferred modes . This can be understood via reference to the derivation in the Methods , where assumptions regarding temporal smoothness play no role . For example , a condition-mode preference will be observed even if dynamics cause rapid fluctuations in the neural state , and indeed even if the dynamics are themselves rapidly time-varying . It is the ‘smoothness’ across conditions versus neurons that determines the preferred mode , not the smoothness across time . This fact is also illustrated in Fig 5 , where control manipulations alter the preferred mode while leaving temporal smoothness unchanged . For the simulations in Fig 8 and the models in Fig 6 the preferred mode always reflected the dominant source of temporal structure . Yet with the exception of some idealized models , reconstruction error was rarely perfectly stable even for the preferred mode . The lack of perfectly stability arises from multiple sources including nonlinearities , simulated noise in the firing rate , and contributions by the non-dominant source of structure . We therefore stress that it is difficult , for a given empirical dataset , to ascertain why the preferred mode shows some instability in reconstruction error . For example , in the case of M1 it is likely that the modest rise in condition-mode reconstruction error with timespan ( e . g . , Fig 4C and 4D ) reflects all the above factors .
Given the relationship between model class and preferred mode , the neuron-preferred structure in V1 is entirely expected: all V1 datasets were recorded in the presence of strong visual inputs that are expected to drive the observed response structure [53] . In contrast , the condition-preferred structure of the M1 population response could not be anticipated from first principles because there is little agreement regarding the source of temporal response structure in M1 . Several existing M1 models assume that time-varying responses are a function of time-varying movement variables such as reach direction , velocity , and joint torques ( for a review see [21] ) . These variables may be ‘dynamic’ in the loose sense ( they change with time and some may be derivatives of the others ) but their values typically do not follow a single dynamical rule that is consistent across conditions . Other recent models are explicitly dynamics-based: the future population state is a function of the present population state , with external inputs serving primarily to set the initial state of the dynamics [30 , 34 , 36] . Tuning-based and dynamics-based models lie on a continuum , but occupy opposing ends and thus make different predictions regarding the tensor structure of the population response . Existing dynamics-based models predict condition-preferred tensor structure , in agreement with the M1 data . Existing tuning-based models predict neuron-preferred structure , in opposition to the M1 data . Our results thus place strong constraints on models of M1: to be plausible a model must replicate the condition-preferred structure of the empirical population response . Our exploration of current models indicates that this happens naturally for models that include strong dynamics within the recorded population . It does not occur naturally for tuning-based models . We cannot rule out the possibility that future elaborations of tuning-based models might be able to replicate the empirical condition-preferred structure , but the practical possibility of such elaborations remains unclear . There also exist a number of M1 models that we did not examine [35 , 37 , 54 , 55] . It remains an empirical question whether the tensor structure of such models is compatible with the data . We stress that all current M1 models ( including those that successfully predict the empirical preferred mode ) are incomplete in key ways and will need to be elaborated or unified in the future . For example , the dynamics-based models we examined do not yet capture the influence of external , sensory-based feedback which is known to be a driver of M1 responses [38 , 39 , 56] . Conversely , a recent model of feedback control ( not tested here ) captures only the dynamics of external feedback loops; the M1 population was modeled as a feedforward network [37] . As future models are developed that incorporate both internal recurrence and sensory feedback , tensor structure provides a simple test regarding whether those models produce realistic population-level responses . Tensor structure is a basic feature of data , much as the frequency spectrum or the eigenvalue spectrum of the neural covariance matrix are basic features of data . ( Indeed , tensor structure is a simple extension to a three-mode array of the standard method of applying principal component analysis to a two-mode array . ) Thus , any model that attempts to explain data should succeed in replicating the preferred mode . This requirement is particularly important because , while models can often be easily modified to produce obvious surface-level features , it is more challenging to also reproduce the underlying tensor structure . Just as importantly , the preferred mode of recorded data can be informative regarding how an appropriate model should be constructed . For every model tested we found that tensor structure is condition-preferred only when the measured population reflects most of the state variables in a dynamical system . In the context of M1 , this suggests that successful models will be those where a large percentage of the relevant state variables ( sensory feedback , muscle commands and the dynamics that link them ) are observable in the M1 population response . It should be stressed the preferred mode is likely not a feature of a brain area per se , but rather of a neural population in the context of the computation being performed by that population . For example , M1 has strong responses to sensory stimuli , especially stretching of the tendons and muscles [56] . In an experiment where responses are driven primarily by externally imposed perturbations of the arm [57 , 58] it seems likely that M1 would exhibit a neuron-mode structure like that of V1 in the present study . If so , then it would be natural to apply a model in which responses are largely externally driven . If not , then one would be motivated to consider models in which external events set in motion internal dynamics . In either case , knowing the preferred mode would be valuable because it would constrain the set of plausible models . Interpretation of the preferred mode is most straightforward when there exists one or more models that seek to explain the data . Any model ( or model class ) that does not replicate the empirical preferred mode must be modified or discarded . Can similarly strong inferences be drawn directly from the preferred mode of the data , without comparison with models ? In short they cannot: while a robust preferred mode may suggest a particular class of model , caveats apply . As shown in the derivation ( Methods ) idealized models produce neuron-preferred structure when responses are driven by unconstrained external variables , and condition-preferred structure when responses are shaped by internal dynamics . We found that this pattern was robust under less-idealized circumstances: all of the models we examined exhibited a preferred mode consistent with the idealized pattern , even though they departed from idealized assumptions ( in particular they were not linear ) . Such robustness is largely expected . For example , non-linear dynamical systems can often be well approximated by time-varying linear systems , which is all that is required to produce the idealized pattern . Similarly , a non-linear dependency on external variables can often be reconceived as a linear dependency via a change in variables . That said , there will be limits to the observed robustness . It is possible that a model of one class ( e . g . , a dynamical systems model ) can produce a paradoxical preferred mode ( e . g . , a neuron-mode preference ) under certain circumstances . This might , for example , occur for a neural circuit with strongly nonlinear dynamics that produces long motor sequences . Such a system might be poorly approximated by time-varying linear dynamics , which would result in compromised condition-mode reconstructions . In the case where responses are driven by external variables , an unclear or even paradoxical preferred mode could occur if there is something ‘ill-conditioned’ about the input . For example , the input could be highly redundant across conditions , resulting in responses that lack enough structure to allow meaningful comparison of reconstruction quality for the neuron mode versus the condition mode . Along similar lines , it would be difficult to interpret the preferred mode in the case where there is little variation in the motor output that can be captured across conditions . An attractive feature of the preferred mode analysis is that it can be applied without knowledge of the inputs to a system , and provides constraints on potential hypotheses without requiring fully mature models that are ready to be fit directly to data . These advantages are large but , as discussed above , not absolute . First , although potential inputs need not be known , one must have reasonable confidence that the task evokes a range of reasonably rich responses , such that a clear preferred mode can emerge . Second , interpretation of the preferred mode will always be most certain in the case where the preferred mode of the data can be compared with the preferred mode displayed by competing models . In the present case , the preferred mode of the M1 datasets consistently disagreed with the preferred mode of models where time-varying responses are a function of time-varying movement variables . As this accords with formal expectations , such models are unlikely to provide a good account of the data without major modification . It is likely that neural populations outside of areas V1 and M1 will also display clear preferred modes , which could be diagnostic regarding candidate models . Applicable datasets are those that are sufficiently rich: the experimental task must elicit time-varying responses where PSTHs vary across neurons and conditions . Further , there must be sufficiently many neurons and conditions such that certain low-rank conditions are met ( an explanation of these conditions are in Methods under Low-rank assumptions ) . As a potential example , some models of decision-making assume that neural responses reflect a small number of task variables ( e . g . , a ‘decision variable’ whose value codes the evolving tendency towards a given choice [59] ) . Other models include internal dynamics that implicitly gate when information is integrated or ignored [60] . None of these decision models sits fully at an extreme—all assume both sensory inputs and some form of integration—but they possess large qualitative differences that may predict different tensor structure . Given the ease with which the preferred mode can be computed for both real and simulated data , the preferred-mode analysis provides a natural way to test whether a given model matches the data at a basic structural level .
All methods were approved in advance by the respective Institutional Animal Care and Use Committees at Albert Einstein College of Medicine ( protocol #20150303 ) and the New York State Psychiatric Institute ( protocol #1361 ) . To minimize any potential suffering non-survival surgeries were performed under deep anesthesia with sufentanil citrate , adjusted per the needs of each animal . Survival surgeries were performed under isoflurane anesthesia with carefully monitored post-operative analgesia . We analyzed 9 physiological datasets . Eight have been analyzed previously and one was recorded for the present study . All datasets were based on the spiking activity of a neural population recorded using either multi-electrode arrays ( the datasets analyzed in Fig 4A , 4B and 4C ) or sequential individual recordings ( the neural dataset analyzed Fig 4D and the muscle dataset analyzed in Fig 6C ) . Datasets are available from the Dryad repository ( http://dx . doi . org/10 . 5061/dryad . 92h5d ) . One V1 dataset ( analyzed in Figs 1 , 2 , 3 , 4A and 7A ) was collected using natural-movie stimuli from an anaesthetized adult monkey ( Macaca fascicularis ) implanted with a 96-electrode silicon ‘Utah’ array ( Blackrock Microsystems , Salt Lake City , UT ) in left-hemisphere V1 . These data were recorded in the laboratory of Adam Kohn ( Albert Einstein College of Medicine ) specifically for the present study . The left eye was covered . Receptive field centers ( 2–4 degrees eccentric ) were determined via brief presentations of small drifting gratings . Stimuli , which spanned the receptive fields , were 48 natural movie clips ( selected from YouTube ) with 50 repeats each . The frame rate was 95 Hz . Each stimulus lasted 2 . 63 s ( 100 movie frames followed by 150 blank frames ) . Spikes from the array were sorted offline using MKsort ( available at https://github . com/ripple-neuro/mksort/ ) . Single units and stable multi-unit isolations were included . Some neurons showed weak responses and were not analyzed further . Similarly , some stimuli ( e . g . , those where the region within the receptive fields was blank or relatively unchanging ) evoked weak responses overall . Again , these were not analyzed further . Finally , to ensure we were analyzing a neural population that responds to a shared set of stimulus features , all analyses focused on the subset of units with strongly overlapping receptive fields , defined as the 25 units with receptive fields closest to the center of the stimulus . We insisted upon this criterion because our central analyses would not be as readily interpretable if applied to a set of neurons with distant receptive fields , as they would effectively be responding to different stimuli . We analyzed two further V1 datasets ( Fig 4B ) recorded from cat V1 as described in [4 , 50] using Utah arrays implanted so as to overlap areas 17 and 18 ( collectively , cat area V1 ) . Stimuli were large stationary gratings , ~30 deg in diameter , and thus spanned the receptive fields of all neurons . Gratings were presented in a rapid sequence—one every 32 ms—each with one of 4 spatial phases and one of 12 orientations . One dataset had five sequences of ~12 s in length . The other dataset had nine such sequences . We wished to segment these long-duration stimuli into ‘conditions’ with a timescale comparable to that of the other V1 and M1 datasets analyzed here . To do so , we divided the first 10 s of each sequence into 10 one-second segments , which we treated as separate conditions ( the stimuli in each second were unrelated to the stimuli in the last second , and are thus effectively different conditions ) . The two datasets ( Fig 4B , top , bottom ) thus yielded a total of 50 and 90 conditions , respectively . Each condition was observed across multiple ( ~10 ) trials . Each dataset consisted of 96 well-tuned multiunit recordings ( see [4 , 50] for details ) , which were down-selected to match condition counts ( 50 and 90 ) of the datasets . Four M1 datasets were recorded from two male macaque monkeys ( Macaca mulatta ) trained to perform a delayed reach task . These datasets have been described and analyzed previously [29 , 30] . Briefly , reaches were performed on a fronto-parallel screen for juice reward . To begin each trial the monkey touched a central spot . After a >400 ms hold period , a reach target and up to nine ‘barriers’ appeared ( see Fig 1 of [29] ) . The monkey was required to hold its position for a 0–1000 ms delay until a ‘go cue’ , and to then briskly reach to the target while avoiding the barriers . A juice reward was delivered after a 450 ms hold period . This task evoked a large variety of conditions: each corresponding to a particular target and arrangement of barriers . For a given condition , reach trajectories were highly stereotyped across trials ( there was only one allowable route through the barriers ) allowing a meaningful computation of the average across-trial firing rate . Only trials with delays >450 ms were analyzed ( 5–40 trials per condition , depending on the dataset ) ; shorter delays simply provided incentive to prepare their movement during the delay . For present purposes , the primary value of the barriers was that they increased the variety of reach conditions , thus increasing the size of the tensor that could be analyzed . In the original dataset some conditions included ‘distractor’ targets that the monkey had to ignore while preparing the reach . The purpose of those conditions was incidental to the present study and they were not included in the analysis ( results were virtually identical if they were included ) . Neural responses were recorded from M1 and the adjacent region of caudal PMd . Single-electrode and array datasets employed 18 and 72 conditions respectively . Single-electrode datasets consisted of ideally isolated single neurons . Array datasets included both ideal isolations and good multi-unit isolations ( e . g . , two clear units that could not be separated from one another ) . Unit counts for the four datasets were 170 , 218 , 55 , and 118 ( corresponding , respectively , to panels c-d in Fig 4 ) , which were down-selected to 72 , 72 , 18 , and 18 to match condition counts . Two datasets of the responses of muscle populations ( analyzed in Fig 6C ) were recorded using the same monkeys and task as for the M1 datasets . Muscle datasets used the same 18 conditions as the single-electrode datasets . EMG responses were recorded percutaneously using electrodes inserted for the duration of the recording session . Recordings were made from six muscle groups: deltoid , biceps brachii , triceps brachii , trapezius , latissimus dorsi and pectoralis . Multiple recordings were often made from a given muscle ( e . g . , from the anterior , lateral and posterior deltoid ) . For monkey J the triceps was minimally active and was not recorded . Muscles were recorded sequentially and then analyzed as a population ( just as were the single-electrode datasets ) . For the two monkeys the resulting populations consisted of 8 and 12 recordings . We analyzed multiple datasets produced via simulation of published models . The velocity model from [30] was analyzed in Fig 6A ( here , referred to as the simple tuning model ) . The complex-kinematic model from [30] was analyzed in Fig 6B ( here referred to as the complex tuning model ) . The generator model from [30] is analyzed in Fig 6D . The network model of Sussillo et al . [34] is analyzed in Fig 6E . The network model of Hennequin et al . [36] is analyzed in Fig 6F . Both network models are instantiations of a recurrent neural network ( RNN ) : dx ( t , c ) dt=−x ( t , c ) +Ar ( t , c ) +Bu ( t , c ) ( 7 ) r ( t , c ) =ϕ ( x ( t , c ) ) y ( t , c ) =Wr ( t , c ) , where x ∈ ℝN is the network state , u ∈ ℝM is the vector of inputs , y ∈ ℝP is the vector of outputs . The function ϕ is an element-wise nonlinear function , r ∈ ℝN is interpreted as a firing rate , and the matrices A , B , and W are of appropriate dimensions . The output y is interpreted as muscle activity . All datasets were from the original simulations analyzed in those publications , with the exception of the RNN model of [36] . We re-simulated that model based on similar procedures described in [36] . After stabilizing the network using their procedure , we needed to specify each of the 72 initial states x ( 1 , c ) ( one for each condition ) . We first computed the controllability Gramian of the linearized network ( the matrix Q in [36] ) . The orthonormal columns of Q correspond to potential choices of initial states; the first column is an initial state that evokes the ‘strongest’ response ( in terms of the total energy of the corresponding signals r ) ; the second column gives the next strongest , and so forth . We selected the initial state for each condition to roughly match the temporal pattern of total energy ( summed across all neurons ) of the empirical neural data . Namely , we first considered the instantaneous power P ( t ) ≔ r ( t ) ⊤r ( t ) . Next , for a given column of Q ( a possible choice of initial state ) , we simulated the network and measured the correlation across times between P ( t ) of the simulated data and P ( t ) of the empirical data for a given condition . After determining the 5 columns of Q that yielded the highest correlations , we chose each x ( 1 , c ) to be the weighted sum of those 5 columns that best matched P ( t ) for that condition . The net effect of this procedure was to produce a rich set of dynamics , flowing from 72 initial states , that provided a possible basis set for producing patterns of EMG for the 72 conditions . We confirmed the network did indeed provide such a basis set ( e . g . , that the EMG could be fit as a weighted sum of the responses in the network ) . For all experimental neural data , spike trains were smoothed with a Gaussian kernel ( 20 ms standard deviation ) and sampled every 10 ms . Firing rate values were averaged across trials resulting in a population tensor of size N × C × T . Each element of this tensor is simply the firing rate for the corresponding neuron , condition and time . To ensure that analysis was not dominated by a few high-rate neurons , we normalized firing rates . Because normalization can occasionally lead to an undesirable expansion of sampling noise for low-rate neurons , we employed a ‘soft-normalization’ procedure ( this same normalization is used in [30] ) . Each neuron was normalized according to: xn ( c , t ) ←xn ( c , t ) 5+rangec , t ( xn ( c , t ) ) , ( 8 ) where i = 1 , … , N . The function rangec , t ( ⋅ ) returns the difference between the maximum and minimum firing rates across all conditions and times for a given neuron . The soft normalization constant 5 mapped high firing rate neurons ( e . g . , 100 Hz ) to a new range close to one . Low firing rate neurons were mapped to a range somewhat less than one ( e . g . , a neuron with a range of 5 spikes/s would be mapped to a new range of 0 . 5 ) . This preprocessing allows neurons to contribute roughly equally regardless of their firing rate range . This is especially desirable when analyses involve the mean squared error . For example , without normalization the same relative error will be 25 times greater for a neuron with a 0–100 Hz firing rate range relative to a neuron with a 0–20 Hz firing rate range . That said , we emphasize that our results ( e . g . , the preferred mode of a given dataset ) did not depend on the choice of soft normalization constant . We wished to analyze temporal response structure that was different across conditions . We therefore removed the ‘cross-condition mean’ from the entire population tensor . We averaged the tensor across conditions resulting in an N × T matrix that we subtracted from every N × T matrix of data . This is related to the standard PCA step of first removing the mean value of each variable , and ensured that the analysis did not consider response structure that was identical across conditions , such as an elevation of firing rates for all visual stimuli or all reach directions . All datasets naturally had an unequal number of neurons ( N ) and conditions ( C ) . To ensure that basis-neuron and basis-condition reconstructions were compared on similar footing , we removed excess neurons or conditions in each dataset so that N = C . In most datasets there were more neurons than conditions . In such cases we kept the N = C neurons with the highest ratio of signal to noise . In the V1 dataset of Fig 1A there were more conditions than neurons . In this case we retained the N = C conditions that elicited the most temporal complexity in the population response ( assessed via the standard deviation of the firing rate across all neurons and times ) . The specific preprocessing choices ( filter length , normalization , equalizing N and C ) were made to minimize any potential bias toward basis-neurons or basis-conditions . Still , none of these choices were found the affect the outcome of the analyses . For each population tensor X∈ℝN×C×T we quantified how well it could be reconstructed from a small set of k basis-neurons or k basis-conditions ( the method for choosing k is described later ) . To illustrate , we first consider the case of basis-neurons ( the case of basis-conditions is entirely parallel ) . Each of the recorded neurons is a set of T datapoints ( one per time ) for C conditions and thus forms a C × T matrix . Each basis neuron is also a C × T matrix . The data for each of the N neurons ( each C × T matrix within the full population tensor ) was approximated as a weighted sum of k basis-neuron matrices . Weights and basis neurons were chosen to provide the reconstruction with the lowest error . To find those weights and basis neurons we applied SVD along the neuron mode of the population tensor . This procedure amounts to ‘unfolding’ ( or reshaping ) the tensor into a matrix , X ( 1 ) ∈ ℝN×CT , where the subscript in parentheses indicates which mode appears as the row index in the matrix ( see [49] ) . The order in which the columns appear in the matrix does not affect our analysis . We applied the SVD to X ( 1 ) . The right singular vectors of X ( 1 ) correspond to vectors of dimension CT , which can be reshaped into C × T matrices corresponding to ‘basis-neurons . ’ The singular values ( squared ) of X ( 1 ) indicate how much variance is explained by each basis-neuron . The approach to finding basis-conditions is parallel to the above and involves the SVD of X ( 2 ) ∈ ℝC×NT . For both reconstructions we assessed the mean squared error between the elements of the original tensor and those of the reconstructed tensor . The reconstructed tensor was produced by multiplying the matrices produced by the SVD after appropriately limiting the inner dimensions based on the number of basis elements k . For example , if X ( 1 ) = USV⊤ , then X ( 1 ) rec=U: , 1:kS1:k , 1:kV1:k , :⊤ . We note that for practical convenience reconstruction error can also be readily computed from the first k singular values . For visualization we express reconstruction error in normalized form , relative to the total variance of the data . We extended the above analysis to quantify reconstruction error as a function of the number of time-points included in the tensor ( Figs 3 , 4 and 6 ) . We began by considering a single time-point halfway through the response: thalf = round ( T/2 ) . We used this time to ask how many basis elements ( basis-neurons and basis-conditions ) were necessary to achieve low reconstruction error . As above we applied the SVD , in this case to the matrix X: , : , thalf∈ℝN×C×1 . We chose the smallest number k such that normalized reconstruction error using the first k basis elements was less than 5% . Because X: , : , thalf is a matrix , the value of k is the same for basis-neurons and basis-conditions . We then analyzed X: , : , thalf−1:thalf+1∈ℝN×C×3 and quantified reconstruction error when using k basis-neurons versus k basis-conditions ( i . e . , the standard procedure described above was applied , but to a tensor that contained three times rather than all times ) . We repeated this for X: , : , thalf−2:thalf+2∈ℝN×C×5 and so forth until the full N × C × T tensor was analyzed . To assess statistical reliability , we computed reconstruction error independently for each condition . This yielded a distribution of errors with a given mean and standard error . It is that mean and standard error that are plotted in Figs 2C , 3C , 3E , 4 and 6 , and the right columns of Fig 8 . We chose to compute the standard error across conditions rather than across both neurons and conditions to be conservative ( the latter would have yielded even smaller error bars ) . We performed a three control analyses to assess the robustness of the central method . The outcome of the first of these is shown in the Results; the outcome of the other two are shown here . First , we analyzed two control datasets intentionally constructed to have surface-level features similar to the original empirical datasets . To generate the manipulated V1 dataset , we first extracted the top 24 basis-conditions ( out of 25 ) from the original dataset using SVD . We randomly partitioned the basis set into 6 partitions ( 4 elements each ) , and summed the elements within a partition to create a single basis-condition , resulting in 6 total basis-conditions . We then reconstructed the manipulated dataset neuron-by-neuron: each new neuron was a least-squares fit to the original neuron , but using the 6 basis-conditions derived above . This ensured that the manipulated V1 data had relatively few degrees of freedom across conditions , yet resembled the original V1 neurons in terms of basic response properties . The manipulated M1 dataset was constructed analogously , but using 6 basis-neurons derived from the original 72 . The outcome of this analysis is shown in Fig 5 . Second , to assess robustness of the central method with respect to the number of recorded conditions , we repeated the analysis for one M1 dataset ( the dataset from Fig 3E ) that originally contained 72 conditions . We down-sampled the data by selecting 10 , 20 , and 30 conditions . Conditions were selected randomly , but via a procedure that also ensured that the selected conditions were sufficiently different ( e . g . , that they were not all rightwards reaches ) . The preferred mode was indeed robust even when the number of conditions was reduced ( Fig 9 ) . Finally , we analyzed the effect of spike filter widths on the preferred mode for the V1 and M1 datasets ( Fig 10 ) . This analysis served two purposes . First , spike filtering is a standard preprocessing step and we wanted to ensure that results were not dependent on the particular choice of filter width . Second , the analysis reveals that the preferred mode is not in some way to due to the smoothness or frequency content of neural signals—a potential concern when comparing brain areas whose neurons have fundamentally different response properties , as is the case with V1 and M1 . In Fig 8 we illustrated some basic properties of the preferred mode using simulations of linear dynamical systems ( Eq ( 6 ) ) . These simple simulations were separate from the simulations of published models described above . For these simple simulations we chose N = C = 20 , and T = 300 . We set M = 10 ( i . e . the input u was ten-dimensional ) . We first generated the matrices A and B with orthonormal columns; for A , eigenvalues were random but were clustered near 1 to ensure smooth trajectories for our choice of T ( this was not a necessary step , but yielded roughly comparable oscillation frequencies to those observed in the datasets of Fig 4 ) . Each input um was composed of a randomly weighted sum of 20 sinusoids . Sinusoid frequency was determined by the same procedure that generated the eigenvalues of A . Thus , inputs had the same frequency components as the dynamics , ensuring similar single-neuron response properties across simulations . Initial states across conditions were chosen randomly and were constrained to span 10 dimensions . With these parameters fixed , we simulated the system x ( t + 1 , c ) = aAx ( t , c ) + bBu ( t , c ) , where a ∈ [0 , 1] and b ∈ [0 , 1] determined the strength of dynamics and inputs , respectively . In Fig 8A–8D , values of a were 0 , 0 . 98 , 0 . 99 , and 1 ( Note that values of a even slightly lower than unity lead to rapidly decaying ‘weak’ dynamics ) . Values of b were 1 , 0 . 05 , 0 . 03 , and 0 ( note that inputs need to be quite weak before they cease to have a strong effect on a system with persistent dynamics ) . Each panel in Fig 8 involved the same choices of A and B , and the same initial states . Data in Fig 8E–8H were simulated as above , with a = 1 and b = 0 . However , the ‘data’ for which the preferred mode was computed consisted not of the values of the dynamic variable x , but rather of the values of an observation variable y . We treated y as the neural population being driven by ‘observing’ the dynamic state variable x , with y ( c , t ) = Cx ( c , t ) . The observation matrix C had different ranks depending on how fully y reflected x . Specifically , C was diagonal with 1s on the first 3 , 4 , 8 , and 20 diagonal entries for Fig 8 panels e , f , g , h , respectively ( and 0s elsewhere ) . Here we show that neuron-preferred structure is expected when responses are driven by unconstrained external variables , while condition-preferred structure is expected when neural responses are shaped by internal dynamics . We consider a dataset X∈ℝN×C×T , where N , C and T are the number of recorded neurons , experimental conditions , and times . We also consider a set of external signals , or inputs , U∈ℝM×C×T , where M is the number of external variables . The column vector x ( t , c ) ∈ ℝN is the firing rate of every neuron at time t ∈ {1 , … , T} for condition c ∈ {1 , … , C} . An N × C matrix ‘slice’ of X is denoted X ( t ) ∈ ℝN×C , and is the population state across all conditions for time t . We define the ‘mode-1’ and ‘mode-2’ matrix unfoldings of X: X ( 1 ) ≔[X ( 1 ) X ( 2 ) ⋯X ( T ) ]∈ℝN×CT , ( 9 ) X ( 2 ) ≔[X ( 1 ) ⊤X ( 2 ) ⊤⋯X ( T ) ⊤]∈ℝC×NT . Each row of X ( 1 ) corresponds to one neuron , and each row of X ( 2 ) corresponds to one condition . Importantly , rank ( X ( 1 ) ) is the number of basis-neurons needed to reconstruct X . Similarly , rank ( X ( 2 ) ) is the number of basis-conditions needed to reconstruct X . Definition: A dataset X∈ℝN×C×T is called neuron-preferred ( condition-preferred ) when the rank of the matrix unfolding X ( 1 ) ( X ( 2 ) ) of its sub-tensors XTi∈ℝN×C×Ti does not increase with Ti , while the rank of X ( 2 ) ( X ( 1 ) ) does increase with Ti . We evaluate the rank of each unfolding in datasets X generated by the following model classes: x ( t , c ) =Bu ( t , c ) , ( 10 ) and x ( t+1 , c ) =Ax ( t , c ) . ( 11 ) We term Eq ( 10 ) the tuning model class ( B ∈ ℝN×M defines each neuron’s tuning for external variables ) , and Eq ( 11 ) the dynamical model class ( A ∈ ℝN×N specifies linear dynamics ) . Claim: Models of the form Eq ( 10 ) ( Eq ( 11 ) ) generate datasets having neuron-preferred ( condition-preferred ) structure . To begin , note that Eq ( 10 ) can be written as a matrix equation , X ( t ) =BU ( t ) . ( 12 ) For any Ti ∈ {1 , … , T} , Eq ( 12 ) implies , [X ( 1 ) X ( 2 ) ⋯X ( Ti ) ]=B[U ( 1 ) U ( 2 ) ⋯U ( Ti ) ] , ( 13 ) or , more compactly , X ( 1 ) = BU ( 1 ) . For the mode-2 unfolding , given Eq ( 12 ) we can also write , [X ( 1 ) X ( 2 ) ⋮X ( Ti ) ]=[B0⋯00B⋯0⋮⋮⋱⋮00⋯B][U ( 1 ) U ( 2 ) ⋮U ( Ti ) ] , ( 14 ) i . e . , X ( 2 ) =U ( 2 ) ( ITi⊗B⊤ ) where ITi is the Ti × Ti identity matrix and ⊗ denotes the Kronecker product . Thus , x ( t , c ) =Bu ( t , c ) ⟺X ( 1 ) =BU ( 1 ) ⟺X ( 2 ) =U ( 2 ) ( ITi⊗B⊤ ) . ( 15 ) We can take without loss of generality rank ( B ) = M . Thus , rank ( X ( 1 ) ) = rank ( BU ( 1 ) ) = min ( M , rank ( U ( 1 ) ) ) ≤ M . On the other hand rank ( X ( 2 ) ) = rank ( U ( 2 ) ) ≤ min ( C , MTi ) . ( To see this note that U ( 2 ) is size C × MTi and ( ITi⊗B⊤ ) is size MTi × NTi and full rank ) . Thus , the rank of the mode-1 unfolding is strictly bounded by M ( which is fixed by the model ) while the rank of the mode-2 unfolding can grow arbitrarily with C and Ti ( which can be increased by the experimenter ) . Thus , datasets generated by the tuning model class are neuron-preferred when the inputs are unconstrained , i . e . when rank ( U ( 2 ) ) grows beyond M with increasing Ti . This shows part 1 of the claim . Eq ( 11 ) can be written X ( t + 1 ) = AX ( t ) , which admits the solution X ( t ) =At−1X ( 1 ) , ( 16 ) where the matrix At−1 maps initial states to the state at time t . We define the tensor A∈ℝN×N×T to be the collection of all matrices At−1 for t = 1 , … , T ( from here , the definitions of A ( 1 ) and A ( 2 ) follow ) . We can now write [X ( 1 ) X ( 2 ) ⋮X ( Ti ) ]=[INA⋮ATi−1]X ( 1 ) . ( 17 ) More compactly: X ( 2 ) = X ( 1 ) ⊤A ( 2 ) . To find X ( 1 ) , given Eq ( 16 ) we can write [X ( 1 ) X ( 2 ) ⋯X ( Ti ) ]=[INA⋯ATi−1][X ( 1 ) 0⋯00X ( 1 ) ⋯0⋮⋮⋱⋮00⋯X ( 1 ) ] . ( 18 ) More compactly: X ( 1 ) =A ( 1 ) ( ITi⊗X ( 1 ) ) . Thus , x ( t+1 , c ) =Ax ( t , c ) ⟺X ( 1 ) =A ( 1 ) ( ITi⊗X ( 1 ) ) ⟺X ( 2 ) =X ( 1 ) ⊤A ( 2 ) . ( 19 ) We note that the rank of the mode-1 unfolding can grow with Ti , rank ( X ( 1 ) ) ≤rank ( [X ( 1 ) AX ( 1 ) ] ) ≤rank ( [X ( 1 ) AX ( 1 ) A2X ( 1 ) ] ) ≤⋯ , ( 20 ) and can eventually reach the maximum of rank ( A ) ( due to the Cayley-Hamilton theorem ) . On the other hand , rank ( X ( 2 ) ) = rank ( X ( 1 ) ) , where equality follows because X ( 1 ) ⊤ is a submatrix of X ( 2 ) . The rank of the mode-2 unfolding thus does not grow with Ti . Therefore , datasets generated by the dynamical model class are condition-preferred when rank ( [X ( 1 ) AX ( 1 ) ] ) > rank ( X ( 1 ) ) , i . e . whenever the matrix A maps the initial states into a subspace not spanned by the columns of X ( 1 ) . This completes part 2 of the claim . Given the above , a natural expectation is that X ( t ) = BU ( t ) ⇒ rank ( X ( 1 ) ) ≤ rank ( X ( 2 ) ) with rank ( X ( 2 ) ) growing as more times are considered . Similarly one expects X ( t + 1 ) = AX ( t ) ⇒ rank ( X ( 2 ) ) ≤ rank ( X ( 1 ) ) with rank ( X ( 1 ) ) growing as more times are considered . These expectations will indeed hold given reasonable low-rank assumptions . The first inference ( that tuning models imply a neuron-mode preference ) depends upon recording more neurons and conditions than the presumed number of represented variables , i . e . , we need N > M and C > M . Otherwise it is possible for min ( C , MTi ) ( the limit on rank ( X ( 2 ) ) ) to be smaller than M ( the limit on rank ( X ( 1 ) ) ) . In practice , the adequacy of the data can be evaluated by testing whether results change when more neurons/conditions are added . Importantly , the present results did not depend upon neuron/condition count . For example , effects are equally strong in Fig 4F and Fig 4G despite a threefold difference in the number of analyzed neurons and conditions . Still , the possibility of data being neuron- or condition-limited is a real one , and provides strong motivation to analyze datasets with many neurons and many diverse conditions . The second inference ( dynamical models imply a condition-mode preference ) depends upon the assumption rank ( X ( 1 ) ) < rank ( A ) . In other words , the set of initial states ( one per condition ) must occupy a proper subspace of all states visited as the dynamics governed by A unfold . Otherwise rank ( X ( 1 ) ) = rank ( X ( 2 ) ) regardless of how many times are considered ( i . e . , the red and blue traces in Fig 4 would be equal and would not rise with time ) . In practice the assumption rank ( X ( 1 ) ) < rank ( A ) is reasonable , both because we never observed the above signature for any dataset and because we have recently shown that M1/PMd preparatory states do not occupy all dimensions subsequently explored during movement [61] . In summary , the key low-rank assumptions are likely to be valid when considering many neurons and diverse conditions . Models of the form X ( t ) = BU ( t ) will thus have a stable rank ( X ( 1 ) ) and an unstable rank ( X ( 2 ) ) . Models of the form X ( t + 1 ) = AX ( t ) will have a stable rank ( X ( 2 ) ) and an unstable rank ( X ( 1 ) ) . The converse inferences will also hold . If rank ( X ( 1 ) ) is stable as times are added then the data can be factored as in Eq ( 13 ) and thus modeled as X ( t ) = BU ( t ) . If rank ( X ( 2 ) ) is stable then the data can be factored as in Eq ( 17 ) ( possibly requiring a time-varying A ) and thus modeled as X ( t + 1 ) = AX ( t ) . Part 2 of the above claim extends naturally to the equation X ( t + 1 ) = A ( t ) X ( t ) , a time-varying linear dynamical system . As long as the dynamics—the ( potentially time-varying ) vector fields—are the same across conditions then the above arguments hold . Thus , while the appearance of condition-preferred structure depends on the constraints imposed by dynamics , such structure does not depend on time-invariant dynamics . Because dynamical systems can often be approximated as time-varying linear systems ( especially over short timescales ) , condition-preferred structure is likely to be common whenever population structure is shaped by strong dynamics . Empirical neural data inevitably include sampling noise in the estimated firing rates , due to finite trial-counts from spiking neurons . Similarly , some degree of nonlinearity is always present in the form of spiking thresholds or deeper nonlinearities in the underling representations or dynamics . Thus , the measured X ( 1 ) and X ( 2 ) will always be full rank . In practice , we therefore evaluated not the ranks of X ( 1 ) and X ( 2 ) per se but the success of rank-k reconstructions of X ( 1 ) and X ( 2 ) . In simulations we found that this approach works well . Reconstruction error is increased by the addition of noise or nonlinearities , but this occurs approximately equally for both X ( 1 ) and X ( 2 ) . Thus , the preferred-mode analysis is still able to successfully differentiate datasets generated by static nonlinear tuning models from autonomous nonlinear dynamical models ( e . g . , Fig 4 ) . | Neuroscientists commonly measure the time-varying activity of neurons in the brain . Early studies explored how such activity directly encodes sensory stimuli . Since then neural responses have also been found to encode abstract parameters such as expected reward . Yet not all aspects of neural activity directly encode identifiable parameters: patterns of activity sometimes reflect the evolution of underlying internal computations , and may be only obliquely related to specific parameters . For example , it remains debated whether cortical activity during movement relates to parameters such as reach velocity , to parameters such as muscle activity , or to underlying computations that culminate in the production of muscle activity . To address this question we exploited an unexpected fact . When activity directly encodes a parameter it tends to be mathematically simple in a very particular way . When activity reflects the evolution of a computation being performed by the network , it tends to be mathematically simple in a different way . We found that responses in a visual area were simple in the first way , consistent with encoding of parameters . We found that responses in a motor area were simple in the second way , consistent with participation in the underlying computations that culminate in movement . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"decision",
"making",
"population",
"dynamics",
"vertebrates",
"neuroscience",
"animals",
"mammals",
"simulation",
"and",
"modeling",
"primates",
"systems",
"science",
"mathematics",
"cognition",
"computational",
"neuroscience",
"population",
"biology",
"neuronal",
"tuning",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"monkeys",
"animal",
"cells",
"dynamical",
"systems",
"cellular",
"neuroscience",
"cell",
"biology",
"single",
"neuron",
"function",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"computational",
"biology",
"cognitive",
"science",
"amniotes",
"organisms"
] | 2016 | Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1 |
Kyasanur Forest disease virus ( KFDV ) and Alkhumra hemorrhagic fever virus ( AHFV ) are genetically closely-related , tick-borne flaviviruses that cause severe , often fatal disease in humans . Flaviviruses in the tick-borne encephalitis ( TBE ) complex typically cause neurological disease in humans whereas patients infected with KFDV and AHFV predominately present with hemorrhagic fever . A small animal model for KFDV and AHFV to study the pathogenesis and evaluate countermeasures has been lacking mostly due to the need of a high biocontainment laboratory to work with the viruses . To evaluate the utility of an existing mouse model for tick-borne flavivirus pathogenesis , we performed serial sacrifice studies in BALB/c mice infected with either KFDV strain P9605 or AHFV strain Zaki-1 . Strikingly , infection with KFDV was completely lethal in mice , while AHFV caused no clinical signs of disease and no animals succumbed to infection . KFDV and high levels of pro-inflammatory cytokines were detected in the brain at later time points , but no virus was found in visceral organs; conversely , AHFV Zaki-1 and elevated levels of cytokines were found in the visceral organs at earlier time points , but were not detected in the brain . While infection with either virus caused a generalized leukopenia , only AHFV Zaki-1 induced hematologic abnormalities in infected animals . Our data suggest that KFDV P9605 may have lost its ability to cause hemorrhagic disease as the result of multiple passages in suckling mouse brains . However , likely by virtue of fewer mouse passages , AHFV Zaki-1 has retained the ability to replicate in visceral organs , cause hematologic abnormalities , and induce pro-inflammatory cytokines without causing overt disease . Given these striking differences , the use of inbred mice and the virus passage history need to be carefully considered in the interpretation of animal studies using these viruses .
Kyasanur Forest disease virus ( KFDV ) and Alkhumra hemorrhagic fever virus ( AHFV ) are tick-borne flaviviruses that cause severe hemorrhagic disease in humans . KFDV and AHFV share a high degree of genetic sequence similarity ( >90% amino acid identity for the E glycoprotein ) despite occupying very different ecological niches [1]–[4] . KFDV was first isolated in 1957 [5]–[7] and causes outbreaks with 400–500 cases annually in Karnataka State , western India . AHFV was first isolated in 1995 [8]–[10] and has caused recent outbreaks of febrile disease in Saudi Arabia [11] , [12] , as well as illness in travellers returning to Europe from southern Egypt [13] . KFDV infection is primarily associated with bites from Haemaphysalis ticks [14] , [15] , while AHFV is thought to be transmitted by Ornithodoros savignyi and Hyalomma dromedarii ticks [16] , [17] . Both viruses have case fatality rates of up to 20% [reviewed in 18] , and work with infectious material is restricted to biosafety level 4 ( BSL4 ) laboratories in North America . Flaviviruses in the tick-borne encephalitis ( TBE ) complex typically cause neurological disease in humans . The TBE complex includes human pathogens such as tick-borne encephalitis virus ( TBEV ) , Powassan virus ( POWV ) , and Louping ill virus ( LIV ) , as well as a number of other viruses that are apathogenic in humans [19] . TBEV , POWV , and LIV cause encephalitis of varying severity in humans , but some members of the TBE complex , such as Omsk hemorrhagic fever virus ( OHFV ) , cause predominantly hemorrhagic disease with very little neurological involvement [20] , [21] . KFDV and AHFV typically cause hemorrhagic disease as well [6] , [8] , [19] , [21] , but there is some evidence of central nervous involvement in infections by KFDV [22] , [23] and AHFV [10] , [11] , [24] . Tick-borne flaviviruses therefore cause a spectrum of disease ranging from neurological to hemorrhagic manifestations , and KFDV and AHFV may occupy an intermediate disease phenotype between TBEV and OHFV . This question needs to be addressed in a small animal model . Furthermore , regulatory bodies such as the World Health Organization ( WHO ) require that flavivirus countermeasures have to be initially evaluated in a mouse model [25] . In recent laboratory studies , mice have been successfully used as a model for OHFV disease that generally follows the clinical presentation seen in humans [26]–[28] . However , no studies of KFDV pathogenesis in small animal models have been published in the past decade . Older publications from the 1960s and 70s sought to evaluate lethality of KFDV in wild-caught mammals from southwestern India and surrounding regions , but the amount of information contained in these studies is rather limited [29]–[35] . The body of scientific literature on the related AHFV is even smaller , and no animal studies have been published . To evaluate the utility of existing mouse models for tick-borne flavivirus pathogenesis studies , we performed serial sacrifice studies of immunocompetent mice infected with either KFDV or AHFV to determine the tissue distribution of these viruses and to assess the physiological effects of infection . Although genetically closely related , KFDV and AHFV have very different clinical and virological presentations in BALB/c mice . Our data show that KFDV replicates primarily in the brains of infected animals , while AHFV replicates in visceral organs ( kidneys , spleen , and liver ) that are commonly associated with hemorrhagic diseases . Given the striking differences in pathogenesis and tissue tropism , the use of inbred mice as well as the passage history needs to be carefully considered in the interpretation of animal studies using these viruses .
Vero E6 were maintained in Dulbecco's modified Eagle's medium ( DMEM ) containing 5% fetal bovine serum ( FBS ) and BHK21 clone 13 cells were maintained in minimal essential medium ( MEM ) containing 10% FBS at 37°C with 5% CO2 . All virus isolates were obtained from the World Reference Center for Emerging Viruses and Arboviruses ( WRCEVA ) , which is housed at the University of Texas Medical Branch ( UTMB ) , Galveston , Texas . KFDV P9605 ( Genbank accession number JF416958 ) had 9 suckling mouse brain ( SMB ) passages and 2 Vero E6 passages; AHFV 200300001 ( AHFV 2003; accession number JF416954 ) had 1 SMB passage and one Vero E6 passage; AHFV Zaki-1 ( accession number JF416956 ) had one SMB passage , 2 mouse brain passages , 4 Vero passages , and 3 Vero E6 passages; and OHFV Guriev ( accession number AB507800 ) had at least 27 SMB passages and one Vero E6 passage . All infections were performed under biosafety level 4 ( BSL4 ) conditions at the Galveston National Laboratory ( GNL ) , UTMB . Virus stocks were grown in Vero E6 cells . Titrations were performed using BHK21 cells by limiting dilution in MEM containing 5% ( v/v ) FBS and are expressed as the 50% tissue culture infectious dose ( TCID50 ) . For growth kinetics , 5×105 cells per well were seeded into 12-well dishes and were allowed to attach . The respective viruses were added at a multiplicity of infection ( MOI ) of 0 . 01 ( 5×103 TCID50 per well ) in a total volume of 500 µl per well . After incubation at 37°C for 1 h , virus inoculum was removed and replaced with 500 µl per well of fresh MEM containing 5% FBS . Beginning at 1 day post-infection , culture supernatants were harvested every 24 h and centrifuged for 5 min at 500×g . The clarified supernatants ( ∼500 µl ) were transferred to fresh tubes and stored at −80°C . Cell-associated virus was collected by scraping the cell monolayers into 500 µl of fresh MEM containing 5% FBS . The resuspended cell fractions were stored at −80°C . All animal procedures were reviewed and approved by UTMB's Institutional Animal Care and Use Committee ( IACUC ) in strict compliance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Four- to six-week old female BALB/c mice ( strain code 028 ) were purchased from Charles River ( Wilmington , Massachusetts ) . Subdermal transponders IPTT-300 measuring body temperature ( Biomedic Data Solutions , Seaford , Delaware ) were implanted . Subsequently , animals were moved into the ABSL-4 in the GNL and allowed to acclimate for 5 days before challenge . Mice were kept under barrier conditions in individually ventilated micro-isolator cages at five mice per cage ( Tecniplast , Buguggiate , Italy ) with corn-cob beading . Food and water was provided ad libitum and environmental enrichment material such as nestles were provided . All animal procedures were reviewed and approved by UTMB's Institutional Animal Care and Use Committee and in strict compliance with the guide for the care and use of laboratory animals . Mice were infected via the footpad with 2×103 TCID50 per animal ( 20 µl total volume ) or by intraperitoneal infection with 103–104 TCID50 per animal of virus diluted in serum-free MEM . Uninfected control animals were injected with serum-free MEM . Blood was collected by intracardial terminal bleeds in EDTA tubes . The whole blood was centrifuged for 5 min at 9 , 300×g and the resulting plasma was removed to fresh cryovials . For quantification of virus in post-mortem tissue specimens , a maximum of 0 . 3 g of tissue was homogenized in 0 . 6–1 . 0 ml of MEM containing 5% FBS for 2 rounds of 2 . 5 min each at 30 cycles s−1 using a Tissuelyser II homogenizer ( Qiagen ) , followed by a 30 s spin at 9 , 300×g to pellet debris . The virus titers in tissue lysate supernatant and plasma samples were determined by limiting dilution and are expressed as TCID50 per gram of tissue or milliliter of plasma . Additional samples of each tissue were collected in PBS-buffered 4% paraformaldehyde . The fixed organs were kept at 4°C in the BSL4 laboratory , after which the fixative was changed completely and the tissue samples were brought out of the BSL4 according to institutional UTMB operating procedures . For complete blood counts , 25 µl of EDTA blood was used . All cell counts were quantified using HemaVet 950FS hematology analyzer equipped with software to measure white blood cell count , red blood cell count , platelet count , hemoglobin concentration , hematocrit , mean corpuscular volume , mean corpuscular hemoglobin , and mean corpuscular hemoglobin concentration . For clinical chemistry , 100 µl of lithium heparin plasma was used . Analysis was performed using a VetScan2 Chemistry Analyzer ( Abaxis Inc . , Sunnyvale , CA , USA ) , which provides a complete diagnostic panel that includes albumin , alkaline phosphatase , alanine aminotransferase , amylase , total bilirubin , blood urea nitrogen , calcium , creatinine , glucose , and potassium . These hematological and clinical chemistry analyses were performed using whole blood in the BSL4 laboratory . Brain , spleen , liver , kidney , and lung were harvested during necropsy , fixed in 10% neutral buffered formalin , and removed from the BSL4 based on Galveston National Laboratory ( GNL ) inactivation procedures . Tissues were then routinely processed by UTMB's Research Histopathology Core , cut , and stained with hematoxylin and eosin ( H&E ) for histopathologic examination . For immunohistochemistry , 5 µm sections were cut , air dried overnight , and placed into a 60°C oven for 1 h . The deparaffinized and rehydrated sections were quenched for 10 min in aqueous 3% hydrogen peroxide and rinsed in deionized water . Epitopes were retrieved using Biocare's rodent decloaking solution at 98°C for 40 min . Once slides were cooled , they were placed into Tris buffered saline containing 0 . 05% Tween-20 ( TBS-T ) for 5 min . Slides were blocked for 30 min with Rodent Block M from the MM HRP-polymer kit ( Biocare Medical , USA ) , and were then rinsed with TBS-T . The slides were incubated with mouse ascites fluid antibodies against KFDV strain P9605 ( R157 HMAF ) and AHFV strain Zaki-1 ( R204 HMAF ) , both kindly provided by Dr . T . Ksiazek ( WRCEVA , UTMB ) at a dilution of 1∶1000 overnight at 4°C . Slides were rinsed with TBS-T , incubated for 20 min with MM polymer-HRP , and rinsed again in TBS-T . Betazoid DAB Chromogen Kit ( Biocare Medical , USA ) was used as the substrate chromogen and the slides were counterstained with Gill's hematoxylin . To determine cytokine levels in the plasma and tissues of mice , 25 µL of plasma or organ homogenate was run in duplicate with a Bio-Plex Pro Mouse Cytokine Assay kit ( Bio-Rad ) in the BSL4 laboratory based on the manufacturer's instructions . The kit simultaneously quantifies interleukin ( IL ) -1β , IL-5 , IL-6 , IL-10 , IL-13 , interferon ( IFN ) -γ , monocyte chemotactic protein ( MCP ) -1 ( also known as CCL-2 ) , and tumor necrosis factor ( TNF ) -α . The samples were run on the Bio-Plex 200 ( Bio-Rad ) and analyzed using the Bio-Plex Manager software version 6 . 0 . Sample groups were compared by 1-way analysis of variance ( ANOVA ) with Tukey post-test using GraphPad Prism version 5 . 03 . Levels of statistical significance are given as either p<0 . 05 or 0 . 01 .
We performed growth kinetics experiments with OHFV Guriev , KFDV P9605 , AHFV 200300001 ( AHFV 2003 ) , and AHFV Zaki-1 by infecting BHK21 cells at an MOI of 0 . 01 and followed the virus titers daily . All viruses reached peak replication in the supernatant ( Figure 1A ) and cell-associated ( Figure 1B ) fractions at day 2 post-infection , and titers decreased thereafter . There was approximately 5- to 10-fold more virus found in the supernatant than in cell-associated samples . The growth patterns of AHFV Zaki-1 and KFDV P9605 were very similar in both the supernatant ( Figure 1A ) and cell-associated fractions ( Figure 1B ) , whereas AHFV 2003 was more comparable to OHFV Guriev ( Figures 1A and 1B ) . The cytopathic effects were similar for all viruses ( data not shown ) . Taken together , these data indicate that none of the viruses have obvious growth defects . We next determined the lethality of KFDV P9605 , AHFV Zaki-1 , and AHFV 2003 by infecting groups of 5 BALB/c mice with 103 TCID50 per animal via footpad inoculation . BALB/c mice have been used in prior studies of tick-borne flavivirus infection and disease [26] , [27] . KFDV P9605 was uniformly lethal , and animals died between days 6 and 12 post-infection ( Figure 2A ) . Animals typically developed ruffled fur and hunched posture the day before death ( data not shown ) and had elevated temperatures and weight loss beginning at day 6 ( Figures 2B and 2C ) . In contrast , mice infected with AHFV 2003 ( data not shown ) or AHFV Zaki-1 did not succumb to infection ( Figure 2A ) , nor did they exhibit any signs of disease ( data not shown ) or significant weight loss ( Figure 2C ) . AHFV Zaki-1 animals had slightly elevated temperatures on day 3 post-infection , but then returned to normal levels for the duration of the study ( Figure 2B ) . We also infected mice with AHFV 2003 at doses of 104 and 103 TCID50 via the footpad and intraperitoneal route , respectively , but neither route of infection was lethal ( data not shown ) . In order to demonstrate that animals were infected and the evaluate virus dissemination in infected mice , we performed serial sacrifice studies of BALB/c mice infected by footpad inoculation with 2×103 TCID50 per animal of either KFDV P9605 or AHFV Zaki-1 . We chose to use KFDV P9605 and AHFV Zaki-1 for detailed pathogenesis studies because they had similar growth kinetic profiles in cell culture ( see Figure 1 ) . Groups of five mice were sacrificed on days 2 , 4 , 6 , and 8 post-infection and organ homogenates were titrated to determine the presence of infectious virus . Mice infected with KFDV P9605 had small amounts of virus in the lung on day 6 , which increased on day 8 ( Figure 3A ) . The same pattern was observed in the brain , where several animals were positive on day 6 , but all animals had virus in the brain on day 8 ( Figure 3B ) . Virus was not detected in the homogenates of the kidney , spleen , or liver from animals infected with KFDV P9605 ( Figure 3C , 3D , and 3E ) . AHFV Zaki-1 was only detected in the lung and brain of 1 of 5 infected mice on day 2 post-infection , but others were negative throughout the study ( Figures 3A and 3B ) . However , relatively high amounts of AHFV Zaki-1 were found in kidney , spleen , and liver ( Figures 3D , 3D , and 3E ) . Peak titers in the kidney were detected on day 2 post-infection , but declined during the study and virus was cleared by day 8 ( Figure 3C ) . Few animals had virus in the spleen and liver , with the highest titers detected on day 4 post-infection , and were then cleared by day 8 ( Figures 3D and 3E ) ; we did not detect virus in the plasma ( Figure 3F ) . In the brain , lesions were first observable at day 6 in some of the animals and were characterized by mild meningitis with infiltrates primarily composed of lymphocytes ( data not shown ) . KFDV P9605 immunoreactivity was observed in the brains of two of the five animals euthanized at this time ( data not shown ) . Positive immunostaining was observed in scattered neurons of the superficial cerebral cortex . At day 8 brain lesions were more widespread and severe ( Figure 4B ) . Meninges were expanded by inflammatory infiltrates including lymphocytes , plasma cells , and neutrophils ( Figure 4B ) . There were few migrating inflammatory cells directly underlying the affected pia mater within the molecular layer of the cerebral cortex . There was occasional perivascular cuffing and endothelial hypertrophy . KFDV P9605 immunoreactivity was observed in the brains of all animals at day 8 within scattered foci of neurons and occasionally in astrocytes ( Figure 5B ) . Interestingly , meninges and affected vessels were commonly devoid of antigen . Two animals showed mildly increased cellularity of alveolar walls in the lung at day 8 due to infiltration of macrophages and lymphocytes in localized foci ( data not shown ) . Antigen could be detected sporadically by immunohistochemistry in these foci . No lesions or antigen were detected in the liver , spleen , or kidney of mice infected with KFDV P9605 . Small , scattered foci of inflammatory cells were detected throughout the liver of most of the animals infected with AHFV Zaki-1 at days 6 and 8 post-infection . Foci were composed of macrophages and lymphocytes ( Figure 4D ) . Interestingly , viral antigen could not be detected in these foci but was rather found in Kupffer cells lining the sinusoids ( Figure 5F ) . Kidneys appeared congested and an increased cellularity of the glomeruli was noted at day 4 in all animals ( Figure 4F ) . AHFV Zaki-1 immunoreactivity was observed in the kidneys of three out of the five animals from days 2 through 6 post-infection ( data not shown ) . AHFV Zaki-1 immunostaining was observed in cells of the distal cortex , outer and inner medulla ( Figure 5D ) , and sporadically in the mesangial cells of the glomeruli ( Figure 5D ) . At day 4 there was dropout of lymphocytes in the germinal centers of the spleen and increased numbers of macrophages . By day 6 there was pronounced reactive hyperplasia . Immunoreactivity for AHFV Zaki-1 was observed in scattered mononuclear cells in three animals at day 2 , one at day 4 , and two at day 6 post-infection ( data not shown ) . Antigen was detected within mononuclear cells scattered throughout the parenchyma . No lesions or antigen was detected in the brains of mice infected with AHFV Zaki-1 . There were also no lesions or antigen detected in any of the tissues harvested from mock-infected control mice . We further characterized the responses of mice to KFDV P9605 and AHFV Zaki-1 by evaluating panels of hematological and clinical chemistry parameters . Both KFDV P9605 and AHFV Zaki-1 induced leukopenia on day 2 post-infection , although these levels recovered partially in mice infected with KFDV P9605 after this point ( Figures 6A and 6C ) . The recovery of white blood cell levels was delayed until day 6 in mice infected with AHFV Zaki-1 ( Figures 6A and 6C ) . There was a specific decrease in lymphocyte levels in mice infected with both viruses and these levels recovered over the course of the study ( Figures 6B and 6C ) . The proportion of white blood cells ( lymphocytes , monocytes , neutrophils , basophils , and eosinophils ) in mice infected with AHFV Zaki-1 remained stable , whereas lymphocyte proportions decreased and neutrophil proportions increased in mice infected with KFDV P9605 ( Figure 6D ) . AHFV Zaki-1-infected mice also had decreased red blood cells counts ( Figure 7A ) , hemoglobin ( Hb ) levels ( Figure 7B ) , hematocrit ( Figure 7C ) , and platelet counts ( Figure 7D ) , whereas these parameters remained stable in KFDV P9605-infected mice . Thus , despite a lack of clinical disease , AHFV Zaki-1 induces several of the hallmark signs of hemorrhagic fever observed in other animal models [36]–[39] . We have already shown that the highest KFDV P9605 virus titers were present in the brain at days 6 and 8 post-infection , while no AHFV Zaki-1 was detected in the same tissue ( Figure 3B ) . Likewise , AHFV Zaki-1 virus titers are the highest at day 2 and 4 in the kidney and spleen , but KFDV P9605 was not detected in this organ ( Figures 3C and 3D ) . We therefore analyzed the homogenates of these organs from the indicated time points by Bio-Plex Pro Mouse Cytokine Assay for a panel of cytokines . In the brain , KFDV P9605 induced large increases in the amount of IL-10 and IFN-γ , and MCP-1 at day 8 , while IL-6 levels were elevated at both days 6 and 8 ( Figure 8A ) . Mice infected with AHFV Zaki-1 had levels of IL-6 , IL-10 , IFN-γ , and MCP-1 in the brain that were lower than those found in the control or KFDV P9605 animals ( Figure 8A ) . The levels of TNF-α in the brain were generally lower than what was found in the control animals for both viruses ( Figure 8A ) . AHFV Zaki-1 infection induced the production of IL-6 , IL-10 , IFN-γ , MCP-1 , and TNF-α in the kidney at days 2 and 4 post-infection , while the response to KFDV P9605 infection was similar to control animals for these cytokines , with the exception of significantly elevated levels of MCP-1 on day 4 ( Figure 8B ) . Finally , infection with KFDV P9605 also resulted in a significant induction of IL-6 , IFN-γ , and MCP-1 in the spleen on day 2 , while both KFDV P9605 and AHFV Zaki-1 induced significantly higher levels of IL-10 ( Figure 8C ) . However , the levels of these cytokines diminished rapidly by day 4 for both viruses ( Figure 8C ) . The levels of TNF-α were lower than in control animals on day 2 after AHFV Zaki-1 infection but increased by day 4 ( Figure 8C ) , while the levels of IL-6 and IFN-γ were unchanged on both days ( Figure 8C ) . Both KFDV P9605 and AHFV Zaki-1 are therefore able to induce pro-inflammatory cytokines in organs where virus replication is detected , although the inflammatory response in the spleen is brief .
Previous studies of tick-borne flavivirus pathogenesis have used either of the immunocompetent mouse strains BALB/c or C57BL/6 mice [26]–[28] and the Bogoluvovska strain of OHFV , which has approximately two suckling mouse brain ( SMB ) passages , and extensive tissue culture passages [26] . This virus caused enlarged spleen and some evidence of hemorrhages in the liver , as well as rapid onset of clinical signs with death occurring around day 9 post-infection [26] . Of particular note is the lack of neurological disease and detection of virus only within the cerebellum of infected animals [26] . Further studies with OHFV strain Guriev resulted in high virus titers in the brain and relatively low titers in other peripheral organs [27] . Although KFDV P9605 does not have as many SMB passages as OHFV Guriev ( 9 compared to 24 for OHFV Guriev ) , we found that its distribution in tissues was similar to the results of Tigabu et al . [27] , namely that the highest virus titers were found in the brain , but only at low levels elsewhere . This suggests that this neuroinvasive phenotype is perhaps due to neuroadaptation over the course of many SMB passages . In prior studies , mice infected with OHFV Guriev showed some signs of mild neurological disease [27] , but we did not observe comparable signs in mice infected with KFDV P9605 . KFDV with higher 9 SMB passages ( strain 1639 , which is similar to KFDV P9605 used in our experiments ) has also been found in the CSF of infected macaques at later stages of disease [29] , supporting the conclusion that KFDV may have acquired a neuroinvasive phenotype during SMB passage . The practice of passaging virus isolates in mouse brains was common in the 1950s before the widespread availability of tissue culture , so it is consequently quite challenging to obtain virus isolates that have not been extensively passaged in mice . We found virus distributed in tissues commonly associated with hemorrhagic fever ( kidneys , liver , and spleen ) in mice infected with AHFV Zaki-1 , which appeared at early time points , but was then cleared . We did not find significant amounts of virus in the brain or lungs , which also corresponds with what has been observed for OHFV [26] . Similar to what has been observed by others upon OHFV infection , AHFV induces a pro-inflammatory cytokine response early in the kidney and spleen , and differences in some hematologic parameters , namely leukopenia combined with decreases in lymphocytes , RBC count , hemoglobin levels , and hematocrit [28] , indicating that AHFV Zaki-1 causes a sub-clinical hemorrhagic-like syndrome . Lethal infection of mice with TBEV is not associated with elevated body temperatures [40] , while animals infected with KFDV in our study had transient fever during the later phase of disease . This suggests that tick-borne flaviviruses that are primarily associated with neurological disease do not induce fever . The presence of KFDV in the brain may therefore reflect a spillover event into the CNS due to factors such as increased vascular permeability rather than a true neuroinvasive phenotype as is seen with TBEV infection . Initial studies with AHFV showed that it was lethal in suckling mice when injected intracerebrally ( IC ) or intraperitoneally ( IP ) , and in adult mice when administered via the IP route [8] . This discrepancy likely reflects the fact that injection by the IC and IP is a more efficient means of infecting mice compared to footpad injection , which we used for our experiments . We infected mice IP with AHFV Zaki-1 , but the dose was not sufficient to cause disease or death . Footpad injection also requires a much smaller volume of inoculum than other routes , so higher doses of virus are not always practical or even possible . We were unable to detect virus in the plasma of infected animals for both KFDV P9605 and AHFV Zaki-1 . This confirms the findings of previous studies , which also failed to detect free virus in animals infected with other tick-borne flaviviruses [27] , [28] . It is unclear why there is no detectable viremia in infected mice , especially since we found that there was generally more virus in the in vitro culture supernatant than in the cellular fraction ( see Figure 1 ) . We suspect that the virus is associated with circulating leukocytes ( macrophages or dendritic cells ) , as has been described for other viruses such as morbilliviruses [41] , [42] and henipaviruses [43] . Interestingly , another study found that KFDV was lethal in bonnet macaques ( Macaca radiata ) and high virus titers were detected in the serum [29] , which suggests that the lack of free virus in the serum may be restricted to rodent species . In this study we evaluated the pathogenesis of KFDV and AHFV in the BALB/c mouse model . Here we found that AHFV Zaki-1 infection did not cause a clinically evident disease but showed viral tropism , hematological changes , and pro-inflammatory cytokine response suggestive of a viral hemorrhagic fever . In contrast , infection with KFDV was uniformly lethal with evidence of a neuro-inflammatory disease . Histopathology and immunohistochemistry findings support the tissue distribution phenotype of KFDV P9605 and AHFV Zaki-1 observed by titration of virus in organs , and demonstrates the invasiveness of these viruses in the respective tissues . KFDV P9605 seems to invade the brain on or before 6 days post-infection and causes moderate meningitis , with virus replicating in neurons in all areas of the brain . Surprisingly , KFDV P9605 replication could not be detected in any other visceral organs , with the exception of the lung . AHFV Zaki-1 was found in a range of visceral tissues where it only causes mild histopathologic changes . It should be noted that although there is no detectable viremia , AHFV Zaki-1 immunoreactivity was found in multiple tissues with filtering function such as glomeruli in the kidney , Kupffer cells in the liver , and macrophages in the spleen . Despite finding AHFV Zaki-1 antigen and infectious virus in multiple organs , mice appear to be able to control and eliminate the virus effectively . These data also suggest that KFDV is more neurotropic than AHFV and support the hypothesis that KFDV infection can cause neurological disease despite its infrequent occurrence in humans . It is possible that serial passage of KFDV P9605 in the brains of suckling mice contributed to the enhanced neurovirulence . Clearly , the use of authentic low-passage KFDV isolates would be desirable for this study to empirically evaluate the neurovirulence of KFDV . Unfortunately , low-passage KFDV strains were not available to us . Future studies will examine the influence of progressive virus passage of AHFV and KFDV in different organs on disease presentation . | Kyasanur Forest disease virus ( KFDV ) and Alkhumra hemorrhagic fever virus ( AHFV ) are tick-borne flaviviruses that cause severe hemorrhagic disease in humans . The pathogenesis of the disease is still not very well understood mostly due to the lack of suitable animal models . Despite sharing a high degree of genetic sequence similarity , KFDV replicates primarily in the brain and is uniformly lethal for BALB/c mice . In contrast , AHFV does not cause clinically overt signs in mice , replicates in the visceral organs , and induces pro-inflammatory cytokines and hematological changes . Given the striking differences in pathogenesis and tissue tropism , the use of inbred mice as well as the passage history of the virus needs to be carefully considered in the interpretation of animal studies using these viruses . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"viral",
"classification",
"emerging",
"viral",
"diseases",
"tropical",
"diseases",
"microbiology",
"viruses",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"veterinary",
"science",
"animal",
"models",
"of",
"infection",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"veterinary",
"diseases",
"zoonoses",
"medical",
"microbiology",
"microbial",
"pathogens",
"pathogenesis",
"hemorrhagic",
"fevers",
"flaviviruses",
"host-pathogen",
"interactions",
"virology",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"omsk",
"hemorrhagic",
"fever",
"viral",
"diseases",
"organisms"
] | 2014 | Comparative Pathogenesis of Alkhumra Hemorrhagic Fever and Kyasanur Forest Disease Viruses in a Mouse Model |
Trypanosoma cruzi , the etiological agent of Chagas disease , is highly genetically diverse . Numerous lines of evidence point to the existence of six stable genetic lineages or DTUs: TcI , TcIIa , TcIIb , TcIIc , TcIId , and TcIIe . Molecular dating suggests that T . cruzi is likely to have been an endemic infection of neotropical mammalian fauna for many millions of years . Here we have applied a panel of 49 polymorphic microsatellite markers developed from the online T . cruzi genome to document genetic diversity among 53 isolates belonging to TcIIc , a lineage so far recorded almost exclusively in silvatic transmission cycles but increasingly a potential source of human infection . These data are complemented by parallel analysis of sequence variation in a fragment of the glucose-6-phosphate isomerase gene . New isolates confirm that TcIIc is associated with terrestrial transmission cycles and armadillo reservoir hosts , and demonstrate that TcIIc is far more widespread than previously thought , with a distribution at least from Western Venezuela to the Argentine Chaco . We show that TcIIc is truly a discrete T . cruzi lineage , that it could have an ancient origin and that diversity occurs within the terrestrial niche independently of the host species . We also show that spatial structure among TcIIc isolates from its principal host , the armadillo Dasypus novemcinctus , is greater than that among TcI from Didelphis spp . opossums and link this observation to differences in ecology of their respective niches . Homozygosity in TcIIc populations and some linkage indices indicate the possibility of recombination but cannot yet be effectively discriminated from a high genome-wide frequency of gene conversion . Finally , we suggest that the derived TcIIc population genetic data have a vital role in determining the origin of the epidemiologically important hybrid lineages TcIId and TcIIe .
At least 10 million people are thought to carry the infectious agent of Chagas disease , Trypanosoma cruzi , which is considered to be responsible for ∼13 , 000 deaths annually ( www . who . int , [1] ) . The disease is a vector-borne zoonosis and transmission in its wild transmission cycle is maintained by numerous species of mammal reservoir and over half of approximately 140 known species of haematophagous triatomine bug [2] . The geographical distribution of silvatic T . cruzi stretches from the Southern States of the USA to Southern Argentina . Domestic transmission is limited to Central and South America where domiciliated vector species occur . Human infection occurs primarily through mucosal or broken skin contact with contaminated triatomine faeces egested by the insect during feeding . Consistent with an ancient association with South America [3] T . cruzi populations are highly diverse , with at least six stable discrete typing units ( DTUs ) reported: TcI , TcIIa , TcIIb , TcIIIc , TcIId , and TcIIe . Among these , TcI and TcIIb are the most divergent groups in molecular terms - estimates based on nuclear genes date their most recent common ancestor at 3–10 million years ago ( MYA ) [4] . The phylogenetic status of TcIIc and TcIIa is in full debate [5] , [6] . Based on mosaic patterns of nucleotide diversity across nine nuclear genes , Westenberger et al . , ( 2005 ) proposed that both are the product of an early hybridisation event ( s ) between lineages TcI and TcIIb [6] . Others argue that TcIIc and TcIIa represent a single ancestral group in their own right [5] , whereby these lineages share a characteristic mitochondrial genome distinct from both TcI and TcIIb . These hypotheses are not mutually exclusive and TcIIa and TcIIc are not easily distinguished based on mitochondrial sequences [4] . However , nuclear gene sequences consistently support their status as genetically separate clades [4] , [6]–[8] and flow cytometric analysis across a panel of representative strains reveals that TcIIc and TcIIa genomes are divergent in terms of their absolute size [9] . The current tendency to group TcIIc and TCIIa as a single lineage is an oversimplification that may arise from Miles's original Z3 classification [10] . In fact Miles clearly defines an additional lineage in later publications – Z3/Z1 ASAT , which corresponds to TcIIc [11] , [12] . Researchers attempting to classify a third major lineage , TcIII - corresponding loosely to TcIIc - almost entirely ignore TcIIa [5] , as well the large divergence between North and South American TcIIa isolates [13] . By contrast , there is general consensus in the literature regarding the evolutionary origin of the two remaining lineages , TCIId and TCIIe . These are almost certainly hybrids and nucleotide sequence [4] , microsatellite [5] , and enzyme electrophoretic [14] , [15] data show that the parents are TcIIc and TcIIb . In line with experimental data [16] , maxicircle kinetoplast DNA inheritance in TcIId and TcIIe appears to have been uniparental [4] , [5] , and both retain a mitochondrial genome similar to that of TcIIc . TcIIc is infrequently isolated from domestic transmission cycles . Sporadic reports of this lineage occur from domestic mammals in the Chaco region of Paraguay and Argentina as well as southern Brazil ( Canis familiaris [17]–[19] ) from humans in Brazil [19] , [20] and from domestic triatomine bugs in Argentina and Peru ( T . infestans [17] Miles M A , unpublished ) . In total , domestic TcIIc isolates make up only a handful of strains over >30 years of sampling . By contrast , other lineages - in particular TcI , TcIIb , TcIId and TcIIe - are common in humans , domestic mammals and vectors [21] , TcI in northern South America and TcIIb , IId and IIe in the Southern Cone region . Although rare to domestic transmission cycles , TcIIc occurs with relatively high frequency in the silvatic environment . We have shown that this DTU is almost exclusively associated with terrestrial transmission cycles and fossorial mammalian genera , including the Cingulata ( armadillos ) and terrestrial marsupials ( Monodelphis spp . & Philander frenata ) [19] , [22] . Terrestrial rodents ( Dasyprocta spp . , Proechimys iheringi , Oryzomys spp . and Oxymyctereus sp . [15] , [19] ) and Carnivora ( Conepatus spp . [17] ) have also been implicated . Among these hosts , the nine-banded armadillo , Dasypus novemcinctus , is probably the most important . In Paraguay [22] and Bolivia ( Llewellyn et al . , unpublished data ) prevalence of infection in this mammal is consistently 33%–57% across distinct geographic foci . Although D . novemcinctus does account for most of the TcIIc isolates sampled from mammalian reservoirs in the silvatic environment , it is unclear to what extent D . novemcinctus and TcIIc have shared a common evolutionary relationship . Trypanosomes rarely co-speciate with their hosts or vectors , instead ‘ecological-host fitting’ is thought to be the major driver behind parasite diversification [23] whereby parasite clades are associated with distinct vector/host cliques characteristic of a particular ecological niche . Thus far , few vector species have been incriminated in silvatic transmission of TcIIc . Pantrongylus geniculatus and Triatoma rubrovaria , both principally silvatic vectors and often , although not exclusively , associated with terrestrial ecotopes [24] , as well as Dasypus sp . armadillos [2] , [12] , [25] , [26] , are both recorded with TcIIc infection [12] , [19] , [27] The occurrence of TcIIc in domestic transmission cycles , albeit infrequently , implies a role as an agent of human disease . In addition , it is likely that TcIIc is under-reported from both domestic and silvatic transmission cycles because some typing methodologies fail to distinguish between TcIIa and TcIIc ( e . g . [28] ) . Furthermore , TcIIc is one of the parents of the hybrid lineages TcIId and TcIIe [4] , which are predominant agents of severe Chagas disease in the Gran Chaco and adjacent regions [21] . TcIIc therefore represents an important focus for study . As we have recently shown for TcI , an understanding of the dynamics of silvatic T . cruzi infection is a vital step before evaluating the nature of domestic parasite transmission [9] . For TcIIc , this rationale becomes important as human populations expand into previously undisturbed cycles of natural transmission and secondary vector species re-emerge from the silvatic environment after the eradication of major domestic species [29] , [30] . With the aim of establishing the diversity of silvatic TcIIc , here we use 49 microsatellite loci , 12 newly identified in this study , in conjunction with sequence from the glucose-6-phosphate isomerase ( GPI ) gene to examine the population genetics of this lineage from foci across South America . We demonstrate that TcIIc populations are diverse , spatially structured and well established across different climatic regions in South America . By comparison to a newly available TcI microsatellite dataset , we are able to shed light on the ecological and evolutionary significance of our findings .
A c . 1 kb fragment of the glucose-6-phosphate isomerase ( GPI ) gene was sequenced across a representative subset of 22 TcIIc isolates . Genbank accession numbers for the corresponding strains are included in Table S2 . Amplification was achieved according to Gaunt et al . , ( 2003 ) using primers gpi . for ( 5′-CGC ACA CTG GCC CTA TTA TT ) and gpi . rev ( 5′-TTC CAT TGC TTT CCA TGT CA ) [16] in a final reaction volume of 25 ul containing containing 1× Taq polymerase reaction NH4+ buffer ( Bioline , UK ) ) , 2 mM MgCl2 , 200 uM dNTPs; 25 pM of each primer , 1 . 25 units of Taq polymerase , and 35 ng of parasite DNA . The reaction cycle involved an initial denaturation step for five minutes at 94°C , followed by 28 amplification cycles ( 94°C for 30 seconds , 60°C for 30 seconds , 72°C for 30 seconds ) and a final ten minute elongation step at 72°C . PCR products were prepared for sequencing with a BigDye® v3 . 1 sequencing kit ( Applied Biosystems , UK ) , according to the manufacturer's instructions . In addition to forward and reverse external primers , one internal primer was also employed , gpi . 1 ( 5′TGT GAA GCT TTG AAG CCT TT ) [16] . Samples demonstrating two or more heterozygous sequence profiles at individual nucleotide sites were cloned individually using the pGEM T easyVector® system ( Promega , UK ) to derive sequence haplotypes . Owing to the reported occurrence ( c . 20% e . g [32] ) of artefactual recombinant sequence haplotypes derived from Taq DNA polymerase template switching during PCR amplification , ten different clones were sequenced from each sample . Minority recombinant sequence artefacts were identified and excluded from the analysis . Analysis was undertaken of a 980 nucleotide sequence alignment of all experimentally derived haplotypes . Also included in this alignment were selected fragments available on Genbank from a recent study of T . cruzi GPI sequence diversity ( AY484472–AY484478 ) [7] . Tree topology was defined using Kimura-2-parameter ( k2p ) distances and reconstructed through Neighbour-Joining ( NJ ) in the PHYLIP v3 . 67 software package [33] . A thousand bootstrapped datasets were generated in SEQBOOT , analysed using k2p distances , and the resultant NJ trees assessed for congruence in CONSENSE , all in PHYLIP v3 . 67 [33] . The resulting tree ( Figure 1 ) was visualised and prepared for publication using FigTree v1 . 1 . 1 ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Repetitive motifs were extracted from the draft sequence of the T . cruzi genome available at www . genedb . org for analysis of all 53 TcIIc isolates . Four Mb of sequence , including at least 13 syntenous sequence fragments ( SSFs ) , were scanned for di- and tri-nucleotide repeats using a pattern matching script ( regular expression ) written in sed . An extension of the algorithm was included to extract the up and downstream flanking regions of the microsatellite sequence ( ∼200 bp ) . Primer design was achieved in PRIMER3 [34] Over 200 microsatellite loci were identified and screened against a representative subset of five TcIIc isolates . Forty-nine markers , polymorphic across the test group , were selected for further use , including two employed in previous studies [35] . Thirty-seven markers correspond to those we have employed in a recent study of TcI intra-lineage diversity [9] . Twelve are unique to this study . Primer codes , sequences and binding sites are listed in Supplementary Information ( Table S3 ) . After optimisation of annealing temperatures , the following reaction cycle was implemented across all loci: a denaturation step of 4 minutes at 95°C , followed by 30 amplification cycles ( 95°C for 20 seconds , 57°C for 20 seconds , 72°C for 20 seconds ) and a final 20 minute elongation step at 72°C . Reaction conditions , with a final volume of 10 ul , were as follows: 1× ThermoPol Reaction Buffer ( New England Biolabs ( NEB ) , UK ) , 4 mM MgCl2 , 34 uM dNTPs; 0 . 75 pmols of each primer , 1 unit of Taq polymerase ( NEB , UK ) and 1 ng of genomic DNA . Five fluorescent dyes were employed to label forward primers – 6-FAM and TET ( Proligo , Germany ) as well as NED , PET & VIC ( Applied Biosystems , UK ) . Microsatellite allele sizes were determined using an automated capillary sequencer ( AB3730 , Applied Biosystems , UK ) in conjunction with a fluorescently tagged size standard and were manually checked for errors . All isolates were typed “blind” to control for user bias . Allelic richness estimates were calculated in FSTAT 2 . 9 . 3 . 2 [36] and corrected for sample size using Hurlbert's rarefaction method [37] in MolKin v3 . 0 [38] to obtain an unbiased measure of genetic polymorphism among those populations studied . Heterozygosity indices ( Table 1 ) were estimated in ARLEQUIN 3 . 0 [39] . They include mean expected ( under Hardy-Weinberg ( HW ) expectations ) and observed heterozygosity over loci , as well as tests for deviation from HW equilibrium at the level of individual loci within populations . Pair-wise FST values were also estimated in ARLEQUIN v3 . 0 [39] , and represent the proportion of variation accounted for by the sub-division between each population pair by comparison to the total level of variation across both populations . P-values for multiple tests were corrected using a sequential Bonferroni correction [40] to minimise potential Type 1 errors . A further statistic , FIS was applied as an alternate measure of heterozygosity by assessing the level of identity of alleles within individuals compared to that between individuals where +1 represents all individuals homozygous for different alleles and −1 all individuals heterozygous for the same alleles . Mean FIS values per SSF per population were calculated in FSTAT 2 . 9 . 3 . 2 . to examine the genomic distribution of heterozygosity . Multilocus linkage disequilibrium , estimated by the Index of Association ( IA ) , was calculated in MULTILOCUS 1 . 3b [41] , [42] ( Table 1 ) and tests for evidence of the non-random association of alleles across multiple loci . Genetic distances between isolates were evaluated in MICROSAT under an infinite alleles model of microsatellite evolution using DAS ( 1-proportion of shared alleles at all loci / n ) [43] ( Figure 2 ) . To accommodate multi-allelic loci , and asses their influence on the stability of the resulting tree , a script was written in Microsoft Visual Basic to make multiple random diploid re-samplings of each multilocus profile . Individual-level genetic distances were calculated as the mean across multiple re-sampled datasets . A single randomly sampled dataset was used for population-level analysis . A Mantel's test for matrix correspondence was executed in GENALEX 6 to compare pair-wise geographical ( km ) and genetic distance ( DAS ) [44] ( Figure 3 ) . Samples were assigned to populations on an a priori basis according to geography and transmission cycle . DAS - defined sample clustering was also used to inform population identity , and obvious outliers assigned to the correct genetic group ( Figure 2 ) .
53 TcIIc isolates were assembled among which 33 are original to this study and were collected in Venezuela and Bolivia between 2005 and 2007 . Venezuelan isolates were collected from the tropically forested foothills of the Cordillera Oriental to the west of the country around the town of Curbati , Barinas state ( Sample prefix M & PARAMA ) . Three study sites in Bolivia fall across different ecological zones . The first , comparable in terms of ecotope but not elevation to the Venezuelan site was in low-lying Beni state ( Sample prefix SJMO & SJM ) . The second was located in semi-arid Chiquitania dry forest c . 60 km east of Santa Cruz de la Sierra ( Sample prefix CAYMA ) and the last in the arid Chaco region c . 200 km south of Santa Cruz de la Sierra ( Sample prefix MA & SAM ) . Isolates from Paraguay where collected by M . Yeo ( MY ) between 2001 and 2003 [22] . The northern study site at Campo Lorro lies in the arid Paraguayan Chaco ( Sample prefix MA ) and the southern site in semi-arid savannah in the central department of San Pedro ( Sample prefix SP & ARMA ) . Four further historical isolates: M5361 , CM17 , CM25 & 85/847 are from North-Eastern Brazil , Eastern Colombia ( CM ) and Alto Beni ( Bolivia ) respectively . Among numerous mammal species sampled ( >25 - Llewellyn et al . , unpublished; = 10 - MY [22] ) , most isolates originated from D . novemcinctus , including animals from Venezuela , Brazil , Colombia , Bolivia , and Paraguay . However , a number of secondary hosts were also present . In Colombia these included the terrestrial agouti , D . fugilinosa , in Bolivia armadillo genera Euphractus sexcinctus and Chaetophractus vellorosus , and in Paraguay E . sexcinctus and C . vellorosus , as well as the terrestrial marsupial Monodelphis domestica . A single isolate originates from a Panstrongylus spp . triatomine nymph found infesting a D . novemcinctus burrow at Curbati , Venezuela . The tree resulting from sequence analyses is shown in Figure 1 . Nine GPI sequence haplotypes were resolved among the 25 isolates analysed , and nine variable sites identified - equating to ∼0 . 9% sequence diversity within the TcIIc group . TcIIc emerged as a moderately well supported sister group to TcI ( 72% bootstrap support ) and clearly distinct to those TcIIa strains included in the analysis . Among the TcIIc group , some correlation with geography was observed . The following , weakly supported ( >50% ) , clades were apparent: a ‘northern’ group , corresponding to isolates from Brazil , Venezuela , Colombia and Bolivia; and a southern group , corresponding exclusively to isolates from Paraguay and Bolivia . This subdivision corresponds to two fixed single nucleotide polymorphisms between the two groups . One sequence haplotype , ( sjmc19_h1 and m10_hap1 ) fell as an outlier , and could not be assigned to either group . Removal of these isolates from the analysis improved resolution of the subdivision within the TcIIc group . Phylogenetic clustering occurred independently of host species . A final dataset of 4 , 585 alleles ( excluding missing data ) was subjected to analysis . Most strains presented one or two alleles at each locus . Multiple ( ≥3 ) alleles were observed at a small proportion of loci ( 0 . 45% ) , only among uncloned strains , and indicate the possible presence of polyclonal infections in reservoir hosts sampled . Four populations were defined: Venezuela , Colombia and Brazil ( NORTHBraz/Ven/Col ) ; Northern Bolivia ( BOLNorth ) , Southern Bolivia ( BOLSouth ) and Paraguay ( PARANorth/Central ) . Measures of sample size-corrected genetic diversity ( Allelic richness ( Ar ) , were relatively homogeneous across all populations ( Ar = 2 . 58–2 . 83 , Table 1 ) , and no support for a specific correlation between genetic diversity and geographic origin was identified . Diversity indices across all TcIIc populations were equivalent to those observed in lowland silvatic TcI populations ( Ar = 2 . 23–2 . 34 ) [9] . Identical TcIIc multilocus genotypes ( MLGs ) were not observed , and clone correction ( removal of identical MLGs ) unnecessary in the calculation of parameters from the current dataset . Isolate clustering based on pair-wise DAS values ( Figure 2 ) revealed clades broadly defined by geographical origin . Strong bootstrap support ( 92 . 2% ) was found for a division between isolates from Northern and Southern South America . SJMC19 ( Table S2 ) , isolated from D . novemcinctus in BOLNorth , and defined by GPI sequence data as an outlier on the basis of one halplotype , represents a possible migrant and fell within the Northern cluster on the basis of microsatellite variation . As such it was assigned to NORTHBraz/Ven/Col for population level analyses . Consistent with physical proximity , no bootstrap support was apparent between clades from Bolivia and Paraguay . As with GPI sequence data , partitioning of isolates by host was not apparent in this dataset . A tree based on pair-wise distances was also constructed under a step-wise model of microsatellite mutation ( δμ2 [45] ) and bootstrapped using the same methodology as that in Figure 2 . Overall the result was poor by comparison to the DAS derived topology . The bootstrap value for the major division between northern and southern South America , for example , was c . 3% . The extent of spatial structuring among isolates was tested by examining the relationship between genetic ( DAS ) and geographical distance ( km ) . Strongly significant ( RXY = 0 . 687 , p<0 . 001 ) isolation by distance was apparent across all TcIIc isolates . To facilitate a direct comparison between the spatial dynamics of two distinct T . cruzi major genotypes with their principal reservoir species , TcIIc isolates drawn exclusively from D . novemcinctus were compared with a larger dataset of TcI isolates from Didelphis spp . ( D . marsupialis and D . albiventris ) [9] ( Figure 4 ) . The following conclusions can be drawn: 1 ) Both D . novemcinctus TcIIc isolates and D . marsupialis TcI isolates show significant spatial structure ( TcIIc - RXY = 0 . 658 , p<0 . 001; TcI - RXY = 0 . 429 , p<0 . 001 ) . Furthermore , the standard error ( SE ) about the regression gradient ( RG ) for each does not encompass zero , confirming this result . 2 ) TcIIc isolates from D . novemcinctus show greater spatial structure than TcI from D . marsupialis as the RG of the former ( TcIIc - RG = 6 . 445×10−5+/−SE 2 . 401×10−6 ) is greater than the latter ( TcI -RG = 2 . 234×10−5+/−SE 1 . 049×10−6 ) and the respective error bars do not overlap . Importantly TcIIc and TcI isolates from their respective principal host species were sampled across approximately the same geographical range , validating a direct comparison between the two ( Figure 2 , [9] ) . Heterozygous deficiency with respect to HW expectations was a consistent phenomenon across all population examined ( Table 1 ) . This effect was most pronounced in NORTHBraz/Ven/Col . To explore the genomic distribution of homozygosity , mean FIS values were calculated from each SSF ( as defined by the online CL Brener genome – www . tigr . com ) containing ≥2 microsatellite loci . The results of this analysis are displayed in Figure 3 and suggest that homozygosity is fairly evenly distributed across the SSFs studied and by extension homozygosity is likely to be a genome-wide phenomenon . Notably , when Brazilian , Colombian and Bolivian isolates were excluded from NORTHBraz/Ven/Col , a marked reduction in FIS was observed ( Figure 3 ) . Thus , to an extent , high levels of homozygosity within this population may be partially attributable to intra-population subdivision ( Wahlund effect [46] sensu lato as in [47] ) . Significant pair-wise inter-population subdivision ( FST ) ( p<0 . 004 ) after a sequential Bonferroni correction ( Table S1 ) indicates that all populations studied are fairly discrete in population genetic terms , and values broadly correspond to the geographical distances involved ( e . g . lowest subdivision is observed between populations closest geographically - BOLNorth and BOLSouth ( FST = 0 . 051 ) ) . In support of differential levels of spatial structuring between TcIIc and TcI as summarised earlier , gene flow between a silvatic TcI population from BOLNorth and populations from lowland Venezuela and North-Eastern Brazil was higher than that observed from the TcIIc dataset [9] . However , a possible confounder was the anomalous position of isolate SJMC19 , which clustered alongside isolates from NORTHBraz/Ven/Col . In this case subdivision between BOLNorth and NORTHBraz/Ven/Col ( FST = 0 . 284 ) is likely to have been marginally overestimated . Strongly significant ( p<0 . 001 , Table 1 ) linkage disequilibrium ( measured using the Index of Association ( IA ) ) was detected in all populations except BOLNorth where only marginal significance was observed ( p = 0 . 032 , Table 1 ) . Predominantly clonal parasite propagation is thus supported by the non random association of alleles at different loci in most populations . However , given that the IA is a highly conservative measure , some level of recombination cannot be ruled out in any population , especially BOLNorth .
The widespread spatial distribution and genetic diversity of the TcIIc isolates studied here point to an possible ancient origin for this DTU and certainly a long-term association with terrestrial transmission cycles . Historically , most TcIIc isolates have originated from the Southern Cone region of South America [19] , [22] . We can now confirm that TcIIc occurs as far north as Western Venezuela , and by implication throughout the continent . Levels of genetic diversity among populations studied are comparable to those observed in arboreal silvatic TcI from lowland moist forest ecotopes [9] . Indeed there is no evidence from the current dataset to suggest that TcIIc is any ‘younger’ than TcI in evolutionary terms , although microsatellites may be a poor estimator of ancient evolutionary events . Nonetheless , the divergent TcIIc mitochondrial genome ( i . e . kinetoplast maxicircle ) does suggest an ancient origin for this lineage [4] , [5] and lends support to our data . Also , observed heterozygous deficiency is not superficially consistent with a hybrid origin for TcIIc [6] . Again , however , microsatellites are an imperfect tool for detecting ancient hybrid signatures . Informative variation will be lost rapidly via mutation and/or gene conversion . Genetic diversity was surprisingly homogenous across the populations studied , an observation interesting in the context of the major host species examined . Molecular dating of the long-nosed armadillos , the Dasypodini ( which includes Dasypus spp . ) suggests an early emergence for this group ( c . 40 MYA ) , if not for the species D . novemcinctus itself , which is likely to have emerged later [48] . The ancestors of extant Dasypus species were presumably widespread in the tropical-temperate forest environments that predominated throughout South America around this time [49] . The emergence of the extant Euphractinae ( which include Chaetophractus and Euphractus spp ) is thought to have occurred very recently ( c . 5 MYA ) in response to climatic cooling and the formation of the arid southern Chaco and Pampas ecotopes [48] . Diversity estimates from our data reject a recent radiation of TcIIc into Paraguay and Southern Bolivia in conjunction with the emergence of Euphractinae hosts . It seems instead that residual populations of Dasypus spp . have maintained TcIIc transmission in dryer areas , and indeed these mammals demonstrate a much higher infection rate in Southern Bolivia ( Llewellyn et al . , unpublished data ) and Northern Paraguay [22] than other dry-adapted armadillo genera , despite being less abundant . This observation could be related to the ease with which the burrows of different armadillo genera are infested with triatomines . Our field observations suggest that Tolypeutes matacus ( rarely , if ever infected - Llewellyn et al . , unpublished , [22] ) does not dig burrows; E . sexcinctus and Chaetophractus spp ( infrequently infected Llewellyn et al . , unpublished , [22] ) dig very deep burrows; whereas D . novemcinctus burrows are shallower , subject to repeated use by the same individual and provide an easily accessible long-term refuge for triatomines . Nonetheless , triatomines do transmit TcIIc to other terrestrial genera and secondary hosts must have fairly frequent contact with this DTU as D . novemcinctus and non-D . novemcinctus isolates are not clearly distinguishable at discrete foci . TcIIc is thus eclectic in terms of host in terrestrial transmission cycles , as expected under a model of ‘ecological host-fitting’ [23] . It follows that a stringent co-evolutionary relationship with D . novemcinctus can be ruled out in the context of the current dataset , and , in the context recent data from Brazil , with other known hosts of TcIIc [19] . Interestingly , a new isolate from a Panstrongylus spp . nymph in Barinas , Venezuela ( M3-CU ) , recovered from the burrow of D . novemcinctus corroborates earlier reports of TcIIc from this vector genus in North-Eastern Brazil [12] , and provides more support for ‘divergence by niche’ in T . cruzi silvatic populations [22] . On the basis of microsatellite diversity , and concordant with a related study in Brazil [19] , TcIIc is highly spatially structured across South America . This observation corresponds with the general epidemiology of silvatic disease transmission , where endemic parasite populations at distinct foci exchange little genetic content in the absence of rapid and long distance host or vector dispersal . SJMC19 , a strain isolated from D . novemcinctus in Northern Bolivia , is an exception being apparently a northern migrant . However , the grouping of SJMC19 with isolates from NORTHBraz/Ven/Col could be an artefact of poor sample coverage from Western Brazil and warrants more intensive sampling from this region . A statistical comparison between TcI and TcIIc isolates from their major reservoirs ( D . marsupialis and D . novemcinctus respectively ) reveals greater spatial structuring among the latter . This perhaps relates to the larger home range of D . marsupialis as compared to D . novemcinctus [50] , but also to the greater number of secondary hosts involved in TcI transmission [22] , if historical records are broadly representative of the relative abundance of the two lineages among mammalian genera . GPI sequence data provide a more confused pattern of spatial diversification , where , among the 24 TcIIc strains analysed , the North-South divide is less pronounced . A single nuclear locus , especially from a relatively conserved sequence class , is clearly insufficient to address a population genetic question . However , sequence data ( Figure 1 ) do corroborate the anomalous status of SJMC19 , and two highly divergent haplotypes are evident in this sample , one identical to a Venezuelan haplotype ( itself an outlier ( M10 A1 ) ) , and the other occurring alongside haplotypes from Paraguay , Central and Southern Bolivia , potentially consistent with recombination and worthy of further study . A common feature between both TcI [9] and TcIIc isolates , and consistent within T . cruzi as a whole [4] , [6] , [35] , with the exception of hybrids TcIId and TcIIe , is an apparent lack of heterozygosity as compared to Hardy-Weinberg expectations . Heterozygous deficiency is also incongruent with extreme models of long term clonal evolution in diploids , where haplotypes are expected to become increasingly divergent over time in the absence of recombination [47] , [51]–[53] . Excess homozygosity in sexual populations , assuming strict neutrality , zero allele drop out and discounting Wahlund effects , is normally indicative of inbreeding [54] . Heterozygosity in predominantly clonal diploids such as T . cruzi can theoretically be reduced by several processes including gene conversion and occasional recombination ( both out-crossing and selfing events ) , but distinguishing between these processes is challenging . As in our recent study of TcI microsatellite diversity [9] , we can show that homozygosity in T . cruzi is genomically diffuse . This suggests that infrequent , localised ( e . g . whole chromosomes or chromosome fragments ) gene conversion events can , therefore , be ruled out in the context of those SSFs we examined . A recent population genetic study of a related trypanosomatid ( Leishmania braziliensis ) , previously thought to be clonal , partially attributes excess homozygosity to endogamic recombination [55] . We found no concrete evidence for sexuality within the TcIIc populations studied , but some level of recombination cannot be ruled out , especially in BOLNorth , where only marginal significance could be attributed to the Index of Assocation ( multilocus linkage disequilibrium [42] ) , which is considered a conservative measure of clonality [47] . Two important issues must be considered when attempting to distinguish between the various non-exclusive sources of homozygosity in a predominantly clonal diploid: 1 ) It seems illogical to correct for Wahlund effects using population assignment programs that explicitly rely upon Hardy-Weinberg assumptions with the aim of demonstrating endogamic sexuality [55] , [56] – this argument is circular . 2 ) In order to discount gene conversion by disproving a negative relationship between allele size differences in heterozygotes and the number of heterozygotes across samples , one must assume a purely stepwise model of microsatellite mutation [55] , without significant frequencies of back mutation or homoplasy . In our analysis we were able provide some evidence of a Wahlund effect sensu lato by manual exclusion of outlying samples from NORTHBraz/Ven/Col . However , we were unable to discount gene conversion as a source of homozygosity as the step-wise model we applied to our data seemed to give poor results by comparison to DAS . Null loci could contribute to the homozygosity observed in our dataset , however , primers were designed against the CL-Brener genome , of which one haplotype belongs to a TcIIc parent , and we do not therefore expect major sequence divergence between the microsatellite flanking regions of this and our TcIIc isolates . We would therefore cautiously suggest that both a high frequency of gene conversion acting across the genome as well as hybridisation involving fusion of highly similar or identical individuals could have played a role in generating the observed diversity but we are unable to discriminate between the two processes with any confidence . Additionally we suggest that the latter process would be best demonstrated experimentally in TcIIc , as it has been in TcI [16] , before drawing direct conclusions from variation at microsatellite loci , about which the mutational mechanism is still poorly understood . Whether or not recombination is an important factor , we believe it is a valid interpretation of our data , and that of others [4] , [6]–[8] , that TcIIc represents an ancient , discrete and diverse T . cruzi lineage with well defined ecological associations and a continental distribution among silvatic cycles of parasite transmission . Despite a strong association with D . novemcinctus , TcIIc is eclectic within the terrestrial ecotope and parasite diversification occurs independently of host species . We can also confirm that the dispersal of this lineage between foci of transmission occurs at a significantly lower rate than that of TcI , a phenomenon that may be partly explained by differential primary host dynamics . While we recognize that the inclusion of TcIIc within the ‘TcII’ group makes increasingly little taxonomic sense , it also makes no more or less sense than the inclusion of any of the other TcII groups under the same heading . T . cruzi is certainly overdue a taxonomic overhaul , but , until further clarification - which must include multilocus analysis of a larger number of strains , especially from TcIIa and TcIIb – we believe that the DTU definition [57] , which implies monophyly within clades but makes no assumptions about the evolutionary relationship between clades , is currently the ‘least wrong’ in terms of T . cruzi population structure . Interestingly , TcIIc appears to be absent from the USA on the basis of the current literature , and among the D . novemcinctus so far sampled only TcI ( N = 2 ) and TcIIa ( N = 1 ) have been identified [58] . D . novemcinctus is widespread in the Southern USA , and if overall T . cruzi prevalence is comparable to that we have identified in South America [22] ( Llewellyn et al . , unpublished ) this species has been heavily under-sampled . In terms of human transmission , our dataset and analytical methodology will be applicable in pinpointing the geographical and/or ecological origin of the predominantly domestic T . cruzi strains TcIId and TcIIe . Westenberger et al . , 2006 [59] provide evidence from the composition of 5S rRNA arrays that the TcIIb ancestor of TcIId and TcIIe lies within the western portion of the Southern Cone of South America . Our microsatellite panel can now provide information with regards to the TcIIc ancestor , as well as more fine scale determination of the likely TcIIb ancestor , so long as adequate samples are available . In doing so it may be possible to clarify the ecological circumstances around the emergence of these epidemiologically important hybrids , and perhaps help predict similar events in the future . | Trypanosoma cruzi , the etiological agent of Chagas disease , infects over 10 million people in Latin America . Six major genetic lineages of the parasite have been identified with differential geographic distributions , ecological associations and epidemiological importance . With the advent of the T . cruzi genome sequence , it is possible to examine the micro-epidemiology of T . cruzi using high resolution genetic markers that assess diversity within these major types . Here we examine the genetic diversity of TcIIc , a poorly understood T . cruzi genetic lineage found predominantly among wild cycles of parasite transmission infecting terrestrial mammals and triatomine vectors , but also a potentially important emergent human disease agent . Amongst a number of findings , we show that TcIIc genetic diversity is comparable to other ancient T . cruzi lineages , highly spatially structured , and that a stringent co-evolutionary relationship with its principal reservoir host can be ruled out . Additionally , TcIIc is one of the two parents of hybrid lineages TcIId and TcIIe , which cause most of the Chagas disease that occurs in the Southern Cone of South America . The system we have developed will help to clarify the ecological circumstances around the emergence of these epidemiologically important hybrids , and perhaps help predict similar events in the future . | [
"Abstract",
"Introduction",
"Methods",
"and",
"Analyses",
"Results",
"Discussion"
] | [
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics",
"ecology/evolutionary",
"ecology",
"molecular",
"biology/molecular",
"evolution",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"microbiology/parasitology",
"ecology/population",
"ecology"
] | 2009 | Trypanosoma cruzi IIc: Phylogenetic and Phylogeographic Insights from Sequence and Microsatellite Analysis and Potential Impact on Emergent Chagas Disease |
Increasing evidence has indicated that microRNAs ( miRNAs ) play vital roles in various pathological processes and thus are closely related with many complex human diseases . The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis . Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations , they suffer from various limitations that affect the prediction accuracy and their applicability . In this study , we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning ( AMVML ) . Specifically , considering the inherent noise existed in the current dataset , we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles . We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning . In particular , we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate . To comprehensively illustrate the prediction performance of our method , we compared AMVML with four state-of-the-art methods under different validation frameworks . As a result , our method achieved comparable performance under various evaluation metrics , which suggests that our method is capable of discovering greater number of true miRNA-disease associations . The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker . Together , the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs .
MiRNAs are a group of short non-coding RNAs that mediate post-transcriptional gene silencing[1] . Accumulating evidence has proved that miRNAs play crucial roles in a variety of cancer-related pathways . Therefore , the identification of miRNA-disease associations can shed new light on understanding possible pathogenesis of diseases . To compensate for the limitations of experiment-based approaches , a great number of computational models have been proposed to identify potential disease-related miRNAs in recent years[2] . Under the assumption that functionally similar miRNAs tend to be associated with phenotypically similar diseases , Jiang et al . prioritized the entire microRNAome for over a thousand diseases by constructing an integrated phenome-microRNAome network[3] . Chen et al . measured the global network similarity and inferred potential miRNA-disease interactions based on random walk with restart[4] . Shi et al . adopted a similar idea and further integrated the protein-protein interactions into the prediction process[5] . Chen et al . proposed a novel heterogeneous graph inference method by iteratively updating the association probability[6 , 7] . Liu et al . constructed a heterogeneous network in which they integrated the miRNA-target gene and miRNA-lncRNA associations[8] . Specifically , the methods introduced above mainly predicted disease-related miRNAs by applying random walk algorithms to the reconstructed similarity networks[9] . Another family of prediction methods was generally based on network topological characteristics and also achieved remarkable performance . For instance , Zou et al . computed the similarity score based on walks of different lengths between the miRNA and disease nodes[10] . Sun et al . exploited the potential disease-related miRNAs based on known miRNA-disease network topological similarity[11] . You et al . proposed to measure the association score for a miRNA-disease pair by calculating the accumulative contributions from all paths between them[12] . Li et al . used DeepWalk to enhance the existing associations through a topology-based similarity measure[13] . Chen et al . computed the association possibility between a disease node and a miRNA node in the corresponding graphlet interaction isomers[14] . Although effective , these methods are sensitive to the change of the network topological structures , which might affect the prediction accuracy . Alternatively , prediction methods that were based on semi-supervised learning as well as supervised learning have been well developed . Xiao et al . introduced a graph regularized non-negative matrix factorization to effectively discover sparse miRNA-disease associations[15] . Both Chen et al . and Yu et al . adopted matrix completion to recover the potential missing miRNA-disease associations[16 , 17] . Zeng et al . used a derivative algorithm structural perturbation method to estimate the link predictability with structural consistency as the indicator[18] . Chen et al . used an ensemble model where a sequence of weak learners were trained to collectively obtain a predicted association score[19] . Recently , we reconstructed the miRNA and disease similarity matrices based on global linear neighborhoods and then applied label propagation to predict potential associations between diseases and miRNAs[20 , 21] . Chen et al . extracted novel feature vectors for both miRNAs and diseases to train a random forest classifier for the prediction task[22] . Although great efforts have been made to efficiently uncover potential miRNA-disease associations , most existing computational approaches still suffer from several limitations . Specifically , the inherent noise in the current datasets resulted in incomplete and sparse similarity matrices and thus inevitably affected the prediction accuracies of these methods . Moreover , the integration of multiple biological data sources in calculating the similarity matrices for both miRNAs and diseases was generally performed by averaging the input similarity information , which might lead to suboptimal results . Lastly , the predicted association scores from miRNA space and disease space were often updated separately during the learning process . To solve these problems , in this paper , we propose a novel Adaptive Multi-View Multi-Label ( AMVML ) learning framework to infer disease-related miRNAs . In particular , our method adaptively learns a new affinity graph for miRNAs and diseases respectively from multiple data sources ( i . e . miRNA sequence similarity , Gaussian interaction profile kernel similarity and so on ) . In addition , we unify the optimization process for both disease space and miRNA space based on multi-label learning . The experimental results under several different evaluation metrics clearly demonstrate the superior performance of our method over previous methods . We further carry out a case study on thyroid cancer to identify potential prognostic biomarkers .
The known human miRNA-disease associations were retrieved from HMDD v2 . 0 database[23] . HMDD is a database for experimentally supported human miRNA and disease associations that were manually collected from all the miRNA-related publications in PubMed . Each entry in HMDD contains four items , i . e . miRNA name , disease name , experimental evidence for the miRNA-disease association and the publication PubMed ID . To keep consistent of data from different sources , we also downloaded the annotation information of 4796 human miRNAs released on March 2018 from miRBase[24] . We then downloaded the latest MeSH descriptors from the National Library of Medicine ( https://www . nlm . nih . gov/ ) and only retained items from Category C for diseases , which resulted in 11572 unique items . After mapping the miRNA names and disease names involved in each association with miRBase records and MeSH descriptors , we finally obtained 6088 associations between 328 diseases and 550 miRNAs for subsequent analysis ( S1 File ) . Specifically , we classified the 328 diseases based on the Diseases Categories provided in MeSH . For diseases belonging to multiple categories , we increased the count by one for each category accordingly . As a result ( Fig 1 , S2 File ) , we can see that most diseases recorded in HMDD were cancers . For convenience , we used a binary matrix Y ∈ ℝ328×550 to represent the miRNA-disease associations . For a given disease i and miRNA j , Yij = 1 if i is related to j , and Yij = 0 otherwise . As described in [25] , the disease semantic similarity can be calculated based on Directed Acyclic Graphs ( DAGs ) . Specifically , for a given disease d , its DAG is composed of three items , i . e . DAG = ( d , T ( d ) , E ( d ) ) , where T ( d ) represents d itself together with all its ancestor nodes , and E ( d ) contains all direct links connecting the parent nodes to child nodes . The contribution Dd ( t ) of a disease t in a DAGd to the semantics of disease d was defined as follows: {Dd ( D ) =1Dd ( t ) =max{0 . 5*Dd ( t′ ) |t′∈childrenoft}ift≠d ( 1 ) The semantic similarity score between two diseases i and j can then be calculated by: S ( i , j ) =∑t∈T ( i ) ∩T ( j ) ( Di ( t ) +Dj ( t ) ) ∑t∈T ( i ) Di ( t ) +∑t∈T ( j ) Dj ( t ) ( 2 ) Moreover , the similarity between a given disease d and a group of diseases Dt = {dt1 , dt2 , … , dtk} was defined by: S ( d , Dt ) =max1≤i≤k ( S ( d , dti ) ) ( 3 ) Finally , we obtained the semantic similarities for each disease pair according to ( Eq 2 ) . We denoted the semantic similarity matrix as AD ( 1 ) ∈ ℝ328×328 where ADij ( 1 ) represents the semantic similarity between disease i and disease j ( S3 File ) . In this subsection , to comprehensively characterize similarities between miRNAs , we adopt three measures using different biological data sources for subsequent predictions[26] . Gaussian interaction profile kernel similarity has been widely used in previous studies and proved effective in measuring both miRNA and disease similarities . For a given miRNA i or disease j , its interaction profile IP ( mi ) or IP ( dj ) was a binary vector extracted from the i-th row or the j-th column of the association matrix Y . The kernel similarity between two miRNAs or two diseases could then be computed by: KM ( mi , mj ) =exp ( −βm‖IP ( mi ) −IP ( mj ) ‖2 ) ( 6 ) KD ( di , dj ) =exp ( −βd‖IP ( di ) −IP ( dj ) ‖2 ) ( 7 ) where βm and βd are defined as follows: βm=βm′/ ( 1550∑i=1550‖IP ( mi ) ‖2 ) ( 8 ) βd=βd′/ ( 1328∑i=1328‖IP ( di ) ‖2 ) ( 9 ) where β'm and β'd are two parameters controlling the kernel bandwidth . As a result , we used AM ( 4 ) ∈ ℝ550×550 and AD ( 2 ) ∈ ℝ328×328 to represent the obtained Gaussian interaction profile similarity matrices for miRNAs and diseases , respectively . We summarize the notations used throughout this paper . Given a matrix M , Mij and Mi represent its ij-th element and i-th row , respectively . The transpose of M is denoted by MT . Tr ( M ) denotes the trace of M and the Frobenius norm of M is represented as ||M||F . For a similarity matrix S , its Laplacian matrix LS is defined as LS=DS−ST+S2 , where DS is a diagonal matrix with its i-th diagonal element equal to ∑j ( Sij + Sji ) /2 .
To systematically evaluate the performance of our method and illustrate its superiority over existing alternatives , we compared AMVML with fourstate-of-the-art methods , i . e . IMCMDA[37] , SPMMDA[38] , PBMDA[12] and EGBMMDA[19] under several evaluation metrics . All these methods have been proved effective in predicting reliable disease-associated miRNAs . First of all , we adopted the global Leave-One-Out Cross-Validation ( LOOCV ) and five-fold cross-validation to test the general prediction performance . Specifically , in the framework of global LOOCV , each known miRNA-disease association was selected as a test sample while the remaining associations were considered as training samples . For five-fold cross-validation , all known miRNA-disease associations were randomly divided into five subsets and each subset was chosen as the test samples . Besides , the five-fold cross-validation was repeated 10 times to eliminate the potential bias caused by the sample division . The prediction performance was illustrated by Receiver Operating Characteristic ( ROC ) curve and the accuracy was quantified by the Area Under the ROC Curve ( AUC ) . As shown in Fig 3 , AMVML achieved the highest accuracy among all methods in both global LOOCV and five-fold cross-validation . Next , we employed another evaluation metric called Leave-One-Disease-Out Cross-Validation ( LODOCV ) to verify the prediction performance when no prior information is available . Specifically , for each disease d , we removed all known miRNAs associated with d and carried out predictions based on miRNA association information of the other diseases . Since there are no known associations for each tested disease in advance , LODOCV is more difficult than global LOOCV and five-fold cross-validation . We calculated an AUC value for each disease in LODOCV and thus obtained a vector consisting of 328 AUC values for each method . We then demonstrated the comparison results by density plots ( Fig 4A ) . As a result , the AUC values obtained by our method mainly concentrated over the interval [0 . 9 , 1] , indicating a better performance than that of the other methods in terms of LODOCV . Wilcoxon signed-rank test further confirmed the statistical significance of the comparison results ( Table 1 ) . Lastly , we conducted experiments on real datasets to further demonstrate the prediction ability of our method . To this end , we first downloaded the older version of HMDD ( v1 . 0 ) which contains 1474 known associations between 129 diseases and 280 miRNAs after filtering ( S7 File ) . Compared to HMDD v1 . 0 , there were 4614 ( i . e . 6088–1474 ) new miRNA-disease associations , 199 ( i . e . 328–129 ) new diseases and 270 ( i . e . 550–280 ) new miRNAs involved in HMDD v2 . 0 . In particular , among the 4614 newly recorded associations in HMDD v2 . 0 , 2445 associations were related with miRNAs and diseases already existed in HMDD v1 . 0 , while 2169 associations were related with either new miRNAs or new diseases only contained in HMDD v2 . 0 . Moreover , the degree distribution of miRNAs as well as that of diseases for the 4614 associations indicating that only a minority of these associations were related with highly connected miRNAs and diseases ( S1 Fig ) . We then applied each method on HMDD v1 . 0 and validated the prediction results by the 4614 associations newly added in HMDD v2 . 0 . Therefore , for each method , the greater the number of true positives predicted , the better the performance . Specifically , we compared the number of true positives in the top-N miRNAs predicted by each method with N ranging from 10 to 50 and an interval of 10 . As exhibited in Fig 4B , AMVML obtained greater number of validated disease-associated miRNAs than the other methods . Similar results were also obtained with increased N and larger intervals ( S2 Fig ) . Taken together , the experimental results under various evaluation metrics proved the effectiveness of our method . There were two trade-off parameters α and β in our method which balance the learned similarity matrices and the predicted association matrix . Generally , since our objective function is a minimization problem , setting a large value to α or β indicates a large impact of the label consistency between diseases or miRNAs on the learned disease or miRNA similarity matrix . To show a reasonable searching range of these two parameters as well as a general trend of the prediction performance affected by varying their values , in this subsection , we analyzed their influences on the prediction accuracy in terms of five-fold cross-validation ( Fig 5A ) . Similar trends were also observed in global LOOCV . In particular , when β was fixed , the smaller the α , the better the performance . In contrast , when α was fixed , the performance varied in a "U" shape with the change of β . We can see that the proposed method reached the best performance when both α and β were equal to 1e-4 . As described in previous section , we have theoretically proved the convergence of our algorithm . Here we investigated the convergence rate of our method by analyzing the variations of the objective function value in ( Eq 11 ) with respect to the number of iterations . As demonstrated in Fig 5B , the objective function value reached a steady state within 5 iterations , indicating a fast convergence rate of our method . In this section , we conducted a case study on thyroid neoplasms to identify potential miRNA biomarkers for this disease . The overall prediction results and the differential expression analysis for several other diseases were also provided on Github ( https://github . com/alcs417/AMVML ) . Thyroid cancer is the most common endocrine cancer and its incidence rate has increased rapidly over recent years[39] . We first downloaded the miRNA expression profiles together with the clinical information of thyroid carcinoma from GDC data portal ( https://portal . gdc . cancer . gov/projects/TCGA-THCA ) . Concretely , the downloaded data contained 506 tumors samples and 59 normal samples and each sample measured the expression level of 1881 miRNAs . We then applied our method on the given disease to obtain the top-10 predicted miRNAs ( Table 2 ) . Specifically , we evaluated the classification power of these miRNAs in differentiating tumor samples from normal samples according to their expression profile and the results of five-fold cross-validation illustrated that they could achieve a mean classification accuracy of 0 . 983 ( S3 Fig ) . Next , we calculated for each miRNA the fold-change as well as the statistical significance of differential expression using the R package edgeR ( Table 2 ) [40] . Besides , we searched in another two databases dbDEMC and miR2Disease to see if the predicted miRNAs were also recorded in them[41 , 42] . dbDEMC is an integrated database that designed to store and display differentially expressed miRNAs in human cancers detected by high-throughout methods while miR2Disease is a manually curated database providing information about miRNA deregulation in various human diseases . As a result , the expression level of the top predicted miRNA hsa-mir-181a-2 was significantly altered between tumor samples and normal samples ( log2 fold-change > 1 and adjusted p-value< 0 . 05 ) , which is consistent with the records in both db2DEMC and miR2Disease . Therefore , we further checked whether this miRNA could serve as a potential biomarker for thyroid cancer . Specifically , we carried out one-way ANOVA test to validate whether its expression level at different tumor stages also significantly altered . The tumor stages of all patients were retrieved from the clinical information and there were six pathologic stages after filtering . As expected , the expression level of hsa-mir-181a-2 varied significantly among different stages ( Fig 6A ) . Furthermore , the Kaplan-Meier survival analysis confirmed that the survival rates of patients were also significantly related with its expression level ( Fig 6B ) [43] . Taken together , our results provided new evidence for the functional role of hsa-mir-181a-2 in the development of thyroid cancer .
Identification of disease-associated miRNAs has drawn much attention during the past decade and it still remains a challenging task . In this study , we proposed a novel computational framework to effectively uncover the potential links between miRNAs and diseases . Our method integrated datasets from multiple sources and adaptively learned two new similarity graphs . Specifically , instead of assigning predetermined weight values to each input similarity matrix , the proposed method automatically updated the view weights according to the reliability of each view . It is also worth mentioning that our method could be easily extended if there are new data sources available . Besides , our method could simultaneously update the prediction results from both disease space and miRNA space . The convergence of our method has been proved both theoretically and experimentally . To demonstrate the utility of our method , we compared AMVML with five state-of-the-art methods and the experimental results confirmed the superiority of our method . We then applied our method on thyroid cancer and found that hsa-mir-181a-2 could be a potential prognostic biomarker . Notably , our method is not limited to discover miRNAs for which an association is already known between its paralogous miRNA and the same disease . In essence , as a semi-supervised learning model , our method could fully take advantage of the limited number of known miRNA-disease associations together with multiple sources of biological datasets to reliably predict novel associations . Therefore , we anticipate that our method could serve as an effective tool for miRNA-disease association prediction . The superior performance of our model can be largely attributed to the following two reasons . First , the consensus similarity matrices obtained from multiple biological datasets based on multi-view learning for both miRNAs and diseases are more robust to outliers and noises compared to existing methods . Second , the graph-based multi-label learning unified the two prediction spaces into one optimization framework , which enhances the inherent correlations between miRNAs and diseases from the label-consistency point of view . Nevertheless , our method still has some limitations . Specifically , there are two parameters α and β in the objective function that need to be tuned in advance , and it is a non-trivial task to find the best combination of the two parameters . In addition , although our method can adaptively learn a new affinity graph from different data sources , the integration of unreliable similarity matrices might weaken the overall prediction accuracy . | MiRNAs are a class of small non-coding RNAs that are associated with a variety of complex biological processes . Increasing studies have shown that miRNAs have close relationships with many human diseases . The prediction of the associations between miRNAs and diseases has thus become a hot topic . Although traditional experimental methods are reliable , they could only identify a limited number of associations as they are in general time-consuming and expensive . Consequently , great efforts have been made to effectively predict reliable disease-related miRNAs based on computational methods . In this study , we develop a novel method to discover potential miRNA-disease associations based on Adaptive Multi-View Multi-Label learning . Considering the inherent noise existed in the current dataset , we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple biological data source , including miRNA sequence similarity , miRNA functional similarity and Gaussian interaction profile kernel similarity . Notably , our method is applicable to diseases without any known associated miRNAs and also obtains satisfactory results . The case study conducted on thyroid neoplasms further confirms the prediction reliability of the proposed method . Overall , results show that our method can predict the potential associations between miRNAs and diseases effectively . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"linguistics",
"medicine",
"and",
"health",
"sciences",
"natural",
"antisense",
"transcripts",
"gene",
"regulation",
"endocrine",
"tumors",
"carcinomas",
"applied",
"mathematics",
"cancers",
"and",
"neoplasms",
"social",
"sciences",
"biomarkers",
"simulation",
"and",
"modeling",
"oncology",
"algorithms",
"optimization",
"micrornas",
"mathematics",
"research",
"and",
"analysis",
"methods",
"lung",
"and",
"intrathoracic",
"tumors",
"gene",
"expression",
"thyroid",
"thyroid",
"carcinomas",
"biochemistry",
"rna",
"anatomy",
"nucleic",
"acids",
"semantics",
"thymic",
"tumors",
"genetics",
"endocrine",
"system",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"non-coding",
"rna",
"neoplasms"
] | 2019 | Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs |
Septic shock caused by Neisseria meningitidis is typically rapidly evolving and often fatal despite antibiotic therapy . Further understanding of the mechanisms underlying the disease is necessary to reduce fatality rates . Postmortem samples from the characteristic purpuric rashes of the infection show bacterial aggregates in close association with microvessel endothelium but the species specificity of N . meningitidis has previously hindered the development of an in vivo model to study the role of adhesion on disease progression . Here we introduced human dermal microvessels into SCID/Beige mice by xenografting human skin . Bacteria injected intravenously exclusively associated with the human vessel endothelium in the skin graft . Infection was accompanied by a potent inflammatory response with the secretion of human inflammatory cytokines and recruitment of inflammatory cells . Importantly , infection also led to local vascular damage with hemostasis , thrombosis , vascular leakage and finally purpura in the grafted skin , replicating the clinical presentation for the first time in an animal model . The adhesive properties of the type IV pili of N . meningitidis were found to be the main mediator of association with the dermal microvessels in vivo . Bacterial mutants with altered type IV pili function also did not trigger inflammation or lead to vascular damage . This work demonstrates that local type IV pili mediated adhesion of N . meningitidis to the vascular wall , as opposed to circulating bacteria , determines vascular dysfunction in meningococcemia .
Fulminant meningococcal septic shock is the most lethal outcome of infection with the human-specific bacterium Neisseria meningitidis with a case fatality rate of 16–52% [1] . Despite the apparent virulence of the bacteria , between 5 and 30 % of the human population carry N . meningitidis in their throat asymptomatically [1] . Pathology is initiated when the pathogen accesses the bloodstream , and can lead to distinctive disease progressions , meningitis ( 37–49% ) , septic shock ( 10–18% ) or a combination of the two ( 7–12% ) [1] . Meningococcal septic shock , either alone or in addition to meningitis , is typically rapidly evolving and responsible for 90% of fatal cases [2] . Despite antibiotic treatment , severe sequelae and death rates remain high for meningococcal septic shock , and a better understanding of the mechanisms of the infection is required . Clinical studies have provided a detailed description of the late stages of the disease . Post mortem histological studies have shown bacterial aggregates inside the lumen of blood vessels in several organs including skin , liver , brain , and kidney [3] , [4] , [5] , [6] . A characteristic site of infection is the skin . Dermal lesions , including petechial and purpura rashes , are considered one of the cardinal features of meningococcal septic shock , occurring in 28–78% of cases [1] , [7] . Bacteria can be isolated from the skin of 86% of patients with meningococcal sepsis by needle aspiration [8] . Close association of bacterial aggregates with endothelial cells suggests that bacteria are adhering along the vessel wall although this remains to be demonstrated [3] , [6] . The presence of bacteria inside microvessels is associated with a potent inflammatory response and vascular damage . Infection triggers the secretion of high levels of inflammatory cytokines including IL-6 , IL-8 and TNFα in patient serum [9] , [10] , [11] . A cellular infiltrate primarily consisting of monocytes and polymorphonuclear neutrophils ( PMN ) is observed in infected areas [3] , [4] , [12] and bacteria are frequently found inside these phagocytic cells [4] , [6] . Widespread thrombosis of the small vessels is associated with alterations in blood flow , congestion of red blood cells and subsequently dilation and engorgement of the vessels . Endothelial damage results in vascular leakage , and the development of purpura [1] , [5] , [12] , [13] . Despite the strong association of meningococcal disease and purpura as well as the serious prognosis associated with it , the bacterial factors involved and the sequence of events leading to the histological observations remain poorly understood . How the bacterial aggregates form inside the vessels is still unknown . It may be that bacteria associate as pre-formed aggregates that get lodged in the microvasculature , possibly proliferate after reduction in circulation following coagulation , or it may be an active adhesion process . One of the major obstacles in the study of meningococcal septic shock and meningitis has been the absence of an experimental model , primarily due to the strong human specificity of N . meningitidis . Adhesion to endothelial cells is a factor likely to contribute to the difficulty in developing animal models that reproduce the human disease . N . meningitidis adhesion to human cells in culture is several orders of magnitude more efficient than to mouse cells in vitro [14] . A variety of bacterial adhesins have been described in vitro including type IV pili ( tfp ) , Opa proteins , OpC , TspA or NadA but their relative importance for adhesion in vivo is unknown [15] , [16] , [17] , [18] . The objective of this work was to investigate the impact of bacterial adhesion to the vessel on vascular dysfunction during septic shock and purpura in vivo . We report the development of a humanized mouse model for N . meningitidis based on the previously described xenografting of human skin onto SCID/Beige mice [19] . In this model the grafted human dermal microvasculature anastomoses with the mouse circulation thus introducing functional human microvessels in these animals . We show that infection of this humanized mouse model results in extensive bacterial adhesion , which leads to vascular dysfunction and reproduces the cutaneous lesions found in acute meningococcemia .
Our first objective was to develop an experimental model for meningococcal infection , which included bacterial adhesion . Our strategy was to introduce human dermal vessels into a mouse model . Human skin , 200 µm thick , including the corneum , epidermis and the papillary layer , containing an abundance of capillaries , was grafted onto SCID/Beige mice [19] . Three to four weeks post graft ( Fig . 1A ) , the human skin displayed classical morphology with a clearly detectable epidermis and dermis and no evident inflammation ( Fig . 1B ) . The graft border between the human graft and mouse skin was identified by reduced epidermal thickness and presence of hair follicles in the mouse skin ( dotted line , Fig . 1C ) . The human origin of the microvessels , and retention of human endothelial cells following grafting was confirmed by staining with the lectin Ulex europaeus agglutinin ( UEA ) , a marker of human endothelial cells ( Fig . 1D ) , as well as a monoclonal antibody specific for human CD31 ( PECAM-1 ) ( Fig . 2F ) . At the interface of the grafts , junctions between human and mouse vessels could be identified ( Fig . 1E ) . Circulation of blood in the human vessels was demonstrated by intravital imaging . Introduction of fluorescently labeled UEA lectin and 150 kDa FITC-dextran in the circulation of the grafted animals allowed the identification of the human vessels and blood flow respectively . Red blood cells were visible in the human vessels as black silhouettes in the fluorescent plasma ( Fig . 1F and Movie S1 ) . This shows that grafting of human skin resulted in perfused , functional human vessels in the SCID/Beige mouse . In patients presenting with fatal meningococcemia , the bacterial load , as determined by qRT-PCR , has a median value of approximately 105 colony forming units ( CFU ) /ml [20] . To mimic this scenario , a total of 106 CFU bacteria per mouse were introduced intravenously into 15 grafted mice leading to an average of 1 . 5×105 CFU bacteria/ml effectively circulating within 5 min of injection ( Fig . 2A ) . One aspect of the human specificity of N . meningitidis is iron uptake , necessary for bacterial growth [21] , [22] . In line with previous findings [23] , [24] intraperitoneal injection of human transferrin prior to infection increased bacterial loads at 6 h and therefore all experiments presented were performed with the addition of human transferrin ( Fig . S1E ) . In 5 mice the infection was allowed to proceed for 6 h and in the other 10 mice for 24 h . These 15 mice represent data pooled from 4 separate experiments performed at different times using skin from 7 different donors . Bacterial loads in the blood averaged 4 . 8×104 CFU/ml at 6 h and 2 . 4×104 CFU/ml at 24 h ( Fig . 2A ) . At 24 h 5/10 mice had no detectable bacteria in their circulation . Bacterial counts were then determined from the grafted human skin and other organs . Bacterial counts were consistently high in the grafted human skin averaging 2 . 1×104 CFU/mg of tissue after 6 h of infection , and 4 . 4×102 CFU/mg after 24 h ( Fig . 2B , Hu skin ) . In the same mice , contralateral mouse skin contained virtually no detectable bacteria ( Fig . 2B , Ms skin ) . As a control of the grafting procedure , skin from C57/BL6 mice was grafted in place of the human skin on SCID/Beige mice . No bacteria were found in the grafted mouse tissue ( Fig . 2I , MOM for ‘mouse on mouse’ graft ) . This data is pooled from 7 mice from 3 separate experiments . This data showed that N . meningitidis associated exclusively , and in significant numbers , to the microvessels in the grafted human skin . It is noteworthy that 3 of the 5 mice with no detectable bacteria in their blood at 24 h had substantial bacterial counts in the human skin graft . In general no bacteria were found in other organs after 24 h of infection except in 2 mice , with higher than average blood counts that showed bacteria in their liver and spleen ( Fig . 2C ) . Expression of green fluorescent protein ( GFP ) facilitated visualization of N . meningitidis in live animals and on tissue sections . Intravital imaging allowed for the observation of the initial steps of bacterial association with the vessel endothelium . At 30 min post infection individual bacteria and small aggregates were observed seemingly adhering to human blood vessels labeled with UEA lectin ( Movie S2 ) . Both live and fixed tissue analysis confirmed that bacteria were exclusively found in vessels staining positive for human endothelium ( Fig . 2D–F ) . Bacteria were observed in the lumen of human vessels , either lining the endothelium or forming aggregates typically between 20 and 150 bacteria ( Fig . 2D ) . Occasionally , bacteria filled the entire vessel ( Fig . 2E ) . Bacterial aggregates were embedded in a heterogeneous but locally dense meshwork of type IV pili ( Fig . 2G ) and also expressed a polysaccharidic capsule forming a thick ring around individual bacteria ( Fig . 2H ) . These data show that introduction of human vessels promoted the association of N . meningitidis with microvessels . Abnormal regulation of inflammation , a ‘cytokine storm’ , is an important feature of meningococcal septic shock [10] , [11] . In this model we had the unique opportunity to differentiate the signaling occurring locally at the site of adhesion ( human ) from the circulating cells ( mouse ) . Despite the small size of the human skin graft relative to the whole animal , expression of human IL-6 and IL-8 was detectable in the serum of human skin grafted animals , infected for 24 h ( Fig . 3A–B , Hu skin 24 h ) . Human cytokine detection showed no cross reaction with purified mouse cytokines . No expression was seen in either of the control groups: ( i ) mice grafted with human skin and injected with PBS ( Fig . 3A–B , PBS ) or ( ii ) mice grafted with mouse skin and infected with N . meningitidis for 24 h ( Fig . 3A–B , MOM ) . TNFα , IL-1α , IL-1β , IL-10 , IFNγ , MIP1α , MIP1β , IFNα and GM-CSF were measured but not detected . As the circulating cells are of mouse origin it can be inferred that the human cytokines were produced by the endothelium . To confirm this , human endothelial cells ( HUVEC ) were exposed to N . meningitidis in vitro and they displayed the same cytokine profile with expression of only IL-6 ( 1200 pg/ml 24 h post infection ) and IL-8 ( 1200 pg/ml , 24 h post infection ) strengthening the implication of the endothelium as a key mediator of this response in vivo . These results point to a direct role of the endothelium in contributing to the IL-6 and IL-8 cytokine response seen in meningococcal patients . Microscopy analysis revealed a massive recruitment of inflammatory cells to the skin at 24 h post infection ( moderate in 6/10 and severe in 4/10; Fig . 3C , 4D , G , H and Table 1 ) . Vessels were often filled with a combination of bacteria and infiltrating cells . Bacteria were commonly found phagocytosed by neutrophils ( Fig . 3E ) . At 6 h post infection the recruitment was less pronounced ( mild in 5/5; Fig . 3D , 4A and Table 1 ) . Mice grafted with mouse skin and infected for 24 h had a very low level of inflammation ( very mild 3/4 , none in 1/4 , Table 1 ) . This data shows an increased infiltration of inflammatory cells as the infection progresses from 6 to 24 h . We next analyzed the impact of infection on vasculature integrity . At 6 h post infection the morphology of the tissue was generally well preserved although signs of mild vascular thrombosis and inflammation were apparent ( Fig . 4A , arrow ) . Endothelial staining revealed occasional vessels with non-continuous staining ( Fig . 4B , arrow ) , suggesting endothelial loss . Sloughed endothelial cells could be found in the lumen of larger vessels ( Fig . S1A ) . Mild vascular congestion was seen in 2/5 mice with one mouse showing the first signs of vascular leakage ( Table 1 ) . Mild thrombosis in 3/5 mice was confirmed by staining of fibrin and platelet aggregation and was both in close proximity to and distal from bacterial colonies ( Fig . 4C , and Fig . S1B–D ) . At 24 h post infection tissue damage was extensive with widespread vascular damage ( Fig . 4D ) . Staining of collagen IV , a major constituent of the basement membrane ( BM ) , showed bacteria in close proximity to the BM , again suggesting loss of endothelium ( Fig . 4E ) . In some vessels the integrity of the basement membrane appeared compromised ( Fig . 4F ) . Vascular congestion and engorgement were consistently seen ( moderate 1/10 and severe in 9/10 ) along with thrombosis ( moderate in 6/10 and severe in 4/10; Fig . 4G , arrow ) . Vascular leakage was found in all infected mice ( mild 1/10 , moderate in 4/10 , severe in 5/10; Table 1 ) , varying from small perivascular hemorrhages ( Fig . 4H , arrow ) through to lakes of RBCs ( Fig . 4I , J ) . Erythrocyte leakage was located primarily at the dermal/epidermal border ( Fig . 4I , J ) . Importantly , 3/10 animals ( 30% ) developed macroscopically identifiable non-blanching rashes reminiscent of clinical purpura ( Fig . 4K , L ) . Control mice grafted with human skin and injected with PBS had typical non-infected morphology . Mice grafted with mouse skin and infected with bacteria had mild inflammation ( moderate 1/7 and mild 7/7 ) and vascular congestion ( moderate 2/7 and mild in 5/7 ) but no vascular leakage was detected ( Table 1 ) . Association of N . meningitidis with the human dermal vessels in this model led to vascular damage and cutaneous lesions replicating what is seen in clinical samples . This experimental model of infection allowed for evaluation of the effect of specific N . meningitidis virulence factors in the process of vascular adhesion and cutaneous lesion development in vivo . Type IV pili ( tfp ) have been found to be a key mediator of adhesion in vitro . In addition , clinical strains are invariably piliated making tfp the prime candidate to mediate interaction with the vascular wall in vivo . To test the role of tfp we took advantage of two isogenic bacterial strains deficient in type IV pili function , pilD and pilC1 . The pilD mutant is deficient in the biosynthesis of type IV pili and does not express any pili on its surface [25] . Infection of 5 human skin grafted mice with the pilD mutant strain did not reveal any significant difference in the circulating bacterial CFU counts in the blood as compared to the wild type ( Fig . 5A , pilD , WT ) . No bacteria were however found associated with the human skin graft ( Fig . 5B–C , Hu skin pilD ) . Data shown is from 2 separate infection experiments . This indicated that tfp are indeed important for bacterial association with the human vessels . To investigate specifically the adhesive properties of the tfp we used a pilC1 mutant that displays pili on its surface but fails to mediate adhesion despite maintaining other type IV pili dependent properties such as bacterial aggregation [26] . Again circulating blood CFU counts of the pilC1 mutant in 5 mice were not significantly different to wild type bacteria ( Fig . 5A , pilC1 ) . In the grafted human skin no pilC1 mutant bacteria were detected ( Fig . 5B , Hu skin pilC1 ) . Data shown is from 2 separate infection experiments . This showed that it is the adhesive property of tfp that is essential for bacterial association with the human vascular endothelium in vivo . Morphologically , tissues showed a low level of inflammation and neutrophil infiltration ( mild in 5/5 pilD and 4/5 pilC1; Fig . 5D , arrow ) . Occasional vessels showed signs of congestion and thrombosis ( none in 2/5 and mild in 3/5 pilD , mild 5/5 pilC1; Fig . 5E , arrow ) . No significant loss of vascular integrity or leakage was noted in either group ( Table 1 ) . Infection with neither the pilD or pilC1 mutant strain resulted in any detectable expression of human IL-6 or IL-8 ( Fig . 5F , G ) , suggesting tfp mediated adhesion is crucial for this endothelial signaling . This data shows that bacterial adhesion is mediated by tfp and that adhesion is essential for triggering the cascade of inflammation and vascular damage that results in cutaneous lesions in vivo .
Several attempts have been made to generate in vivo models of Neisseria meningitidis infection; each producing important advances [24] , [27] , [28] , but lacking crucial aspects of the development of fulminant meningococcal septic shock . The human skin graft model described here reproduces several key features of acute infections caused by N . meningitidis [5] , [13] , [29]: ( i ) adhesion of bacteria to the microvasculature; ( ii ) induction of inflammation and ( iii ) vascular damage with coagulopathy and loss of vascular integrity . This model allows for studies aiming to understand the molecular mechanisms underlying the interaction of this bacterium with the microcirculation in vivo and the pathology of the cutaneous lesions which occur during meningococcemia . In addition , this model may prove to be an interesting new tool to evaluate new prevention strategies , which remains a key approach for the future due to the rapid evolution of meningococcemia . A large-scale effort is currently underway in industrial and academic research to design alternative vaccine antigens for serogroup B meningococcus [30] , [31] . Evaluation of the efficiency of vaccines in experimental models is a critical preclinical step and remains a limitation in the development of meningococcal vaccines . The mouse background used in our xenograft model is unable to generate antibodies but serum generated in other mouse backgrounds against candidate antigens , passive immunization , could be tested in this model . Although this model shows promise for future studies it cannot reproduce every aspect of the human infection . The human graft is a relatively small fraction of the organism and subsequently some systemic aspects of the human disease may be lacking . For instance , blood bacterial counts might be expected to increase after 24 h rather than decrease as we observed in some cases , although the dynamics of bacterial proliferation during the human disease is difficult to assess . It should also be noted that the crossing of the nasopharyngeal epithelium or the blood-brain barrier are not taken into account . A major result of this study is that local bacterial adhesion to the endothelium is essential for triggering a cascade that results in cutaneous lesions in vivo . We also show that this adhesion in vivo is largely mediated by type IV pili . In contrast to type IV pili , which are expressed by all clinical strains , different strains express different sets of additional adhesins . The strain used in this study does not contain the opc gene and the opa genes are mostly in the OFF phase [32] . The possible participation of these adhesins will thus require further studies . The pathogenic effects of meningococcemia , particularly on the skin have previously been attributed to circulating LPS [1] , [33] , [34] . In this model , non-adherent bacteria , despite circulating in the same numbers as the wild type strain and thus releasing similar amounts of LPS and other bacterial compounds , do not trigger cytokine secretion in the human tissue or lead to significant vascular damage . This highlights the importance of local adhesion events in the vasculature , a process we refer to as vascular colonization [35] , in triggering the disease . This represents a change of paradigm from the idea that vascular damage is caused solely by large amounts of circulating bacteria and bacterial products in the blood . Once adherent to the endothelium , bacteria remain in tight aggregates and do not appear to seed the circulation in large quantities , as demonstrated by mice with no circulating bacteria despite high bacterial counts in the skin . Patients with meningitis frequently display no circulating bacteria in their blood [1] but based on our results large amounts of bacteria could still be bound to microvessels . These results point to the importance of designing new therapies targeting the interaction of the bacteria to the endothelium . The infection of an average of 1 cm2/200 µm thick human skin graft into the mouse model was sufficient to enable the detection of human cytokines in the serum , emphasizing the intensity of the response following bacterial adhesion . The small size of the graft may however mean that other human cytokines that may be expected , such as TNFα , are not detected due to insufficient sensitivity . A previous report has shown a role for monocytic cells in the regulation of TNFα by endothelial cells [36] and the lack of human monocytes in this model may be another explanation to the lack of this cytokine . As the circulating cells are of mouse origin it can be inferred that the grafted human tissue produced the human cytokines . The combination of the in vivo data , the complimentary in vitro data and the fact that they are directly in contact with bacteria , suggests that the endothelium contributes to the secretion of these cytokines but in this study we cannot exclude the role other dermal cell types may play . While adhesion was the only factor definitively linked to increased cytokine production in this study , the intense inflammatory response seen in the tissue could also be related to the high local concentrations of bacterial factors such as LPS subsequent to adhesion . The alterations in blood flow or endothelial damage could also trigger the specific endothelial inflammatory signaling that is seemingly responsible for the recruitment of inflammatory cells . Further work will be needed to delineate the specific role of the inflammatory response in vascular damage . Vascular colonization led to extensive vascular and tissue damage in the grafted human skin . It has been reported in vitro that large bacterial aggregates cause endothelial junctional openings on cells within 4 hours . This response depends on tfp and recruitment of junctional proteins and may be responsible for the vascular leak observed here [37] , [38] . The animal model presented here provides information on the kinetics of meningococcal infection in vivo for the first time . At 6 h post infection vessel damage is relatively mild despite large numbers of bacteria whereas at 24 h , when damage is severe , bacterial quantity has not increased . This late onset of vascular leak is different to the junctional openings seen on isolated cells , suggesting that other mechanisms are involved in vivo . A major difference between the 6 and 24 h time-points is that the recruitment of inflammatory cells is much more pronounced at 24 h and a role for local inflammation in the alteration of vessel integrity and the development of the cutaneous lesion may be envisaged . Macroscopically identifiable purpura or a petechial rash was seen in 30% ( 3/10 ) of the grafted mice despite the small size of the graft in this model , falling into the range of the 28–78% quoted for clinical cases [1] , [2] . This result again points to the importance of the local events of infection as opposed to systemic infection as is frequently proposed . The purpura only developed where bacteria bind the vessel wall whereas circulating bacteria did not lead to such effects . In conclusion , the experimental model depicted here faithfully reproduces key clinical features associated with meningococcal septic shock in the skin . By taking advantage of this new tool , we provide new insights to better understand the pathogenesis process by demonstrating the importance of local vascular colonization in triggering disease .
All experimental procedures involving animals were conducted in accordance with guidelines established by the French and European regulations for the care and use of laboratory animals ( Décrets 87–848 , 2001–464 , 2001–486 and 2001–131 and European Directive 2010/63/UE ) and approved by the local ethical committee Comité d'Ethique en matière d'Expérimentation Animale , Universite Paris Descartes , Paris , France . No: CEEA34 . GD . 002 . 11 . All surgery was performed under anesthesia , and all efforts were made to minimize suffering . For human skin , written informed consent was obtained and all procedures were performed according to French national guidelines and approved by the local ethical committee , Comité d'Evaluation Ethique de l'INSERM IRB 00003888 FWA 00005881 , Paris , France Opinion: 11–048 . N . meningitidis 8013 clone 12 is a serogroup C clinical isolate , expressing a class I SB pilin , Opa− , Opc− , PilC1+/PilC2+ [32] . N . meningitidis was grown on GCB agar plates ( Difco , France ) containing Kellogg's supplements and 100 µg/ml kanamycin , 50 µg spectinomycin or 5 µg/ml chloramphenicol as required at a humid 37°C , 5% CO2 . Mutations in the pilD and pilC1 genes were introduced into the N . meningitidis chromosome by natural transformation of chromosomal DNA , and selected with integrated antibiotic resistance cassette . The pilD mutation was extracted from a library of transposition mutants [39] . The pilC1 mutant is described elsewhere [26] . meningitidis expressed the green fluorescent protein ( GFP ) under the control of the pilE promoter using the pGCC2 plasmid for chromosomal insertion [40] . A 300 base pair fragment of the pilE promoter was amplified from chromosomal DNA with the following primers: PrPilEF , 5′-AGTACTCCATGCCAATAGAGATACCCCACG-3′ introducing a ScaI site and PrPilER , 5′-TTAATTAAAATTGGAAAGGAAATGCCTCAAGC-3′ introducing a PacI site . The amplicon was cloned into the PCR2 . 1 Topo plasmid , sequence was checked , the insert restricted with PacI and ScaI and cloned into the pGCC2 vector restricted with the same enzymes generating the pGCC2PrPilE plasmid . The GFP ORF was amplified by PCR from the pAM239 [41] plasmid with the following primers: GFPF 5′-TTAATTAATTTAAGAAGGAGATATACATATGAGTAAAG-3′ introducing a PacI site and GFPR , 5′-GTCGACTTATTTGTATAGTTCATCCATGCCATGTG-3′ introducing a SalI site . The amplicon was cloned into the PCR2 . 1 Topo plasmid , sequence was checked , the insert restricted with PacI and SaII and cloned into the pGCC2PrPilE vector restricted with the same enzymes . SCID/Beige ( SOPF/CB17 SCID BEIGE . CB17 . Cg-Prkdc-Lyst/Crl ) mice ( Charles River , France ) 5–8 weeks of age were used ( 20 . 3±3 g ) . Animals were housed in disposable cages ( Innovive , France ) with sterile water and mouse chow . Normal human skin was obtained as surgical excess from plastic surgery at Hôpital Européen Georges-Pompidou ( HEGP ) . The superficial 200 µm of skin was harvested using a Sober dermatome ( Humeca BV , Holland ) and cut to approximately 2 cm2 . Skin was stored in DMEM with 10% serum at 4°C until grafting . Grafting normally occurred within 2–4 h of surgery . Mice were anesthetized with ketamine ( 100 mg/kg ) and xylazine ( 10 mg/kg ) IP . Hair was removed from the posterior thorax , and a local anesthetic applied ( Tronothane , Lisa Pharm ) . A graft bed was prepared by excising an area of skin approximately 1 . 5–2 cm2 . Human skin was immediately placed over the graft bed , trimmed to size and fixed in place with Vetbond ( 3M , USA ) . Band-aids and dressing tape were applied . Dressings were removed 10–15 days post graft . Mice were infected between 21–28 days post graft . The engraftment rate was very high throughout the study with a successful graft occurring in more that 95% of cases . There was no apparent evidence of graft rejection and the very few that failed were due to physical disruption of the graft in the early stages post-surgery . Bacteria from overnight plates were grown to an OD600 0 . 1 in Endo-SFM with 10% FBS ( Gibco , France ) at 37°C , 5% CO2 shaking , for 2 hours . Bacteria were washed twice in PBS and resuspended to 107 CFU/ml in PBS . 100 µl ( 106 CFU ) were injected IV into anesthetized grafted mice . Mice were injected IP with 8 mg of human transferrin ( Sigma Aldrich , France ) prior to infection . Blood samples were taken before infection , 5 min and 6 h post infection and at sacrifice . Biopsies ( 4 mm ) of tissues were homogenized using a MagNA Lyser ( Roche , France ) homogenizer . Liver , spleen and brain samples were homogenized using MagNA Lyser Green Beads ( Roche , France ) at 6000 rpm for 30 sec . Skin samples were homogenized using Fast-Prep lysing matrix M tubes ( MPBio , France ) at 6000 rpm for 2×30 sec . All homogenates were plated with dilutions immediately onto GCB agar with appropriate antibiotics . Tissue fixed in PFA 4% was frozen in OCT ( Tissuetek , Sartorius , USA ) and sliced at 10 µm . For histology , tissues were stained with hemotoxylin and eosin ( H&E ) . For fluorescence microscopy the following reagents were used: Ulex Europaeus Agglutinin lectin – Rhodamine ( Vector Laboratories , Eurobio ABCys , France ) ; Monoclonal anti-human-CD31 clone JC70A ( Dako , Denmark ) ; anti-mouse CD31 ( BD Pharmingen , France ) ; Monoclonal anti-human-Collagen IV ( AbD Serotec , France ) ; anti-human-fibrinogen ( Dako , Denmark ) ; anti-CD42b clone R300 ( Emfret Analytics , Germany ) ; anti-mouse TER-119/Erythroid cells ( BD Pharmingen , France ) ; anti-mouse Ly-6G and Ly-6C , clone RB6-8C5 ( BD Pharmingen , France ) ; Neisseria meningitidis anti-serum Group C ( Difco , USA ) ; Polyclonal anti PilE antiserum [42] . Secondary antibodies used were: anti-Rat Alexa 488 , anti-rabbit Alexa 568 , ( Molecular Probes Invitrogen , France ) Texas-Red Streptavidin , ( Vector , USA ) ; anti-rat Cy3 , ( Jackson Lab USA ) . Monoclonal antibodies were detected using the Mouse on Mouse detection kit ( Vector Laboratories , Eurobio ABCys , France ) . All samples were mounted in Vectashield mounting reagent ( Vector Laboratories , Eurobio ABCys , France ) . Intravital and fluorescence confocal microscopy was performed using an inverted Nikon Eclipse Ti microscope equipped with a CSU-X1 M1 confocal head with a Yokogawa spinning disk head and an Evolve ( Photometrics ) camera ( Roper Scientific , France ) . Images were captured using MetaMorph software ( Molecular Devices , USA ) . Histology was imaged using a Nikon Eclipse TC600 with a Baumer camera and Archimed software ( Microvision Instruments , France ) . Image processing was performed using MetaMorph and Image J [43] . Final figures were created in Photoshop ( Adobe , USA ) . Animals were anesthetized and a mid-line dorsal incision made from the neck to mid-back . The skin supporting the human graft was carefully separated from underlying tissue . The skin flap was attached with Vetbond ( 3M , USA ) to a 35 mm μ-Dish with a thin bottom ( Ibidi , Germany ) and bathed in 37°C saline . The microscope imaging chamber was maintained at 37°C . To visualize blood flow 150 kDa-FITC dextran ( TdB Consultancy , Sweden; 2 mg/animal ) was injected IV . UEA lectin–Rhodamine ( Vector Laboratories , Eurobio ABCys , France ) , was desalted , resuspended in PBS and injected IV ( 200 µg/ml per animal ) . Animals were injected IV with 107 CFU of N . meningitidis expressing GFP . Cytometric Bead Array ( CBA ) was performed according to the manufacturer's protocol ( BD Bioscience , France ) . Human cytokines were detected using a combination of CBA Flex Set kits for IL-1α , IL-1β , IL6 , IL-8 , IL-10 , IL-12p70 , INFγ , INFα , TNF , MIP1α , MIP1β and GM-CSF ( BD Bioscience , France ) . Statistical data is represented as raw data points and median . Statistical analysis was performed using a two-tailed Mann-Whitney test . P values of <0 . 05 was considered significant . Ranking: <0 . 0001 = **** , 0 . 0001 to 0 . 001 = *** , 0 . 001 to 0 . 01 = ** , 0 . 01 to 0 . 05 = * . Graphs were prepared using Prism ( Graphpad , USA ) . | Certain bacterial pathogens access the bloodstream during infection and this is associated with extremely severe conditions such as septic shock . A central feature of these infections is the rapid alteration of blood vessel function with deregulated inflammation , coagulation and loss of vessel integrity . Studying the mechanisms of infection of Neisseria meningitis in vivo for the first time , we show that the ability of this bacterium to adhere to and proliferate in the blood vessel , a process we refer to as vascular colonization , is a prerequisite to the alteration of vascular function . Previously , circulating bacteria were thought to be responsible . We identified the bacterial factors involved in this process by showing that it is largely dependent on type IV pili , long filamentous appendages that allow bacteria to stick to the vessel walls . To study infection by N . meningitidis , a highly human specific pathogen , we introduced human vessels into mice by grafting human skin onto immunodeficient mice . This work thus also introduces a new animal model of infection that reproduces the cardinal features of meningococcal infections . Such a model will be useful to the scientific community to explore the mechanisms of disease , and test new treatment or preventive strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"bacterial",
"pathogens"
] | 2013 | Adhesion of Neisseria meningitidis to Dermal Vessels Leads to Local Vascular Damage and Purpura in a Humanized Mouse Model |
Genetic recombination during meiosis functions to increase genetic diversity , promotes elimination of deleterious alleles , and helps assure proper segregation of chromatids . Mammalian recombination events are concentrated at specialized sites , termed hotspots , whose locations are determined by PRDM9 , a zinc finger DNA-binding histone methyltransferase . Prdm9 is highly polymorphic with most alleles activating their own set of hotspots . In populations exhibiting high frequencies of heterozygosity , questions remain about the influences different alleles have in heterozygous individuals where the two variant forms of PRDM9 typically do not activate equivalent populations of hotspots . We now find that , in addition to activating its own hotspots , the presence of one Prdm9 allele can modify the activity of hotspots activated by the other allele . PRDM9 function is also dosage sensitive; Prdm9+/- heterozygous null mice have reduced numbers and less active hotspots and increased numbers of aberrant germ cells . In mice carrying two Prdm9 alleles , there is allelic competition; the stronger Prdm9 allele can partially or entirely suppress chromatin modification and recombination at hotspots of the weaker allele . In cell cultures , PRDM9 protein variants form functional heteromeric complexes which can bind hotspots sequences . When a heteromeric complex binds at a hotspot of one PRDM9 variant , the other PRDM9 variant , which would otherwise not bind , can still methylate hotspot nucleosomes . We propose that in heterozygous individuals the underlying molecular mechanism of allelic suppression results from formation of PRDM9 heteromers , where the DNA binding activity of one protein variant dominantly directs recombination initiation towards its own hotspots , effectively titrating down recombination by the other protein variant . In natural populations with many heterozygous individuals , allelic competition will influence the recombination landscape .
Genetic recombination in mammals is restricted to hotspots: short , 1–2 kb-long sites scattered throughout the genome [1 , 2] . With the exception of canids [3 , 4] , their locations in mammals are determined by the sequence-specific DNA binding protein , PRDM9 ( MGI:2384854 ) [5 , 6 , 7] . PRDM9 initiates recombination by binding DNA at hotspots where it locally trimethylates histone H3 at lysine 4 ( H3K4me3 ) using a conserved PR/SET domain [8 , 9 , 10 , 11] . This signals the correct locations of programmed meiotic double-strand breaks ( DSB ) that are required for the physical exchange of material between homologous chromatids during meiosis and the eventual formation of genetic crossovers and noncrossovers [9 , 10 , 12] . Prdm9 function is essential for meiosis; null alleles lead to sterility in both sexes of mice [13] , and point mutations in PRDM9 are found in azoospermic human patients [14 , 15] . In addition , Prdm9 is a key player in evolution by creating hybrid sterility . Male intersubspecific F1 hybrid mice that are heterozygous for particular Prdm9 alleles and carry the M . m . musculus-derived chromosome ( Chr ) X are infertile , thus creating postmating reproductive barriers that contribute to incipient speciation [16] . Prdm9/PRDM9 is highly polymorphic , both within and between mammalian species . This includes humans [5 , 6 , 7 , 17 , 18 , 19] , mice [5 , 7 , 9 , 20] , chimps [21 , 22 , 23] , cattle [24] , and equids [25] , which all harbor diverse alleles of Prdm9 . Most of the naturally occurring sequence polymorphisms in Prdm9 change the identity of the amino acids contacting DNA and/or the number and arrangement of individual fingers in the DNA-binding zinc-finger domains . This allows PRDM9 variants to target a large number of DNA sequences , thereby expanding the distribution of recombination sites . Three laboratories simultaneously came to the identification of PRDM9 as the key protein determining the location of mammalian hotspots [5 , 6 , 7] . In our case , we identified hotspots in genetic crosses between C57BL/6J ( B6 ) and CAST/EiJ ( CAST ) mice whose activation depended on a trans-acting factor [26] . Genetic mapping identified the key factor as the CAST allele of Prdm9 [7] . Importantly , the same experiments also identified hotspots whose activities were quantitatively reduced rather than activated by the presence of CAST alleles , and others whose activities were completely suppressed . Similar variation in recombination rates has been observed at human hotspots depending on the identities and combinations of PRDM9 alleles present [17 , 18 , 27] . These observations coincide with previous evidence that Prdm9 alleles in heterozygous individuals do not show simple additive behavior . In both humans [28 , 29] and mice [10 , 30] there is allelic dominance in which a predominance of hotspots in heterozygotes are activated by one of the alleles present . This phenomenon is of considerable biological importance given the extensive polymorphism of Prdm9 and that heterozygotes represent a considerable majority of some natural populations . Together , the available evidence indicates a complex regulation of hotspot activity in heterozygous individuals . However , little is known of the specific mechanisms and molecular players involved in hotspot suppression and the observed competition between Prdm9 alleles . Here we report that both of these observations are the functional consequence of a direct interaction between PRDM9 protein variants in a limited pool of PRDM9 molecules in meiotic cells . Using genetic strategies , we now show that , while Prdm9 is required for activation of hotspots , it is also the trans-acting factor responsible for the quantitative modulation of recombination rate and allelic dominance in heterozygous mice . In cell cultures , we show that PRDM9 protein variants form both homo- and heteromeric complexes , and that heteromeric complexes bind and trimethylate nucleosomes at hotspot DNA sequences . We find that Prdm9 function is dosage-sensitive; in heterozygous male Prdm9+/- null mice , where Prdm9 is present in a single copy , the numbers and activity of PRDM9-defined H3K4me3 hotspots are reduced and animals have increased abnormalities in meiotic prophase I . In addition , replacing the null allele with one from a different mouse subspecies is sufficient to fully suppress recombination at some hotspots , suggesting direct interaction between protein variants . Taken together , the data point to a model in which quantitative activity at recombination hotspots is partially controlled through PRDM9 occupancy at hotspots , which in turn is dependent on both the number of PRDM9 molecules available in meiotic cells and the DNA-binding affinity of each allele . Our data suggests that in heterozygous individuals , PRDM9 forms heteromers that preferentially bind to and activate recombination at the stronger allele’s hotspots , thereby suppressing recombination at hotspots otherwise activated by the weaker allele .
To determine factors regulating recombination rate , we first focused on the hotspot Pbx1 , as evidence indicates that genetic background has a strong effect on the recombination rate at this hotspot in mice [26] . Pbx1 has a sex-averaged recombination rate of 2 . 38 cM in the B6 background , but shows a significant 5 . 8-fold down-regulation when CAST alleles are introduced in ( B6xCAST ) F1 hybrids ( 0 . 41 cM , Fisher’s exact test p < 10−4 ) [31] . To genetically map factors controlling the quantitative activity of Pbx1 we used N2 and F2 mapping crosses ( S1A Fig ) that allowed us to detect the influence of dominant , recessive or additive alleles [26] . We collected 49 N2 and 75 F2 males heterozygous for B6/CAST on distal 100 Mb on Chr 1 , the region containing the Pbx1 hotspot , genotyped them at 165 markers spaced across the genome , and isolated their sperm DNA to measure the recombination rate at Pbx1 . Crossing over at Pbx1 was determined using a DNA sequencing assay that takes advantage of SNPs located on either side of the hotspot and counts the number of recombinant and parental molecules in sperm samples ( S1B Fig ) . Comparing the frequencies of parental and recombinant molecules from many thousands of individual sperm ( each representing a potential offspring ) provided a measure of recombination rates at Pbx1 in individual male mice . Using the recombination rate at Pbx1 as our phenotype , genome scans performed on individual crosses , and pooled data from both crosses , resulted in a single significant QTL peak on proximal Chr 17 ( Figs 1A , S2A and S2B ) . The 1 . 5-LOD support interval for this QTL is from ~4–30 Mb along Chr 17 , with the approximate QTL located around 14 Mb . Mice homozygous for B6 at Chr 17 had the highest rate of recombination; heterozygous mice had an intermediate level of recombination , and mice homozygous for CAST had the lowest ( Fig 1B ) . This pattern suggests an additive effect of the QTL dependent on the B6 haplotype on Chr 17 . The position of the QTL on Chr 17 implicates Prdm9 in regulating recombination at the Pbx1 hotspot . Prdm9 is located on Chr 17 at 15 . 5 Mb , is currently the only known recombination regulator locus in mice , and B6 and CAST mice carry two different Prdm9 alleles ( Prdm9Dom2 and Prdm9Cst respectively ) [7 , 26] . Furthermore , the PRDM9Dom2 protein variant , found in B6 mice where Pbx1 is active , shows binding to the Pbx1 DNA sequence in vitro and regulates H3K4me3 level in the surrounding region in vivo [31] . The QTL analysis above suggests that Prdm9 is also a modifier of recombination rate at Pbx1; two copies of Prdm9Dom2 result in a higher recombination rate at Pbx1 compared to one copy . The apparent low recombination rate in homozygous CAST at the QTL locus mice is largely a measure of the frequency of false-recombinants ( see methods ) . To test for the presence of additional modifiers of hotspot activity , we conditioned on the identity of the Prdm9 allele present by selecting the set of N2 and F2 mice that were homozygous Prdm9Dom2/Dom2 and performed an additional genome scan on these mice alone; however , we did not detect any other significant QTLs ( S2C Fig ) . The genetic evidence above suggests that PRDM9 activation of hotspots is sensitive to Prdm9 dosage , indicating that PRDM9 is limiting in meiotic cells , or sensitive to competition between alleles . A plausible molecular mechanism by which two alleles can directly influence each other is through their physical interaction [28 , 29] . To test if this is the case and PRDM9 interacts with itself , we cloned both the human PRDM9A allele , the primary allele found in humans from European ancestry , and the PRDM9C allele , more prevalent in populations with African ancestry [7 , 18] , for expression in cultured human HEK293 cells similar to previous reports [32 , 33] . Expressing PRDM9 in cultured HEK293 cells resulted in a significant increase in total H3K4me3 levels , which depended on the conserved PR/SET methyltransferase domain present in PRDM9 ( Fig 2A ) , similar to results previously described [13 , 33] . In order to test if PRDM9 retains DNA-binding specificity in HEK293 cells we expressed PRDM9A , PRDM9C , or empty vector and performed ChIP for H3K4me3 . Several human hotspots have previously been characterized as being responsive to either PRDM9A ( for example hotspots S and F ) or PRDM9C ( hotspots 5A and 22A ) by measuring recombination in pooled sperm samples [17 , 18] . We found that expression of either PRDM9A or PRDM9C in HEK293 cells resulted in increased H3K4me3 levels at the center of these hotspots in an allele-specific manner as measured by qPCR ( Fig 2B and 2C ) . To identify genome-wide PRDM9-defined H3K4me3 sites we used ChIP DNA for deep sequencing , for each allele , and compared these H3K4me3 maps to the recently published genome-wide position of meiotic DSBs identified by chromatin immunoprecipitation of the meiotic recombinase DMC1 from men ( S3 Fig and S1 Table , DMC1 SSDS data available at GEO: GSE59836 ) [28] . DSB hotspots were classified as PRDM9C-defined if they were uniquely identified in the DMC1 SSDS data from the heterozygous A/C individual but not found in the homozygous A/A1 individual ( S3B Fig ) . For both alleles , approximately one-third of unique allele-specific H3K4me3 sites identified here in HEK293 cells overlap with DSB hotspots identified in testis ( S3C and S3D Fig ) . To visualize allele-specificity , heat maps were generated for each H3K4me3 ChIP and DMC1 ChIP data set at shared hotspots by aligning the position of identified PRDM9 motifs ( Fig 2D ) . H3K4me3 signal at PRDM9C-defined DSB hotspots was increased only after expression of PRDM9C but not after expression of PRDM9A or in empty vector controls . Similarly , expression of PRDM9A in HEK293 cells resulted in increased H3K4me3 only at PRDM9A-defined hotspots . H3K4me3 signal is readily detected at promoter regions , including empty vector control , highlighting the PRDM9-defined H3K4me3 at hotspots ( S4 Fig ) . For both PRDM9 alleles , H3K4me3 modified nucleosomes are organized in a symmetrical pattern around a central PRDM9 sequence motif as previously seen in mouse germ cells [9] , near the maximum signal of DSB intensity found from testis ( Fig 2E and 2F ) . These data show that ectopically expressed PRDM9 can bind and modify chromatin at hotspot sequences in somatic cells in an allele-specific manner . To examine if PRDM9 can interact with itself , we assessed interaction between these two human alleles in HEK293 cells . Both alleles were cloned to contain either an N-terminal FLAG or N-terminal V5 epitope tag to facilitate detection and allow discrimination of protein variants . Both the FLAG- and V5-tagged versions of each PRDM9 allele were expressed , either separately or together , in HEK293 cells ( Fig 3A ) . Immunoprecipitation using the FLAG monoclonal antibody directed against FLAG-PRDM9A or FLAG-PRDM9C showed an enrichment for the V5-tagged PRDM9C only when the two proteins were co-expressed ( Fig 3A , lanes 11 and 12 ) . Likewise , reciprocal immunoprecipitation with V5-PRDM9C displayed the same result , as it enriched for both FLAG-PRDM9 protein variants ( Fig 3A , lanes 17 and 18 ) . These data show that PRDM9 can form both homo- and heteromeric protein complexes when co-expressed . A macromolecular complex containing two PRDM9 protein variants would have two distinct zinc-finger arrays , with the potential capability to bind two motifs . This predicts that in cells expressing two PRDM9 alleles , the non-activating protein variant might be found at hotspots at which it does not typically bind . For example , human PRDM9A would be found at a C-defined hotspot only when in a heteromeric complex with PRDM9C . To test for the presence of heteromeric complexes at hotspots , we expressed V5-PRDM9C , FLAG-PRDM9C , and FLAG-PRDM9A alone , or co-expressed V5-PRDM9C and FLAG-PRDM9A together , and tested for their presence at several C- and A-defined hotspots using ChIP ( Figs 3B–3E and S5 ) . As expected , expression of FLAG-tagged PRDM9C ( FLAG-C ) resulted in enrichment for DNA at C-hotspots following immunoprecipitation using anti-FLAG antibody ( Fig 3B , 3C and 3D ) ; while expression of FLAG-PRDM9A ( FLAG-A ) did not . In addition , using anti-FLAG antibody there was no enrichment for C-hotspots after expression of V5-PRDM9C ( V5-C ) , showing antibody specificity . The lack of PRDM9A signal at C-defined hotspots is not due to inactivity of the protein variant , as PRDM9A can readily bind to an A-hotspot ( Fig 3E ) . Importantly , there were increases in enrichment at C-defined hotspots when co-expressing FLAG-PRDM9A along with V5-PRDM9C ( V5-C + FLAG-A ) compared to either protein expressed alone ( Fig 3B , 3C and 3D ) . Thus PRDM9A , which does not bind to or modify C-defined hotspots alone , is nevertheless found at C-hotspots when co-expressed with PRDM9C , a situation potentially similar to heterozygous individuals . Given that in heteromeric complexes at least two PRDM9 molecules can be found at hotspot sequences , we wanted to test if the protein variant that does not bind DNA can still catalytically function to modify hotspot nucleosomes . To do so we expressed the catalytically-dead FLAG-PRDM9C-G278A alone or co-expressed FLAG-PRDM9C-G278A with V5-PRDM9A and performed ChIP for H3K4me3 ( Fig 3F ) . As expected , expression of FLAG-PRDM9C-G278A alone did not lead to H3K4me3 at C-hotspots . However , when co-expressed with a functional V5-PRDM9A protein variant , C-defined hotspots had a clear , albeit weak , H3K4me3 signal and organized the nucleosome pattern at these hotspots . These data show that bringing together a functional PR/SET domain of one allele , with the zinc-finger DNA-binding domain of a different allele , is sufficient to mark hotspots . Together , these data provide strong evidence for the formation of functional heteromeric complexes that can bind and modify hotspots . The above cell culture data show that two PRDM9 variants can be found in the same protein complex bound at hotspot DNA sequences . If PRDM9 activity is limiting in meiotic cells , the two protein variants might directly compete for hotspot activation in heterozygous individuals by influencing which allele’s hotspots the heteromeric complexes bind . To investigate if PRDM9 activity is limiting in meiotic cells we next characterized mice made heterozygous null at Prdm9 . To test the effect of lowered Prdm9 dosage on hotspots in vivo , we measured genome-wide H3K4me3 levels in male germ cells from mice heterozygous for the targeted null allele Prdm9tm1Ymat ( B6-Prdm9Dom2/- ) and compared them to those from homozygous B6 ( Prdm9Dom2/Dom2 ) . B6-Prdm9Dom2/- males have reduced level of PRDM9 protein compared to homozygous littermates [30 , 34] , suggesting reduced availability of the catalytic domain . Using H3K4me3 ChIP-seq we identified approximately half the number of detectable H3K4me3 hotspots seen in mice with two copies of Prdm9Dom2 . Among 97 , 117 total H3K4me3 peaks identified in B6-Prdm9Dom2/- germ cells , 9 , 707 were associated with PRDM9Dom2-defined H3K4me3 hotspots , the remainder being associated with promoters and other functional elements . This is in contrast to nearly twice as many H3K4me3 hotspots previously measured in Prdm9Dom2/Dom2 mice ( 18 , 849 ) [9] . The PRDM9-defined H3K4me3 hotspots identified in the heterozygous null mice correspond to those with the highest level of H3K4me3 in homozygous B6 ( Fig 4A ) . Next , we compared the relative activity ( normalized read counts ) of H3K4me3 hotspots present in both B6 and B6-Prdm9Dom2/- heterozygous null males . As a class , PRDM9Dom2-defined H3K4me3 hotspots have one-third of the level of H3K4me3 in heterozygous null mice compared to B6 ( Fig 4B ) . This reduction in H3K4me3 is also sensitive to the intrinsic strength of the hotspot ( as measure by average H3K4me3 level ) ; the weaker the hotspot is ( lower average H3K4me3 ) , the greater the fold difference in H3K4me3 levels between B6-Prdm9Dom2/- and B6 mice . Importantly , other PRDM9-independent H3K4me3 sites , such as gene promoters , are not affected by Prdm9 copy number ( Fig 4B , blue points ) . Together , the H3K4me3 ChIP-seq data show that B6-Prdm9Dom2/- male mice have about half the number H3K4me3 hotspots compared to B6 mice , and those that are present have reduced levels of H3K4me3 , with the greatest reductions seen at the weakest hotspots . Male mice that are homozygous null for Prdm9 have a complete meiotic arrest [13 , 35 , 36 , 37] . However , there is conflicting evidence on the effect of removing one allele of Prdm9 on fertility and meiotic progress . Heterozygous male mice for the targeted null mutation Prdm9tm1Ymat have testes weights and sperm counts similar to wild-type males when on a mixed ( 129*B6 ) [13] or B6 [36] background . Males heterozygous for another allele , Prdm9M1045Lja , that expresses a truncated protein [34] displayed lower testes weight , reduced number of spermatids , and azoospermia [37] . To determine the effect of heterozygosity of the Prdm9 tm1Ymat allele in the B6 background ( B6-Prdm9Dom2/- ) on meiotic progress and fertility , we used indirect immunofluorescence labeling of spread adult testicular cells to detect meiotic arrest . Compared to homozygous Prdm9Dom2/Dom2 B6 littermate controls , B6-Prdm9Dom2/- males displayed a mild , but significantly increased fraction of abnormal pachytene stage cells , either completely lacking or having an abnormal sex body ( Fig 4C and 4D ) . This increased number of abnormal pachytene spermatocytes was also seen in heterozygotes when using a different M . m . domesticus allele , Prdm9Dom3 , on a C3H/HeN genetic background ( Fig 4D ) . To assess the effect of lowered Prdm9 dosage on overall fertility , we crossed heterozygous null ( B6-Prdm9Dom2/- ) males to homozygous B6 ( Prdm9Dom2/Dom2 ) females . The B6-Prdm9Dom2/- males produced fewer offspring compared to B6 controls ( 4 . 3±1 . 4 versus 6 . 2±1 . 0 per female per month , p = 0 . 01 , Welsch’s t-test ) and needed on average 7 . 4 more days to sire their first litter ( p = 0 . 01 , Welsch’s t-test ) . Thus Prdm9 is partially haploinsufficient for meiotic progress and fertility . In total , these data comparing Prdm9+/- heterozygous mice to homozygous mice demonstrate that PRDM9 function is dosage-sensitive . Because the number of H3K4me3 hotspots decreased with lowered Prdm9 dosage , and B6-Prdm9Dom2/- males have increased abnormal pachytene stage cells , we wanted to test if meiotic DSBs are reduced in B6-Prdm9Dom2/- males . To accomplish this , meiotic DSBs were counted in early zygonema using indirect immunofluorescence microscopy with a mix of antibodies directed against DSB-repair proteins RAD51 and DMC1 on staged surface-spread testicular nuclei ( S6 Fig ) . The B6-Prdm9Dom2/- males displayed 189±18 ( mean ± standard deviation ) DSBs per cell and their B6-Prdm9Dom2/Dom2 littermates 202±29 DSBs per cell; this difference was not significant ( p = 0 . 12 , Welsch´s t-test ) , confirming a previous report of no reduction using different combinations of Prdm9 alleles [34] . The combined evidence suggests that PRDM9 activity is limiting in meiotic cells and that PRDM9 variants can self-interact . Together these data suggest that , if heteromeric complexes exist in meiotic cells , the two variants might compete for DNA binding and activation of hotspots in heterozygous individuals . Recombination at some hotspots in Prdm9Dom2/Cst heterozygous mice are completely suppressed when CAST alleles are introduced in trans [26] . One such example is the PRDM9Dom2-defined hotspot Ush2a ( genomic position: Chr 1 190 , 124 , 179–190 , 127 , 477 Mb ) . By genotyping progeny from crosses [26] , we found that the sex-averaged recombination rate at Ush2a is 0 . 61 cM in the B6 background and completely suppressed when CAST alleles are present ( Fisher’s exact test p < 10−4 ) . Nested allele-specific PCR , using primers to amplify either parental or recombinant molecules from pooled sperm , confirms the genetic cross data ( Fig 5A ) . Crossovers at Ush2a are detected in sperm DNA from ( B6 x B6 . CAST-1T ) F1 hybrids that are heterozygous B6/CAST at the hotspot on distal Chr1 and otherwise homozygous B6/B6 ( Fig 5A , lanes 3 and 4 ) , but fully suppressed in sperm DNA from ( B6 x CAST ) F1 hybrids that are heterozygous B6/CAST across all of the genome ( Fig 5A , lanes 1 and 2 ) . To test if suppression of recombination is due to reduced Prdm9Dom2 dosage , competition between PRDM9Dom2 and PRDM9Cst , or the action of a novel regulatory factor , we compared recombination at Ush2a in sperm DNA from co-isogenic mice that are either heterozygous Prdm9Dom2/Cst or heterozygous Prdm9Dom2/- , heterozygous B6/CAST on distal Chr 1 ( to allow detection of crossing over at Ush2a ) , and uniformly B6/B6 over the rest of the genome . We did so by using appropriate progeny from two crosses: B6-Prdm9CAST-KI ( KI ) , a co-isogenic strain in which the Prdm9Dom2 allele has been replaced by the Prdm9Cst allele from CAST mice [9] , crossed to B6 . CAST-1T ( KI x CAST-1T ) , and B6-Prdm9Dom2/- crossed to B6 . CAST-1T ( KO het x CAST-1T ) . Importantly , similar to ( B6 x CAST ) F1 hybrid males , recombination at Ush2a is suppressed in ( KI x CAST-1T ) F1 hybrid males , where the only difference is the presence of Prdm9Cst in one copy ( Fig 5A , lanes 5 and 6 ) . However , recombination persists in ( KO het x CAST-1T ) F1 hybrid males , which only have one allele of Prdm9Dom2 ( Fig 5A , lanes 7 and 8 ) . Moreover , the rate of recombination at Ush2a is similar in B6 mice with two doses of Prdm9Dom2 and heterozygous B6-Prdm9Dom2/- mice with one dose ( S2 Table ) . These data show that the Prdm9Cst allele alone is sufficient to directly suppress the activity of the Prdm9Dom2 allele at the Ush2a hotspot . We next tested the extent to which Prdm9Cst can influence Prdm9Dom2 activity on a genome-wide basis . Previous reports found that , when tested for either H3K4me3 initiation sites or DMC1 DSB sites , the number of PRDM9Dom2-defined hotspots represent much less than the predicted 50% of all hotspots in F1 hybrids carrying two different Prdm9 alleles , indicating some form of competition between alleles [9 , 10] . In progeny from both B6xCAST and CASTxB6 crosses , the majority ( 65% ) of all hotspots were PRDM9Cst-activated [30] . However , interpretations of genome-wide hotspot behavior in traditional F1 hybrids between inbred strains are complicated by the presence of novel hotspots not found in either parent that result from the action of one parents Prdm9 allele on the genome of the other parent [30] , and by the fact that the entire genome is heterozygous , potentially introducing additional trans control mechanisms . To remove these complications and test for competition in a genetically uniform background , we crossed B6 mice to co-isogenic B6-Prdm9CAST-KI mice and measured H3K4me3 levels in germ cells of the resulting heterozygous Prdm9Dom2/Cst F1 male progeny . The total number of putative PRDM9-defined H3K4me3 hotspots in Prdm9Dom2/Cst progeny ( n = 21 , 894 , Fig 5B ) is less than the sum of the parental strains ( n = 18 , 849 and n = 28 , 475 , for B6 and CAST-KI respectively ) [9] , similar to previous result for DSBs in crosses involving Prdm9Dom2 [10] , likely reflecting the sensitivity of hotspot numbers to the total amount of PRDM9 protein . In addition , only ~ 26% of H3K4me3 hotspots in these F1 mice are PRDM9Dom2-activated , while ~ 74% are PRDM9Cst-activated ( Fig 5B ) . The PRDM9Dom2-activated H3K4me3 hotspots that are found in the ( B6xKI ) F1 mice are a subset of the PRDM9Dom2 hotspots found in Prdm9Dom2/- heterozygous mice ( Fig 5C ) , and are therefore those with the highest activity in B6 mice . Together these data confirm competition between alleles in mice heterozygous for Prdm9 and may suggest that PRDM9Cst has a greater affinity for its binding sequence .
In both mouse and humans , recombination rates can be influenced by heterozygosity at Prdm9 [17 , 18 , 26] . Here , using mouse genetics , we identified a single QTL influencing the recombination rate at the Pbx1 hotspot that overlaps with Prdm9 ( Fig 1 ) . The QTL mapping data suggested that Prdm9 function is more complex than simple activation of hotspots; in particular , that it is dosage-sensitive and subject to competition between alleles in heterozygous individuals . We found that PRDM9 can form homo- and heteromeric complexes , and that these complexes are bound to DNA at hotspots ( Fig 3 ) , providing a molecular explanation for competition between alleles in both mouse and humans . Moreover , measuring H3K4me3 levels at hotspots in heterozygous null and homozygous mice confirmed that Prdm9 is dose-sensitive ( Fig 4 ) . Finally , we found that hotspot suppression extends beyond simple dosage of Prdm9 in heterozygous mice , showing that the PRDM9Dom2-activated hotspot Ush2a is directly suppressed by the presence of only the Prdm9Cst allele ( Fig 5 ) . Our data indicate that Prdm9 is partially haploinsufficient for mouse fertility on the B6 background . Further phenotypic evidence for Prdm9 dosage sensitivity comes from genetic studies of hybrid sterility [16 , 36 , 38] . Crosses between certain M . m . musculus-derived mice and M . m . domesticus strains carrying Prdm9Dom2 result in sterile males with the severity of the pachytene-stage arrest in spermatogenesis being dependent on the parental origin of Chr X [39] . Thus , there are complex genetic interactions between PRDM9 protein variants and another locus on Chr X [38 , 40] . The F1 hybrid male sterility can be rescued by either making the Prdm9Dom2 allele homozygous; replacing the Prdm9Dom2 allele with another M . m . domesticus allele; or adding extra copies of Prdm9 ( independent of which M . m . domesticus allele is added ) ; or it can be partially rescued by removing Prdm9Dom2 , creating a heterozygous null M . m . musculus state , these results together further implicate allelic interactions in the hybrid sterility phenomenon [36] . These data , together with our findings on the capacity of PRDM9 to form homo- and heteromeric complexes , indicate that the sterility phenotype is Prdm9 dosage-sensitive and may be partially explained by incompatibilities of different homo- versus heteromeric PRDM9 complexes . In the absence of Prdm9 , meiotic DSBs persist [13] , although they are relocated away from recombination hotspots to other Prdm9-independent H3K4me3 sites such as those found at promoters and enhancers [10] , resulting in complete meiotic arrest . We found that B6-Prdm9Dom2/- heterozygous mice have a partial failure in meiotic progression ( Fig 4 ) . One possible explanation is that the reduced number of PRDM9-dependent H3K4me3 hotspots in a single cell may lead DSBs to be redirected to other , PRDM9-independent , H3K4me3 sites , which are subsequently not properly repaired , as occurs in the homozygous null mouse [10] . Evidence for competition between PRDM9 alleles is also seen in humans [17 , 18 , 29] . The recombination rate at several hotspots was measured in men carrying various combinations of A-type and C-type PRDM9 alleles . While men homo- or heterozygous for PRDM9C have similar recombination rates at C hotspots , recombination rates at A hotspots are reduced in heterozygous PRDM9A/C men when compared to homozygous PRDM9A/A men [29] . In addition , in one heterozygous PRDM9A/C man , 56% of the DSBs were due to PRDM9C protein variant , and PRDM9C hotspots were on average stronger than PRDM9A hotspots [28] . Data from these observations , combined with our finding that PRDM9A and PRDM9C can form heteromers and that PRDM9A can be found at C hotspots , support the idea that competition between human PRDM9 alleles results from PRDM9C being partially-dominant to PRDM9A , a relationship similar to that of PRDM9Cst and PRDM9Dom2 in mice . In addition to Prdm9 , there are 16 orthologous PRDM genes in primates and 15 orthologs in rodents , many of which function in multi-protein complexes [41 , 42] . PRDM proteins are characterized as containing a PR/SET domain , which can catalyze a variety of chromatin modifications , and most also have C-terminal DNA-binding zinc finger domains . Two other PRDM-family proteins , PRDM6 and PRDM2 ( also known as Riz1 ) , also form homomeric complexes , in part through interactions involving their PR/SET domains [43 , 44] . PRDM9 also contains a KRAB domain known to facilitate protein-protein interactions [45] . The mouse and human genomes both contain hundreds of other KRAB-Zinc finger proteins [46] , and several are known to form both homo- and heterodimers [47] . Together , these observations suggest that multimer formation may be a common feature of PRDM and KRAB domain containing proteins . The phenomenon of dominance among Prdm9 alleles is most simply explained by assuming that different alleles have different intrinsic DNA binding affinities determined by the allele-specific zinc finger domains . For example , in a heterozygous mouse , such as the F1 offspring of a cross between B6 and CAST mice , putative PRDM9 dimers would consist of PRDM9Dom2 homodimers , PRDM9Dom2-PRDM9Cst heterodimers , or PRDM9Cst homodimers , in approximate ratios of 1:2:1 ( Fig 6 ) . If PRDM9Cst is dominant over PRDM9Dom2 , as the suppression of Ush2a suggests , and overall PRDM9 activity is limiting , as the results from the B6-Prdm9Dom2/- studies indicate , PRDM9Dom2-PRDM9Cst heterodimers would activate PRDM9Cst-defined hotspots more often than PRDM9Dom2 hotspots , predicting the 3:1 over-representation of PRDM9Cst-defined hotspots that are active in ( B6 x KI ) F1 hybrids ( Figs 5B and 6 ) . The dominance relationship seen between these two alleles is enhanced by the fact that PRDM9Dom2 hotspots have undergone greater evolutionary hotspot erosion in B6 mice compared to PRDM9Cst-defined hotspots , resulting in PRDM9Cst hotspots having greater binding affinity in the B6 background [9 , 30] . However , not all allelic pairs show such large bias in hotspot selection . For example , in ( WSB x PWD ) F1 hybrids , containing two different PRDM9 alleles , 32% of hotspots are defined by the WSB allele and 40% of hotspots are defined by the PWD allele , and the remaining hotspots are unique to the F1 [30] . In any particular combination of alleles , relative dominance will be determined by the intrinsic binding strength of each allele for the hotspots found in that genetic background . This model is supported by the following evidence: Prdm9 activity is dosage dependent ( Fig 4 ) , suggesting a limited molecular activity within meiotic cells , PRDM9 protein variants directly compete for hotspot binding [30] , for H3K4me3 activity ( Fig 5B ) , DSBs [10 , 28] , and genetic recombination ( Fig 5A ) [17 , 18 , 29] , and finally , PRDM9 can form heteromeric complexes that allow protein variants to directly influence each other ( Fig 3 ) . In general , if the average affinity of a PRDM9 allele for its hotspots is appreciably stronger than that of a different PRDM9 allele for its hotspots , and the two protein variants are found in complex together , this difference in affinity in heterozygotes would create a molecular tug-of-war with the stronger allele winning more often , further diminishing the effective dose of the weaker allele . As a result , in heterozygotes , a complex containing both PRDM9 protein variants would more often be bound at hotspots corresponding to the stronger allele . Given the very high population frequencies of Prdm9 heterozygotes [17 , 18 , 23 , 28 , 48 , 49] , these effects can seriously influence patterns of inheritance in some natural populations .
The animal care rules used by The Jackson Laboratory and Institute of Molecular Genetics are compatible with the regulations and standards of the U . S . Department of Agriculture , National Institutes of Health , and European Union Council Directive 86/609/EEC and Appendix A of the Council of Europe Convention ETS123 . The protocols were approved by the Animal Care and Use Committee of The Jackson Laboratory ( Summary #04008 ) and Committee on the Ethics of Animal Experiments of the Institute of Molecular Genetics ( Permit Nos . 137/2009 , 61/2013 ) . C57BL/6J ( stock number 000664 ) and CAST/EiJ ( stock number 000928 ) mice were used . The generation and characterization of the B6 . CAST-1T , B6-Prdm9CAST-KI/Kpgn and B6-Prdm9tm1Ymat strains were described previously [9 , 13 , 26 , 38 , 50] . The C3H-Prdm9tm1Ymat mice were derived from Prdm9tm1Ymat mice by repeated backcrossing to C3H/HeN resulting in a 98% C3H/C3H background; the differential segment of Chr 17 carrying Prdm9 was approximately 36 Mbp . Surface-spread testicular nuclei were prepared using the hypotonic treatment protocol described previously [51] with a few modifications [36 , 38] . The following antibodies were used: rabbit polyclonal anti-RAD51 ( Santa Cruz , sc-8349 ) , anti-DMC1 ( Santa Cruz , sc-22768 ) , and anti-SYCP1 ( Abcam , ab15087 ) ; mouse monoclonal anti-γH2AFX ( Upstate , #05–636 ) and anti-SYCP3 ( Santa Cruz , sc-74569 ) ; goat anti-Rabbit IgG Alexa Fluor 488 ( Molecular Probes , A-11034 ) ; and goat anti-Mouse IgG Alexa Fluor 568 ( Molecular Probes , A-11031 ) and IgG Alexa Fluor 647 ( Molecular Probes , A-21236 ) . The images were acquired using a Nikon E400 microscope with a DS-QiMc mono-chrome CCD camera ( Nikon ) and processed in the NIS-Elements program ( Nikon ) . The spread nuclei were staged based on axis development and synaptonemal complex formation . An important goal for counting recombination at Pbx1 was to bring distant ( 1–2 kb ) SNPs that define recombination hotspots into close proximity for DNA sequencing in a single molecule , while being able to multiplex DNA from hundreds of animals in one sequencing lane . This was achieved using a series of enzymatic steps designed to reduce false-recombinant molecules and incorporate DNA barcoded primers ( S1B Fig ) . Using this system each molecule sequenced represents a single sperm DNA and therefore a potential recombinant DNA . Epididymal sperm was collected from adult mice and DNA purified using the automated sample handling system Maxwell 16 ( Promega ) with the Tissue LEV Total RNA Purification Kit ( Promega ) . All mice were genotyped as described previously [26] . Step 1: First-round of PCR . DNA primers were design to amplify Pbx1 and contained NotI restriction sites ( all primers are listed in S3 Table ) . PCR reactions for each sample were seeded with ~20 , 000–25 , 000 haploid genomes ( 75 ng total sperm DNA ) using 0 . 25 μl Phusion II enzyme with the HF Buffer ( New England Biolabs ) , 0 . 8 μM of each primer , 5% DMSO , 0 . 2 mM dNTPs ( NEB ) in a total reaction volume of 25 μl . First-round PCR conditions include an initial 98°C 30 second denaturing step followed by 11 cycles of 98°C for 10 seconds , and a 70°C annealing step for 30 seconds followed by 72°C extension step for 45 seconds . The final cycle was followed by 10 minutes at 72°C . Step 2: The entire PCR reaction was brought to 50 μl supplemented with 1 μ NotI ( NEB ) and appropriate restriction buffer and incubated for 60 minutes at 37°C , followed by heat-inactivation at 80°C for 15 minutes . Step 3: To facilitate intra-molecular ligation and create circularized DNA , the restriction digests were diluted to 200 μl using 5% polyvinylpyrrolpidone ( SIGMA ) , 20 μl T4 ligase buffer , and 1 μl T4 Ligase ( NEB ) . Ligations were performed at 15°C for 15 minutes . The ligation reactions were treated with Exonuclease I and III and incubated at 37°C for 15 minutes to digest any remaining linear DNA molecules . Exonuclease was heat-inactivated by incubating at 95°C for 2 minutes . DNA was concentrated using standard ethanol precipitation and diluted in 10 mM Tris pH 8 . 0 . Step 4: The second round of PCR was performed to generate small DNA molecules amenable to paired-end sequencing . PCR reaction conditions were similar to the first round of PCR in a total reaction volume of 25 μl . Second-round PCR primers were designed to include an 8-bp DNA barcode on the 5’ end in order to allow multiplexing different mouse samples . PCR cycling conditions were also similar to the first round of PCR using 24 cycles . Step 5: After the second round of PCR all individual 25 μl reactions were pooled together and concentrated using ethanol precipitation and resuspended in 10 mM Tris pH 8 . 0 . DNA was run on 2% agarose gel for size selection and purification using QIAquick Gel Extraction Kit ( Qiagen ) . The resulting samples were then subject to high throughput DNA sequencing ( see below ) . Even with the protocol described above , the DNA sequences generated by high-throughput sequencing consistently reported a low rate of recombination in control samples . To measure false-recombination using deep sequencing we mixed equal amounts of spleen DNA prepared from B6 and CAST mice separately prior to the first-round of PCR; this analysis resulted in a false-recombination rate of 0 . 22 ± 0 . 05 cM ( mean ± standard deviation ) . Because recombination cannot occur in these control samples , we conclude that the observed chimeric molecules are created from incomplete extension in one PCR cycle synthesizing a DNA molecule that is subsequently used to prime DNA in following rounds of PCR , so called template-switching or ‘jump-PCR’ . QTL analysis was performed using R ( http://www . R-project . org/ ) and the r/qtl package [52] . Single-QTL scans were performed using the scanone function using imputation method . Genome-wide LOD significance thresholds were defined by performing 5 , 000 permutations . The PRDM9B allele was purchased from OriGene ( Rockville , MD ) . Oligonucleotide primers were designed to include a 5’ V5 epitope tag and used to amplify the full-length PRDM9 and cloned into pCEP4 expression vector ( Invitrogen ) to create pCB09 . A 6X-HIS-3X-FLAG tag was inserted in frame replacing the V5-tag using yeast-based homologous recombination [53] . The zinc-finger arrays for both PRDM9A and PRDM9C were amplified from human genomic DNA [7] and cloned into pBAD-HisC ( Invitrogen ) . These zinc-finger arrays were subcloned into the pCEP4 vectors using restriction enzymes AflII and HindIII ( NEB ) to create full-length tagged versions of FLAG-PRDM9C ( pCB51 ) , V5-PRDM9C ( pCB47 ) , and FLAG-PRDM9A ( pCB53 ) , and V5-PRDM9A ( pCB48 ) for expression in mammalian cell culture . The V5-PRDM9C-G278A allele was created using QuikChange II site-directed mutagenesis ( Agilent Technologies ) to change glycine 278 to alanine to create pCB56 . All cloning oligonucleotides are listed in S3 Table . HEK293 cells were cultured in DMEM ( Gibco , Life Technologies ) supplemented with 10% FBS ( Gibco ) at 37°C and 5% CO2 . 24 hours prior to transfection , cells were seeded at with 10 ml 2 . 5·105 cell/ml in 10-cm culture-treated plates . Cells were transfected using X-tremeGene HP transfection reagent ( Roche ) following manufacturer’s protocol using a ratio of 3:1 reagent to DNA with 10 μg total plasmid DNA . H3K4me3 ChIP-seq from mouse spermatocytes was performed as previously described [9] . ChIP from HEK293 cell cultures were performed with modifications . After transfection cells were allowed to grow for 48 hours . For H3K4me3 ChIP cells were crosslinked by adding formaldehyde ( SIGMA ) to a final concentration of 1% , and incubated for 10 minutes . For FLAG-tagged PRDM9 ChIP , cells were crosslinked using freshly prepared paraformaldehyde added to a final concentration of 1% and incubated for 5 minutes . Excess formaldehyde was quenched by adding glycine to a final concentration of 125 mM . The medium was removed and the cells were washed once with phosphate-buffered saline ( PBS , SIGMA ) . The PBS was removed and 2 ml of fresh PBS was added supplemented with protease inhibitor cocktail ( SIGMA ) . The cells were collected by scrapping into a 2-ml Eppendorf tube and pelleted by centrifugation at 5000 x g at 4°C for 5 minutes . The PBS was removed and the cell pellet frozen in liquid nitrogen and stored at -80°C . For H3K4me3 ChIP chromatin isolation , MNase digestion , and immunoprecipitation steps were carried out as previously described for spermatocytes . For FLAG ChIP , chromatin was sheared using sonication and immunoprecipitation performed as described for mouse PRDM9 [30] . Pooled DNA samples from the Pbx1 recombination assay were prepared for sequencing using the TruSeq DNA PCR-Free Sample Preparation Kit ( Illumina ) in order to avoid PCR amplification , which could lead to template switching during amplification , in turn leading to false recombinant molecules . After library preparation Pbx1 DNA was size-selected using the Pippin Prep ( Sage Science ) . DNA from ChIP experiments was prepared for sequencing using NEXTflex ChIP-Seq Kit ( Bioo Scientific ) for H3K4me3 ChIP from mouse spermatocytes , or Kapa Hyper Prep Kits ( Kapa Biosystems ) for H3K4me3 ChIP from HEK293 cells without size-selection and 14-cycle PCR amplification . Sequencing for mouse samples was performed at The Jackson Laboratory using the Illumina HiSeq 2000 platform . Sequencing for HEK293 samples was performed at the New York Genome Center using Illumina HiSeq 2500 platform . Base calls were made using CASAVA and mapped to either the mouse genome ( mm9 ) or the human genome ( hg19 ) using BWA [54] with default settings . Custom software was developed to count parental and recombinant molecules , and to de-multiplex individual mice from the Pbx1 recombination assay . For ChIP-seq , alignment files were filtered to keep only uniquely mapped reads . DMC1 SSDS ( DSB ) ChIP data was previously described [28] ( GEO accession no . GSE59836 ) . Peak calling was performed using MACS ( v . 1 . 4 . 2 ) [55] using ChIP samples for treatment and , for H3K4me3 ChIP , sequenced input DNA as controls with the following settings:-p 1e-5 –keep-dup = ‘all’ . Coverage profiles presented in figures were generated with the UCSC genome browser ( settings: mean , smoothing window 5 ) using bedgraphs generated from MACS after tag-shifting . Motif identification and searching for PRDM9C and PRDM9A allele-specific motifs was performed using the MEME Suite ( v . 4 . 9 . 0 ) [56] . To locate hotspot centers for heat maps , for each hotspot with more than one motif instance only the top scoring motif was retained ( threshold—p-value < 0 . 0001 ) . Analysis of H3K4me3 peak differences between B6 and heterozygous B6-Prdm9Dom2/- null mice was performed using the R package DiffBind [57] . Heat maps for H3K4me3 ChIP from HEK293 cells and DMC1 ChIP were created using seqMiner [58] for peaks with identified PRDM9 motifs that overlapped both H3K4me3 and DMC1 datasets . For heat maps , tag extension was set at 150 bp for H3K4me3 and 450 bp for DMC1 ChIP , determined by the MACS tag-shifting model , and a wiggle step of 1 bp . Summaries of H3K4me3 ChIP-seq and DMC1 SSDS datasets are presented in S1 Table and S3 Fig . Analysis of peak locations between datasets was performed using bedtools [59] . Quantitative PCR ( qPCR ) was performed using Quantifast SYBR Green PCR Kit ( Qiagen ) on the real-time PCR system MasterCycler ep realplex ( Eppendorf ) . Primers were designed using OligoPerfect primer design software ( Life Technologies ) with 40–60% GC with a product size of 80–120 bps ( all primer sequences are listed in S3 Table ) . All PCR reactions were set up in technical triplicates with 2 μl of ChIP DNA and 0 . 5 μM forward and reverse primers . Reactions were run for 40 cycles followed by melting curve analysis , and cycle threshold numbers were determined by automated threshold . All ChIP samples were normalized to purified input DNA controls . Whole-cell protein was extracted from HEK293 cells using RIPA buffer ( SIGMA ) supplemented with 1 mM PMSF , 1X protease inhibitor cocktail ( SIGMA ) , 1 mM EDTA , 1 mM DTT , and 1 μl Benzonase ( SIGMA ) . Cells were lysed at 4°C for 30 minutes mixed every 5 minutes . For Histone extraction , cells were first incubated for 30 minutes with rotation in hypotonic lysis buffer ( 10 mM Tris-HCL , pH 8 . 0; 1 mM KCl , 1 . 5 mM MgCl2 ) supplemented with 1 mM PMSF and 1X protease inhibitor cocktail . Nuclei were pelleted by centrifugation at 10 , 000 x g for 10 minutes at 4°C . Histones were recovered by diluting nuclei in 0 . 2 N HCl and incubating at 4°C with rotation for 2 hours . Cell lysate was cleared by centrifugation at 10 , 000 x g for 10 minutes at 4°C . Protein samples were normalized for equal loading using Bradford Reagent ( BioRad ) and diluted in SDS gel-loading buffer and heat-denatured for 5 minutes at 98°C . For immunoprecipitation , cleared whole-cell lysate was diluted to 500 μl in RIPA buffer . Magnetic protein-G Dynabeads ( Invitrogen ) were pre-washed with RIPA and treated with anti-FLAG or anti-V5 antibodies for 20 minutes with rotation at room temperature , and washed again with RIPA . Dynabeads were added to the whole-cell lysates and incubated with rotation at 4°C for 3 hours . Immunocomplexes bound to beads were washed 3 times with 500 μl RIPA buffer and eluted using 2X SDS loading buffer . For western blotting , protein was loaded into 4–15% Tris-Glycine gels ( mini-protean , BioRad ) and electrophoresis was carried out at 150 V for 60 minutes . Protein was transferred to nitrocellulose membranes using the iBlot system ( Invitrogen ) with a 7-minute transfer . Westerns were developed using the SNAP i . d . 2 . 0 Protein Detection System ( Millipore/EMD ) with the following antibodies diluted 1:1000: anti-FLAG M2 ( SIGMA , F1804 ) , anti-V5 ( Invitrogen , R960-25 ) , anti-H3K4me3 ( Millipore/EMD , 07–473 ) , anti-H3 ( Millipore/EMD , 06–755 ) , and VeriBlot secondary antibody HRP ( Abcam , ab131366 ) . Blots were visualized using enhanced chemiluminescent substrate SuperSignal West Femto ( Life Technologies ) and images digitally captured using a G:BOX gel document system ( SYNGENE ) . Sperm DNA was amplified by two rounds of nested PCR using allele-specific primers in each PCR reaction similar to previously described [7] . The two pairs of primers were orientated in 5’-3’ CAST-B6 combination . The 5’ forward primers were both designed to the CAST haplotype . The 3’ reverse primers were designed as either CAST or B6 . Primers were PTO-modified at the last two nucleotides in the 3’ end ( primer sequences found in S3 Table ) . The first-round PCR was performed using 50 ng sperm DNA , 0 . 25 mM of each dNTP , 0 . 25 μM of each primer , 1x Titanium Taq PCR buffer , and 0 . 5 U Titanium DNA Taq Polymerase ( Clontech Laboratories Inc ) . PCR cycling conditions included an initial denaturizing step at 94°C for 5 minutes , then 12 cycles of 94°C for 1 minute , 64°C for 40 seconds , and extension time of 68°C for 3 minutes , followed by a final extension time at 68°C for 10 minutes . The amplified DNA product was diluted 10 times and 2 μl used for the second-round allele-specific PCR . Second-round PCR cycling conditions used an initial denaturing step at 94°C for 5 minutes , then 40 cycles of 94°C for 1 minute , 55°C for 40 seconds , and extension time of 68°C for 3 minutes , followed by a final extension time at 68°C for 10 minutes . Quantitation of recombination rates was done by determining the number of crossover and parental molecules in the same sample of sperm DNA . PCR amplification was carried out in serial dilutions where the starting amount of DNA was diluted two times in each consecutive reaction . The last positive and the first negative dilution reactions were used to perform 20 PCR reactions each in parallel . The number of negative reactions in each pool determines the number of amplifiable molecules through the Poisson distribution . High-throughput sequencing files and processed data for ChIP-seq experiments associated with this manuscript can be found at Gene Expression Omnibus under accession numbers GSE52628 and GSE67673 . | During formation of sperm and eggs chromosomes exchange DNA in a process known as recombination , creating new combinations responsible for much of the enormous diversity in populations . In some mammals , including humans , the locations of recombination are chosen by a DNA-binding protein named PRDM9 . Importantly , there are tens to hundreds of different variations of the Prdm9 gene ( termed alleles ) , many of which are predicted to bind a unique DNA sequence . This high frequency of variation results in many individuals having two different copies of Prdm9 , and several lines of evidence indicate that alleles compete to initiate recombination . In seeking to understand the mechanism of this competition we found that Prdm9 activity is sensitive to the number of gene copies present , suggesting that availability of this protein is a limiting factor during recombination . Moreover , we found that variant forms of PRDM9 protein can physically interact suggesting that when this happens one variant can influence which hotspots will become activated . Genetic crosses in mice support these observations; the presence of a dominant Prdm9 allele can completely suppress recombination at some locations . We conclude that allele-dominance of PRDM9 is a consequence of protein-protein interaction and competition for DNA binding in a limited pool of molecules , thus shaping the recombination landscape in natural populations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Multimer Formation Explains Allelic Suppression of PRDM9 Recombination Hotspots |
Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior . Sets of genes could be over-expressed or repressed when anomalies due to disease appear , and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues . This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers .
Understanding diseases within the context of biological networks is one of the major challenges in systems biology . Diseases often persist and resist therapeutic intervention . The persistence of a disease in a system must be reflected in the ability of the system's networks to maintain the state underlying the disease . In other words , networks are “locked-in” to disease states and maintain their stability . Thus , it is important to understand how such multi-stable states are achieved within the context of network topology and to understand the dynamics of these states . A network robust against a range of perturbations can maintain a healthy state but can also , when affected by a disease , transition to a new steady state that is often also robust against perturbations , making the disease state persistent . A series of disease progressions may be the result of a sequence of state transitions in the network dynamics ( Fig . 1A ) . Bistable circuits may drive such transitions and are thus critical in enabling the initiation and progression of diseases to be understood ( Fig . 1 B ) . Complex networks exhibiting such multi-stability must have a set of bi-stable or multi-stable circuits consisting of proteins and genes . The identification of circuits that exhibit bi- or multi-stability within large protein-interaction and gene-regulation networks would provide information useful for understanding the mechanism ( s ) of network bistability . Furthermore , circuits exhibiting bistability can be potential drug targets or biomarkers for classifying disease states . Network dynamics are regulated by the structure of the network and the flow of information through feedforward and feedback loops . Mutual activation or mutual inhibition configurations can maintain the flow of biological information between two molecules and act as network memories or switches . Furthermore , an activation-inhibition configuration , in which one molecule stimulates the other while the latter inhibits the former , generates dynamics with periodicity like that seen in circadian rhythms and cell cycles [1] . The stability and characteristics of Boolean networks comprising these configurations were studied in detail by Kauffman et al . [2] . In the study reported here , we focused on mutual inhibition , which is thought to be involved in the stable deviations of a system observed during the progression of tissue from a normal to a diseased state . There are several important network motifs for system configurations [3]–[6] in protein-protein networks . One of them , a toggle switch that converts a continuous input signal into a discontinuous ON or OFF response , plays a fundamental role in information processing and decision making . Among the naturally occurring toggle switches that have been reported are the lambda phage lysis–lysogeny switch [7]–[9] , switches in the lactose operon repressor system [10]–[12] , the mitogen-activated protein kinase ( MAPK ) cascade [13]–[20] , the Sonic hedgehog network in stem-cell differentiation [21] , cell-cycle regulatory circuits [22]–[24] , and the rapid lateral propagation of receptor tyrosine kinase activation [25] . Genetically engineered toggle switches have been constructed experimentally in Escherichia coli [26] , [27] and in mammalian cells [28] . A robust toggle switch behaves as a signal memory unit by using a hysteresis mechanism [29] . Once in the ON state , a toggle switch remains in the ON state even if the stimulus concentration falls below the threshold level [11] , [13] , [23] , [24] , [30] , [31] . A molecular network's persistence in a disease state might be due to the hysteresis of toggle switches . To identify circuits exhibiting bi- and multi-stability , we topologically analyzed activation and inhibition in proteins on a large scale by using various databases containing expression array data for various diseases . We compared the progression stages of these diseases with those of control samples by using data for healthy individuals taken from available databases , and we identified sets of switch circuits possibly responsible for maintaining the persistent disease states by using network topologies to analyze that data .
There are theoretically many system configurations that can lead to bistability [18] , [32]–[35] . We focused on bistable toggle switches ( BTSs ) with double-negative feedback . Such switches can be constructed from any two genes that mutually repress their expression . We considered three types of network motifs that can exhibit bistable behavior ( Fig . 2 ) . It is possible that double-negative feedback can be a bistable toggle switch when both nodes have positive feedback loops . Two BTSs can share their mutual inhibition configurations as positive feedback loops and can form network configurations . Next , bistable toggle switches defined above was extracted from large-scale databases ( ResNet 3 . 0 , Ariadne Genomics Inc . ) containing data for interaction networks . We detected 6585 pairs of bistable toggle switches , and these switch nodes formed a large network . Four-hundred and forty-two genes are involved in these BTS pairs , and the hubs of switch nodes in the network are clearly visible because of their high degree of connectivity ( Fig . 3 ) . A complete list of the BTS pairs is provided in Protocol S1 , and a Cytoscape session file is provided in Protocol S2 . It should be noted that this network was constructed using text mining and that the molecular details of each interaction were not verified . It is nevertheless a reasonable starting point , and whether or not a listed BTS actually exhibits bistability can be further examined using microarray data . ArrayExpress microarray data were used to further examine the states of the BTS pairs . It is obvious that a BTS has four possible states: “ON/ON , ” “ON/OFF , ” “OFF/ON , ” and “OFF/OFF . ” Mathematical analysis of bistability for the chosen parameter condition demonstrated that the probability of “ON/OFF” and “OFF/ON” states is high , that of “ON/ON” is low , and that of “OFF/OFF” is extremely low [38] . This is the reason we focused on the BTSs that demonstrated “ON/OFF” or “OFF/ON” states . The ArrayExpress experimental categories and the mean number of corresponding BTS pairs with a significant ON/OFF change are shown in Fig . 4 . In the set of 6585 candidate BTSs the number of pairs with a significant ON/OFF change ranged from 0 to 1927 ( mean = 298 . 6 ) , while in a set of 6585 randomly selected gene pairs the number of pairs with a significant ON/OFF change ranged from 0 to 273 ( mean = 72 . 1 ) . The switching of a molecule's function to the ON state generally means the molecule's intrinsic function related to intracellular molecular systems has become stronger , whereas switching to the OFF state means it has become weaker . The ON state of a molecule is produced not only by an increase in the absolute amount of that molecule but also by actions such as activation due to phosphorylation-induced transformation of the molecule's three-dimensional structure or to translocation of the molecule to an location where it can carry out its function properly . In these studies using mRNA expression data from microarrays , the toggling of a BTS pair was defined as an instance in which a sample's mRNA level for one of that pair's molecules increased ( relative to a control ) and the mRNA level for the other of that pair's molecule decreased ( relative to the same control ) . A notable finding is that when mRNA levels were compared between induced pluripotent stem ( iPS ) cells and donor controls , more than 1000 BTS pairs demonstrated significant changes in the ON/OFF states . The high frequency of these changes in iPS cells is reasonable in that an iPS cell is in an undifferentiated state committed to differentiation to a particular lineage , in which many BTSs might be involved [39] . iPS cells have been generated from mouse and human somatic cells by using retroviruses or lentiviruses to introduce Oct3/4 and Sox2 with either Klf4 and c-Myc or Nanog and Lin28 [40] . These factors have been reported to result in bistability when they combine with other factors and form mutual-activation and mutual-inhibition motifs [41]–[43] . Lung cancer is the leading cause of cancer-related deaths [44] , and tobacco smoking is the strongest etiological factor associated with lung cancer . Prior studies have demonstrated that smoking creates a field of molecular injury throughout the airway epithelium exposed to cigarette smoke [45] . Figure 5A depicts the toggling of BTS ON/OFF states inferred from time-dependent data ( ArrayExpress ID: E-GEOD-10700 and E-GEOD-10718 ) for the mRNA expression in normal human bronchial epithelial cells exposed to cigarette smoke for 24 hours . Toggling began at 2 hours ( Fig . 5B ) and was observed most frequently at 4 hours ( Fig . 5C ) . SOCS3 ( suppressor of cytokine signaling 3 ) was observed early , while BTSs related to HMOX1 ( heme oxygenase 1 ) , CSF2 ( colony stimulating factor 2 ) , and SPP1 ( secreted phosphoprotein 1 ) were observed throughout the 24-h period . SOCS3 inhibits cytokine signaling via the JAK ( Janus kinase ) /STAT ( signal transducers and activators of transcription ) pathway . Recent research has demonstrated that the activation of SOCS3 in the lung occurs during the acute inflammatory response [46] . Frequent hypermethylation in the CpG islands of the functional SOCS3 promoter has been found in lung-cancer tissue samples to correlate with its transcription silencing [47] . The OFF states of EGF ( epidermal growth factor ) and MAPK8 ( mitogen-activated protein kinase 8 ) were linked to the ON states of CSF2 and HMOX1 , which became the main players at four or more hours of exposure . CSF2 and HMOX1 were connected through several genes in the OFF state , including IL13 ( interleukin 13 ) , IFNG ( interferon gamma ) , and FN1 ( fibronectin 1 ) , which are related to inflammatory responses and wound healing . Figure 6 illustrates the state of BTS toggling for a comparison of mRNA expression ( ArrayExpress ID: E-GEOD-10072 ) in non-small cell lung carcinoma ( NSCLC ) patients with a history of smoking ( Fig . 6A ) along with those currently smoking ( Fig . 6B ) with mRNA expression seen in normal lung tissue . The bold black frames surround molecules that are also in the BTS molecules whose toggling is shown in Fig . 5A . ON/OFF patterns of FN1-SPP1 ( Fig . 6A ) and IGF1-SPP1 ( Fig . 6B ) were observed in the data gathered in experiments exposing normal human bronchial epithelial cells to cigarette smoke . SPP1 is a secreted integrin-binding glycoprotein that is overexpressed in various tumors and has been reported to be involved in tumorigenesis and metastasis . High expression of SPP1 is a significantly unfavorable prognostic factor for the survival of patients with NSCLC [48] . In addition , although some EDN1 ( endothelin-1 ) -related BTS pairs and SHC1 ( Src homology 2 domain containing transforming protein ) -related BTS pairs are shared in lung cancer tissue in current and former smokers , a considerable number of differing patterns are evident . This suggests that the mechanisms for carcinogenesis differ depending on the lengths of time that current and former smokers have smoked . EDN1 , which is a hypoxia-inducible angiogenic growth factor for surrounding epithelial and endothelial cells , plays an important role in cancer-stromal interactions and tumor progression , and its expression is related to poor prognosis in NSCLC [49] . Small molecules that can put these BTS pairs into normal ON/OFF states might be useful in preventing the progression of lung cancer in both current and former smokers . Hepatocellular carcinoma ( HCC ) is a primary cancer that originates in hepatocytes and typically follows cirrhosis or chronic-hepatitis virus infections [50] , and the most significant risk factors for HCC are chronic infections with either hepatitis B virus or hepatitis C virus ( HCV ) . Figure 7 is a BTS toggling graph in which mRNA expression data ( ArrayExpress ID: E-GEOD-6764 , [51] ) for tissues from patients with HCV-induced dysplasia and HCC are compared with mRNA expression data for normal liver tissue . The molecules surrounded by bold lines are BTSs for which toggling was observed when comparing dysplastic liver tissue ( cirrhotic tissue and dysplastic nodules ) , a precursor of liver cancer , with normal liver tissue . The two tissue types share many BTSs associated with PTGS2 ( prostaglandin-endoperoxide synthase 2; COX-2 ) and IL1B ( interleukin 1 , beta ) . It has been demonstrated that the expression pattern of PTGS2 , a key enzyme of the prostaglandin metabolism , is closely correlated with the differentiation grade of HCC [52] . Nonsteroidal anti-inflammatory drugs targeting PTGS2 have been shown to inhibit the proliferation of cultured hepatocellular cancer cells by inducing cell-cycle arrest [53] . When HCC tissue was compared with healthy liver tissue , toggling was most evident for CCNA2 ( cyclin A2 ) –related BTSs ( Fig . 7 ) We therefore analyzed how the toggling of CCNA2-related BTSs rippled out to other BTS pairs during the malignant transition of HCC ( Fig . 8 ) . CCNA2 activates CDC2 or CDK2 kinases and regulates the cell cycle positively by promoting G1/S and G2/M transitions in both the G1 and G2 phases of the cell cycle [54] , while EGR1 ( early growth response gene 1 ) has suppresses transformation [55] . The upregulation of CCNA2 and downregulation of EGR1 might thus play a key role in the dysregulation of normal growth in HCC carcinogenesis [56] . The downregulation of IL6 ( interleukin 6 ) is involved in dysregulation of the immune response in early carcinogenesis . After the toggling of CCNA2-related BTSs but still in the early stage of carcinogenesis , the OFF state of IL6 is related to the ON states of PTK2 and SMAD3 ( SMAD family member 3 ) . PTK2 and SMAD3 play important roles in cell growth and the activation of intracellular signal transduction pathways , suggesting that cell proliferation might accelerate during this stage . Toggling of PTK2 ( ON ) -BCL2 ( OFF ) was observed in advanced and very advanced stages . BCL2 ( B-cell CLL/lymphoma 2 ) suppresses apoptosis , and the downregulation of BCL2 might be involved in the acceleration of apoptosis in cancer cells . Notably , the ON/OFF state of the TP53-IGF1 BTS was changed from “OFF-OFF” to “ON ( TP53 ) –OFF ( IGF1 ) ” in advanced HCC . And in very advanced HCC , almost all IGF1-related BTS pairs demonstrated “ON ( other ) –OFF ( IGF1 ) ” patterns . In the very advanced stage , many IGF1 ( insulin-like growth factor-1 ) -related BTS pairs demonstrated significant ON/OFF changes . The liver is the main source of IGF1 , and the development of HCC is accompanied by significantly reduced serum IGF1 levels [57] . The downregulation of IGF1 and upregulation of a set of another pair of genes might affect a wide variety of cellular functions .
We constructed bistable switch networks , compared their ON/OFF states with those of control ( healthy ) samples , and found that their states changed with disease progression and differed between patient subtypes . Since most disease states exhibit a certain level of resilience against therapeutic intervention , each can be considered to be homeostatic to some extent . This homeostasis implies the robust status of a dynamical network and could not be maintained without mechanisms that drive a network to maintain a certain state . One such mechanism is a bistable switch , so we should look for sets of bistable switch circuits in large-scale protein interaction networks . Our analysis revealed that BTS states change with disease progression , and the implications of this are far reaching . For example , it might be possible to prevent or delay disease progression by perturbing one or more such switches . Such switches may be novel drug-target candidates for controlling disease progression . Analysis of the ON/OFF states of genes constituting bistable circuits revealed similarities between disease subtypes . While our analysis has provided insightful information , it has shortcomings . First , the network topologies were based on commercial databases created using a text-mining system . This means that the details of the molecular interactions were not verified . The development of a more accurate interaction database would enable more precise and accurate analysis of bistable network behaviors and of the contributions of switch circuits to those behaviors . Second , the analysis was based solely on network topologies—no parametric features were considered . Although topological analysis enabled us to identify circuits exhibiting bistable behavior , whether circuits exhibiting bistable behavior apparently exhibit bistable behavior depends on the kinetic parameters associated with each interaction [58] . Using microarray data , we determined that the pairs of genes in the circuits we identified are polarized into ON and OFF states . Two mutually inhibitory nodes polarized into ON and OFF states do not function as a bistable switch if both genes are ON or OFF . This is why we focused on BTSs , which demonstrated “ON/OFF” or “OFF/ON” states . We should , however , note that the “ON/ON” states of some BTSs play important roles in mammalian embryogenesis [59] , T-cell differentiation [60] , and visual-system specification [61] . Cluster analysis of transcriptome data in microarrays is useful for classifying disease characteristics according to differences in expression patterns . Although several disease types that are difficult to classify morphologically have been classified using this approach , the rules underlying the cluster structure of the data are unclear , and the importance of each of the molecules in a cluster cannot be determined with a reasonable degree of certainty . The analysis of changes in gene-expression levels can also be used to create a list of molecules whose levels increase or decrease significantly over time or whose levels differ significantly between healthy and diseased tissues . Although examinations of gene interrelations using gene-ontology classification and analysis of the classification results using network diagrams have led to a greater degree of understanding of the changes in molecular networks , it is difficult to infer the meanings of biological interactions between molecules . Our proposed method ( i . e . , focusing on BTS ON/OFF changes ) takes as the starting point the interactions between molecules . This makes it easy to infer biological meaning and makes it possible to analyze time-dependent data for time periods corresponding to that of disease progression ( from hours to years ) . In addition , while conventional methods sometimes neglect molecules that are downregulated , our method places equal importance on both increases and decreases in expression . DNA microarray technology makes it possible to study the expression of thousands of genes at the same time , but much of the microarray data consists of low signal intensities that can produce erroneous gene expression ratios between control and experimental samples [62] . The distribution of the ratio of two random variables approaches a Cauchy , or Lorentzian , distribution , which has longer tails than Gaussian distributions [63] , [64] . In our results , far more BTS pairs had significant toggling scores than did random gene pairs , but a considerable number of random gene pairs did show significant ON/OFF changes . We should therefore consider the possibility of random error in the analysis of BTS pairs . We used the transcriptome of normal tissue as the control in our analyses . This means that the identification of the molecular ON/OFF states inherent to normal tissue was unclear . Even if the ON/OFF state of a molecular pair for a certain switch is important for a particular tissue , if this state is retained in the diseased tissue , we would be unable to detect it in the present study because the ON and OFF states are not mutually exclusive . Therefore , molecules exhibiting even the slightest change are emphasized while those showing no change are ignored . We aim to overcome this drawback by identifying what types of ON/OFF changes occur in switches when embryonic stem ( ES ) cells or iPS cells undergo differentiation . Since proteins are responsible for cell function , the ON/OFF state of a molecule must be determined at the protein level when searching for molecular-network structures mediating cell functions . Because there are more than 20 control steps along the way from mRNA to functional proteins [65] , the reported expression levels of mRNA do not always agree with those of proteins—their translated products [66] . And even if there were a quantitative correlation between the levels of mRNA and functional protein , the efficiency of the translation process would be greatly affected by factors such as structural change and protein localization . Proteomics data for proteins in different cellular contexts is useful but is available for only some proteins . Transcriptome data analysis is the only method currently available for examining molecular networks on a large scale , but when testing the quality of BTS pairs in the future we will use all the relevant available data for the target proteins . Furthermore , to ensure bistability , the hysteresis phenomena must be confirmed when a perturbation has vanished . By conducting time-scale experiments in both directions when applying and removing perturbations , we should be able to further test the quality of BTS pairs . Despite its shortcomings , the approach presented here provides useful insights into the states of biological networks , insights that may lead to discovery of novel drug targets and therapeutic interventions .
The lists of molecular interactions were constructed using the Ariadne Genomics ResNet human protein interaction database ( ver . 3 . 0 ) compiled , using MedScan [67] natural language processing technology , from more than 13 , 000 , 000 PubMed abstracts and 43 publicly available full-text journals . The database contains data on over 200 , 000 objects ( proteins and small molecules ) and over 100 , 000 interactions . The interactions can be divided into two major classes: direct physical interactions ( binding , protein modifications , and promoter binding ) and indirect regulatory interactions ( regulation , expression regulation , direct regulation , molecular transport regulation , and molecular synthesis regulation ) . MedScan also extracted information on the relation direction and the effect on the target molecule . The “Effect” attribute has three possible values: “positive , ” “negative , ” and “unknown . ” The BTS pairs were extracted from the database on the basis of five rules . We extracted 19 , 178 relationships involving 3 , 682 genes ( basic interaction datasets ) . Using basic interaction datasets , we extracted possible network motifs for toggle switches . We defined these motifs as follows . The type-1 BTS contains two genes that have positive autoregulation and inhibit each other's expression . The type-2 BTS also contains two genes that suppress each other's expression , but each gene also has a positive or negative loop with the other gene . One of the four subtypes of type-2 BTSs ( corresponding to the four possible combinations of double positive and/or negative feedback ) shows the same function as the type-1 BTS . The type-3 BTS was based on a theoretical study of the modeling of genetic switches with positive feedback loops [37] . The BTS motifs are illustrated in Fig . 2 , and we extracted 6585 BTSs ( see supporting Table 1 ) . We used mRNA microarray data to examine the changes in the ON/OFF states of BTS candidates . CEL format files or tab-limited text files were downloaded via ArrayExpress ( http://www . ebi . ac . uk/arrayexpress/ ) , which is a public repository provided by the European Bioinformatics Institute [68] . We only used microarray data obtained from experiments with humans and with platforms of Affymetrix HG-U133A&B ( 631 sets ) and HG-U133Plus2 . 0 ( 404 sets ) . These data were normalized and summarized using the robust multichip analysis method [69] implemented in the Affymetrix Expression Console software . The toggling of a BTS pair was defined as instances in which the mRNA levels of a sample increased for one molecule of the pair and decreased for the other . To remove background noise , we calculated the toggling score using where SW1 and SW2 are the two molecules in alphabetical order . Changes in the ON/OFF states were considered significant when the toggling score was more than two standard deviations greater than the mean of all the toggling scores . For pathway visualization , we used Cytoscape ( Version 2 . 6 . 3 ) , which is widely used open-source software for visualization and analysis of networks [70] . The nodes in the visualized BTS network represent genes , the edges between nodes represent the pairing of bistable toggle switches , and the color of nodes were automatically assigned as a continuous color gradient from red for ON ( upregulated ) to blue for OFF ( downregulated ) according to relative gene-expression levels of the nodes . | Since most disease states exhibit a certain level of resilience against therapeutic interventions , each disease state can be considered to be homeostatic to some extent . There must be one or more mechanisms that cause the gene-regulatory network to maintain a certain state , and one such mechanism is a bistable switch . In this work , bistable switch networks were constructed and their ON ( upregulated ) /OFF ( downregulated ) states were compared between human cancers and healthy control samples . Changes in the ON/OFF state with the progression of cancer were demonstrated . A series of genes that might serve as a drug target or diagnosis biomarker was identified . The approach presented here should provide useful insights into the states of biological networks , which may lead to the discovery of novel drug targets and therapeutic interventions . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology/lung",
"cancer",
"computational",
"biology/systems",
"biology",
"oncology/gastrointestinal",
"cancers"
] | 2010 | Large-Scale Analysis of Network Bistability for Human Cancers |
Numerous constraints significantly hamper the experimental study of hepatitis C virus ( HCV ) . Robust replication in cell culture occurs with only a few strains , and is invariably accompanied by adaptive mutations that impair in vivo infectivity/replication . This problem complicates the production and study of authentic HCV , including the most prevalent and clinically important genotype 1 ( subtypes 1a and 1b ) . Here we describe a novel cell culture approach to generate infectious HCV virions without the HCV replication requirement and the associated cell-adaptive mutations . The system is based on our finding that the intracellular environment generated by a West-Nile virus ( WNV ) subgenomic replicon rendered a mammalian cell line permissive for assembly and release of infectious HCV particles , wherein the HCV RNA with correct 5′ and 3′ termini was produced in the cytoplasm by a plasmid-driven dual bacteriophage RNA polymerase-based transcription/amplification system . The released particles preferentially contained the HCV-based RNA compared to the WNV subgenomic RNA . Several variations of this system are described with different HCV-based RNAs: ( i ) HCV bicistronic particles ( HCVbp ) containing RNA encoding the HCV structural genes upstream of a cell-adapted subgenomic replicon , ( ii ) HCV reporter particles ( HCVrp ) containing RNA encoding the bacteriophage SP6 RNA polymerase in place of HCV nonstructural genes , and ( iii ) HCV wild-type particles ( HCVwt ) containing unmodified RNA genomes of diverse genotypes ( 1a , strain H77; 1b , strain Con1; 2a , strain JFH-1 ) . Infectivity was assessed based on the signals generated by the HCV RNA molecules introduced into the cytoplasm of target cells upon virus entry , i . e . HCV RNA replication and protein production for HCVbp in Huh-7 . 5 cells as well as for HCVwt in HepG2-CD81 cells and human liver slices , and SP6 RNA polymerase-driven firefly luciferase for HCVrp in target cells displaying candidate HCV surface receptors . HCV infectivity was inhibited by pre-incubation of the particles with anti-HCV antibodies and by a treatment of the target cells with leukocyte interferon plus ribavirin . The production of authentic infectious HCV particles of virtually any genotype without the adaptive mutations associated with in vitro HCV replication represents a new paradigm to decipher the requirements for HCV assembly , release , and entry , amenable to analyses of wild type and genetically modified viruses of the most clinically significant HCV genotypes .
HCV infects 2–3% of the world population . A majority of infected people fail to clear the virus and are at risk for developing serious liver complications ( reviewed in [1] ) . HCV belongs to the genus Hepacivirus in the Flaviviridæ family , and at least six genotypes have been identified so far [2] . Greater than two thirds of HCV infections diagnosed worldwide are of subtypes 1a or 1b [2] . There is no approved vaccine and available treatments are much less effective against genotype 1 compared to other genotypes . The limited experimental availability of chimpanzees , the primary animal model for HCV [3] , [4] , and difficulties encountered in reproducing true infection in small animals have significantly limited the use of in vivo models to study the biology of this virus . The structure of the intact virion is unknown , and it is still unclear how the RNA genome [5] circulates in infected patients . In addition , although the natural target cells of HCV are primarily hepatocytes in the liver , in vitro most human hepatic cells poorly propagate HCV isolates from patients ( e . g . [6] ) . In vitro studies were nevertheless marked by two breakthroughs allowing for the screening of new antiviral compounds . First , subgenomic replicons ( i . e . without structural genes ) of subtypes 1b [7] , [8] and 1a [9] were established in selected subclones of the human hepatic Huh-7 cell line that are highly permissive for HCV replication , e . g . Huh-7 . 5 cells [10] . Subsequently , a full infectious cycle was reproduced in cell culture with JFH-1 , a particular strain of genotype 2a [11] , [12] , or with a J6/JFH-1 chimera [13]; the released particles are referred to as HCVcc . Although propagation of a few HCV strains in replication-permissive cell lines has been an important contribution to the field , it has long been recognized that these models are complicated by the particularly high error rate of the HCV RNA replicase [14] . Combined with the in vitro selective pressure , e . g . associated with the modifications acquired by the permissive cell lines [15] , or viral recombination between genotypes [16]–[18] , it inevitably results in the emergence of adaptive/escape variants [19] . However , cell culture-adapted HCV most often displays lack of infectivity or impaired fitness in vivo [20] , [21] . Conversely , HCV genomes with a consensus sequence that are infectious in chimpanzees are not infectious in cell culture , e . g . in Huh-7 . 5 cells [9] , [22] . This issue is especially perplexing with genotype 1 strains , for which the accumulation of cell-adaptive mutations that enhance its RNA replication results at best in low yields of HCVcc with impaired infectivity [19] , [23] . Intergenotypic JFH-1 chimeras have been engineered to tentatively overcome such limitations [16]–[18] but have been shown to accumulate structural gene compensatory mutations [16] . As such mutations and their associated complications result from the viral RNA replication process , we reasoned that uncoupling the production of infectious HCV particles from HCV RNA replication would circumvent major limitations associated with existing in vitro systems requiring such coupling . All known Flaviviridæ members replicate in the cytoplasm of their target cells and induce membrane rearrangements mostly deriving from the endoplasmic reticulum ( ER ) [24] , [25] . Strongly connected to RNA replication [26] , assembly of infectious flavivirus particles occurs within a distinct sub-compartment of rearranged membranes [27] , [28] . It has been possible to produce flavivirus virions by providing their structural genes in trans . Thus , upon expression of WNV structural genes: core , pre-membrane ( prM ) and envelope ( E ) , baby hamster kidney ( BHK ) -21 cells carrying a WNV subgenomic replicon encoding a reporter gene release infectious WNV reporter-particles ( WNVrp ) containing subgenomic replicon RNA [29] , [30] . Although distantly related within the Flaviviridæ family , the Flavivirus and Hepacivirus genera display common features [31] . We therefore examined whether , as for WNV , infectious HCV particles could be formed when the structural proteins are encoded in trans . While we did not observe such trans-complementation in a HCV replication-permissive cell line , we made the surprising observation that non-hepatic mammalian cells previously used to study the biology of Flaviviridæ ( including HCV ) and bearing a flavivirus subgenomic replicon can produce infectious HCV of diverse genotypes from genomic RNA produced by a plasmid-based system involving cytoplasmic transcription by bacteriophage T7 RNA polymerase . The lack of involvement of the HCV RNA replication machinery avoids the occurrence of cell-adaptive mutations in the HCV genomes .
In initial analyses of the possible effects of flavivirus replicons on HCV virus particle production from proteins provided in trans , we observed that release of HCV structural proteins ( expressed from a cytomegalovirus immediate early promoter and harvested by ultracentrifugation ) was dramatically enhanced in BHK-21 cells carrying a lineage II WNV subgenomic replicon [32] ( referred to as BHK-WNV cells in this study ) compared to parental cells; a less pronounced increase was observed in the cell lysate ( Fig . 1A ) . This result suggests that , in the complete absence of HCV RNA replication , the WNV subgenomic replicon had generated a permissive environment for releasing HCV particles . Surprisingly , these effects were not observed in the seemingly more relevant Huh-7 . 5 human hepatocyte cell line , in which we found that the presence of an HCV subgenomic replicon inhibited rather than stimulated release of HCV structural proteins ( both of genotype 1a ) provided in trans ( Fig . S1A ) . Moreover , we were unable to stably establish an HCV subgenomic replicon in BHK-21 cells . Based on these results , we considered the potential of the BHK-WNV cell system to produce infectious HCV particles if appropriate HCV-based RNA molecules were generated in the cytoplasm . Such a system might potentially enable virus production of the most prevalent but experimentally difficult genotype 1 strains . To test this hypothesis , we devised a strategy for generating HCV-based RNA molecules in the cytoplasm of BHK-WNV cells ( Fig . 1B ) . One component of this approach consisted of a dual-plasmid bacteriophage polymerase ( p2B ) system consisting of the genes for the DNA-dependent RNA polymerases from both bacteriophages T7 and SP6 ( T7pol and SP6pol , respectively ) , each linked to their reciprocal promoter . The other component was a plasmid encoding the HCV genomic sequence of interest flanked at the 5′ end by the bacteriophage T7 promoter , and at the 3′ end by a hepatitis delta virus antisense ribozyme ( HDVrbz; cf . Materials and Methods ) . We reasoned that co-transfection of these two components into BHK-WNV cells would result in cytoplasmic co-amplification of both bacteriophage polymerases; T7 Pol would then drive high level cytoplasmic production of uncapped HCV genomic RNA with correct 3′ termini ( by HDV rbz self-cleavage ) that would serve as template for translation of HCV proteins ( driven by the HCV IRES ) , including the structural proteins core , E1 and E2 . Assembly and release of particles composed of HCV structural proteins and containing the HCV-based RNA might then occur ( Fig . 1C ) , and such particles might be infectious for appropriate target cells . We first generated HCV bicistronic particles ( HCVbp ) using a plasmid encoding HCV 5′-UTR to NS2 sequence upstream of the encephalomyocarditis virus ( EMCV ) IRES of a Huh-7 . 5 cell-adapted HCV subgenomic replicon of subtype 1a [9] , thereby yielding a bicistronic RNA ( Fig . 2A , top ) capable of replicating in Huh-7 . 5 cells . Co-transfection of BHK-WNV cells with this plasmid plus the p2B system resulted in the formation of large vesicles ( not classical multi-vesicular bodies ) filled with 50–60-nm particles in the vicinity of dilated rough ER protrusions and mitochondria , as observed by transmission electron microscopy ( Fig . 2B , top panels ) . In contrast , BHK-WNV cells transfected with a control plasmid ( HCV subgenomic replicon minus the HCV structural genes ) displayed the extensive membrane rearrangements previously shown to be triggered by the WNV subgenomic replicon [25] ( Fig . 2B , lower left panel ) , such as vesicle packets ( site of WNV RNA replication ) and convoluted membranes ( site of WNV RNA translation and polyprotein processing ) ; however the large vesicles containing particles were not observed ( Fig . 2B , lower right panel ) . Immuno-gold electron microscopy analysis with anti-HCV E1 and anti-core antibodies revealed the presence of the corresponding HCV proteins within membrane rearrangements or large vesicles ( Fig . S2 ) in BHK-WNV cells expressing the bicistronic HCV full length construct . Fig . 3A shows quantitation of viral RNA ( WNV and HCV ) in BHK-WNV cells and the corresponding culture supernatants ( SN ) after their ultracentrifugation . As expected , the cells contained a large amount of WNV RNA generated by the WNV subgenomic replicon , independent of transfection with the HCVbp-encoding plasmid . In contrast , HCV RNA was observed only in cells expressing this plasmid , at levels comparable to the WNV RNA . Strikingly , this was accompanied by the appearance in the SN of a large amount of HCV RNA , which was highly enriched ( approximately 100-fold ) relative to the WNV RNA . Sucrose density gradient analysis of particulate material from the culture supernatant indicated that the HCV-based RNA migrated over a broad buoyant density range of 1 . 05 to 1 . 20 g/cm3 ( Fig . 3B ) . The HCV E1 glycoprotein was detected across the gradient , as were the other structural proteins core and E2 ( Fig . S3A , upper panels ) . These results suggest that the BHK-WNV cell system is capable of releasing particles composed of HCV structural proteins that are preferentially associated with the HCV-based RNA from which they were translated . We determined that the harvested particles were not exosomes or cell debris , consistent with a requirement for maturation of HCV envelope proteins for particle release in our system ( Text S1 and Fig S3A–C ) . We also excluded that the WNV RNA released upon transfection of the HCVbp plasmid in BHK-WNV cells ( Fig . 3A ) was associated with infectious particles . First , previous reports suggest a requirement for WNV core protein [26] . In addition , after the transfection of BHK-WNV cells with a plasmid encoding WNV structural proteins , the secreted particles ( WNVrp ) were infectious for Huh-7 . 5 cells ( Fig . S3D ) , consistent with previous findings using other target cells [32] . However , incubation of Huh-7 . 5 cells with HCVbp did not yield any Renilla luciferase activity . Finally , BHK-WNV cells were treated with antiviral drugs for two weeks , which inhibited the WNV replicon ( measured by the reduced expression of Renilla luciferase ) but did not affect the release of HCV particles ( Fig . S3E ) . It is therefore highly unlikely that HCV RNA replication is responsible for the production of HCV in this system ( data on the mechanism will be presented elsewhere ) . Several criteria were examined to test the infectivity of the HCVbp in Huh-7 . 5 cells . First , we used RT-qPCR for the 5′-UTR to test individual fractions from the sucrose density gradient in Fig . 3B for their ability to induce HCV RNA replication . As shown in Fig . 4A , the amounts of HCV RNA in target cells at day 3 post-infection were negligible for nearly all fractions , and increased substantially by day 4 . As previously reported for HCVcc [23] , [33] , we observed that the infectivity of HCVbp was spread over a broad range of buoyant densities , and that it did not directly correlate with the detected amounts of viral RNA . The peak of infectivity generally ranged between 1 . 08–1 . 13 g/cm3 ( Fig . 4A ) , which corresponded to a low peak of HCV RNA ( Fig . 3B ) . Infectious titers of HCVbp in the supernatants of BHK-WNV cells were measured in Huh-7 . 5 cells . TCID50 were between 0 . 6×104 units/ml at day 3 and 2 . 5×105 units/ml at day 4 ( cf . Text S1 ) , consistent with data presented in Fig . 4A . Such viral titers are about one log lower than with the JFH-1 strain [11] , [12] and genotype 2a chimera [13] after a two-day incubation in permissive cell lines , but at least 10-fold higher than with HCVcc obtained with a cell culture-adapted strain of genotype 1 [23] . The relatively low buoyant density of most infectious particles could relate to their association with lipids , since lipid droplets were detected in the vicinity of non-structural proteins in BHK-WNV cells expressing the HCVbp-4cys construct , encoding a tetracysteine tag within NS5A ( Fig . S4A ) . As BHK-21 cells express functional LDL receptor [34] , another non-exclusive possibility is that HCV particles interacted with lipoproteins from the culture medium . Incubating HCVbp with ( up to 0 . 15 µg/ml ) human VLDL , LDL or HDL in vitro prior to Huh-7 . 5 cells enhanced the amount of viral RNA accumulating in target cells up to 5-fold ( not shown ) , which would be consistent with a specific interaction of lipoproteins with pre-assembled HCVbp , as previously reported for HCV-like particles ( HCV-LPs ) [35] , lentiviral particles pseudo-typed with HCV envelope proteins ( HCVpp ) and HCVcc [36] . We also analyzed HCVbp-induced synthesis of HCV proteins and RNA in Huh-7 . 5 target cells by laser-scanning confocal microscopy . Based on staining with a polyclonal antibody against NS5A ( Fig . 4B; specificity of antibody validated in Fig . S4C ) , NS5A-positive patches were detected in the cytoplasm of Huh-7 . 5 cells infected with HCVbp for two days ( center and right panels ) , but not in uninfected cells ( left panel ) . Albeit in close proximity with ERGIC53 , these patches did not co-localize with this lectin that transports glycoproteins from the ER to the Golgi apparatus , suggesting that NS5A was not associated with a ‘classic’ membrane compartment . We also examined HCV RNA replication in Huh-7 . 5 cells incubated with HCVbp; after several hours , the cells were treated with actinomycin D to block RNA polymerase II-dependent nuclear transcription , then loaded with 5-bromo-UTP , a nucleotide analog that is incorporated into elongating RNA . Staining of HCVbp-infected cells with anti-bromo-uridine ( BrU ) and NS5A antibodies resulted in the detection of both signals in a cytoplasmic subcompartment of Huh-7 . 5 cells incubated with HCVbp ( Fig . 4C , center panels ) . This staining pattern was very similar to that observed in positive control cells , i . e . Huh-7 . 5 cells bearing an HCV subgenomic replicon ( Fig . 4C , right panels ) , but not observed in the uninfected negative control cells ( Fig . 4C , left panels ) . This result presumably reflects the local incorporation of BrU into replicating HCV-based RNA , as has been shown for flaviviruses [37] . Consistent results were obtained with live cells infected with particles encoding a tetra-cysteine tag in NS5A ( Fig . S4D ) . Treatment of cells with viral inhibitors ( interferon α or β plus ribavirin ) prior to their inoculation with HCVbp inhibited the accumulation of viral RNA by ∼10-fold ( not shown ) . The sensitivity of HCV replication to these agents [38] suggests that HCVbp-mediated increase in HCV RNA reflects the activity of the introduced subgenomic replicon . The pre-incubation of HCVbp with serum from an HCV-cured patient ( without circulating HCV RNA by PCR ) decreased the amount of intracellular RNA ( Fig . S4B ) detected by RT-qPCR , compared to that with normal/naive human serum , suggesting the existence of a specific interaction of HCVbp with the immune serum ( presumably IgG ) interfering with their infectivity . The CD81 tetraspanin has been implicated as an important receptor for HCV entry [39] . Albeit of human hepatic origin , the HepG2 cell line lacks CD81 and is poorly permissive for HCV entry but can be rendered permissive by CD81 expression , as previously shown by infection with HCVpp [40] or HCVcc [41] . We found that stable transduction of these cells with a recombinant lentivirus encoding human CD81 resulted in its surface expression ( Fig . S5A ) ; it enhanced the NS5A signal triggered by the incubation of HepG2 cells with HCVbp ( Fig . 5A; compare right and left panels ) . For more quantitative analyses , we devised a variation of the system involving the production of HCV reporter particles ( HCVrp ) . To this end , the fragment encoding NS3 up to the last third of HCV NS5B in the HCVbp construct was replaced with one encoding the ORF of bacteriophage SP6 RNA polymerase ( SP6 Pol; Fig . 5B , top ) . After HCVrp entry into target cells , these cells were co-transfected with the p2B system plus a plasmid encoding EGFP fused with Firefly luciferase , linked to the T7 and SP6 promoters and an EMCV IRES ( Fig . 5B , bottom ) , and were treated with actinomycin D to decrease the background reporter gene expression in the absence of incoming SP6 Pol-encoding RNA , which triggers reporter gene expression in a dose-dependent manner , independent of most post-entry processes . As predicted , parental BHK failed to release infectious HCVrp ( Fig . S5B ) . Although EGFP expression was also observed , only luciferase activity is reported . We tested the dependence of HCVrp entry ( cf . Text S1 and Table S1 ) on surface molecules previously implicated as essential entry receptors in target cells ( Fig . 5C ) . Inhibition of the Firefly luciferase signal generated by HCVrp entry occurred when Huh-7 . 5 cells were pretreated with siRNA pools targeting several HCV candidate receptor molecules: SR-B1 [42] , CD81 [39] , ASGP-R subunits 1 and 2 [43] , and to a lesser extent claudin-1 [44] ( Fig . 5C , filled bars ) . The same siRNA treatments had little effect on entry of WNVrp ( generated by transfecting BHK-WNV cells with a plasmid encoding WNV structural proteins ) , as measured by the Renilla luciferase activities encoded by the WNV subgenomic replicon packaged into WNVrp ( Fig . 5C , open bars ) . SR-B1 siRNA was the most effective at blocking both the protein expression ( Fig . S5C ) and HCVrp entry ( Fig . 5C ) . Consistently , pre-incubation of Huh-7 . 5 cells with antibodies against CD81 and SR-B1 significantly inhibited HCVrp entry signal ( Fig . S5D ) . The interaction of HCV E2 hypervariable region 1 ( HVR-1 ) interaction with SR-B1 is critical for infection [42] and in vivo infection has previously been neutralized by an antiserum against HVR-1 [45] . Preliminary data ( reagent was made available in very limited quantity ) shows that incubation of HCVrp with these anti-HVR-1 antibodies also inhibited their entry into Huh-7 . 5 cells ( Fig . S5E ) . We also tested the possibility of producing infectious particles based on the ability of the JFH-1 strain to infect Huh-7 . 5 cells [11] . Plasmids encoding the genomic RNA of JFH-1 [11] or a Con1-JFH1 ( 1b-2a ) chimera [18] under a T7 promoter were transfected into BHK-WNV producer cells and HCV particles were harvested , then incubated with Huh-7 . 5 cells . Fig . 6A–B shows the detection of viral RNA in the target cells for both constructs . Starting at day 3 , increasing RNA amounts were measured , whereas in cells treated with interferon plus ribavirin no such increase was detected ( Fig . 6A–B ) . As an additional variation of this HCV expression approach , we tested the possibility that BHK-WNV cells could produce authentic infectious HCV particles . We co-transfected BHK-WNV cells with the p2B system and a plasmid encoding a full-length genomic RNA with the consensus sequence of a strain of genotype 1a ( H77 , Fig . 7A ) , which has been shown to be infectious in chimpanzees [46] . The ‘wild type’ particles ( HCVwt ) released into the supernatants were harvested by ultracentrifugation and analyzed by sucrose density gradient centrifugation . In fractions with buoyant densities of 1 . 08–1 . 13 g/cm3 , spherical particles of 50–60 nm in diameter were observed by negative staining electron microscopy; these were not observed with corresponding fractions from control BHK-WNV cells . Some of these particles were positive by immuno-gold electron microscopy , indicating their recognition by immunoglobulins from an HCV-cured patient ( Fig . 7B ) . As HCV isolates from patients are poorly infectious in Huh-7 cells [6] , the infectivity of HCVwt was tested in liver slices from non-infected patients ( negative for HCV , HBV and HIV ) . Like primary human hepatocytes [47] , liver slices can be infected ex vivo with HCVcc ( unpublished ) . The liver slices presumably better reflect the real situation than cell lines do , as both the architecture and cell type diversity of the liver are maintained in their original configuration . After incubation of liver slices with BHK-WNV cell-produced HCVwt ( H77 strain ) [46] or Huh-7 . 5 cell-produced HCVcc ( JFH-1 strain ) [11] , specific staining by anti-HCV antibodies was analyzed by multifocal confocal microscopy; after a few days , the signal appeared within the slices at various locations of a few lobules , and increased up to 6–10 days . Fig . 7C ( upper panels ) shows data obtained at day 8; positive staining by both serum from HCV-infected and monoclonal antibodies against structural proteins was observed ( middle panel ) within two to five lobules of HCVwt-infected slices ( area >1 cm2 ) . The results were similar to those obtained after infection with HCVcc ( right panel ) , and specificity was verified by the undetectable staining in uninfected liver slices ( left panel ) . Infection was detected in clusters of cells within a few lobules , consistent with a recent report showing that HCV infection of the liver involves a limited number of hepatocytes [48] . Results varied in shape and intensity with liver donor , but the specificity of the detected infection signal was further confirmed by additional analyses with control antibodies ( Fig . S6 ) . Similar results were obtained with HCVwt-4cys , encoding a tetracysteine tag within its non-structural gene NS5A , as previously validated in Huh-7 . 5 cells ( cf . Text S1 ) . Six to eight days after their infection with HCVwt-4cys , liver slices were incubated with a permeable biarsenical dye and observed with a two-photon confocal microscope . Specific staining was detected predominantly in a few periportal spaces , and also in mediolobular areas ( Fig . 7C , lower middle and right panels ) of HCVwt-4cys-incubated slices . In spite of a high background that reduces the sensitivity of detection with this technology , the appearance of small clusters of positive signals ( generated in live cells ) is consistent with the local synthesis of HCV non-structural proteins in human liver slices after their ex vivo infection with HCVwt-4cys produced in BHK-WNV cells . As HCV isolates from patients are poorly replicating in Huh-7 cells [6] , [9] , [22] and access to naïve human liver slices of good quality is limited , we tested the possibility that HCVwt could infect HepG2-CD81 cells , which have been previously reported to support replication of patient isolates [6] . To some extent these cells support HCVbp replication ( Fig . 5A ) . The incubation of HepG2-CD81 cells with HCVwt ( produced in BHK-WNV cells ) of subtypes 1a , 1b , and to a lesser extent 2a ( or 1b/2a chimera; not shown ) , resulted in high readings starting at day 0 ( Fig . 8A–C ) . Although the detected amounts of HCV RNA sharply decreased during the first 24–48 hr , which could relate to some non-productive binding/uptake , it raised again afterward; the later increase was abolished by a treatment with interferon and ribavirin added to the cells both prior to and after infection ( cf . results in Huh-7 . 5 cells ) . Incubation of HepG2-CD81 cells with HCVwt of subtypes 1a resulted in more intracellular accumulation of HCV RNA than what was measured after their incubation with HCVbp of the same genotype ( not shown ) ; one possible interpretation is that Huh-7 . 5 cell-adaptive mutations were detrimental to HCVbp replication in HepG2-CD81 cells , similar to what has previously been reported in the liver , in vivo [20] .
Producing large amounts of infectious HCV virions in cultured cells has been difficult , especially for the most prevalent and clinically problematic genotype 1 , which in part relates to its poor ability to replicate in vitro and the subsequent appearance of cell culture-adaptive mutations interfering with its propagation and infectivity . Here , we produced HCV particles of genotype 1 containing a genome previously shown to be highly infectious in vivo [7] , [46] . Their ability to infect human liver slices demonstrates the biological relevance of the particles produced in this in vitro system . Two major features underlie independence from HCV replication , which avoided adaptive mutations typically associated with HCV propagation in cell culture: first , the unique and robust strategy for producing HCV genomes in the cytoplasm independent of HCV replication , and second , the WNV subgenomic replicon that created an appropriate cellular environment for HCV RNA translation as well as particle assembly and release . HCV particle formation likely took place within membrane rearrangements derived from those induced by the WNV subgenomic replicon , as suggested by immuno-gold electron microscopy results . We also observed that the release of HCV particles by BHK cells was enhanced by lineage I WNV [49] and serotype-2 dengue virus [50] subgenomic replicons , but not by one of Semliki Forest virus [51] , an alphavirus belonging to the Togaviridæ family ( Fig . S1B ) . This indicates that , beyond similarities in genomic organizations and sequences [31] , the increased production of infectious HCV could result from common functional properties conserved amongst members of the Flaviviridæ family rather than strict sequence specificity of the proteins encoded by the flavivirus subgenomic replicons . In BHK-WNV cells , this possibility is further substantiated by the lack of correlation between HCV production and translation of the WNV subgenomic replicon , upon inhibition of the latter's activity . The replication of flaviviruses and HCV induce similar membrane rearrangements in the cytoplasm of infected cells [24] , [25] , and our data confirmed that flaviviruses also infect hepatocytes [28] , [52] . In Huh-7 . 5 cells , cholesterol metabolism has been implicated in HCV replication [53] and lipid droplets in its assembly [54] . Likewise , WNV replication involves cholesterol metabolism [55] and , for dengue virus particle formation , the interaction of the viral core with lipid droplets [56] . As the mechanisms involved in the production of HCV by hepatocytes are still debated , these similar features perhaps underlie part of the BHK-WNV cell permissiveness for HCV particle formation . The correlation between reversal of membrane rearrangements and loss of HCV particles production ( not shown ) suggests that these rearrangements , and perhaps related cellular changes ( e . g . cholesterol metabolism and lipid droplet formation ) , are playing a major role in the permissiveness of BHK-WNV cells . However , the down or up regulation of other cellular factors could be involved as well . Thus , several intracellular mechanisms involved in innate immunity interfere with flavivirus propagation [e . g . 57]–[59] , and knockdown of interferon stimulating mechanisms or signaling pathways enhance WNV [58] , [59] and HCV [60] productions in cell culture; WNV [61] and HCV [62] proteins have been shown to directly target such pathways . Here we cannot exclude that such a mechanism took place prior to or upon expression of HCV genes . However , the introduction of a BHK cell-adapted WNV subgenomic replicon into naïve BHK-21 cells rendered them rapidly permissive for the production of WNV , whereas that of HCV appeared after many more passages ( not shown ) . One possible interpretation is that co-evolution of WNV subgenomic replicon and BHK cells under antibiotic selection led to the regulation of additional cellular factors , probably involved in fine tuning WNV replication and/or translation , but absolutely required for the production of infectious HCV . This prompted us to identify such cellular factors in BHK-WNV cells and test their relevance with the JFH-1 strain/Huh-7 . 5 cells paradigm , the results of which will be presented elsewhere . The entry assay with particles produced in BHK-WNV cells ( HCVrp ) requires only the delivery of the associated RNA molecule into the cytoplasm of the target cell where it can be translated at sufficient levels to trigger the dual bacteriophage RNA polymerase amplification system . Thus , the target cell needs to be permissive only for viral entry , and possibly a limited number of post-entry steps ( e . g . RNA uncoating ) . Most importantly , the non-involvement of RNA replication for the signal readout allows assessment of the entry permissiveness of diverse cell types , independent of their ability to support HCV replication . This represents a significant advantage over the HCVcc system that also relies on viral spreading to amplify the read out signal , and the involvement of only HCV structural proteins and RNA clearly distinguishes this system from HCVpp , which is based on non-HCV protein and nucleic acid platform . We had previously observed that both ASGP-R subunits were required for internalization of HCV materials into hepatocellular carcinoma as well as non-target cells [43] . Here we show that these subunits are involved in delivering HCV reporter RNA into HCV-permissive hepatic cells . It is not yet known whether the role of ASGP-R in HCV uptake relates to incomplete maturation of E1 and/or E2 carbohydrate residues , as previously observed [63] , [64] , or involves another mechanism [65] , [66] . HCV has been reported to enter cultured cells via clathrin-coated pits [67]–[69] , and ASGP-R internalization follows the same path [66] , [70] . Yet , ASGP-R can be targeted to various intracellular compartments including ER [43] , which leaves open the possibility that this receptor plays a role at an early as well as a late step of the HCV entry process and RNA delivery . As inter-genotypic differences and cell-adaptive mutations could affect viral production in hepatic cells , the BHK-WNV paradigm provides an alternative model to produce wild type virus for in vivo or ex vivo studies without the concern that adaptive mutations develop . It could also present major advantages for deciphering mechanisms of viral translation , assembly , release and entry , including involvement of non-structural genes in viral production independent of their role in replication .
BHK-21 cells were grown in E-MEM supplemented with 10% fetal bovine serum ( FBS; HyClone ) , GlutaMax-I ( Invitrogen ) ; BHK cells harboring WNV lineage II SG-replicon encoding Renilla luciferase , BHK WNIIrep-REN cells [32] , herein simply called BHK-WNV cells , were propagated in D-MEM supplemented with 10% FBS , GlutaMax-I and 5 µg/ml blasticidin ( Invitrogen ) . Huh-7 . 5 cells and Huh-7 . 5 cells harboring HCV SG-replicon of 1a genotype ( H77 ) with mutations in NS3 and NS5A ( Huh-7 . 5-SG 1a rep ) were maintained as described [9] , [10] . HepG2 cells were grown in E-MEM supplemented with 10% FBS , GlutaMax-I and non-essential amino acid mix . Cells were cultured in an incubator with a 95% air/5% CO2 atmosphere saturated in humidity . A new system of plasmids ( p2B ) was designed to amplify the cytoplasmic transcription of plasmids in which the gene of interest is under the control of a DNA-dependent RNA polymerase ( DdRp ) 's cognate promoter; this system consists of a set of two plasmids generating T7 polymerase ( T7 Pol ) : 1 ) pCR-T7p/SP6pol in which bacteriophage SP6 DdRp ( SP6pol ) gene was cloned into pCR2 . 1 plasmid ( Invitrogen ) in frame with the second ATG start codon of EMCV IRES under the control of T7 promoter; 2 ) pSL-SP6p/T7pol in which bacteriophage T7 DdRp ( T7pol ) gene was cloned into pSL1180 plasmid ( Clontech ) in frame with the second ATG start codon of EMCV IRES under the control of SP6 promoter . This p2B system was used for all T7 Pol promoter-driven HCV coding plasmids , in which a sequence coding for an HDV antigenomic ribozyme [71] was added at their C termini . p90 HCVconFLlongpU encoding the FL genome of infectious H77 strain [46] , or , pH-Neo-SG ( L+I ) encoding a subgenomic replicon of the same strain with cell-culture adaptive mutations [9] were used as templates to construct all HCV coding plasmids of genotype 1a . HCVbp was produced from p684-SG ( L+I ) -HDV plasmid , in which the neomycin resistance gene of pH-Neo-SG ( L+I ) -HDV , i . e . pH-Neo-SG ( L+I ) encoding an hepatitis delta virus antisense ribozyme ( HDV rbz ) after the HCV 3′-end , was replaced with HCV 5′-UTR to NS2 coding sequence . An HDVrbz gene was introduced at the 3′-end of p90HCVconFLlongpU to create p90-T7p/H77FL-HDV plasmid that will produce HCVwt , i . e . virus particles containing the full-length , consensus sequence of H77 strain . HCVbp-4cys and HCVwt-4cys were obtained using modified p684-SG ( L+I ) -HDV and p90-T7p/H77FL-HDV plasmids , in which a tetracysteine tag-encoding sequence [72] had been inserted within the NS5A gene . HCVrp was produced from pCMV ( - ) T7p/HCV-SP6pol-HDV plasmid that encodes HCV 5′-UTR and structural genes followed by those of SP6pol ( entry signal ) gene in frame with EMCV IRES and a sequence encoding carboxy-terminus of HCV NS5B ( kissing loops ) [73] and 3′-UTR . To detect incoming-SP6pol RNA upon HCVrp entry into target cells , pT7-SP6p2/EGFPLuc reporter plasmid was made . This plasmid was derived from pEGFPLuc plasmid ( Clontech ) in which EMCV-IRES-EGFPLuc expression is under the control of both bacteriophage T7 Pol and SP6 Pol cognate promoters in tandem . This construct lacks eukaryotic promoter and therefore is responsive either to T7 Pol , SP6 Pol , or both; it was found responding to either incoming DdRp , be it in the form of protein or DdRp encoding RNA ( not shown ) . Two additional constructs , pHCVp7 and pHCVcore-NS2 are pcDNA3 . 1 ( + ) -based plasmids ( Invitrogen ) , respectively encoding HCV 1a structural genes ( core , E1 , E2 , p7 ) and HCV 1a structural genes plus NS2 . pIRES1hyg-WNV [32] encodes WNV structural genes ( core , prM and E ) . These three plasmids are under CMV early promoter ( not shown ) . pJFH1 [11] , pFK1-Con1 ( 9605Con1 ) [7] and pFK-JFH1Con1C-842 [18] are plasmids encoding from a T7 Pol promoter the genomic RNA of , respectively , the JFH-1 strain ( genotype 2a ) , the Con1 strain ( genotype 1b ) and a Con1-JFH1 chimera ( 1b/2a ) . A DNA fragment encoding an HDV rbz was inserted at the 3′-end of the HCV RNA coding region of each plasmid . Anti-E2 monoclonal antibodies ( ALP98 and AP33 ) [74] and anti-E1 ( A4 ) monoclonal antibody were used for Western blot analysis , and rabbit polyclonal antibody against HVR1 of E2 [45] for inhibition of HCVrp entry . Anti-NS5A rabbit polyclonal antibody ( in-house ) was used for confocal microscopy analysis . To produce rabbit antibody against NS5A of genotype 1a , 48-amino-acid peptide: NH2-AEEDEREVSVPAEILRKSRRFARALPVWARPDYNPPLVETWKKPDYEP-COOH , corresponding to position 2261–2308 of the H77 strain was synthesized by Peptide Synthesis and Analysis Laboratory ( RTB/NIAID/NIH ) ; a cysteine residue was introduced at the amino-terminus and the peptide was coupled to KLH . Two rabbits were immunized from which two sera were harvested; both IgGs were peptide affinity-purified . Sequence of the peptide is almost identical ( but amino acids 22 , 25 , 43 and 46 ) to that of Con1 ( genotype 1b ) . Monoclonal antibody against HCV core protein ( clone C7-50; Thermo Scientific ) was used to analyze Huh-7 . 5-produced JFH-1 ( HCVcc ) infection by confocal microscopy . Antibodies against HCV candidate receptors and cellular proteins are as follow: anti-CD81 mAb ( JS-81 , BD Biosciences ) ; anti-SR-BI rabbit polyclonal antibody ( Novus Biologicals ) ; anti-ASGPR-1 mAb ( clone 8D7 , Santa Cruz Biotechnology ) ; anti-claudin mAb ( Invitrogen ) ; anti-Hsp70 ( BD Biosciences ) ; anti-ERGIC-53 ( Alexis Biochemicals ) and anti-BrdU ( Invitrogen ) . FIAsH- and ReAsH-EDT2 labeling reagents were obtained from Molecular Probes ( Invitrogen ) . For flow cytometry and immunofluorescence ( confocal microscopy ) analysis , the secondary antibodies used were Alexa Fluor 488- , 594- , or 635-conjugated goat anti-mouse and anti-human antibodies , and Alexa Fluor 594- , 635- , or 680-conjugated goat anti-rabbit antibodies from Molecular Probes ( Invitrogen ) . One day before transfection , BHK-WNV cells were seeded at a density of 6×106 cells per 162-cm2 flask . Plasmids encoding HCV sequence under the control of bacteriophage T7 promoter ( or CMV early promoter where specified ) were transfected using Lipofectamine LTX and Plus reagent according to the manufacturer's protocol ( Invitrogen ) . Culture medium after transfection was D-MEM supplemented with 10% FBS , 1% non-essential amino acid mix , GlutaMax-I , 25 mM Hepes; cells were incubated at 37°C . One or two days later , 2 . 5 to 3 . 7 g/L sodium bicarbonate was added ( to prevent further acidification of the medium ) , and culture medium were harvested at day 3 , centrifuged at 30 , 000× g for 30 min at 4°C to remove cell debris , then clarified supernatants were centrifuged at 100 , 000× g for 3 hrs at 4°C . Pellets were either resuspended in culture medium and filtered through 0 . 45 µm PVDF membrane ( Millipore ) , or loaded on the top of a 20–60% sucrose gradient in phosphate-buffered saline solution ( PBS; Quality Biologicals , MD ) , then centrifuged in a SW55Ti rotor ( Beckman ) at 100 , 000× g for 16 hrs at 4°C . Gradients were manually harvested from the top in 150 µl fractions . HCVcc ( Huh-7 . 5-produced JFH-1 ) was obtained by electroporating IVT RNA into Huh-7 . 5 cells as described [11] . Virus stock was concentrated , aliquoted and stored at −80°C . BHK-WNV cells ( 2 . 5×105 ) seeded in a 6-well plate were transfected with HCVbp-coding plasmid . Three days later , cells were fixed in 2% glutaraldehyde in 0 . 1 M sodium cacodylate for 1 hr at RT , then at 4°C , overnight . Cells were subsequently processed for TEM as described [75] . Pooled sucrose fractions containing HCVwt were diluted with PBS then pelleted in Beckman SW55Ti ( 100 , 000× g , for 2 hr ) at 4°C . Pellets were resuspended in 4% paraformaldehyde in PBS and analyzed for negative staining EM . Serum from a cured HCV patient previously infected with genotype 1a was used to detect HCVwt in the immuno-EM analysis . Virus-containing supernatant from BHK-WNV cells were clarified at 30 , 000× g in SW28 Beckman rotor for 30 min , filtered through 0 . 45 µm PVDF membranes then concentrated ( 60-fold ) with 106 MWCO Vivaspin filters ( Sartorius Stedim , Gottingen , Germany ) . Huh-7 . 5 cells ( 7×103 ) were seeded in a 8-well chamber coverglass ( Lab-Tek II , Nalge Nunc ) and incubated with HCVbp for 2 hr at 37°C . After virus inoculum removal , cells were grown for another 48 hr to analyze the expression of HCV NS5A protein . Briefly , cells were washed twice with ice-cold PBS and fixed with 4% paraformaldehyde and 0 . 15 M sodium cacodylate buffer , pH 7 . 4 , for 20 min at room temperature , followed by washing ( 5 minutes , twice ) with PBS containing 50 mM glycine . After washing with PBS , cells were permeabilized with 0 . 3% Triton X-100 in PBS for 15 minutes at room temperature , then incubated with blocking solution ( 10% FBS , 3% BSA , 0 . 3% Triton X-100 in PBS ) for 30 min . Cells were then incubated with primary antibodies: rabbit anti-NS5A IgG and anti-ERGIC-53 mAb ( in 1% BSA , 0 . 1% Triton X-100 , in PBS ) overnight at 4°C . The fluorescent secondary antibodies were Alexa Fluor 488-conjugated anti-mouse IgG antibody and Alexa Fluor 594- or 635-conjugated anti-rabbit IgG antibodies . Nuclei were labeled with DAPI with antifade ( Chemicon , CA ) . To test the infectivity of HCVwt ( 1a , 1b and 2a ) produced by BHK-WNV cells , HepG2-CD81 cells were seeded on 24-well collagen plates , and the following day , cells were incubated with particles in the presence or absence of IFN-α and ribavirin . Total RNA was harvested daily and intracellular HCV RNA was measured by RT-Taqman PCR . Cells were infected with HCV particles containing a genome encoding a tetracysteine-tag ( HCVbp-4cys or HCVwt-4cys ) : Huh-7 . 5 cells were infected with HCVbp-4cys for 3 days , then incubated with the cell-permeant FIAsH-EDT2 or ReAsH-EDT2 biarsenical dye according to the manufacturer's protocol ( Molecular Probes , Invitrogen ) . Adding FIAsH ( or ReAsH ) dye onto live cells expressing TC-tagged proteins should result in a specific fluorescent signal where the tag is present . Samples were observed under a confocal microscope ( SP5 X-WLL ( white light laser ) mono-photon confocal microscope ( Leica , Heidelberg , Germany ) using a 63× oil immersion objective NA 1 . 32 . Images were deconvolved with Huygens Essential software ( Version 5 . 3 , Scientific Volume Imaging BV , Hilversum , The Netherlands ) . A similar procedure was used to stain cultured human liver slices infected with HCVwt-4cys . Huh-7 . 5 cells ( 7×103 ) were seeded in 8-well chamber coverglass and one day later , were infected with HCVbp . At 48 hr post-infection , medium was replaced with D-MEM complete medium containing 2 . 5 µg/ml actinomycin D ( Sigma ) for 30 min and transfected with 5-bromo-uridine 5′-triphosphate ( BrUTP; Sigma ) using Lipofectamine 2000 ( Invitrogen ) . Briefly , 1 µl of Lipofectamine 2000 was added to 10 mM BrUTP , both in 25 µl Opti-MEM I , and incubated for 20 min at room temperature . The BrUTP-Lipofectamine complex was added drop wise onto cells and further incubated for 6 hours . Cells were then fixed , permeabilized and incubated with Alexa Fluor 488 conjugated-anti-BrdU mAb . Confocal microscopy analysis was performed as above . Total RNA from sucrose fractions was extracted with Trizol LS ( Invitrogen ) and RT-TaqMan PCR of HCV 5′-UTR RNA was performed with QuantiTect Probe PCR kit ( Qiagen ) using IVT RNA standard corresponds to the HCV 5′-UTR . HCV RNA was analyzed directly from infected cells harvested daily using lysis buffer of TaqMan Gene Expression Cells-to-CT kit ( Ambion , Applied Biosystems , Invitrogen ) ; RNA was subjected to a RT step followed by HCV TaqMan qPCR analysis performed with HCV specific primers , and HCV 5′-UTR/NH2-core in vitro transcripts as RT-PCR standards . For HCV and WNV RNA analysis from BHK-WNV cells: Total RNA was extracted from cells and pelleted supernatants with Trizol LS followed by RT using random hexamer and Superscript III at 50°C , for 1 hr . Renilla luciferase-specific primers as the target gene for WNV-SG rep RNA . See Text S1 for details . The released particles were filtered , concentrated and serially diluted before incubated with Huh-7 . 5 cells for 3–4 days . NS5A-positive cells were analyzed by immunofluorescence and the number of positive cells was determined using Odyssey In-cell Western system ( Li-Cor Biosciences , Lincoln , NE ) . See Text S1 for details . The cDNA of human CD81 ( hCD81 ) from Huh-7 . 5 cells were amplified by reverse transcription ( RT ) -PCR and cloned into pENTR 2B ( Invitrogen ) followed by recombination with pLenti6 . 2/V5-DEST ( Invitrogen ) to according to manufacturer's recommendation . See Text S1 for details . Human liver slices were infected with HCVcc ( JFH-1 ) produced in Huh-7 . 5 cells , HCVwt produced in BHK-WNV cells , or not infected . Six-to eight days after infection , co-immunostaining was performed with HCV serum or monoclonal antibodies , followed by DyLight 488 conjugated-anti-human IgG F ( ab′ ) 2 ( Jackson ImmunoResearch Laboratories , West Grove , PA ) , or Alexa Fluor 546 conjugated-anti-mouse IgG goat antibody ( Invitrogen ) . Liver slices were analyzed with a mono-photon multi-focal confocal microscope ( Leica SP5 Resonant Scanner , Heidelberg , Germany ) coupled to a high resolution CCD . For live-cell staining , human liver slices were infected with HCVwt-4cys ( HCVwt encoding a tetracysteine tag ) for 6 days , incubated with the cell-permeant TC-FIAsH dye and analyzed ( over a thickness of 100–150 µm ) as above , using a multi-photon mono-focal confocal microscope ( Leica TCS SP5 Resonant Scanner , Heidelberg , Germany ) . See Text S1 for details . See Text S1 for details . All human samples were obtained during routine medical care and in compliance with the standard Ethical Guidelines of the Institutional Review Board of Cochin Hospital ( Paris ) that approved the study . | Two decades after its identification , hepatitis C virus ( HCV ) remains a leading cause of serious liver diseases worldwide . The poor in vitro propagation of patient isolates has impaired their study . Conversely , viral strains of the most prevalent ( ∼70% of total infections ) and clinically problematic ( ∼45% cured with the standard of care ) genotype 1 adapted for in vitro replication display mutations impairing yield and/or in vivo infectivity . We established a new cell culture model for producing infectious HCV in a cell line stably bearing a subgenomic replicon from West Nile virus ( a flavivirus belonging to the same family as HCV ) that circumvents the requirement for HCV RNA replication . To study viral infectivity in vitro , we devised several HCV genome-based constructs . This system produced wild type HCV particles of subtypes 1a , 1b , 2a and a 1b/2a chimera . All specifically infected permissive target cells , and HCV particles containing wild type genomes known to be infectious in vivo infected human liver slices ex vivo . The production of authentic HCV particles independent of HCV RNA replication represents a new paradigm to decipher requirements for HCV assembly , release , and entry , amenable to analyses of wild type and genetically modified viruses of the most clinically significant genotypes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"virology/virion",
"structure,",
"assembly,",
"and",
"egress",
"gastroenterology",
"and",
"hepatology/hepatology",
"infectious",
"diseases/viral",
"infections",
"virology/host",
"invasion",
"and",
"cell",
"entry"
] | 2011 | A New Model to Produce Infectious Hepatitis C Virus without the Replication Requirement |
Recent genome-wide association ( GWA ) studies have identified dozens of common variants associated with adult height . However , it is unknown how these variants influence height growth during childhood . We derived peak height velocity in infancy ( PHV1 ) and puberty ( PHV2 ) and timing of pubertal height growth spurt from parametric growth curves fitted to longitudinal height growth data to test their association with known height variants . The study consisted of N = 3 , 538 singletons from the prospective Northern Finland Birth Cohort 1966 with genotype data and frequent height measurements ( on average 20 measurements per person ) from 0–20 years . Twenty-six of the 48 variants tested associated with adult height ( p<0 . 05 , adjusted for sex and principal components ) in this sample , all in the same direction as in previous GWA scans . Seven SNPs in or near the genes HHIP , DLEU7 , UQCC , SF3B4/SV2A , LCORL , and HIST1H1D associated with PHV1 and five SNPs in or near SOCS2 , SF3B4/SV2A , C17orf67 , CABLES1 , and DOT1L with PHV2 ( p<0 . 05 ) . We formally tested variants for interaction with age ( infancy versus puberty ) and found biologically meaningful evidence for an age-dependent effect for the SNP in SOCS2 ( p = 0 . 0030 ) and for the SNP in HHIP ( p = 0 . 045 ) . We did not have similar prior evidence for the association between height variants and timing of pubertal height growth spurt as we had for PHVs , and none of the associations were statistically significant after correction for multiple testing . The fact that in this sample , less than half of the variants associated with adult height had a measurable effect on PHV1 or PHV2 is likely to reflect limited power to detect these associations in this dataset . Our study is the first genetic association analysis on longitudinal height growth in a prospective cohort from birth to adulthood and gives grounding for future research on the genetic regulation of human height during different periods of growth .
Height is a continuous complex trait which family and twin studies suggest is 80–90% heritable [1]–[3] . Recent genome-wide association ( GWA ) studies have found and replicated associations between common genetic variants from several genomic regions and adult height [4]–[7] . Each of the variants typically has only a small ( ∼0 . 2–0 . 6 cm/allele ) effect on height [4] . Some of the SNPs identified lie in genes which are related to rare and severe monogenic syndromes impacting height in humans , or that can cause growth defects in mice when mutated [4] . Patterns of height growth vary from infancy to early adulthood and are controlled by a number of interacting mechanisms . The fastest gain is observed during the first year of life , followed by a period of slower growth , with another peak in puberty [8] . Longitudinal height growth analysis involves individual growth curve fitting and derivation of growth parameters from the fitted curves . Commonly derived biologically meaningful growth parameters include peak velocities at periods of fast growth and the timing of these peaks [8] , [9] . The choice of periods of fast growth is based on prior knowledge of the biological regulation of height growth during these periods [10] , [11] . Nutritional factors are known to have a considerable role in infancy whereas sex steroids and other hormones strongly regulate height growth in adolescence [12] , [13] . This indicates that different biological pathways are involved in the augmentation of height at different stages of growth [10] , [14] . We therefore expect that different patterns of genetic variation are associated with regulation of height growth at different stages , specifically at the two stages of fast growth: infancy and puberty . This hypothesis has been introduced before [15] but it has not yet been explored in population based genetic association studies . This is the first study to evaluate the effect of genetic variants on different stages of height growth in a large prospective cohort from birth to adulthood . We assessed the associations between variants identified for adult height in GWA studies [4]–[7] and peak height velocities in infancy ( PHV1 ) and puberty ( PHV2 ) and two measures of timing of pubertal growth spurt: age at height growth spurt take-off ( ATO ) and age at peak height velocity in puberty ( age at PHV2 ) . These parameters were derived from longitudinal height growth measurements from birth until adulthood ( on average 20 measurements per person ) in the Northern Finland Birth Cohort 1966 ( NFBC1966 ) . The association between these variants and adult height in this sample was also assessed .
Table 1 describes the growth outcomes in the NFBC1966 . Males had a greater birth length , PHV1 and PHV2 while females had about two years earlier timing of pubertal growth spurt , measured by ATO and age at PHV2 ( see Figure 1 which also shows how height velocity varies by age and sex between 8 and 16 years ) . The correlations between derived growth parameters and birth measures , adult height and body mass index ( BMI ) and age at menarche are as expected , showing internal consistency ( Text S1 , Table S1 ) . For example , age at PHV2 had a correlation of r = 0 . 58 with age at menarche in girls and a weaker but still robust ( p<0 . 0001 ) inverse correlation with BMI at 31 y in both sexes ( r = −0 . 19 in girls , r = −0 . 17 in boys ) . Adult height was more strongly correlated with PHV1 ( r = 0 . 45 in girls , r = 0 . 46 in boys ) than PHV2 ( r = 0 . 14 in girls , r = 0 . 09 in boys ) whereas age at PHV2 did not have a correlation with adult height at p<0 . 05 level . Table 2 shows the associations between all SNPs , growth parameters and adult height from additive models per adult height increasing allele identified in previous studies . To assess age-dependent effects of the variants on growth velocity , the p-value for interaction between the SNP and age ( puberty vs . infancy ) on PHV is shown . The interaction analyses formally tested the hypothesis that different genetic variants are involved in height growth regulation at different stages of life . Due to a high correlation between ATO and age at PHV2 ( Table S1 ) , genetic associations for ATO are omitted from Table 2 but the main results are reported in the text . All the analyses were adjusted for sex and principal components ( PCs; see Materials and Methods: Statistical Analyses ) but not for socio-economic status ( SES ) , birth length or gestational age since the additional adjustment for these variables did not essentially change the results . Table S2 shows further information on these SNPs , including SNP and gene information and allele frequencies . To assess statistical significance , we use p<0 . 05 significance level for adult height , PHV1 , PHV2 and the age-SNP interaction on PHV . For the age at PHV2 and ATO association analyses and for sex-SNP interactions we use Bonferroni-corrected significance level of p<0 . 0011 level ( see Materials and Methods: Statistical Analyses ) because of weaker a priori evidence for the existence of the associations . Based on LD in the NFBC1966 , the 48 SNPs analysed represent 44 independent signals in 43 loci ( see Materials and Methods: Genotyping of SNPs ) . Twenty-four of the 44 signals ( corresponding to 26 of the 48 SNPs ) associated ( p<0 . 05 ) with adult height ( Table 2 ) . All of them had the same direction of effect as identified in GWA studies [4]–[6] . Seven SNPs in or adjacent to the genes SF3B4/SV2A , LCORL , UQCC , DLEU7 , HHIP and HIST1H1D showed an association ( p<0 . 05 ) with PHV1 ( Table 2 ) . All these SNPs except rs6854783 in HHIP were also associated with adult height in our study . All the SNP-PHV1 associations were in the same direction as SNP associations with adult height in the previous GWA studies and in the current study . Five SNPs in or adjacent to the genes SF3B4/SV2A , SOCS2 , C17orf67 , CABLES1 and DOT1L were associated at p<0 . 05 significance level with PHV2 ( Table 2 ) . Of these , three ( related to SF3B4/SV2A , SOCS2 and C17orf67 ) associated with adult height in our sample . All five associated in the same direction as with adult height in the previous studies and in our study . Two of the five ( related to SOCS2 , CABLES1 ) and two additional SNPs ( related to CDK6 , C6orf106 ) associated with timing of pubertal growth spurt ( ATO and/or age at PVH2 ) at p<0 . 05 . However , as we did not have a similar prior evidence for association with the timing of height growth spurt as for height velocities , we cannot declare even the strongest association with age at PHV2 ( C6orf106 , p = 0 . 0057 ) statistically significant after a Bonferroni correction for multiple testing . Only SNP rs11205277 upstream of SF3B4/SV2A showed significant evidence for an association with both PHV1 and PHV2 . SNP rs6830062 in LCORL had a similar effect size on PHV1 ( beta 0 . 74% , 95% CI 0 . 19 to 1 . 21% ) and PHV2 ( 0 . 88% , −0 . 44 to 2 . 17% ) as had SNP rs6842303 in the same gene ( PHV1 beta 0 . 38% , 0 . 01 to 0 . 76% , PHV2 beta 0 . 30% , −0 . 58 to 1 . 19% ) . The associations in LCORL were statistically significant for PHV1 , but not PHV2 , which may reflect inadequate power to detect association with PHV2 . Interaction between SNP and age on PHV was detected for four SNPs that had a main effect ( p<0 . 05 ) on PHV1 and/or PHV2 ( Table 2 ) . For SNPs rs6854783 in HHIP and rs10946808 in HIST1H1D adult height increasing alleles increased PHV in infancy but not in puberty ( p = 0 . 045 and 0 . 0093 ) . SNPs rs11107116 ( in SOCS2 , see Figure 1 for velocity by genotype and age ) , and rs12459350 ( DOT1L ) , showed an effect on PHV in puberty but not in infancy ( p = 0 . 0030 and 0 . 047 ) . Given the strong biological argument for differential effects at different ages [14] , we considered the SOCS2 and HIST1H1D interactions as suggestive and we also found a possible biological explanation for the SOCS2 interaction . The HHIP and DOT1L interactions are borderline significant ( just below p<0 . 05 ) but for the former there is also a possible biological explanation ( see Discussion ) . The interaction between sex and SNP effects on growth was investigated due to differences in growth parameters ( see Table 1 ) . We did not observe any statistically significant sex-SNP interactions on any of the outcomes after Bonferroni correction ( at p<0 . 0011 level ) . The smallest p-value was observed for SNP rs2814933 ( C6orf106 ) which could be associated with timing of pubertal growth spurt in males ( age at PHV2 beta = 0 . 16 years ) while in females there is no effect ( age at PHV2 beta = −0 . 003 years; sex interaction p = 0 . 003 ) . Due to only few interactions that were not significant after Bonferroni correction , the results are shown as sex-adjusted for all SNPs in Table 2 .
Our study is the first genetic association study on longitudinal height growth in a large prospective cohort study from birth to adulthood . Frequent height measurements ( on average 20 measurements/person ) with exact measurement times were obtained from health clinic records . The data are representative of the original cohort and thus the population of Northern Finland ( see Representativeness in Materials and Methods ) . Frequent height measurements from birth to adulthood are rarely available in large population based studies and this makes replication of the results challenging . Fitting similar models and deriving similar phenotypes across study populations would be required to ensure comparability of the results . This is , however , impossible without dense measurement points . One possibility in the future is to combine several smaller studies with dense height growth measurements for replication and meta-analysis . The analyses show high internal quality of the parameters derived from the growth curve models based on their associations with observed birth measures , height , BMI and age at menarche . However , some assumptions had to be made to account for random variation associated with the derived parameters . The weighting of the SNP association analyses by the number of measurements per person within the age period in question assumes that the reliability of the growth data has a proportional relationship with the frequency of measurements taken within the age period , and that the measurement accuracy does not depend on the frequency of the measurements taken . Although these seem reasonable assumptions , they are difficult to verify using this data alone . Ideally the analyses would be weighted by the inverse of the variance attached to the phenotypes derived from the growth models . However , the variances for the derived outcomes could not be directly estimated from the models and we used weighting by the number of measurements as a proxy . We chose a standard parametric approach to model longitudinal growth . This has the advantage of natural biological interpretability of the parameters obtained from the fitted models [9] , and appeared to fit our data well . There are a number of alternative approaches , for instance smoothing or regression cubic splines; these are easy to fit but the interpretation of parameters poses challenges , as does the selection of the degree of smoothness to be enforced . We attempted to fit models based on cubic smoothing splines [16] to these data , but found the results difficult to interpret and sensitive to the number and location of knots selected , and therefore present only the results for the parametric growth models . The results of the model comparison in the NFBC1966 for infant height were consistent with the model comparison on early weight growth in another study [17] in Congolese infants , where the Reed1 model showed the best fit . As far as we know , there are no published model comparisons for early height growth in other studies . For the whole period of growth from birth into adulthood , the superiority of the JPPS model over slightly simpler parametric models such as the Preece and Baines ( PB1 ) and modified Shohoji and Sasaki ( SSC ) models has been described elsewhere [18] , and was not tested in our data set . As expected , JPA-2 fitted better than JPPS into our data . The high correlation between ATO and age at PHV2 ( Table S1 ) estimated from the JPA-2 model largely explains the similarities in the results between the two phenotypes . There was also a moderately high inverse correlation between PHV in puberty with the timing of pubertal height growth spurt . This may contribute to some overlap in the genetic association results , and has to be acknowledged in the interpretation of the results . The power to detect an effect size of 0 . 46 cm per allele with adult height was 60% at level p<0 . 05 using MAF = 0 . 31 ( average MAF among the 48 SNPs ) and an additive genetic model . This contributes to the fact that almost half of the signals were not replicated in our study since the known height variants tested typically have a 0 . 2–0 . 6 cm per allele effect size . The statistical power was slightly lower to identify similar effect sizes for PHV in infancy and puberty , and even lower to identify age-SNP interactions . Despite this , we found an interaction with a p-value of 0 . 0030 that together with a meaningful biological explanation gives suggestive evidence for a differential SNP effect by age . This SNP lies in SOCS2 ( Suppressors of cytokine signalling 2 ) which is a negative regulator of cytokine and cytokine hormone signalling via JAK/STAT pathways , and one of its functions is to influence growth and development through effects on growth hormone/IGF-1 signalling [19] . Estrogen has been shown to induce SOCS2 expression in vitro , with a subsequent decrease in JAK-STAT signalling in response to growth hormone [20] . This potential role for SOCS2 in the interplay between steroid hormones and growth , could explain the association we observe between SOCS2 variation and growth velocity during puberty . The lack of association in early infancy could be explained by the fact that height growth is not yet dependent on growth hormone at that age [14] . Also , we found a possible biological explanation for the interaction ( p = 0 . 045 ) for the SNP in HHIP ( Hedgehog interacting protein ) , suggesting an effect on PHV in infancy but not in puberty . HHIP is a component of the hedgehog signal transduction pathway involved in embryogenesis and development [21] . This pathway influences the transcription of many target genes and is important for development of many tissues and organs . It is important in early embryogenesis and cell proliferation , including limb and central nervous system development [21] , [22] . Therefore it seems plausible that variants in HHIP would only play a role in early infancy but not in puberty . However , since the HHIP interaction does not appear to be very strong in our data , this result needs replication . To summarise , our results show that nearly half of the genetic variants associated with adult height in this sample had a measurable effect on PHV in infancy or puberty . Only one variant was associated with PHV in both infancy and puberty . We found suggestive evidence that the associations of some of the variants may be age-dependent . The majority of signals associated with growth parameters in this study lie close to genes that are involved in recognised growth and development pathways , or have a potential role in growth through an effect on gene expression or regulation ( e . g . cell proliferation , bone formation and growth hormone signalling pathways ) . Heritability of adult height is well documented [23]–[25] but heritability of height velocity at different stages of growth is less well established , although some estimates have been provided from family and twin studies [26] . Our study is the first population based genetic study of longitudinal height growth , and provides an insight into how height in humans may be regulated by its genetic determinants during different periods of growth .
Women expected to give birth in 1966 in the provinces of Oulu and Lapland were invited to participate in the Northern Finland Birth Cohort of 1966 ( NFBC1966 ) . Data were collected in pre-natal clinics and at birth ( e . g . birth weight , length , n = 12 , 058 live births ) [27] , [28] . Details of the measurement protocols are published elsewhere [27] , [29] . Additional data were collected via health clinics at age 1 y ( n = 10 , 821 ) , postal questionnaire at 14 y ( n = 11 , 010 ) and 31 y ( n = 8 , 690 ) , and further data on postnatal growth were obtained from communal health clinics . On average 20 height measurements per person were obtained from birth until adulthood ( most between ages 0–16 y ) . About 25% of the records requested had gone missing over the years or could not be obtained . The final number of individuals with growth data and DNA samples was N = 4 , 311 . The number of singletons with growth and genotype data after exclusions explained in the Statistical Analyses was N = 3 , 538 . The measurement times were chosen by national recommendations but there was some variation between individuals . Individuals still living in northern Finland or the Helsinki area at 31 y were invited to a clinical examination ( n = 6 , 007 attended ) . Anthropometric measurements , samples for biochemical assays and for DNA extraction and genotyping ( n = 5 , 753 ) were collected ( Figure 2 ) . Informed consent for the use of the data including DNA was obtained from all subjects . The present study was approved by ethics committees in Oulu and Oxford universities in accordance with the Declaration of Helsinki . Nineteen SNPs that associated with adult height in Weedon et al , 2008 [4] or their proxies were genotyped using DNA collected as part of the NFBC1966 cohort at age 31 y . 5 , 470 DNA samples were available; maximum 4 , 577 were included in the final analyses due to the exclusions explained in the Statistical Analyses ( see also Figure 2 ) . Genotyping was conducted using TaqMan SNP genotyping assays ( Applied Biosystems , Foster City , California ) . PCRs were carried as recommended in the assay literature and genotypes derived from a 7900HT Sequence Detection System plate reader ( Applied Biosystems , Foster City , California ) . Twelve positive samples and twelve negative wells were used as part of the quality control protocol . Genotyping results were checked to ensure the allele frequencies were in HWE . A full plate ( 384 ) was duplicated for the purposes of quality control . The duplication error rate was calculated as the number of ( genotypes disagreed/number of samples duplicated ) /2 . For most assays the duplication error rate was zero with no discrepancies between the results . There were four assays where one or two samples were discrepant between the two sets of genotyping ( where approximately 340 samples were duplicated on both plates ) . Additional 29 SNPs that associated with height in two other publications [5] , [6] or their proxies were obtained from a genome-wide scan for the NFBC1966 ( original , detailed description in [30] ) using Illumina's HumanCNV370-Duo DNA Analysis BeadChip . All these SNPs were directly genotyped ( no imputed genotypes were used ) . Individuals who refused data delivery to collaborating units or had a gender mismatch between genotype and phenotype data were excluded from all analyses . Of those who had relatedness coefficient >0 . 20 ( twins , half-siblings ) , the one with less complete genotype data was excluded at this stage . The number of exclusions in total was 173 , leaving N = 4 , 763 . Further exclusions explained in the Statistical Analyses reduced the final N to 4 , 682 with genome wide data . Figure 2 shows the identification of SNPs for our analyses , i . e . two “arms” , the one for genotyping done separately for NFBC1966 and the other for identification of SNPs from the NFBC1966 GWA data . Basing our analyses on the sub-sample with GWA data enabled us to correct for cryptic relatedness and population structure via PC analysis ( see Statistical Analyses ) . The genetic association results in the full genotyped sample and the sub-sample with GWA were not materially different . Since Weedon et al , 2008 [4] used a different platform ( Affymetrix 500 K chip ) for genotyping , we could not directly obtain all the SNPs they identified from our GWA data . We could have imputed them but preferred to use directly genotyped SNPs . The 48 SNPs from the recent GWA scans [4]–[6] or their proxies represent 43 separate loci ( TRIP11 , GPR126 , LCORL with two SNPs and CDK6 with three SNPs in or near each ) . The SNPs in or near TRIP11 , GPR126 and CDK6 were in high LD with each other in the NFBC1966 sample ( r2 = 0 . 32–0 . 97 ) and therefore were counted as one signal per gene , giving the total number of 44 independent ( r2≤0 . 12 ) signals within 43 loci . PC analysis was applied in the genome-wide scan sample of N = 4 , 763 to characterize the genetic distances between persons within the sample . The first 20 PCs were analysed in association with birth length , adult height , PHV1 , PHV2 and age at PHV2 by sex . In addition to first five PCs , the PCs that were associated with one or more of the growth outcomes in either sex ( PCs 11 , 13 and 15 ) were adjusted in all SNP association analyses to control for population structure ( see the recommendation by Novembre and Stephens [31] ) . Additional adjustment for socio-economic status at birth ( SES ) did not change the results essentially and was not applied . Unpublished data on this cohort show that adjustment for PCs partly corrects for SES in the ( genome-wide ) analysis of adult height due to a correlation between SES and some of the PCs . Adjustment for PCs also corrects for parental geographic location . Sex was adjusted in all SNP association analyses ( sex-interactions explored and reported separately ) . All remaining twins were removed from the analyses , leaving 4 , 682 for genetic analyses . Number was reduced further due to missing data in the phenotypes , e . g . for final height N = 4 , 677 and for growth data maximum N = 3 , 538 ( Figure 2 ) which was further reduced depending on the minimum number of measurement points required for analysis at certain age windows , as explained in Text S1 . This study is hypothesis based since it utilises prior information from GWA studies and can consequently be likened to candidate gene studies . Therefore statistical significance was considered at p<0 . 05 level for the SNP associations on adult height , PHV1 and PHV2 and the age-SNP interaction on PHV . Since we do not have similar prior information for the timing of height growth spurt , we only declare statistical significance at p<0 . 0011 level for ATO and age at PHV2 . This level is based on Bonferroni correction considering 44 independent signals . Previous GWA studies found no evidence for sex-SNP interactions on adult height , although sex is an important determinant of growth and adult height [4]–[6] . We test sex-SNP interactions on each outcome but due to the absence of prior evidence for interactions use Bonferroni correction ( p<0 . 0011 level ) for assessing their statistical significance . Description of growth curve fitting and derivation of growth parameters from the fitted curves is described in Text S1 . The derived parameters from the Reed1 [32] and Jolicoeur-Pontier-Abidi-2 ( JPA-2 ) [33] models were used separately as outcomes in the SNP association analysis . Due to skewness , natural logarithmic transformation was used for PHV1 and PHV2 . To account for the random variation attached to the derived growth parameters , the association analyses were weighted by the number of measurements per person within the age period in question ( infancy: 0–24 months , puberty: 8–16 years for girls , 9–17 years for boys ) . A regression model assuming an additive genetic effect was fitted between each SNP and each growth parameter , adjusted for sex and PCs . Additionally , the same analyses were run with sex-SNP interaction included . Preliminary analyses showed that adjusting additionally for birth length and gestational age does not essentially change the results , and this adjustment was not done . Results are reported per one allele increase in the genotype , the reference allele being the height decreasing allele in the previous GWA studies . SAS ( version 9 . 1 . 3 . ) was used for all the association analyses of genetic variants and growth parameters . In addition , the interaction between SNP effects and age ( infancy vs . puberty ) on peak height velocity ( PHV ) was tested . This was necessary as especially in the context of low power; finding that some SNPs are statistically significantly associated with PHV at one age and not the other does not automatically indicate different pattern of associations between these ages . Since PHV is much higher in infancy than in puberty , PHV Z-scores were calculated from the log-transformed PHV variables at each age to unify their scale . The data from infancy and puberty were combined into a single data set where most individuals had PHV values for both ages , i . e . two records per person , age indicator variable referring to the time when PHV was estimated ( 0 = infancy , 1 = puberty ) . A mixed model for repeated measures that takes into account the within-person correlation in the outcome values was chosen . The mixed model was fitted between each SNP and PHV Z-score without pre-defined covariance structure for the error matrix ( type = unstructured ) , with SAS PROC MIXED ( version 9 . 1 . 3 . ) . Age was included into the model as a binary variable ( 0 = infancy , 1 = puberty ) and the age-SNP interaction was tested . The analysis was weighted by the number of measurement points at the age window in question ( on average 7–8 measurements per person at both ages ) . The model was additionally adjusted for sex and PCs . Statistical power was 60% to detect a per allele effect size of 6 . 0% SD ( 0 . 24 cm/year ) for PHV1 , 6 . 6% SD ( 0 . 10 cm/year ) for PHV2 , and 4 . 9% SD ( 0 . 46 cm ) for adult height , assuming a MAF of 0 . 31 , which was the average among the 48 SNPs , additive genetic model and significance threshold p<0 . 05 . For comparison , we had 80% statistical power to detect a per allele effect size of 7 . 6% SD ( 0 . 30 cm/year ) for PHV1 , 8 . 4% SD ( 0 . 13 cm/year ) for PHV2 , and 6 . 2% SD ( 0 . 58 cm ) for adult height with the same assumptions . Quanto ( version 1 . 2 . 3 . ) [34] was used for the power calculations . The sub-sample that attended the clinical examination at age 31 y is adequately representative of the NFBC1966 in terms of gender and socio-economic indicators at birth and at age 31 y [35] . Even better representativeness was observed when the sub-group with growth data and height SNP information ( N = 3 , 538 ) was compared with attendees of clinical examination who did not have this information available ( N = 2 , 469 ) . In this comparison , men had data available slightly more often than women ( 61% vs . 57% ) . There were no differences regarding unemployment history or education ( data available for 58–60% in all groups ) . There were small differences between social classes at birth ( data available for 56–62% in all groups ) . At age 31 y , other social classes had more often data available than farmers ( 57–62% vs . 51% ) , but it has to be noted that this may be explained by random variation since the farmers group at 31 years is small ( N = 214 ) . | Family studies have shown that adult height is largely genetically determined . Identification of common genetic factors has been expedited with recent advances in genotyping techniques . However , factors regulating childhood height growth remain unclear . We investigated genetic variants of adult height for associations with peak height velocity in infancy ( PHV1 ) and puberty ( PHV2 ) and timing of pubertal growth spurt in a population based sample of 3 , 538 Finns born in 1966 . Most variants studied associated with adult height in this sample . Of the 48 genetic variants tested , seven of them associated with PHV1 and five with PHV2 . However , only one of these associated with both , and we found suggestive evidence for differential effects at different stages of growth for some of the variants . In this sample , less than half of the variants associated with adult height had a measurable effect on PHV1 or PHV2 . However , these differences may reflect lower statistical power to detect associations with height velocities compared to adult height . This study provides a foundation for further biological investigation into the genes acting at each stage of height growth . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"public",
"health",
"and",
"epidemiology/epidemiology",
"genetics",
"and",
"genomics/complex",
"traits"
] | 2009 | Genetic Determinants of Height Growth Assessed Longitudinally from Infancy to Adulthood in the Northern Finland Birth Cohort 1966 |
We know a great deal about the genes used by the model pathogen Salmonella enterica serovar Typhimurium to cause disease , but less about global gene regulation . New tools for studying transcripts at the single nucleotide level now offer an unparalleled opportunity to understand the bacterial transcriptome , and expression of the small RNAs ( sRNA ) and coding genes responsible for the establishment of infection . Here , we define the transcriptomes of 18 mutants lacking virulence-related global regulatory systems that modulate the expression of the SPI1 and SPI2 Type 3 secretion systems of S . Typhimurium strain 4/74 . Using infection-relevant growth conditions , we identified a total of 1257 coding genes that are controlled by one or more regulatory system , including a sub-class of genes that reflect a new level of cross-talk between SPI1 and SPI2 . We directly compared the roles played by the major transcriptional regulators in the expression of sRNAs , and discovered that the RpoS ( σ38 ) sigma factor modulates the expression of 23% of sRNAs , many more than other regulatory systems . The impact of the RNA chaperone Hfq upon the steady state levels of 280 sRNA transcripts is described , and we found 13 sRNAs that are co-regulated with SPI1 and SPI2 virulence genes . We report the first example of an sRNA , STnc1480 , that is subject to silencing by H-NS and subsequent counter-silencing by PhoP and SlyA . The data for these 18 regulatory systems is now available to the bacterial research community in a user-friendly online resource , SalComRegulon .
Salmonella enterica serovar Typhimurium ( S . Typhimurium ) is an important foodborne pathogen that causes self-limiting gastroenteritis , or more serious systemic infections in susceptible hosts . In the developed world , there are an estimated 93 . 8 million incidences of salmonellosis caused by non-typhoidal Salmonella ( NTS ) strains , resulting in 155 , 000 deaths each year [1] . In developing countries , NTS strains cause bloodstream infections that kill about 20% of patients . This high mortality rate reflects the combination of pre-disposing conditions such as HIV , malaria and malnutrition , and the emergence of invasive NTS strains [2 , 3] . S . Typhimurium colonises a wide range of mammals and birds , and encounters a series of stressful conditions within various host environments . The bacteria express a Type III Secretion System ( T3SS ) encoded on a pathogenicity island ( SPI1 ) that mediates invasion of the host intestinal epithelium . Once internalised , S . Typhimurium expresses a second T3SS , encoded on a second pathogenicity island ( SPI2 ) , which is responsible for its survival and replication in the intracellular environment within the Salmonella containing vacuole ( SCV ) and for the establishment of systemic infection [4 , 5] . Salmonella has the ability to rapidly remodel its gene expression profile when exposed to different spatial and temporal cues . This co-ordinated transcriptional programme allows S . Typhimurium to interact with the microbiota and the mammalian host , to multiply and survive in the host intestine and to cause systemic disease [6] . For Salmonella bacteria , infection involves the generation of genetically-identical cells with different but cooperating phenotypes to ensure fitness and optimal spatio-temporal gene expression [7–12] . RNA-seq analysis of individual host cells has revealed that the gene expression profile of infecting bacterial cells can have profound effects on the resulting host response [13] . Such a flexible bacterial gene expression program must be tightly controlled by the dynamic interactions of hundreds of transcriptional regulators . S . Typhimurium encodes 156 protein factors which act at the level of the initiation of transcription [14] . In addition , gene expression is regulated at the post-transcriptional level , largely by trans-acting small regulatory RNAs ( sRNA ) [15] . Trans-acting sRNAs usually affect expression of target genes through short imperfect base-pairing interactions , and often require the RNA chaperone , Hfq [16] . We require an integrated understanding of the regulatory inputs that coordinate the expression of all RNA transcripts of Salmonella . Here , we explore the interconnections between transcriptional and post-transcriptional regulation of S . Typhimurium protein coding genes and sRNAs using bacterial mutants that lack key components of the global transcriptional networks that are associated with SPI1 and SPI2 expression . We used an RNA-seq-based transcriptomic approach to explore the regulons of 18 virulence-associated regulatory systems including sigma factors , transcription factors , two-component systems and an RNA chaperone , under in vitro conditions . Our particular focus was the regulation of sRNA gene expression and we identified 124 sRNA genes that are controlled by at least one of the regulatory systems tested in this work . Molecular and computational approaches were used to validate regulatory interactions . We showed that the putative virulence-associated sRNA , STnc1480 , is silenced by the nucleoid-associated protein H-NS , and that the transcription factors PhoP and SlyA counteract the repressive effects of H-NS at the STnc1480 promoter . Finally , we describe 13 sRNAs which we predict to play important roles in S . Typhimurium virulence , based on their patterns of regulation . We assembled an online compendium of our RNA-seq-based transcriptomic analysis of the regulons of 18 systems that control S . Typhimurium virulence , as a community resource . One important caveat is that these regulons contain genes that are both directly and indirectly regulated . Further experiments will be required to identify the binding sites for each transcription factor across the chromosome . This investigation of the regulatory inputs to the expression of S . Typhimurium coding genes and sRNAs significantly extends current knowledge about the interconnections between transcriptional and post-transcriptional regulatory elements , and is a step towards the elucidation of the topology of regulatory networks that control S . Typhimurium pathogenesis .
Previously , we profiled the transcriptome of wild-type S . Typhimurium 4/74 under 22 environmental conditions [9] . We discovered that S . Typhimurium possesses 280 sRNAs and that the expression of each sRNA was highly dynamic and environmentally-responsive [9] . However , little was known about the protein factors responsible for modulating sRNA expression . Here we investigated global transcriptional and post-transcriptional regulators of Salmonella gene expression to identify regulatory inputs that control the dynamic expression of S . Typhimurium sRNAs . We selected a panel of 18 virulence-associated S . Typhimurium regulatory proteins , and disrupted their expression by generating a set of isogenic deletion mutants ( Tables 1 and 2; Materials and Methods ) . Our published transcriptomic and transcriptional start site ( TSS ) data from wild-type strain 4/74 [9] were used to investigate whether the expression of downstream genes was affected by the genetic constructions used to delete the individual regulatory genes . Typically , the entire coding regions of 18 regulatory genes were removed . To avoid polar effects , resistance cassettes were removed from mutant strains if the deleted gene had its own TSS and was in the 5’ region of an operon . For the sirA mutation , a 432 bp region at the 5’ end of the sirA coding sequence ( CDS ) was deleted to maintain an intact uvrC TSS . For the slyA mutation , the 5’ and 3’ ends of the slyA CDS were left intact to avoid disruption of anmK expression and deletion of the overlapping 3’ end of the slyB CDS on the opposite strand . It is evident from Fig 1A and 1B that each of the 18 selected regulatory genes was expressed in the wild-type strain under the growth conditions used in this study , and that the gene deletions did not usually cause significant polar downstream effects . Exceptions are discussed in S1 Text . Analysis of the transcriptome and sRNA expression landscape in this panel of mutants , using RNA-seq , revealed the complexity of gene regulation , and offers clues to the function of uncharacterised sRNAs . The panel of regulatory proteins ( Table 2 ) included SPI1-encoded transcription factors ( HilA , HilC and HilD ) and transcription factors previously reported to control SPI1 gene expression ( HilE , BarA/SirA , Fur and FliZ ) [17–24] . Mutants lacking these transcription factors were studied under SPI1-inducing conditions . The SPI2-encoded regulators ( SsrA/B ) and regulatory systems that play an important role in intracellular survival and replication of S . Typhimurium ( SlyA , HilD , OmpR/EnvZ , PhoP/Q , PhoB/R ) [25–30] were investigated during growth in SPI2-inducing conditions [31] . Additionally , the regulons of the DNA adenine methyltransferase protein , Dam , and two alternative sigma factors , RpoS ( σ38 ) and RpoE ( σ24 ) , were investigated in Lennox medium; these three transcription factors ( TF ) also modulate S . Typhimurium virulence gene expression [32–38] . Some of the regulatory proteins are encoded by the core ancestral S . Typhimurium genome , while others are associated with horizontally-acquired pathogenicity islands ( S5 Table ) . The selected regulators represent a diverse range of systems that control the extracellular ( SPI1-dependent ) and intracellular ( SPI2-dependent ) infection modes of S . Typhimurium ( S1 Fig ) , as recently reviewed [39] . The regulatory role of each protein was determined by comparing the transcriptome of isogenic mutant and wild-type strains grown in identical environmental conditions . The rationale and abbreviation used for each growth condition is detailed in Table 2 . The wild-type strain and the regulatory mutants shared a similar growth rate in the relevant media ( S1 Table ) . The mapping statistics from three independent RNA-seq runs are detailed in S2 Table . The genes controlled by each of the 18 regulatory systems were defined , as described in Materials and Methods . S3 Table contains lists of all differentially-expressed protein-coding ( CDS ) genes and sRNA genes ( >3-fold change in expression in mutant strain compared to wild-type ) in the panel of regulatory mutants . As previously mentioned , these regulons contain genes that are both directly and indirectly regulated , and further experiments will be required to identify the binding sites for each TF across the chromosome . As SPI1 and SPI2 are the two main Salmonella pathogenicity islands , we began by investigating the transcriptional landscape of these regions in our panel of regulatory mutants ( Fig 2A and 2B ) . To simplify analysis of the SsrA/B regulon , differentially-expressed genes in the ΔssrA , ΔssrB and ΔssrAB mutant strains were combined as ΔssrAB ( see S2 Fig and S3 Table for individual ΔssrA and ΔssrB datasets ) . Expression of the SPI1 CDS genes was reduced by an average of 40-fold in the absence of the SPI1-encoded transcription factors . The SPI2 CDS gene expression was reduced by an average of 109-fold in the absence of the SPI2-encoded and SPI2-associated transcription factors . Analysis of gene expression across a panel of regulatory mutants , rather than focusing on individual mutants in isolation , allowed us to observe patterns of co-regulation between Salmonella regulons . Our transcriptomic approach highlights the regulatory connections that exist between the two main pathogenicity islands , and shows the HilD-mediated cross-talk between SPI1 and SPI2 that has previously been reported [30] . We also found CDS genes that were differentially-expressed ( >3-fold ) in the absence of both HilD and SsrB , and the majority of these 52 HilD/ SsrB-controlled genes were virulence-associated ( Fig 3A and 3B ) . During growth at ESP , the absence of HilD caused the down-regulation of many SPI2-encoded and SPI2-associated genes which confirmed the previously reported HilD-mediated cross-talk between SPI1 and SPI2 . Mechanistically , HilD indirectly regulates SsrB-regulated genes by antagonizing the H-NS-mediated repression of ssrAB at stationary phase under SPI-1 inducing conditions [30] ( see also Fig 2 ) . However , the up-regulation of SPI1-associated genes in the absence of SsrB suggests an additional layer of transcriptional control for SPI1 genes , via SsrB-mediated repression under SPI2-inducing conditions . Eight of the 52 HilD/ SsrB-controlled genes have not previously been directly linked to the SPI1 or SPI2 regulons and in most cases have no known function ( Function UNknown , FUN ) . Two of the 8 genes , mcpA ( STM3138 ) and mcpC ( STM3216 ) , are predicted to encode Salmonella-specific methyl-accepting chemotaxis proteins [40] and display similar expression patterns to the SPI1-encoded , SPI1-associated and SPI4-encoded genes which are directly or indirectly activated by HilD , and repressed by SsrB , but are not regulated by HilA suggesting they are controlled by the HilD feed-forward loop . The other six genes STM1329 , STM1330 , STM1600 , STM1854 , STM2585 and yneC ( STM4079S ) show a pattern of expression that resembles the SPI2-encoded and SPI2-associated genes , that are positively regulated by both HilD and SsrB under SPI1 and SPI2-inducing conditions , respectively , and are upregulated within macrophages [14] . We speculate that the HilD/ SsrB-controlled FUN genes could play a role in virulence and to further support this hypothesis , we interrogated published data from transposon-directed insertion site sequencing ( TraDIS ) involving oral infections of chicken , pigs and calves [41] and data from intraperitoneal infection of BALB/c mice using single gene deletion mutant libraries [42] . The importance of the 52 HilD/ SsrB-controlled genes during infection of these 4 animal models is summarised in Fig 3C . Seven of the 8 HilD/ SsrB-controlled FUN genes are required for infection of at least one of the animal models . The Gifsy-1-encoded putative transposase gene , STM2585 , is required for virulence in all 4 animal models . Moreover , a deletion mutant of STM2585 ( steE ) is attenuated for colonization of mouse spleen and the SteE protein is translocated into the host cell cytoplasm [43] . Furthermore , STM2585 and STM1330 are regulated by SlyA and PhoP [44] , the master regulators of the SsrB regulon . In contrast , mcpC mutants show increased fitness in all animal models , consistent with a role in down-regulating the expression or function of specific traits that may have a fitness cost for the bacterium during the invasion process . The identification of these putative novel virulence factors through investigation of their regulatory patterns highlights the power of our approach to both recapitulate current knowledge on SPI1 and SPI2 regulatory cross-talk , and to predict the role of novel FUN genes in virulence . The CDS targets of many Salmonella TFs have previously been identified by microarray and single gene analysis studies [37 , 44–50] , and data from these published studies were used to validate our method of investigating bacterial regulons . Table 3 highlights the effects of the panel of regulatory mutants on flagellar , SPI1 and SPI2 gene expression in the context of the literature . Our data confirm published S . Typhimurium virulence gene regulatory interactions , and identify new regulatory links described below . Any differences between the regulons defined in this study and the literature are discussed in S1 Text , and typically arise from differences in the growth conditions that were used , strain background or the increased dynamic range and sensitivity of the RNA-seq-based method of transcriptional profiling . Overall , these comparisons confirm that RNA-seq-based transcriptomics accurately characterises CDS gene expression , and so allows individual sRNAs to be assigned to specific cellular regulons . The Transcripts Per Million ( TPM ) approach was used to generate expression values from the RNA-seq data , and to define whether individual genes were expressed ( threshold = TPM 10 ) . Approximately 75% of the 280 sRNAs of wild-type S . Typhimurium strain 4/74 were expressed in all 5 conditions used in this study , while a further 20% of sRNAs were expressed in at least one environmental condition ( Fig 4A ) . In the panel of transcription factor mutants , 44% of S . Typhimurium sRNA genes ( 124 ) were differentially-expressed by at least 3-fold . Almost 50% of the differentially-expressed sRNAs received only one regulatory input ( direct or indirect ) from the panel of TFs and approximately 11% of differentially-expressed sRNAs received 5 or more regulatory inputs from the panel of TFs ( Fig 4B ) . The sRNA genes in each of the categories shown in Fig 4B are detailed in S6 Table . Fig 5 shows the shared sRNA targets between each regulatory system . We identified many sRNAs that are controlled by both the SPI1 and SPI2 regulatory systems , reflecting the hierarchical regulatory structures that control expression of the two main pathogenicity islands . The majority ( >60% ) of differentially-expressed sRNAs were transcribed from intergenic regions , while over 20% of differentially-expressed sRNAs were located in the 3’UTR of a coding gene , a chromosomal location that constitutes a large reservoir for small non-coding RNAs [51] . Forty-eight percent of the 3’UTR-derived sRNAs are transcribed from their own promoter , while the remaining 52% are likely to be processed from the mRNA of the upstream ORF [9] . We speculate that processed sRNAs are often functionally-related to the upstream co-transcribed ORFs , while expression of the sRNAs that are transcribed from their own promoter may be controlled by alternative transcription factors and these sRNAs have a distinct function . Expression of the majority ( 57% ) of ORFs upstream of putative processed sRNAs follow the same regulatory pattern as their 3’-derived sRNAs in the panel of regulatory mutants ( >3-fold change; S6 Table ) , while only one upstream ORF displays the same regulatory pattern as its non-processed downstream 3’UTR-derived sRNA ( S6 Table ) . Over 60% of differentially-expressed 3’-encoded sRNAs were Hfq-enriched [51] , suggesting that many of the RNAs in this category function as canonical trans-acting sRNAs and require the RNA chaperone Hfq . Conversely , only 20% of antisense-encoded sRNAs were enriched for Hfq binding , highlighting the fact that many of this class of sRNAs are Hfq-independent and may act in cis to their target genes ( S3 Fig and S6 Table ) . The size of the sRNA-based regulons ranges from 63 differentially-expressed sRNAs ( RpoS ) to one differentially-expressed sRNA ( HilA and PhoB/R ) ( Fig 6A and S4A Fig ) . The SPI1-encoded regulators HilC and HilD function as part of a feed-forward regulatory loop [4] , and considerable overlap was observed between the sRNA genes that were controlled by these TFs ( Fig 5 ) . The HilE CDS and sRNA regulons were distinct from the HilA , HilC and HilD regulons at IEP , and show a novel positive regulatory role for HilE in the control of metabolic gene expression ( S3 Table ) , in addition to the reported role as a negative regulator of SPI1 genes [52] . The sRNA genes which were differentially-expressed in the absence of the BarA/SirA regulatory system were a subset of the HilC and HilD-regulated genes , reflecting the indirect activation of SPI1 by BarA/SirA , via the sRNAs CsrB and CsrC ( Fig 5 ) [18] . More sRNA genes were up-regulated than down-regulated in the mutant strain that lacks Fur , consistent with the primary function of Fur as a transcriptional repressor [53] . RyhB-1 and RyhB-2 , the S . Typhimurium homologues of the E . coli Fur-repressed sRNA RyhB , were up-regulated in the absence of Fur . We investigated whether promoters of sRNAs which were up-regulated in the absence of Fur contained “Fur-boxes” or recognition motifs for Fur binding , consistent with direct repression of expression by Fur . The promoters of five up-regulated sRNA genes contained high-scoring putative Fur boxes ( S5 Fig ) . The highest scoring Fur box was present in the promoter of the Fur-dependent RyhB-1 gene [54] . Other high-scoring Fur boxes were located in the promoters of sRNA genes located adjacent to iron-associated or Fur-regulated operons . STnc4000 contains a putative Fur binding site overlapping the -10 site of the promoter region . This sRNA is encoded in an intergenic region and is transcribed divergently from the bfd gene , which encodes a Fur-regulated bacterioferritin-associated ferredoxin protein . The second highest scoring putative Fur binding site was obtained for a sequence overlapping the -10 site of the STnc3250 promoter . STnc3250 is intergenic and transcribed divergently from fhuA , which is the first gene in a Fur-dependent operon that encodes ferrichrome-iron associated proteins . The proximity of STnc4000 and STnc3250 to genes involved in iron homeostasis raises the possibility that Fur regulation of STnc4000 and STnc3250 may be involved in the control of iron metabolism . The Fur-repressed divergently-oriented promoters of STnc3250 and STnc4000 may represent new examples of the bidirectional promoters that belong to the Fur regulon , such as , fepA-fes , fepD-ybdA and fepB-entCEBA [55] . We found that SPI2-associated regulators OmpR/EnvZ , PhoPQ , SsrAB and SlyA control more sRNAs than the SPI1-associated regulators ( Figs 5 and 6A and S4A Fig ) . Many of the sRNA genes which are differentially-expressed in the SPI2-associated regulatory mutants are likely to be indirectly regulated , reflecting the complexities of the SPI2 regulatory hierarchy [56] . The sRNAs which are differentially-expressed in the absence of SsrA/B are a subset of those that are differentially-expressed in the absence of the PhoP/Q and OmpR/EnvZ two-component systems ( TCS ) ( Fig 5 ) , reflecting the roles that PhoP and OmpR play in the control of virulence gene expression as well as the regulation of components of the ancestral genome [57 , 58] . STnc1860 was the only differentially-expressed sRNA in the ΔphoB/R mutant ( >4-fold decrease in expression ) . STnc1860 is located downstream of the phoU gene which encodes a transcriptional regulator of the PstSCAB-PhoU high affinity phosphate transport system operon and is a target of the PhoB/R TCS [59] . STnc1860 is co-transcribed with this operon from the pstS start site [9] and could be involved in the control of phosphate assimilation . Overall , each regulatory system modulated the expression of a similar proportion of sRNAs and CDS ( Fig 6B and S4B Fig ) . The general effect of the panel of regulatory mutations is negative on expression of CDS and sRNA genes . More differentially-expressed CDS genes were controlled by the SPI2-associated regulatory systems than the SPI1-associated regulators . The key difference between the sRNA-based and CDS-based regulatory networks was seen in the context of RpoS-dependent transcriptional control . RpoS modulated the expression of more sRNA genes than other regulatory proteins ( S4B Fig ) , whereas the CDS-based network of RpoS was among the smaller regulons , containing only 190 putative RpoS-dependent coding genes . We propose that RpoS is a hub for sRNA-mediated gene regulation in S . Typhimurium . RpoS-regulated genes play a role in the general stress response at LSP [60] . The prominent role played by RpoS in regulation of sRNA expression at LSP may reflect the pleiotropic functions that sRNAs play in mediating cellular responses to stress [16] , or the lack of expression of many CDS under LSP conditions . The large number of sRNAs which were differentially-expressed in the absence of RpoS highlights the importance of RpoS as a hub for post-transcriptional regulation [61–63] . Of the 63 sRNAs that are differentially-expressed in the absence of RpoS , 36 are Salmonella-specific and 27 are conserved in other species [14] ( Fig 5 ) . ChIP analysis has shown that three sRNAs , OmrA , SibC ( RygC ) and RyeB ( SdsR ) belong to the core RpoS regulon of E . coli [64] . These three conserved sRNAs were also differentially-expressed in the absence of RpoS in our study ( S3 Table ) . Furthermore , Peano et al . identified an RpoS binding site in the intergenic region between the divergently transcribed tisB gene and the conserved sRNA IstR-1_2 . The expression of IstR-1_2 is reduced in the absence of RpoS in our study , suggesting that RpoS activates IstR-1_2 expression in both species . We anticipate that , in future , the joint interrogation of our transcriptomic data with other global ChIP-based studies will elucidate entire regulons . We aligned the promoter sequences of the 49 sRNAs that were down-regulated ( >3-fold ) in the absence of RpoS to determine if the promoters of the Salmonella-specific sRNAs contain hallmarks of RpoS-dependent promoters and are , therefore , likely to be genuine members of the RpoS regulon . A C nucleotide at position -13 relative to the transcriptional start site is highly conserved among RpoS-dependent promoters , and does not favour binding by RpoD [65] . Twenty-one of the 49 RpoS-dependent promoters contained a C at position -13 , including the promoters responsible for the expression of OmrA , SdsR and IstR-1_2 , ( S6 Fig ) . Placing of a G or T at position -14 and degeneracy of the sequence and position of the -35 hexamer are also typical of RpoS-dependent promoters [66] and are common features of the RpoS-dependent sRNA promoters ( S6 Fig ) . sRNA promoters are not qualitatively different from the promoters of CDS [67] and , with the exception of the RpoS regulon , the striking similarities of regulatory input on sRNA promoters with the regulatory input on CDS promoters demonstrate that the transcriptional regulation of sRNAs is mediated by established cellular regulatory networks . Our panel of S . Typhimurium regulatory systems did not reveal a single dedicated TF for transcriptional control of all sRNAs , suggesting that sRNAs are likely to have been integrated into existing networks as required . To confirm the regulatory findings for sRNAs of interest , we used northern blots to confirm differential sRNA gene expression between wild-type and regulatory mutants ( Fig 7 ) . Expression of the sRNA STnc520 decreased 17-fold in the absence of the primary SPI1 regulator , HilD , at ESP ( Fig 7A ) . In S . Typhimurium strain 14028 , expression of STnc520 was directly activated by the SPI1-encoded transcription factor , SprB ( Joseph T . Wade , pers . comm . ) . Direct regulation of STnc520 by SprB was also confirmed in S . Typhimurium 4/74 in this study ( S7A and S7B Fig ) . Expression of the FNR-dependent sRNA FnrS decreased approximately 32-fold in the absence of Fur at ESP ( Fig 7B ) , consistent with the reported overlap between the Fur and FNR regulons [45] . The Fur-mediated up-regulation of FnrS is unlikely to be a direct effect , however , as Fur does not directly activate transcription [53] . The wild-type strain accumulates the sRNA STnc1330 at LSP and , in keeping with previous findings [63] , STnc1330 expression is reduced approximately 61-fold in the absence of RpoS ( Fig 7C ) . RyeF expression was reduced 25-fold in the absence of S . Typhimurium RpoE ( Fig 7D ) and we note that MicL , the E . coli orthologue of the RyeF sRNA , belongs to the RpoE regulon in E . coli [68] . The Salmonella-specific sRNA , STnc1480 , was most highly expressed under environmental conditions that mimic the host intracellular environment and within murine macrophages [9 , 14] . STnc1480 has multiple regulatory inputs from SPI2-associated regulatory systems , with the largest reduction in STnc1480 expression ( approximately 58-fold ) being seen in the absence of both the PhoP/Q and SlyA regulatory systems ( Fig 7E ) . The expression and regulatory profile of STnc1480 suggests that this sRNA may be important during the intracellular lifestyle of Salmonella , leading us to investigate the expression pattern of this sRNA in greater detail . The PhoP and SlyA-dependent expression of STnc1480 was confirmed by ectopic expression of PhoP or SlyA from the arabinose-inducible PBAD promoter , which specifically restored STnc1480 expression in ΔphoP and ΔslyA mutant backgrounds respectively; however ectopic expression of either protein was unable to restore STnc1480 expression in a mutant strain which did not express the other regulator ( Fig 8A and 8B ) . These data argue that both SlyA and PhoP are required for optimal expression of STnc1480 . There is significant overlap between the SlyA and PhoP regulons and a transcriptional requirement for both SlyA and PhoP has previously been reported for horizontally-acquired genes that are subject to silencing by the nucleoid-associated protein H-NS [44 , 69 , 70] . We , therefore , determined whether STnc1480 transcription was subject to H-NS-mediated silencing and subsequent counter-silencing by SlyA and PhoP . Chromatin immunoprecipitation followed by qPCR was used to investigate H-NS occupancy of the STnc1480 promoter under non-inducing conditions ( MEP ) and inducing conditions ( Low Mg2+ ) . The proV promoter and the hemX gene were used as positive and negative control regions , respectively , as H-NS binds to the proV promoter and does not bind to the hemX gene [71] . Fig 9A and 9B are representative of two independent biological replicates and show that H-NS associated with the STnc1480 promoter , with greater enrichment observed for the experimental IP sample ( FLAG ) , compared to the mock IP sample under both inducing and non-inducing conditions . These data indicate that H-NS bound directly to the STnc1480 promoter under non-inducing conditions and that H-NS was not displaced from the STnc1480 promoter under conditions when the sRNA was highly expressed . To further understand the relationship between STnc1480 and the TFs PhoP and SlyA , and to characterise the roles of PhoP and SlyA in counter-silencing , we investigated the expression of STnc1480 in strains lacking a functional H-NS protein in either ΔphoP or ΔslyA mutant backgrounds . Fig 9C confirms that STnc1480 was not highly expressed during logarithmic growth in rich medium in a wild-type background , and that STnc1480 expression was de-repressed in the absence of a functional H-NS protein under this growth condition . SlyA was no longer fully required for STnc1480 transcription under non-inducing or inducing conditions , arguing that the key role of SlyA in STnc1480 expression is to counteract the repressive effects of H-NS , rather than to activate transcription; SlyA plays a similar function at the pagC promoter [70] . Counter-silencing of H-NS is likely to be achieved through SlyA-mediated restructuring of the STnc1480 promoter architecture , rather than displacement of H-NS . We speculate that PhoP plays the role of a classical transcriptional activator at the STnc1480 promoter , rather than counter-silencer , as the presence of PhoP is required for transcription even in the absence of a functional H-NS protein . The “guilt by association” hypothesis posits that groups of genes which perform similar functions are co-expressed and/or co-regulated , allowing transcriptomic data to be used to identify genes which may share related functions [72] . Several approaches are available to reveal novel interactions between TF and co-expressed genes [73] , and correlative patterns are becoming widely used for the inference of causal influence and to define transcriptional networks [74] . We previously identified transcriptional signatures for SPI1- and SPI2-related genes , based on the expression profiles of the archetypical SPI1 gene , prgH , and the archetypical SPI2 gene , ssaG [9] . To identify sRNA genes which may play important roles in S . Typhimurium virulence , we searched for sRNAs with expression profiles that closely correlate to the expression of SPI1 and SPI2 genes . Our global transcriptomic analyses identified 2 sRNA genes that showed a SPI1-like pattern of expression across 22 environmental conditions [9] , within murine macrophages [14] and in our panel of regulatory mutants ( Pearson correlation coefficient > 0 . 7 [prgH] ) ( Table 4 and Fig 10A ) . InvR ( located within SPI1 ) is transcriptionally activated by the primary SPI1 transcription factor , HilD [75] , confirming the value of correlative analysis for identifying genuine regulatory interactions . As previously discussed , the second SPI1-like sRNA , STnc520 , is directly regulated by SPI1-encoded SprB . Eleven sRNA transcripts showed a SPI2-like expression pattern ( Pearson correlation coefficient > 0 . 7 [ssaG] ) ( Table 4 and Fig 10A ) . None of the SPI2-like sRNA genes are located in the SPI2 pathogenicity island . In fact , the SPI2-like sRNA STnc3020 is encoded within the SPI1 island , antisense to prgI , and shows a modest negative correlation with expression of the archetypical SPI1 gene , prgH ( Pearson correlation coefficient: -0 . 21 ) . A high scoring SsrB binding motif was identified adjacent to the putative -35 site of the STnc3020 promoter ( S8 Fig ) . This observation raises the intriguing possibility that STnc3020 has been co-opted by SsrB to integrate regulatory and environmental cues and mediate cross-talk between SPI1 and SPI2 . The STnc1480 sRNA shows a SPI2-like expression profile [9] . The previously-discussed PhoP- and SlyA-dependence of this sRNA suggests that STnc1480 could play a role in the expression of SPI2-associated regulatory systems . Furthermore , a second SPI2-like sRNA , PinT ( STnc440 ) is PhoP-dependent , and was one of the most highly up-regulated sRNA transcripts upon internalisation of Salmonella within macrophages [14] and various other host cell types [76] . PinT represses both SPI1- and SPI2-associated virulence genes , and is a post-transcriptional timer of Salmonella gene expression during infection [76] . We speculated that some uncharacterised sRNAs that show SPI1 or SPI2-like expression patterns could have important functions in S . Typhimurium virulence and we investigated potential virulence phenotypes in the context of TraDIS datasets [41] . The two SPI1-like and four SPI2-like sRNAs were required for optimal fitness during infection of the chicken , pig or calf models ( summarised in Table 4 ) . We present a hypothetical model of a regulatory network which highlights the key regulatory inputs ( >3-fold change in expression ) of the 13 SPI1-like and SPI2-like sRNAs ( Fig 10B ) , showing direct or indirect regulation of the SPI1-like and SPI2-like sRNAs mainly by SPI-associated regulatory proteins . The two SPI1-like sRNAs , InvR and STnc520 , receive negative regulatory inputs from the SPI2-associated regulatory proteins in addition to the positive regulatory inputs from the SPI1-associated regulators , consistent with cross-talk between the expression of the SPI1 and SPI2 systems . The 11 SPI2-like sRNAs are highly interconnected by their regulatory inputs reflecting the complexity of the SPI2 regulatory hierarchy , although a number of the regulatory interactions are likely to be indirect . This hypothetical regulatory network provides the foundation for future experimentation to identify the direct mechanism of transcriptional regulation of the SPI1-like and SPI2-like sRNAs , which present interesting candidates for further analysis . Our laboratory recently published a phylogenetic analysis of the 280 Salmonella sRNAs across 29 enterobacterial genomes [14] . One hundred and seventy-six “Salmonella-specific” sRNAs were identified , that showed >90% sequence identity across the Salmonella genus and <70% sequence identity with other members of the Enterobacteriaceae . Eleven of the 13 SPI1-like and SPI2-like sRNAs were Salmonella-specific . This high proportion of Salmonella-specific sRNAs is consistent with a role for the SPI1-like and SPI2-like sRNAs in the evolution of S . Typhimurium as a pathogen ( S5 Table ) . Despite the fact that none of the 11 SPI2-like sRNAs are encoded within the SPI2 island , six SPI2-like sRNAs are S . enterica-specific and are not conserved in Salmonella bongori . The limited conservation of these sRNAs outwith the S . enterica species suggests that these elements were either acquired with or after their SPI2-encoded regulators , and were co-opted to perform regulatory functions within the intracellular environment after the evolutionary divergence of S . enterica and S . bongori . We performed a similar phylogenetic analysis of the 15 TFs , response regulators and sigma factors used in this study and confirmed that HilD , HilA , HilC , HilE and SsrB are Salmonella-specific regulators , while the remaining 10 regulators are conserved outwith the Salmonella genus ( S5 Table ) . We found that the SPI1-like and SPI2-like sRNAs are controlled by a mixture of regulatory inputs from both Salmonella-specific and Enterobacteriaceae-conserved regulatory systems . The RNA chaperone Hfq facilitates binding between sRNA and target mRNA molecules and contributes to sRNA-mediated post-transcriptional regulation by various mechanisms . Hfq also controls sRNA stability prior to target recognition , either through protection from ribonucleases or through promotion of sRNA decay [77] . The role of Hfq in modulating the global expression of Salmonella sRNAs has not yet been reported as our previous analysis of the Salmonella Hfq regulon relied upon DNA microarrays [78] . Previous co-immunoprecipitation analysis , combined with RNA-seq , demonstrated that approximately half of S . Typhimurium sRNAs were associated with Hfq in a number of environmental conditions [51] . This led us to investigate how the absence of the Hfq protein affects the steady state levels of the Hfq-bound and non-bound sRNAs . Clearly , interactions between Hfq and other proteins or with many mRNAs have pleiotropic effects on gene expression , and these effects cannot always be directly attributed to Hfq [79] . Therefore , we combined the available Hfq co-immunoprecipitation data [51] with our transcriptomic data to identify the Hfq-dependent sRNAs that were physically associated with Hfq , and represent candidate canonical trans-acting sRNAs ( S6 Table ) . Sixty-three of the 280 S . Typhimurium sRNAs were differentially-expressed in the absence of Hfq ( 3-fold or greater change in transcript level ) , and were designated as Hfq-regulated ( S3 Table ) . The majority ( 87%; 55 sRNAs ) of Hfq-regulated sRNA transcripts were enriched by co-immunoprecipitation with Hfq in at least one of the environmental conditions used by Chao et al , and 67% ( 42 sRNAs ) were Hfq-enriched at ESP ( Fig 11A ) . Nineteen sRNAs were enriched for Hfq at ESP but did not show Hfq-dependent expression at ESP . Two of these sRNAs , AmgR and STnc2100 , were not expressed at ESP or in the Δhfq mutant and so were excluded from our analysis . The remaining 17 Hfq-enriched , non-differentially-expressed sRNAs may require Hfq binding for their activity and to aid binding to their target mRNAs but do not require Hfq for stability . A further 8 sRNAs were differentially-expressed in the Δhfq mutant but not enriched for Hfq in any of the conditions used by Chao et al . The differential expression of a number of these sRNAs may have an indirect cause as the absence of Hfq has wide-ranging effects on sigma factors and TFs [78] . Therefore , the differential expression of some sRNAs in the Δhfq mutant may reflect altered transcription rather than an altered rate of turnover . For example , approximately 16% of Hfq-regulated sRNAs were up-regulated in the absence of Hfq ( Fig 11B ) , including the RpoE-dependent sRNAs RybB , MicA and RyeF [68 , 80] . RpoE , and genes in the RpoE regulon , were more highly expressed in a Δhfq mutant due to activation of the extra-cytoplasmic stress response [81] , suggesting that increased levels of RpoE-dependent sRNAs in the Δhfq mutant simply reflect an induction of RpoE . Approximately 30% of up-regulated sRNAs were not enriched for Hfq , compared to only 9% of down-regulated sRNAs which were not enriched for Hfq , reflecting an indirect regulatory effect on the up-regulated sRNA genes ( Fig 11B ) . We investigated how the 55 Hfq-regulated and Hfq-enriched sRNAs ( highlighted using a dashed black box on Fig 11A ) were expressed in the panel of regulatory mutants . The expression of thirty of the 55 Hfq-associated sRNAs was modulated by at least one of the regulatory systems under investigation . We generated a model of the transcriptional regulatory network for these thirty canonical trans-acting sRNAs , based on the sRNA expression patterns in the panel of regulatory mutants ( Fig 11C ) . Approximately half of the Hfq-associated differentially regulated sRNAs were Salmonella-specific , while the other half are conserved in other members of the Enterobacteriaceae family ( S5 Table ) . The potential for diversity of function of Hfq-dependent trans-acting sRNAs is reflected in the diverse range of transcriptional regulators of these sRNAs . The sRNAs with multiple regulatory inputs connect multiple hubs , and we speculate that these sRNAs play a physiological role in connecting regulons to integrate multiple regulatory signals and generate a co-ordinated genetic output in vivo . The sRNAs with fewer regulatory inputs may play more specific roles within their respective regulons . This transcriptomic analysis of the Δhfq mutant , coupled with the Hfq co-immunoprecipitation data [51] , identified a core group of sRNAs that required Hfq for both their activity and stability . Many uncharacterised sRNAs belong to this core group , and represent interesting candidates for further investigation , which are likely to function as canonical trans-acting sRNAs under virulence-associated conditions . We previously developed an online tool , named SalComMac , which allows users to interrogate the expression profiles of S . Typhimurium genes in the wild-type strain grown under a suite of 22 infection-relevant environmental conditions [9] and within murine macrophages [14] . The RNA-seq sequence reads may be visualised in the context of the chromosome using the interactive online browser Jbrowse [9 , 14] . We now provide the gene expression profiles of S . Typhimurium genes in the panel of regulatory mutants as a compendium database ( http://tinyurl . com/SalComRegulon ) , and in the context of the chromosome ( http://tinyurl . com/SalComRegulon-Jbrowse ) . The SalComRegulon tool provides absolute gene expression values in wild-type and mutant strains , and the relative expression of genes in mutant strains compared to the wild-type strain grown in the same environment . This transcriptomic dataset is intended to provide a valuable resource for the investigation of the expression profiles of genes of interest across a panel of regulatory mutants by the bacterial research community . In the past , regulons have been defined in individual Salmonella strains , grown in various conditions and following different experimental criteria . Here , we have used a single S . Typhimurium strain , and just five growth conditions to perform a systematic and high-resolution analysis that offers the first opportunity for direct comparison between these infection-relevant regulatory systems . This analysis has allowed us to examine previously unseen regulatory interactions and uncover novel putative virulence genes . This database will provide the foundation for many hypothesis-driven future studies . We have expanded the regulons of key S . Typhimurium virulence-associated regulatory proteins under infection-relevant growth conditions and we present for the first time a detailed global view of the genes controlled by HilE , FliZ , RpoE and PhoB/R . The RNA-seq approach has given an unprecedented view of S . Typhimurium gene expression at single nucleotide resolution . By analysing the transcriptome of 18 mutants lacking important sigma factors , TFs , TCSs and an RNA chaperone we have identified new interactions between well-characterised regulatory systems , and the regulatory inputs that control the expression of the majority of uncharacterised S . Typhimurium sRNAs . As shown in S1 Fig , the regulatory systems under investigation in this study are key components of the complex transcriptional network responsible for the rapid adaptation required for successful infection of mammalian hosts . The transcriptional control of sRNAs can generate regulatory loops which function via feed-forward and positive or negative feedback [82–84] . We predict that many of the 124 sRNAs that respond to the 18 regulatory systems will belong to new regulatory loops that link TFs to their target genes . The rapid kinetics of sRNA-mediated gene regulation [82 , 83 , 85–87] is likely to extend the flexibility and dynamic range of the regulatory systems of Salmonella . Future analysis of our transcriptomic data in conjunction with other datasets such as the environmentally-responsive and macrophage-regulated sRNA gene expression profiles [9 , 14] , global sRNA target identification and chromatin immunoprecipitation-derived TF binding sites will complete the picture of mixed regulatory interactions within the Salmonella cell . Here , the focus on the SPI1- and SPI2-associated regulons has identified 13 sRNAs that share the transcriptional signatures of S . Typhimurium virulence genes , including six sRNAs that are required for successful infection . We propose that these sRNAs control aspects of the pathogenesis of S . Typhimurium . The study takes us a step closer to the goal of elucidating the high-precision regulatory map of S . Typhimurium that allows this dangerous pathogen to cause hundreds of thousands of human deaths each year .
Salmonella enterica serovar Typhimurium strain 4/74 was used throughout this study [88] . All mutant strains and plasmids used in this study are listed in Table 1 . Cells were routinely cultured in Lennox broth ( 10 g/L tryptone , 5 g/L sodium chloride , 5 g/L yeast extract ) . Unless otherwise stated , overnight cultures were sub-inoculated 1:1 , 000 in 25 mL of Lennox broth in a 250 mL Erlenmeyer flask with appropriate antibiotics . For growth in SPI2-inducing phosphate carbon nitrogen ( PCN ) medium [31] , the inoculum was taken from overnight Lennox broth cultures , as previously described [9] . One mL of the overnight culture was harvested by centrifugation at 13 , 000 rpm at room temperature . The cells were washed 3 times in pre-warmed PCN medium and sub-inoculated 1:500 in 25 mL of minimal medium in a 250 mL flask with appropriate antibiotics . All cultures were incubated at 37°C and 220 rpm in an Innova 3100 water-bath shaker ( New Brunswick Scientific ) . Relative growth rates of wild-type and mutant strains were determined in Lennox and PCN media using a Synergy H1 plate reader ( Biotek ) . Overnight cultures were grown as described above . After overnight growth , strains grown in Lennox medium were diluted in fresh Lennox medium to a normalised OD600 of 0 . 003 . Strains grown under InSPI2 conditions were washed 3 times in InSPI2 PCN medium and diluted in fresh PCN to a normalised OD600 of 0 . 03 . Growth of each strain was assayed in a 96-well plate in a final volume of 200 μL at 37°C , with orbital shaking . Optical density was measured every 10 minutes throughout growth . Cell doubling times were calculated from the optical density measurements taken during exponential growth using www . doubling-time . com . Each strain was assayed in triplicate on 3 independent occasions . For ectopic expression of proteins from the arabinose-inducible PBAD promoter , overnight cultures of strains carrying the pBAD plasmid containing the cloned gene and an empty pBAD vector were set up as previously described . Overnight cultures were diluted in 25 mL of the appropriate medium in a 250 mL flask and grown to the desired OD600 . Cultures were split into two 250 mL flasks and L-arabinose was added at a final concentration of 0 . 2% to one flask , to induce expression from the PBAD promoter , while no arabinose was added to the second culture . Induction proceeded for the indicated length of time and cells were harvested for analysis . Gene deletion mutants were generated as previously described [89] . Genes were tagged in their chromosomal location using an adapted λ Red recombineering protocol as previously described [90] . Mutations or FLAG tags were moved into clean genetic backgrounds by P22 transduction and confirmed by PCR and DNA sequencing ( Source Biosciences , Dublin ) . To ensure antibiotic resistance cassettes would not affect transcription of downstream genes , the resistance genes were removed from mutant strains using the pCP20 plasmid [89 , 91] . Antibiotic resistant mutants or tagged strains were grown to mid-log phase and cells were transformed with the FLP recombinase-harbouring pCP20 plasmid . Cells were recovered for 1 hour at 30°C with aeration and plated on ampicillin or chloramphenicol plates . Transformants were passaged at 37°C without antibiotics to cure the strain of the pCP20 plasmid . Loss of the antibiotic resistance cassette and the pCP20 plasmid were screened for using appropriate antibiotic selection plates and incubation at permissive temperatures . Sequence and ligation independent cloning ( SLIC ) was carried out with modifications [92] . Wild-type chromosomal DNA and plasmid DNA were used as templates to amplify the insert DNA and vector backbone respectively . One μg of DpnI-digested vector backbone and 1μg of insert DNA were treated with 5 units of T4 DNA polymerase in the absence of deoxynucleotide triphosphates to generate single strand overhangs . T4 DNA polymerase treatment proceeded at 23°C for 30 minutes in the presence of 5 mM DTT , 200 mM Urea , 1× BSA , and 1× reaction buffer ( 67 mM Tris-HCl pH8 . 8 , 6 . 6 mM MgCl2 , 1 mM DTT , 16 . 8 mM ( NH4 ) 2SO4 ) . The reaction was stopped by addition of 25 mM EDTA and incubation at 75°C for 20 minutes . 100 ng of T4 DNA polymerase treated vector was mixed with an equal amount of T4 DNA polymerase treated “insert DNA” in a final volume of 10 μL . Samples were incubated at 65°C for 10 minutes followed by “Touch-down” annealing , during which the incubation temperature was reduced from 65°C to 25°C in 1°C decrements and samples were held for 1 minute at each temperature . Annealed vector and insert mixtures were then transformed into chemically competent E . coli TOP10 cells . Positive clones were selected for using appropriate antibiotic selection plates and overnight incubation at 37°C . Clones were screened by colony PCR using plasmid-specific and gene-specific primers . All constructs , gene deletions , and gene tags were confirmed by PCR and sequenced by DNA sequencing . RNA was extracted from S . Typhimurium cells grown to a defined OD600 ( Table 2 ) . Total RNA was isolated from wild-type and mutant strains using TRIzol , as previously described [93] . Briefly , 5 OD600 units were removed from the culture and cellular transcription was stopped using 0 . 4× culture volume of a 5% phenol 95% ethanol “stop” solution . RNA was stabilised on ice , in stop solution , for at least 30 minutes before cells were harvested at 4 , 000 rpm for 10 minutes at 4°C . Pellets were re-suspended in 1 mL of TRIzol Reagent . 400 μL of chloroform was added and the samples were immediately and thoroughly mixed by inversion . Samples were moved to a Phase-lock tube ( 5 Prime ) and the aqueous and organic phases were separated by centrifugation at 13 , 000 rpm for 15 minutes at room temperature . RNA was precipitated from the aqueous phase using isopropanol for 30 minutes at room temperature followed by centrifugation at 13 , 000 rpm for 30 minutes at room temperature . The RNA pellet was rinsed with 70% ethanol followed by centrifugation at 13 , 000 rpm for 10 minutes at room temperature . The RNA pellet was air-dried for 15 minutes and re-suspended in DEPC-treated water at 65°C with shaking at 900 rpm on a Thriller thermoshaker ( Peqlab ) for 5 minutes with occasional vortexing . RNA was kept on ice whenever possible and RNA was stored at -80°C . RNA quality was assessed using a 2100 Bioanalyser ( Agilent ) . RNA to be used for cDNA library preparations was treated with 10 units of DNase I to remove any DNA present in the sample and samples were purified by phenol-chloroform extraction . In vitro transcription by T7 RNA polymerase was used to generate Dig-labelled riboprobes ( Dig Northern Starter kit , Roche ) . Total RNA was electrophoresed through an 8 . 3 M Urea , 1× TBE , 7% polyacrylamide gel . RNA was transferred to a positively charged nylon membrane using the Biometra Fastblot B43 semi-dry blotting apparatus at a constant amplitude of 125 mA for 30 minutes at 4°C . RNA was UV-crosslinked to the membrane at 120 mJ for 2 minutes . The membrane was equilibrated in hybridisation buffer for 1 hour at 62°C in pre-warmed DIG Easy Hyb solution in a rotating hybridisation oven . Boiled riboprobe was added to the pre-hybridising solution and hybridisation proceeded at 62°C overnight . The membrane was washed twice for a total of 10 minutes in pre-heated ( 62°C ) stringency wash buffer 1 ( 2× saline-sodium citrate ( SSC ) buffer , 0 . 1% SDS ) at room temperature with rocking on a see-saw rocker ( Stuart ) , followed by 2 washes for a total of 30 minutes with room temperature stringency wash buffer 2 ( 0 . 5× SSC buffer , 0 . 1% SDS ) with rocking at room temperature . Unspecific sites on the membrane were blocked using 1× casein-based blocking solution , diluted in maleic acid buffer ( 0 . 1 M maleic acid , 0 . 15 M NaCl adjusted to pH 7 . 5 using NaOH pellets ) for 30 minutes at room temperature with rocking . Alkaline phosphatase conjugated polyclonal anti-digoxigenin Fab-fragment was diluted 1:10 , 000 in 1× blocking buffer and immunological detection of the membrane proceeded for 30 minutes at room temperature with rocking . The membrane was then washed twice for a total of 30 minutes in wash buffer ( maleic acid buffer , 0 . 3% Tween-20 ) . The membrane was incubated for 5 minutes in detection buffer ( 0 . 1 M Tris-HCl , 0 . 1 M NaCl , pH 9 . 5 ) and CDP-star was used as the chemiluminescent substrate . Enzymatic de-phosphorylation of CDP-star by alkaline phosphatase was then visualised using an ImageQuant LAS4000 Imager . To determine if RNA samples were equally loaded on the gel , membranes were stripped and re-probed for the 5S ribosomal RNA . Strand-specific cDNA library preparation and high throughput cDNA sequencing ( RNA-seq ) of wild-type 4/74 and isogenic mutants was performed on DNase I-digested total RNA by Vertis Biotechnologie AG ( Freising , Germany ) . No depletion or enrichment methods were used . RNA was fragmented by ultrasound . The 5’ end of each fragment was de-phosphorylated using tobacco acid pyrophosphatase ( TAP ) and a re-phosphorylated using polynucleotide kinase ( PNK ) for 5’ mono-phosphorylation . Poly ( A ) -tails were added to each fragment by poly ( A ) polymerase and an RNA adaptor , containing a 6–10 nucleotide bar-code , was ligated to the 5’-phosphate of each RNA fragment . First strand cDNA synthesis was performed using oligo ( dT ) priming and Moloney murine leukaemia virus reverse transcriptase ( M-MLV RT ) . The resulting cDNA was amplified by PCR to approximately 20–30 ng/μL using a high fidelity DNA polymerase . cDNA was purified using the Agencourt AMPure XP kit ( Beckman Coulter Genomics ) and analysed by capillary electrophoresis . cDNA was sequenced on an Illumina HiSeq 2000 platform . Mutants were always sequenced in the same lane as their wild-type comparator . Sequence reads obtained from RNA-seq experiments were mapped to the 4/74 reference genome using the Segemehl mapping software [94] . To map reads which contained poor quality bases at the 3’ end , we used an iterative trimming approach in which nucleotides were truncated in a stepwise manner from the 3’ end until the reads mapped uniquely or until the read length fell below 20 bases . Reads that did not map uniquely to a single chromosomal location were discarded . Data were normalised using the transcripts per million ( TPM ) method [95 , 96] . A TPM value of 10 was used as the threshold for gene expression based on TPM values of indicator genes which were previously shown not to be expressed under a particular condition [9] . Differential expression of genes between WT and isogenic mutants was calculated from TPM values . Two independent biological replicates of wild-type RNA from ESP , LSP and InSPI2 cultures were used in independent RNA-seq runs . Correlative analysis was used to demonstrate the reproducibility of the RNA extraction , cDNA library preparation and RNA-seq methods ( S4 Table ) . During downstream analyses , independently extracted RNA was used to confirm RNA-seq-based findings by northern blot or quantitative PCR . For visualization of sequence reads in IGB and JBrowse , the read depth was adjusted in relation to the cDNA library with the lowest number of reads . This was achieved by dividing the read coverage at each genomic position by the library size and multiplying it by the size of the smallest library ( ΔompR/envZ ) . ChIP was carried out as previously described [71] . Briefly , protein-DNA complexes were cross-linked by adding formaldehyde to a final concentration of 1% in a drop-wise manner with gentle stirring at room temperature for 30 minutes . Cross-linking reactions were quenched by the addition of ice-cold glycine to a final concentration of 0 . 125 M for 5 minutes with gentle stirring at room temperature . The cross-linked cells were harvested by centrifugation at 4°C at 4 , 000 rpm for 8 minutes and were re-suspended in 600 μL of lysis buffer ( 50 mM Tris-HCl pH 8 . 1 , 10 mM EDTA , 1% SDS , 1× protease inhibitor tablet stock ) and incubated on ice for 10 minutes . 1 . 4 mL dilution buffer ( 20 mM Tris-HCl pH 8 . 1 , 150 mM NaCl , 2 mM EDTA , 1% Trition X-100 , 0 . 01% SDS , 1× protease inhibitor tablet stock ) was added and the chromatin was sonicated on ice to reduce the average DNA fragment length to approximately 500 bp using an MSE Soniprep sonicator ( Sanyo ) . The chromatin was pre-cleared by adding 50 μg of non-species specific IgG ( normal rabbit IgG , Millipore ) . The chromatin was incubated for 1 hour at 4°C on a rotating wheel and 100 μL of protein G-agarose bead suspension was added . The chromatin was incubated for a further 3 hours at 4°C with rotation . Beads were pelleted at 4 , 000 rpm for 4 minutes at 4°C . 200 μL of pre-cleared chromatin was removed and stored at -20°C as “Input” DNA for downstream analysis . The remainder of the pre-cleared chromatin was used to set up Immunoprecipitation ( IP ) reactions . A “mock” IP reaction containing 1350 μL of chromatin and 10 μg of species specific IgG ( normal mouse IgG , Millipore ) was set up to measure background levels of DNA binding to antibodies and beads . Experimental IP reactions contained 1350 μL of chromatin and 10 μg of monoclonal mouse anti-FLAG M2 antibody ( Sigma ) . IP reactions were incubated overnight at 4°C on a rotating wheel . 50 μL of protein G-agarose bead suspension was added to each IP sample and incubation continued for 3 hours at 4°C on a rotating wheel . The beads containing the bound antibody-protein-DNA complexes were carefully washed . Antibody-protein-DNA complexes were eluted from the beads at room temperature by adding 225 μL of elution buffer ( 100 mM NaHCO3 , 1% SDS ) followed by vortexing and pelleting of the beads twice . Both eluates were combined in the same tube . Input and IP samples were treated with 5 ng/μL RNase A and 0 . 3 M NaCl and incubated at 65°C for at least 6 hours or overnight . Protein-DNA cross-links were disrupted by treating Input and IP samples with 9 μg of proteinase K at 45°C for at least 3 hours or overnight . DNA was extracted from Input and IP samples by standard phenol-chloroform extraction followed by ethanol precipitation with yeast tRNA and glycogen as co-precipitants . DNA was re-suspended in nuclease free water at 37°C with shaking at 900 rpm for 1 hour . Input and IP DNA was analysed by quantitative PCR and quantified relative to a standard curve of chromosomal DNA . The quantity of immunoprecipitated DNA is relative to specific protein binding in that region and was calculated as a fraction of the starting amount of DNA ( Input ) . The mock immunoprecipitate and experimental immunoprecipitate were compared to a control region which was negative for specific transcription factor binding . Published data from two studies involving high-throughput analysis of mutant pools during infection showed the virulence-associated roles played by HilD and SsrB-controlled genes . The scoring methods used by the authors of each publication were applied independently to the relevant dataset . High-throughput sequencing of insertion sites of pools of S . Typhimurium transposon mutants was used to compare the ratio of input to output reads to determine relative fitness , following oral infections of chickens , pig or calves . A negative or positive fitness score indicates attenuation ( blue ) or amplification ( orange ) of fitness , respectively , as a result of the transposon insertion . If no output reads were identified for a particular insertion an arbitrary negative fitness score of -15 was assigned [41] . Two S . Typhimurium single gene deletion libraries were used to infect BALB/c mice via intraperitoneal infection , and were isolated from the spleen and liver; DNA microarrays were used to compare the ratio of input to output reads to determine fitness . A negative or positive fitness score indicates attenuation ( blue ) or amplification ( orange ) of fitness , respectively , as a result of the gene deletion [42] . In cases where different fitness scores were assigned to individual mutants isolated from different sites of the host , the most negative or most positive score was chosen . Enrichment for binding by Hfq was determined , as previously described [9] , using published Hfq co-immunoprecipitation datasets [51 , 78] . Briefly , a 5-fold enrichment factor of Hfq immunoprecipitation over a mock immunoprecipitation was used to determine if sRNAs were strongly associated with Hfq . A position-specific scoring matrix ( PSSM ) was generated using alignment of the homologous Salmonella sequences of 15 published Fur binding sites from E . coli ( available at http://arep . med . harvard . edu/ecoli_matrices/ ) , by assigning a score to each possible base at each position within the binding site and normalising to the average G/C content in the S . Typhimurium chromosome . The PSSM was used to scan for direct Fur binding , in the 100 bp upstream of the 30 sRNA genes which were up-regulated in the Δfur mutant , using pattern searching software ( available from rsat . ulb . ac . be ) , as 100 bp was the maximum distance reported for Fur binding upstream of the published Fur-regulated genes used for matrix assembly . The published Fur binding sequences were scanned with the same PSSM to establish a minimum threshold weighted score , below which predicted motifs were considered to be false-positives . Because the lowest weighted score of a published Fur binding site was 7 , and a number of poorly conserved motifs had a score of approximately 7 , we chose the more conservative threshold score of 10 . Only motifs with a weighted score >10 were designated as putative Fur-binding sites . Previously published ChIP-chip analysis of the SsrB regulon identified an 18 bp palindromic sequence with an internal 7-4-7 organisation for SsrB recognition [47] . A position-specific scoring matrix ( PSSM ) was generated from an alignment of these previously identified SsrB-bound sites by assigning a score to each possible base at each position within the binding site and normalising to the average G/C content in the S . Typhimurium chromosome . The PSSM was used to scan approximately 500 bp upstream of the sRNAs which were down-regulated in the absence of SsrB , using pattern searching software available from rsat . ulb . ac . be . A score of 10 was used as the minimum threshold score for an SsrB recognition motif , based on the scores of defined SsrB bound sites scanned with the same PSSM . Investigation of the conservation of 15 transcription factors , TCS response regulators and Sigma factors was determined in 29 enterobacterial genomes using GLSEARCH . 1 . 00 indicates 100% sequence identity . Salmonella-specific regulators were defined as those with >90% sequence identity within the Salmonella genus and <70% sequence identity within other members of the Enterobacteriaceae family . The RNA-seq data generated from this study are deposited at NCBI GEO under the accession numbers GSM2091439 to GSM2091466 , and can be accessed at http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE79314 . | The transcriptional networks and the functions of small regulatory RNAs of Salmonella enterica serovar Typhimurium are being studied intensively . S . Typhimurium is becoming the ideal model pathogen for linking transcriptional and post-transcriptional gene regulation to bacterial virulence . Here , we systematically defined the regulatory factors responsible for controlling the expression of S . Typhimurium coding genes and sRNAs under infection-relevant growth conditions . As well as confirming published regulatory inputs for Salmonella pathogenicity islands , such as the positive role played by Fur in the expression of SPI1 , we report , for the first time , the global impact of the FliZ , HilE and PhoB/R transcription factors and identify 124 sRNAs that belong to virulence-associated regulons . We found a subset of genes of known and unknown function that are regulated by both HilD and SsrB , highlighting the cross-talk mechanisms that control Salmonella virulence . An integrative analysis of the regulatory datasets revealed 5 coding genes of unknown function that may play novel roles in virulence . We hope that the SalComRegulon resource will be a dynamic database that will be constantly updated to inspire new hypothesis-driven experimentation , and will contribute to the construction of a comprehensive transcriptional network for S . Typhimurium . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"pathogens",
"regulatory",
"proteins",
"microbiology",
"dna-binding",
"proteins",
"dna",
"transcription",
"bacterial",
"diseases",
"regulator",
"genes",
"post-transcriptional",
"gene",
"regulation",
"enterobacteriaceae",
"transcription",
"factors",
"gene",
"types",
"bacteria",
"bacterial",
"pathogens",
"salmonella",
"typhimurium",
"infectious",
"diseases",
"proteins",
"medical",
"microbiology",
"gene",
"expression",
"microbial",
"pathogens",
"salmonella",
"regulons",
"biochemistry",
"genetics",
"biology",
"and",
"life",
"sciences",
"organisms"
] | 2016 | The Impact of 18 Ancestral and Horizontally-Acquired Regulatory Proteins upon the Transcriptome and sRNA Landscape of Salmonella enterica serovar Typhimurium |
Cancers arise from successive rounds of mutation and selection , generating clonal populations that vary in size , mutational content and drug responsiveness . Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy . Mutation counts and frequencies resulting from next-generation sequencing ( NGS ) potentially reflect a tumor's clonal composition; however , deconvolving NGS data to infer a tumor's clonal structure presents a major challenge . We propose a generative model for NGS data derived from multiple subsections of a single tumor , and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model . We demonstrate , via simulation , the validity of the approach , and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node . After dividing the tumor into subsections , we perform exome sequencing for each subsection to assess mutational content , followed by deep sequencing to precisely count normal and variant alleles within each subsection . By quantifying the frequencies of 17 somatic variants , we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible . Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time .
Many clones exist within each cancer , and selective pressure imposed by environmental factors , most notably treatments directed at tumor eradication , favors the emergence of clones that grow increasingly resistant to successive rounds of therapy . Incorporating this intra-tumor heterogeneity into strategies for planning , monitoring , and revising cancer treatment could improve outcomes for oncologists and their patients . Therefore , methods for estimating the number , size and mutational content of clones within a patient's tumor are being explored . New approaches are being developed to assess the clonal content of a given tumor . Methods based on the interrogation of individual cells have relied on the use of fluorescent markers [1] , [2] or single cell sequencing [3]–[6] . Whereas fluorescence-based approaches are inevitably limited by the relatively small number of features they can accommodate , single cell sequencing brings the highest possible resolution to characterizing an individual patient's tumor . Nonetheless , single cell sequencing also faces obstacles to its widespread implementation . Evaluating sufficiently large numbers of single cells to obtain statistical power can be prohibitive , for technical or financial reasons . Additionally , it is often difficult to ascertain the identity of the cells being sequenced , and details regarding the spatial positioning of cells relative to each other and to other cells in the tumor are lost when the single cells are obtained . These disadvantages pose significant challenges to the widespread adoption of single cell sequencing as a means for assessing tumor heterogeneity . Complementing single cell approaches are efforts to deconvolve clonal subpopulations based on the frequencies of mutated alleles within one or more bulk tumor specimens . Shah et al . [7] , who sequenced a breast cancer at the time of diagnosis and nine years later , after metastasis , pointed out that allele frequencies of the mutations shared between the two samples could be used to segregate primary mutations into those that occur in a dominant versus subdominant clone . This insight is the basis for a variety of approaches that apply clustering algorithms to mutation allele frequencies , including kernel density estimation [8] and Dirichlet process modeling applied either to the allele frequencies [9] or to a combination of allele frequency , loss-of-heterozygosity status and copy number [10]–[13] . Clearly , statistical power to infer variants and , ultimately , clonal composition , is increased if multiple samples are available for analysis . Accordingly , various studies have examined the progression of cancer within one or more patients over time . Sets of variants that exhibit similar allele frequencies within a single sample are suggestive of a clonal population . Hence , clustering methods to identify groups of mutations associated with a single clone have been applied . For example , kernel density estimation has been applied to allele frequencies from tumor-relapse pairs from eight acute myeloid leukemia ( AML ) patients [14] and from seven secondary AML patients [15] . An orthogonal approach taken by Newberger et al . [16] employs triplet samples of neoplasia , matched normal and carcinoma from six patients to infer lineages of various genetic events . They characterize each locus in terms of a binary vector representing the presence of the mutation across the various samples and then group the loci into classes on the basis of these vectors . After filtering low frequency classes , the classes are used to manually construct a phylogenetic tree . The focus of the study is to identify the shared characteristics of the evolutionary process across six patients with breast cancer . In the current study , we adopt an alternative approach to identify clonal structure . Rather than measuring allele frequencies in multiple samples from the same patient over time , we physically subdivide a single breast cancer specimen and measure allele frequencies within each subsection ( Figure 1 ) . We are aware of two previous studies that have adopted such an approach . Yachida et al . [17] analyzed seven metastatic pancreatic cancers , sequencing from multiple samples per patient . Clones are initially defined relative to sample types ( peritoneal , liver and lung metastases ) . Subsequently , the tumors from two patients are resected and a clonal phylogeny is inferred manually . More recently , Gerlinger et al . [18] carried out exome sequencing followed by targeted deep sequencing on samples from four patients with renal carcinoma . Each primary tumor was divided into 9 regions , and a phylogeny was manually constructed by assuming that higher alternate allele frequencies correspond to earlier mutations . In neither of these studies was an algorithm proposed to automatically infer from such data both the clonal genotypes and the relative frequencies of the clones within each subsection . The method proposed here bears some similarity to the recently proposed Tree Approach to Clonality ( TrAp ) method [19] . The TrAp algorithm aims to identify the number , relative frequencies and genotypes of clones within a tumor using a formalism somewhat similar to ours , based on matrix decomposition . However , rather than analyzing data from multiple sections , the authors use as input a single set of variant allele frequencies and then constrain the resulting optimization problem by introducing a series of four assumptions about cancer evolution . It is not clear whether the method can easily generalize to analysis of data from multiple sections or multiple time points . Here we describe a generative binomial model that incorporates information from multiple sections from a single tumor at a single time point to infer the frequencies and genotypes for a specified number of clones . An implementation of our algorithm is available through Bioconductor as an R package called Clomial ( http://www . bioconductor . org/packages/release/bioc/html/Clomial . html ) . We use Clomial version 1 . 1 . 7 to apply this approach to a breast cancer specimen and demonstrate that the results from our model predict relationships that are phylogenetically and spatially plausible .
We assume that a tumor is comprised of multiple populations of cells ( “clones” ) , each with a unique genotype , and that these populations are heterogeneously distributed within the tumor itself . We collect , from several physical subsections of the tumor , shotgun sequencing reads . We also collect sequencing data from a non-tumor subsection from the same patient . Using the called genotypes from the normal subsection , and restricting ourselves to positions that are homozygous in the normal subsection , each read from a tumor subsection exhibits either a normal allele or a variant allele at each location . We exclude positions that exhibit homozygous normal alleles in all of the tumor subsections . Our goal is to infer , from the remaining mutated positions , the genotype of each clonal population and their relative frequencies within each physical subsection of the tumor . Formally , the problem can be stated as follows . Note that we use bold face letters for random variables , and that and respectively denote the row and the column of matrix . We are given two primary input matrices and , where is the number of mutated loci , is the number of subsections ( of which one is normal and are tumor ) , is the total number of reads ( i . e . , the coverage ) at locus in subsection , and is the number of cancerous reads ( those supporting the mutation ) at locus in subsection . We assume , without loss of generality , that the first of the subsections corresponds to normal tissue , and that the remaining subsections are from the tumor . In addition , we consider , the number of distinct clones in the tumor , as a hyperparameter , and train a model based on a given value of . We assume that the first clone corresponds to the normal cell population and the tumor is composed of tumor clones . Later , we will discuss whether can be estimated from the data . Our task is to infer two matrices: a clone frequency matrix in which is the proportion of cells of clone in subsection , and a genotype matrix in which if clone has the variant allele at locus , and otherwise . The first column of contains all zeroes because it represents the “normal clone . ” By definition , each column of sums to . Also , by construction , the first column of corresponds to the normal subsection and hence consists almost entirely of zeroes , although small non-zero counts may be possible due to contamination from tumor or due to sequencing error . If the first column of consisted entirely of zeroes , then we would expect the first column of to be of the form , but in order to allow for the possibility that the allegedly normal subsection can have slight tumor contamination , we infer the first column of ( as well as the other columns ) . We propose to solve this problem using a generative model whose parameters are learned via expectation-maximization ( EM ) [20] . Accordingly , we define a matrix of hidden variables representing the unknown genotypes of the clones; for instance , if , then the clone has a tumor allele at the locus . We assume that each follows an independent Bernoulli distribution with parameter , i . e . , ( 1 ) We also assume that if a mutation is present in a particular clone , then at that locus the clone is heterozygous with copy number equal to 1 . Therefore , for subsection , if clone has a mutation at locus ( ) , then its contribution to the observed count of cancer alleles is by , half of its proportion in the subsection . Conversely , if a clone does not have a mutation at ( ) , then it does not contribute to the count of variant alleles . By summing up the contributions of all clones , we obtain the total probability that an observed read corresponds to a variant allele rather than a normal allele . Therefore , the probability that a read contains the variant allele at locus in subsection is given by ( 2 ) where is the row of , and is the column of . Finally , we introduce a matrix of random variables representing the observed data , where is the number of reads exhibiting the variant allele at locus in subsection . This matrix encodes our primary assumption about the distribution of the data: for each and , we observe an independent sample of that has a binomial distribution with two parameters and , i . e . , ( 3 ) The first parameter of this distribution is the ( known ) total number of reads at locus in subsection . The second parameter , , is the probability of observing a variant allele; it will be inferred by EM . Given the joint distribution over observed variables and latent variables , governed by parameters , our goal is to maximize the likelihood function . We do so using EM , exploiting three assumptions: ( 1 ) that each subsection contains non-zero normal contamination , i . e . , for all , ( 2 ) independence of the subsections from each other , and ( 3 ) independence of mutations from each other . The first assumption is based on the widely accepted difficulty associated with obtaining perfectly pure samples of tumor cells [21] , [22] . The two independence assumptions essentially state that each locus and each sample is informative . These assumptions are unavoidable: in the presence of very high dependence , only very limited information about the underlying clonal composition of the tumor would be provided by the loci and samples . Furthermore , it is worth noting that these independence assumptions are made conditional on the parameters in the model: that is , the elements of are independent conditional on and . In other words , if we knew the true underlying parameters for the model ( that is , the true genotypes for the clones , and the true proportion of each clone present in each sample ) , then the actual number of “tumor” reads that we would observe for each locus-sample pair would be independent . While the formulation of our inference problem shows some similarity to well-studied matrix factorization problems [23]–[25] , such techniques cannot be directly applied here . Unlike most matrix factorization techniques , which assume a normal distribution , our observations are binomially distributed . Moreover , the elements of the latent matrix are binary , and each column of must sum to 1 . These constraints required us to develop a customized inference algorithm . To frame the EM optimization , we consider the following complete-data log likelihood function of the model: ( 4 ) which can be computed as follows ( for details see Note S4 in Text S1 ) : ( 5 ) where . Our goal is to find the parameters which maximize the likelihood . Because our model involves the hidden variable , we cannot directly maximize the given in Equation 5 with respect to . Instead , we use the EM algorithm to fit the model to the data [26] . EM is an iterative algorithm with two steps—E ( for expectation ) and M ( for maximization ) —in each iteration . In the E step , we use the current estimates of the parameters , , to compute the conditional expectation of . In the M step , we find the new parameters that maximize the conditional expectation . To validate our implementation of the EM optimization procedure and to understand our model's behavior , we produced simulated deep sequencing data and measured the extent to which the model successfully recovers the true clonal structure of the data . For each simulation , we began by randomly generating four matrices . First , we generated a simulated matrix of total read counts with respect to a fixed number ( ) of loci and a fixed number ( ) of subsections with a mean coverage of 1000 reads per locus . The matrix was generated by independently sampling each column ( corresponding to a single subsection ) from a multinomial distribution , where the parameters and correspond to the total number of trials , and the probability of success for each of the loci , respectively . Second , for any clone number , we generated a corresponding Boolean matrix , in which the entry at row and column indicates whether locus exhibits the variant allele in clone . Entries in were generated independently from a Bernoulli distribution with a probability of success , with the exception of the first ( “normal” ) column of , which contains all zeroes . Third , we generated a clone frequency matrix as follows: each element of is independently drawn from a Uniform distribution , and then each column of was divided by the column sum , so that the columns summed to 1 . We then set so that the first column of corresponds to the normal subsection . Finally , for each locus and subsection , we generated the observed number of variant alleles by sampling from a binomial distribution with parameters ( representing the total number of reads ) and ( representing the probability that a given read corresponds to the variant allele ) . This last step complies with our primary assumption about the distribution of the data ( Equation 3 ) . We ran the EM algorithm using the simulated data and and then evaluated the extent to which the estimated clone frequency matrix and mutation probability matrix differed from the corresponding true matrices and . Specifically , we computed the genotype error , defined asand the clone frequency error , Note that , because we did not know which columns of correspond to which columns of , we compared to every permutation of the columns of and selected the permutation that resulted in the smallest genotype error . The selected permutation was then also used in the calculation of the clone frequency error . Our simulation results ( Figure 2 ) exhibit two primary trends . The overall error rate , as measured by either genotype or clone frequency error , decreases systematically as the number of subsections increases , and increases as the number of clones increases . Overall , both error rates are low , especially for . The observed trends are expected: for a fixed number of clones , the availability of more subsections leads to more accurate estimation of the true parameter values; and for a fixed number of subsections , the presence of more clones leads to a greater number of parameters that must be inferred , leading to greater error in estimation . To assess the affect of sequencing error on the performance of Clomial , we added noise to the simulated data and repeated the above experiments . Specifically , we modeled noise by Bernoulli random variables with probability of success interpreted as the probability that a non-tumor allele is read as a tumor allele or vica versa . Running the EM algorithm on the noisy data revealed that Clomial is robust with respect to noise for all reasonable levels of sequencing error ( Figure S6 ) in Text S1 . We obtained breast cancer tissue from a 44 year old premenopausal female with infiltrative ductal carcinoma ( IDC ) with ductal carcinoma in situ ( DCIS ) , stage pT1c pN1 , Grade II/III , estrogen receptor ( ER ) positive , progesterone receptor ( PR ) positive and Her2 negative . Axillary lymph node dissection revealed that one out of 13 nodes was positive for metastatic disease . A total of 6 tissue sections were obtained , including 2 sections from adjacent normal breast tissue , 3 from the primary breast cancer , and 1 from the positive lymph node . The tumor content , including both IDC and DCIS , ranged from 40% to 55% in the primary tumor and axillary lymph node tissue sections based on pathological examination . For subsequent analysis , each tissue section was subdivided into subsections ( Figure 3 ) . To identify mutations and quantify allele frequencies , we performed two rounds of DNA sequencing . Initially , DNA was extracted from each individual subsection and subjected to exome capture followed by Illumina sequencing . Variants were detected independently in each subsection using the SeattleSeq Annotation Server . We focused on single nucleotide variants and short indels that exhibited a coverage of reads in at least one of the subsections , ranking them using DeepSNV [31] and Fisher's exact test ( Methods ) . This analysis produced an initial set of 281 variants ( Dataset S1 ) . To better quantify the allele frequencies at these loci , we designed primer pairs surrounding each locus and used these primers to perform a second round of targeted DNA sequencing . This experiment successfully sequenced 244 of the 281 loci , with a mean and median coverage of 1615 and 1118 , respectively , reads per locus . Each of these loci was individually validated by visual inspection using the Integrative Genomics viewer ( IGV ) . Manual inspection showed that many of the initially identified mutations were flanked by homopolymer repeats , suggesting that the alternate alleles were read calling errors , rather than true mutations [32] . For all downstream analysis we focused on a set of 17 confirmed somatic variants . For clarity of presentation , we refer to each somatic variant by the chromosome where it resides , appending a letter if more than one somatic variant occurred within a chromosome ( Table S1 in Text S1 ) . The targeted sequencing thus produced two 17-by-12 matrices containing , respectively , the total coverage and the tumor allele count at each locus ( Table S1 in Text S1 ) . Visual inspection of the allele frequency profiles shows , not surprisingly , a markedly different pattern of allele frequencies among the subsections from primary and metastatic sites ( Figure 3 ) . In addition , several of the samples ( e . g . , P1-4 and P3-1 ) exhibit consistently lower frequencies across all loci , presumably indicating a higher prevalence of normal cells within these samples . We applied our EM optimization procedure to the two counts matrices , varying the number of assumed clones from C = 3 up to C = 6 . For each value of C , we ran EM 100 , 000 times from different random initializations , and we selected the solution with the highest likelihood ( Figure 4 ) . The resulting three-clone solution identifies two mutations , chr4a and chr9b , that occur in both the primary and metastatic samples and segregate the remaining mutations into nine that occurred in the primary tumor and six that occurred in the metastatic lymph node . The four- and five-clone solutions further subdivide the primary tumor mutations , and the six-clone solution separates the two metastatic mutations into distinct clones . To better understand the inferred clonal landscape , we investigated the relationship between clone frequencies and the anatomy of the three primary and one metastatic tumor sections . We hypothesized that clone frequencies should vary smoothly between adjacent subsections , reflecting the physical spread of successful clonal populations . This hypothesis is supported by the data ( Figure 5 and Figure S1 in Text S1 ) . The trends are most striking in sections P1 and P2 , for which we obtained four separate subsections . In each case , the primary clone frequencies vary in a monotonic fashion as we traverse the sample . Given that the EM inference procedure was provided with no information about which subsection was derived from which section , nor the relative orientation of the subsections to one another , the smoothly varying frequencies among adjacent subsections provides evidence that the method has successfully identified true clonal variation . Cancer progression is an evolutionary process in which clones accrue mutations over time , forming new clones . Accordingly , it should be possible to organize the clonal progression of a tumor into a phylogenetic tree with the founder clone at the root . We therefore investigated whether the clones inferred by our EM procedure obey some simple phylogenetic constraints , with two complementary goals . First , because our EM procedure makes no use of phylogenetic constraints , this analysis can provide further evidence for the validity of our inferred solutions . Second , the phylogenetic analysis has the potential to provide significant insights into the clonal and mutational history of this specific cancer . We started with the C = 3 solution to our EM algorithm , manually constructing a phylogenetic tree in which each node is a clonal population , and edges are marked with the mutations that occurred in the evolution from the parent clone to the offspring ( Figure 6A ) . This particular tree shows two founder mutations , chr4a and chr9b , occurring prior to metastasis , six mutations occurring along the metastatic lineage , and nine along the primary lineage . This is the only phylogenetic tree that is consistent with the inferred clonal genotypes . In contrast , for the solutions inferred from the EM algorithm assuming C = 4 through 6 , we found that it is not possible to construct a tree without requiring that the same mutation occur independently along multiple branches . We therefore considered all possible “nearby” trees ( where “nearby” means that , among the distinct rows of the genotype matrix , the two trees differ by only one bit ) that produce a valid phylogenetic tree with no repeated mutations . For example , for the C = 4 solution , we evaluated the likelihood of six nearby trees , yielding log-likelihoods of −28482 , −21282 , −7500 , −6692 , −5659 , and −4333 ( Table S2 in Text S1 ) . The highest of these likelihoods is −4333 , compared to −4244 for the solution initially inferred by EM . The selected solution requires changing only one bit in the genotype matrix from “0” to “1” ( indicated by asterisks in Figure 4 ) . The resulting phylogenetic tree ( Figure 6B ) closely resembles the C = 3 tree , except that one mutation initially assigned to the metastatic clone C3 is instead assigned to clone C2 in the C = 4 tree . Also , the nine mutations associated with the primary section in the C = 3 tree are further subdivided into three that occur shortly after metastasis and six that lead to clone C1 . Reassuringly , the C = 5 and C = 6 solutions , constructed in a similar fashion ( Figure 6C–D ) , are largely consistent with this story , each introducing a subdivision among the existing sets of mutations to produce a larger set of clones . Among these trees , the only inconsistencies concern ( 1 ) three mutations ( chr5 , chr9a and chr20b ) that occur later according to the C = 4 solution than according to the C = 5 or C = 6 solutions and ( 2 ) two mutations ( chr1 and chr4b ) that are assigned their own branch , directly off the normal clone , in the C = 5 and C = 6 solutions . In practice , the chance that a randomly generated genotype matrix would produce a valid phylogenetic tree is vanishingly small ( Note S3 in Text S1 ) . Therefore , the fact that each of our inferred solutions very nearly produce a valid phylogenetic tree provides evidence for the validity of these solutions . We also investigated the extent to which the observed mutation frequencies obey the phylogenetic tree . In principle , a mutation that occurs earlier in the evolution of the cancer should have a higher frequency than mutations that occur later along the same lineage because a child clone necessarily contains all of the mutations belonging to its parent clone . This investigation is hampered , however , by copy number variation . In practice , we cannot directly compare the allele frequencies of two distal sites because the observed allele frequencies are actually the product of mutation frequency and copy number . Empirically , we observe variation in copy number along the genome and differences in copy number variation from one subsection to the next ( Figure S2 in Text S1 ) . A consistent duplication of a large portion of chromosome 8 is known to occur commonly in breast cancer [33] . We were lucky , however , that two of our mutated loci occur quite close to one another on chromosome 9 ( chr9a and chr9b , separated by only 3 . 3 Mbp ) . Given the observed data , the likelihood that a change in copy number occurring between these two loci is small , thereby allowing us to safely compare the corresponding mutation frequencies . Across all nine primary tumor subsections , we observe that the frequency of the parent mutation ( chr9b ) is higher than that of the child mutation ( chr9a ) . Hence , these mutation frequencies are consistent with the inferred phylogeny . To assess the stability of our inference , we performed leave-one-out analysis and compared the inferred phylogenies as follows . We held out each of the 12 tumor subsections one at a time and trained the model using the data from only 11 subsections for the case of C = 4 . When samples p1-1 or p1-3 were excluded , the inferred genotypes were exactly the same as the genotype obtained from the full data . Excluding any of the other of 10 subsections resulted in a genotype which was different only in one bit; namely , the mutation chr4a was predicted to be present in all clones . However , this difference did not affect the inferred phylogeny because the change of this bit was in fact required to build a valid phylogenetic tree ( Figure 4 ) . In other words , by excluding any of the 12 tumor subsections , the inferred genotype always led to the same valid phylogenetic tree , which suggests that our algorithm is stable .
Once a tumor has been resected , clinicians pay a great deal of attention to characterizing its anatomy . Features such as necrosis , extension beyond normal anatomical boundaries , and microvascular invasion convey important prognostic information . In addition , the cancer cells within any given tumor are frequently heterogeneous with respect to features such as differentiation state , the fraction of cells undergoing mitosis ( as determined by Ki67 staining ) , or ( for breast cancer ) the fraction of cells expressing HER-2 or estrogen receptor . The method described here provides a framework for linking a tumor's molecular anatomy to its structural anatomy as well as its phylogenetic evolution . Several lines of evidence support the validity of the clonal genotypes and relative frequencies inferred by our model . One prediction from our phylogenetic reconstruction is that somatic variants at the trunk will be present at higher frequencies throughout all tumor subsections than variants appearing at the branches . While copy number variation across the somatic genome complicates these comparisons , one of two closely juxtaposed somatic variants ( chr9b ) is positioned at the trunk of our phylogenetic tree , while its neighbor ( chr9a ) arises in one of the branches . Consistent with this representation , the variant allele frequencies for chr9b are consistently higher than for chr9a in all ten tumor subsections examined . Interestingly , phylogenies can be built from the inferred genotypes even given the relatively low purity of the tumor sections: contamination with normal tissue was in 9 out of 12 subsections in our data ( Figure 4 , ) . In particular , although we estimate that the metastatic subsections contained tumor cells in M1-1 and in M1-2 , the corresponding branch of the phylogenies is stable and consistent . Similar to phylogenetic analysis , reassembly of the tumor subsections indicates that our assignment of mutations to clones produces spatial representations that are anatomically reasonable . With further refinements , our method should enable reconstructions that layer a tumor's phylogeny on top of its spatial organization . While our results underscore the potential power of this new method , our study also has several limitations . Our assessments were confined to heterozygous somatic variants , and did not take into account the many chromosomal structural changes that were present in the tumor we examined . A comparison of exome copy numbers between primary tumor and lymph node indicates that the vast majority of these chromosomal changes preceded the divergence shown in our phylogenetic tree ( Figure S2 in Text S1 ) . In theory , one could imagine generalizing our generative model to take copy number variations into account by replacing the 2 in the denominator of Equation 2 with a hidden random variable for each locus , but without some form of aggressive regularization , this formulation would lead to a prohibitively complex and overfit model . Additionally , a key characteristic of our method is the requirement to specify the number of clones prior to the EM inference procedure . It is important to recognize that this choice should depend upon properties of the data set itself , rather than fundamental properties of the cancer . After all , each cell division results in multiple mutations , such that every cancer cell constitutes a distinct clone . Consequently , a picture of the full clonal history of a cancer would consist of a phylogenetic tree with one leaf for each cancer cell . In practice , such a tree would be of limited utility and , more importantly , could not be accurately estimated from any reasonably sized data set . Perhaps the most useful definition of a tumor clone is a population of cells that exhibit distinct spatial or functional properties . Our approach allows the user to specify the number of clones and , hence , the resolution at which the clonal history is viewed . Because Clomial does not impose any assumption on the distribution of mutation frequencies , the number of inferred clones may not exceed the number of samples; otherwise , the resulting optimization problem will be under-constrained . In the particular cancer studied here , the three-clone solution appears to provide an inaccurate view of the clonal history . The placement of the chr17c mutation along the path leading to metastatic clone C2 is surprising , given that this particular locus has such low counts for both metastatic subsections ( 2 counts for subsection M1-1 and 0 counts for M1-2 , Table S1 in Text S1 ) . This apparent anomaly can be explained by the small counts associated with chr17c in four out of the 10 primary tumor subsections ( 3 counts in P1-3 , 4 in P1-4 , and 21 in each of P2-1 and P2-2 ) . Faced with the choice of what genotype profile to assign to this particular locus , the inference procedure selected a solution in which only two subsections , rather than four , are inconsistent . However , given the flexibility of a 4-clone model , the anomaly is resolved , and chr17c defines a novel clone C2 that occurs in the primary tumor samples and is completely absent from the metastatic samples . In practice , it may be possible to estimate how many clones the data set can resolve using a method such as the Bayesian Information Criterion ( BIC ) , with a smaller BIC value indicating a better fit to the data [34]–[36] . This approach has been used previously for estimating tumor clonal composition [37] , [38] . BIC analysis of our model on simulated data suggests that , on average , the BIC accurately estimates the true number of clones , even in the presence of sequencing noise ( Figure S3A–B in Text S1 ) . We also computed the BIC for models trained on our real breast cancer data ( Figure S3C in Text S1 ) and observed a large decrease in BIC ( 45% ) when increases from 3 to 4 , suggesting that the model is too simple to describe the data . However , the subsequent improvements of the BIC are smaller: 29% , 20% , 9% , and 3% respectively , as grows from 4 to 8 . In general , one should avoid increasing the complexity of the model when the BIC improvement is small because , in such situations , adding to the number of free parameters of the model can potentially lead to over-fitting [39]–[47] . Note that , as an alternative to a BIC approach , one could instead take an approach motivated by cross-validation , as has been explored in the context of matrix factorization models [48]–[50] . Running the EM algorithm is very fast . In practice , using a 2 . 40 GHz processor with 2 GB memory , training a single EM instance on the real data set takes a few seconds up to several minutes , depending on the value of the hyperparameter ( Figure S4 in Text S1 ) . However , because the optimization problem in the M step is non-convex , many EM instances must be trained from different random initializations to avoid local optima . We first noted that Clomial achieved good results on simulated data using only 10 random initializations when ( Figure 2 ) . Then , to further assess the appropriate number of EM instances to run , we revisited the solutions from all of our 100 , 000 EM instances , counting how many instances are required to achieve the best observed model ( Figure S5 in Text S1 ) . In practice , while 1000 EM instances is sufficient to find the optimum solution when or 3 , a larger number of random initializations is required as the number of clones grows . This is an expected phenomenon because the complexity of the model grows significantly with , resulting in an optimization surface with many more local optima . Consequently , despite the highly parallel nature of the computation , scaling up to analysis of larger data set with larger numbers of clones will likely require improved EM training strategies , such as noise injection or regularization . Finally , although we used a simple phylogenetic tree construction procedure to evaluate the quality of our inferred clonal genotypes , the EM inference procedure described here does not explicitly model tumor evolution . Ultimately , we aim to produce a model that automatically infers not only clonal genotypes and clonal frequencies , but also the number of clones and the phylogenetic tree relating them . Our method differs significantly from other approaches . A recent characterization of 21 breast cancers defined clones by clustering mutations with similar variant allele frequencies [9] . The success of this strategy hinges on characterizing the frequencies of large numbers ( hundreds or thousands ) of somatic variants . In contrast , our method can reconstruct clonal phylogenies based on accurately measuring alleles of much smaller numbers of somatic variants . The view afforded by our method may provide novel insights into tumor biology . In particular , results from Nik-Zainal and colleagues [9] were interpreted to indicate that cancers become clinically apparent only after one of the competing clones has achieved clonal dominance . In contrast to this “winner takes all” hypothesis , our model suggests that some cancers might be more accurately regarded as ecosystems , in which clones may be subject to spatial influences that affect their competitive fitness , or may even collaborate to support tumor growth . An important difference between our method and many other methods based on clustering [8] , [9] , [12] is our explicit probabilistic modeling of the random selection of normal and variant alleles during sequencing , according to a binomial distribution . By taking into account not just the relative frequency of the two alleles but the separate counts of normal and variant alleles , our model automatically assigns less importance to a locus with lower coverage , even if the locus yields the same variant allele frequency as a high-coverage locus . While this manuscript was under review , two methods called PyClone [13] and PhyloSub [51] were published , which do model allele counts using a binomial distribution . These methods attempt to simultaneously infer not only clonal genotypes and frequencies , as Clomial does , but also infer the number of clones and their phylogeny . Furthermore , PyClone and PhyloSub are not limited , as Clomial is , to situations in which the number of inferred clones is less than or equal to the number of available samples . How is this possible ? To make these inferences feasible , these clustering methods must make certain distributional assumptions about the data . Specifically , PyClone assumes a Dirichlet Process prior for clone frequencies , where the base distribution is Uniform and the concentration parameter is Gamma distributed with shape and scale parameters equal to 1 and , respectively . PhyloSub extends PyClone by using a tree-structured stick-breaking process [52] to directly account for phylogenetic relationships during the inference . In principle , these assumptions enable PyClone and PhyloSub to infer information about a large number of clones from only a single sample . On the other hand , when multiple samples are available , Clomial can draw accurate inferences without requiring these distributional assumptions . In practice , our comparison showed that Clomial and PhyloSub produce similar results on three previously described chronic lymphocytic leukemia ( CLL ) cases [53] ( Tables S3–S5 in Text S1 ) . We note that if is the sequencing error rate at locus , then the probability of observing a variant allele at this locus in subsection is estimated by . In principle , sequencing noise could be incorporated into our model by replacing , defined in Equation 2 , with in the likelihood and EM algorithm . However , given the robustness of the current method to noise ( Figures S6 and S3C in Text S1 ) , we opted to keep our model simple . In future applications , it may be beneficial to model noise in data produced by sequencing technologies that exhibit high error rates ( ) such as PacBio RS [54] . The EM algorithm is not the only option for maximizing the log-likelihood for the observed data . In particular , one could instead treat both and as optimization variables and seek to maximize with respect to and . This would amount to iteratively updating and then updating until convergence , similar to the iterative algorithms typically used for matrix factorization models [23]–[25] , [50] . However , this alternative approach would not have any computational advantage in terms of the update for , which would still not have a closed-form solution , and would need to be solved using BFGS-B or an equivalent approach . Furthermore , the update for would be very complicated under the constraint that is a binary matrix . Therefore , we developed a customized inference algorithm based on EM . Whereas genetic testing for cancer patients today focuses on mutations affecting a relatively small number of cancer-associated genes , most cancers are sustained by networks of aberrantly regulated genes that collaborate to promote tumor growth . The ability to assign mutations to clones , and to layer a tumor's clonal content on top of its structural anatomy in space and over time , can provide new insights into the mechanisms that enable cancers to invade , metastasize and escape treatment .
This research was reviewed and approved by the Cancer Consortium Institutional Review Board ( IRB ) located at the Fred Hutchinson Cancer Research Center ( FHCRC ) . The FHCRC has an approved Federalwide Assurance on file with the Office for Human Research Protections ( number 00001920 ) . The Federalwide Assurance is a formal written , binding commitment that assures that the FHCRC promises to comply with the regulations and ethical guidelines governing research with human subjects , as stipulated by the U . S . Department of Health and Human Services under 45 CFR 46 . Because this study involved the use of de-identified specimens obtained from an IRB-approved repository , we did not interface with patients . Patient consent was administered , in compliance with 45 CFR 46 , by investigators who maintain the repository . Patients gave their consent for their specimens to be stored in the repository and subsequently used for research in cancer . The FHCRC IRB deemed that our research was in concordance with the purpose of the registry and the patient informed consent . We obtained breast cancer tissues from the Breast Cancer Biospecimen Repository of Fred Hutchinson Cancer Research Center after IRB approval . The patient was a 44 year old pre-menopausal woman diagnosed with infiltrative ductal carcinoma ( IDC ) and ductal carcinoma in situ ( DCIS ) , stage pT1c pN1 , Grade II/III , ER positive , PR positive and Her2 negative . Axillary lymph node dissection revealed that one out of 13 nodes was positive for metastatic disease . A total of 5 pieces were obtained from surgical samples including 1 tissue section from adjacent normal breast tissue ( N1 ) , 3 tissue sections from the primary breast cancer ( P1 , P2 , P3 ) , and 1 tissue section from the positive axillary lymph node ( M1 ) . Each section is about 1 cm by 1 cm by 0 . 5 cm . The tumor content , including both IDC and DCIS , ranges from 40% to 55% in the primary tumor and axillary lymph node tissue sections based on pathological examination ( P1 55% IDC , P2 45% IDC , P3 40% IDC and 15% DCIS , M1 50% IDC ) . Each individual section was subdivided into multiple subsections , and the anatomic locations of all the subsections were recorded ( Figure 3 ) . Using Qiagen AllPrep DNA/RNA Micro Kit , DNA was extracted from one normal subsection ( N1-1 ) , seven primary subsections ( P1-2 , P1-3 , P1-5 , P2-1 , P2-3 , P3-3 , P3-4 ) and one metastatic subsection ( M1-1 ) . After quantification , all the DNA samples were subjected to exome capture followed by Illumina sequencing . Next generation sequencing was carried out at the Northwest Genome Center at University of Washington on the normal subsection , seven primary subsections , and one metastatic subsection . For each subsection , one microgram of genomic DNA was used to construct the random-shearing library per standard protocol with Covaris acoustic sonication . Libraries then underwent exome capture using the Mb target from Roche/Nimblegen SeqCap EZ v2 . 0 ( exons and flanking sequence ) . Since each library was uniquely barcoded , samples were performed in multiplex . Massively parallel sequencing was carried out on the HiSeq sequencer . Sequence reads were processed with a pipeline consisting of the following elements: ( 1 ) base calls generated in real-time on the HiSeq instrument ( RTA 1 . 12 . 4 . 2 ) ; ( 2 ) Perl scripts developed in-house to produce demultiplexed fastq files by lane and index sequence; ( 3 ) demultiplexed BAM files aligned to a human reference ( hg19 ) using BWA ( Burrows-Wheeler Aligner; v0 . 5 . 9 ) [55] . Read-pairs not mapping within standard deviations of the average library size ( bp for exomes ) are removed . All aligned read data were subjected to the following steps: ( 1 ) “duplicate removal” was performed , ( i . e . , the removal of reads with duplicate start positions; Picard MarkDuplicates; v1 . 14 ) ; ( 2 ) indel realignment was performed ( GATK IndelRealigner; v1 . 0-6125 ) resulting in improved base placement and lower false variant calls; ( 3 ) base qualities were recalibrated ( GATK TableRecalibration; v1 . 0-6125 ) . All sequence data then underwent a previously described quality control protocol [56] . Variant detection and genotyping were performed using the UnifiedGenotyper tool from GATK ( v1 . 0-6125 ) . Variant data for each sample were formatted ( variant call format ) as “raw” calls that contain individual genotype data for one or multiple samples , and flagged using the filtration walker ( GATK ) to mark sites that are of lower quality/false positives , e . g . , low quality scores ( ) , allelic imbalance ( ) , long homopolymer runs ( ) and/or low quality by depth ( QD ) . Most of the commonly used software for calling SNVs and indels , including SNVMix [57] and VarScan [58] , requires tumor content . To allow identification of low frequency alleles that occur in only one or a few subsections , we did not pool all of the data together . Instead , we designed a method that is appropriate for multiple samples from one patient , with relatively low tumor content , ranging from 45% to 55% . At each chromosomal position ( locus ) , we considered six mutually exclusive possible outcomes: A , C , G , T , deletion , and unknown . The counts of these six outcomes at each locus between normal and each of the multiple tumor subsections were compared with a Fisher's exact test . To correct for multiple testing , we used the qvalue R package to convert to . Only those chromosomal loci with in at least one comparison between normal and tumor samples were accepted for downstream analysis . This analysis identified 6310 loci . For each accepted locus , we used a heuristic procedure to identify which of the six alleles differed between the tumor and normal sample . For each subsection , we carried out six Fisher's exact tests , one for each of the six possible alleles . Thus , each such test compared one allele's counts to the sum of the counts for the other five alleles . Using a p-value threshold of 0 . 01 , an allele was declared to be increased , decreased , or unchanged in the tumor subsection as compared to the normal sample . The changes that were classified as “increased” and had a normal count of zero were called tumor-specific mutations . This procedure identified a total of 268 such tumor-specific mutations , with a mean and median sequencing depth of 92 and 75 , respectively . Corresponding annotations were obtained from SeattleSeq ( http://snp . gs . washington . edu/SeattleSeqAnnotation137 ) . In parallel , we also analyzed our data using deepSNV [31] by comparing the normal subsection to the 8 tumor subsections . We ran deepSNV on the loci with total coverage across all samples more than 50 , which resulted in the identification of 29 loci with . The union of the two lists yielded 281 loci for further validation ( Dataset S1 ) . Mutations were validated by targeted deep sequencing of DNA derived from one normal subsection ( N1-1 ) , 10 primary subsections ( P1-1 , P1-2 , P1-3 , P1-4 , P2-1 , P2-2 , P2-3 , P2-4 , P3-1 , P3-2 ) and two metastatic subsections ( M1-1 and M1-2 ) . The subsections were selected to have low normal content and to span the tumor anatomy . Genomic DNA was prepared as described for the initial exome sequencing . A HaloPlex probe capture library for selective capture of 281 target loci was generated with SureDesign ( Agilent Technologies ) . Target enrichment for deep sequencing was carried out with the HaloPlexTM Target Enrichment System from Agilent Technologies following the manufacturer's protocol . Triplicate enrichments were performed for each sample . Target-enriched samples were sequenced using a MiSeq ( Illumina ) . Of the 281 target loci , 244 were successfully sequenced with coverage more than 100 reads for the normal sample . The mean , median , and the standard deviation of the coverage were 1615 , 1118 , and 1600 , respectively ( Dataset S2 ) . All 244 loci were visualized using the Integrative Genomics Viewer [59] , [60] . A set of 17 loci were selected based upon three criteria: ( 1 ) at least 3 reads cover the locus in the normal sample , ( 2 ) the variant allele is not present in the normal tissue ( allowing for a few variant counts , which may reflect sequencing error ) and ( 3 ) there are no nearby clustered mutations , indicative of sequencing or mapping error . Independently , the data were also analyzed using deepSNV . Applying a threshold of yielded 19 loci , including all 17 of the initially selected loci . The 17 loci were retained for downstream analysis ( Table S1 in Text S1 ) . We computed BIC using the following formula: ( 14 ) where is the expectation of the complete-data log likelihood , which is maximized in the last M step ( see Equations 7 and 13 ) . Also , represents the total number of free parameters , and is the total number of counts . | Cancers arise from a series of mutations that occur over time . As a result , as a tumor grows each cell inherits a distinctive genotype , defined by the set of all somatic mutations that distinguish the tumor cell from normal cells . Acertaining these genotype patterns , and identifying which ones are associated with the growth of the cancer and its ability to metastasize , can potentially give clinicians insights into how to treat the cancer . In this work , we describe a method for inferring the predominant genotypes within a single tumor . The method requires that a tumor be sectioned and that each section be subjected to a high-throughput sequencing procedure . The resulting mutations and their associated frequencies within each tumor section are then used as input to a probabilistic model that infers the underlying genotypes and their relative frequencies within the tumor . We use simulated data to demonstrate the validity of the approach , and then we apply our algorithm to data from a primary breast cancer and associated metastatic lymph node . We demonstrate that our algorithm predicts genotypes that are consistent with an evolutionary model and with the physical topology of the tumor itself . Applying this method to larger numbers of tumors should cast light on the evolution of cancers in space and time . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology",
"medicine",
"and",
"health",
"sciences",
"cancer",
"genetics",
"basic",
"cancer",
"research",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"genomic",
"medicine"
] | 2014 | Inferring Clonal Composition from Multiple Sections of a Breast Cancer |
Gene expression controls how the brain develops and functions . Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia , and very little is known about which genes are expressed in which cells and brain layers . Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum . We learn typical spatial patterns of expression from a few markers that are known to be localized to specific layers , and use these patterns to predict localization for new genes . We analyze images of in-situ hybridization ( ISH ) experiments , which we represent using histograms of local binary patterns ( LBP ) and train image classifiers and gene classifiers for four layers of the cerebellum: the Purkinje , granular , molecular and white matter layer . On held-out data , the layer classifiers achieve accuracy above 94% ( AUC ) by representing each image at multiple scales and by combining multiple image scores into a single gene-level decision . When applied to the full mouse genome , the classifiers predict specific layer localization for hundreds of new genes in the Purkinje and granular layers . Many genes localized to the Purkinje layer are likely to be expressed in astrocytes , and many others are involved in lipid metabolism , possibly due to the unusual size of Purkinje cells .
A key problem in current neuroscience is to characterize how the transcriptome governs the structure and function of the brain [1] . The challenge is particularly hard in the mammalian central nervous system because every brain region contains numerous types of neurons , astrocytes , and other non brain-specific cells such as blood vessels and immune cells . Each of these cell types have their own molecular profile , and typically exhibit unique patterns of gene expression [1] , [2] . These patterns may depend not only on the individual cells , but also on their interaction with neighboring cells . Cell-specific expression patterns determine the formation of both the microcircuitry and the long-range neuronal connections through specific molecules [3] . These patterns also shape the functional properties of neurons and glia . Understanding the molecular basis of brain function therefore requires dissecting gene expression patterns into their cell-specific and layer-specific components . Unfortunately , measuring layer-specific expression is costly and time consuming , and as a result , only a few such datasets have ever been collected [4]–[8] . Cell-type specific data can be collected by growing cell cultures in vitro , which may differ from natural growth conditions , or by sorting cells using known markers [6]–[8] . It is also possible to collect cells from specific cortical layers using laser microdissection [5] . Alternatively , in some cases it is possible to profile the transcriptome of strains that lack a specific type of cells , and compare them to normal developing animals [9] . Here we propose another approach , based on machine vision , to identify layer-specific genes . The method is based on modeling the spatial expression patterns observed in in-situ hybridization ( ISH ) images of a few genes that are known to be expressed exclusively in specific layers ( cell-type markers ) . Using the learned patterns , we then automatically scan the genome-wide ISH database and detect all other layer-specific genes . The current paper focuses on the cerebellum , which has been extensively studied due to its highly organized laminar structure . The cerebellum contains three cortical layers and a white matter layer ( Figure 1 ) . The innermost cortical layer is the granular layer , a densely packed layer containing mossy fibers , the cell bodies of granule cells , uni-polar brush cells , and Golgi cells . The middle cerebellar layer is the Purkinje layer , containing the cell bodies of the Purkinje cells , Candelabrum interneurons and Bergmann glia . The third , outermost , cortical layer is the molecular layer , containing the dendritic arbors of the Purkinje cells and the inhibitory Stellate and basket interneurons . These three cerebellar cortical layers and cell types are illustrated in Figure 1 A . Finally , the white matter layer resides within the inner most part of the cerebellum . For a more detailed review of cerebellar cell types see [10] . We use the large-scale collection of in-situ-hybridization images of the cerebellum collected by the Allen Institute [11] , together with a small list of genetic markers of cell types that are known to reside in specific cerebellar layers . To achieve high classification accuracy , we combine multiple images of each gene . Each ISH image is represented using histograms of local binary patterns ( LBP ) [12] that are collected at multiple resolutions . This representation captures characteristic spatial structures at multiple scales and improves accuracy significantly over a single-resolution representation . When the trained classifiers are evaluated on a held-out data of similar markers , they correctly classify each of the four main cerebellum structures with more than accuracy ( AUC ) . Furthermore , when applied to the full mouse genome , manual inspection of the 250 top predictions of each class shows that the classifiers successfully identify localized genes . Overall , we identify genes localized primarily to the Purkinje layer , genes in the granular layer , and several new layer specific markers for the white matter and the molecular layer . Some of these genes were previously proposed as layer-specific markers , but hundreds of the genes detected have never been associated with these layers .
Our approach is based on learning spatial gene expression patterns measured using non-isotopic ISH . ISH is used to localize RNA expression of a target gene in a tissue . We used ISH data collected by the Allen Brain Atlas ( ABA ) [11] available at http://mouse . brain-map . org . ISH measurements were gathered for the adult mouse full genome , covering genes . For each gene , mice brains were dissected into slices in sagittal and coronal sections , and the slices were fluorescently labeled and imaged to reveal places where the gene is expressed . The outcome of this process is a set of images showing the gene expression pattern across the whole mouse brain in high spatial resolution . This paper focuses on four distinct layers of the mouse cerebellum , each layer contains a different set of neurons and glia cells . Figure 2 A–D shows examples of sections from the mouse cerebellum , stained with four different markers . The leftmost panel depicts the expression of Calbindin1 ( Calb1 ) , a well known marker of Purkinje cell bodies , Figure 2A . Purkinje cells are known to be organized in the thin Purkinje layer , and indeed , the Calb1 expression forms a distinct spatial pattern in the form of a thin stripe . Other layers of the cerebellum can also be observed: Figure 2B shows the expression of Neurod1 , a known marker of granular cells that reside in the granular layer; Figure 2C shows the expression of Plp1 , a myelin proteolipid protein marking cells which reside in the underlying cerebellum white matter; Figure 2 D shows the expression of Gad1 , which is expressed in the molecular layer and also in the Purkinje layer . The molecular layer contains the dendritic arbors of the Purkinje cells , whose bodies lie in the Purkinje layer ( Figure 1A ) . As a result , most genes expressed in the molecular layer are also expressed in the Purkinje layer . We therefore defined a class that contains genes which show expression in the molecular layer and also in the Purkinje layer . While a few dozen genes are already known to be specifically expressed in particular cell types and cerebellar layers , such location information is still unknown for most of the mouse genome . Here we use these few genes that are known to mark a specific cerebellar layer , learn their spatial expression pattern and predict new localization information for many genes . We train a separate binary classifier for each of the four classes: Purkinje , granular , molecular and white-matter . The molecular class includes genes that are expressed both in the molecular and in the Pukinje layer , but we name this class molecular for simplicity . The next section discusses the representation of the ISH images used as input to these classifiers . For natural images , there has been extensive research on extracting features that are useful for object recognition and detection [12]–[16] . However , for ISH images of complex tissues , only little work has been done on developing such discriminative features . ISH should not be confused with single-cell FISH image analysis , which aims to identify subcellular structures . Most existing work on detection and classification of ISH tissue expression images focused on gross anatomy , where the global shape plays a prominent role . [17]–[21] . These methods , which employ advanced machine vision techniques and achieve state of the art results , depend on a pre-processing stage in which the images are normalized and registered . For example , [17] used pyramid kernels to identify expression patterns in fly embryos and [20] have tested a series of techniques with images that were transformed to a standard shape size and orientation . Such standardization is feasible with fly embryos whose shape is largely regular , but for brain layer recognition such standardization poses new challenges as shown below in Figure 3 . As a result , the question of selecting a good representation for analyzing ISH expression images of brains is still open . Since images of different genes were taken from different mouse brains , and brains differ considerably in their detailed anatomy , spatial expression patterns of the same layer vary considerably across brains . Figure 3 shows four genes expressed in the granular layer , illustrating the variability in size and shape across individuals . As a result of these differences , naive approaches that use voxel-to-voxel spatial correlations between images [22] often fail to match images of the same layer . This problem is particularly difficult with the cerebellum with its elongated structures that are sensitive to small shifts of the images . A good representation of an expression image should therefore be invariant to the types of distortion found across brains . An important aspect of layer-specific expression patterns , is that they exhibit structures at multiple scales . At the coarse scale , the gross structure of the cerebellum contains “finger-like” structures , each having a width of a few millimeters ( Figure 4 A ) . At the same time , genes expressed in different layers may also lead to different “textures” that can be observed at a more refined resolution ( Figure 4 B ) . The texture is determined by the particular spatial distribution of the cells in which the genes are expressed . To take advantage of all these sources of information , here we analyze each image at multiple scales , by down-sampling each image before extracting features . Analysis at multiple scales has been used in many other applications , such as texture classification [23] , and we show below that combining multiple resolutions improves classification accuracy in the current problem as well . At every scale , we represented an image using a histogram of local binary patterns ( LBP , [12] ) , together with the mean intensity of the image . We then combine the feature vectors from the different scales into a single representation . This representation captures both fine texture and coarser structures . For example , applying LBP to a coarsely sampled image as in Figure 4 A captures large structures , while applying LBP to a high resolution version of the same image can capture refined texture statistics as shown in Figure 4B . All four cerebellar layers contain recurring patterns at multiple scales . In our data , each gene is associated with multiple ISH images , collected from different brain slices and possibly multiple brains ( typically 2–8 images per gene ) . Our task is to assign a cerebellar layer to each gene , rather than to classify individual images . Therefore , we need to combine classification of images into a single unified decision at the level of a single gene . Common approaches to combine scores from multiple patches , include max-pooling and average pooling [24] . Here we take a discriminative approache to solve this task . First , we train a classifier operating on labeled images , and then classify each gene by combining the scores provided by the image classifier . We compared three different ways to combine image scores into a decision over genes ( Mean image score , Mean LBP features and Two-level classifier ) . We also tested a fourth approach as a baseline , treating each image independently , and making a prediction at the level of single images . Figure 5 shows evaluations of the precision of these four approaches , computed on held-out data . Each panel shows the ROC curve for a different layer , where the classifiers were trained to detect each cerebellar layer from a randomly selected set of genes . All classifiers achieve a very high area under the ROC curve ( AUC for all four categories ) . This shows that the two-level approach succeeds to combine information from multiple images . We further trained classifiers to discriminate between every two classes ( “one vs . one” ) . Table 1 shows that most classes can be easily discriminated , except the pair molecular vs . granular , which are confused of the time . To further evaluate the effect of image scale on classification accuracy , we compared the accuracy obtained using model trained at different scales ( Figure 6 ) . We also tested a model that uses multiple scales . Using a multi-scale representation consistently achieves higher ( or similar ) AUC than using any single scale . Overall , coarse resolutions perform better . Surprisingly , the granular layer and also the molecular layer can be discriminated accurately using high resolution images ( Figure 6B , C ) . The high accuracy obtained when using fine-resolution features suggests that the granular and molecular layers contains texture patterns that are useful for discrimination from other layers even if their coarse expression patterns are similar . We compared our detection results with results obtained using the ABA NeuroBlast [22] http://mouse . brain-map . org/ . NeuroBlast ranks genes according to their spatial correlation to a target gene . First , every in-situ image is registered to a reference template and then the spatial correlation between the registered and normalized images is computed . NeuroBlast provided a good AUC for the Purkinje layer ( 0 . 82 ) and the granular layer ( 0 . 73 ) . However , NeuroBlast was less successful in detecting genes expressed in the molecular layer ( 0 . 34 ) . For the white matter , a high AUC is obtained ( 0 . 98 ) and this is possibly due to the way we manually constructed labeled set of genes . Since we could not find many markers that are primarily expressed in the white matter , we used NeuroBlast to find genes that have white matter expression . NeuroBlast suggested genes which have a close expression pattern to the few known literature white matter markers . We then manually validated these suggestions . More information on the way the dataset was constructed is found in the Methods section . Registration based approaches , such as ABA NeuroBlast , can be sensitive to small shifts and such shifts are prevalent when aligning images of different brains . For example , a shift of a few microns can cause the thin line of the Purkinje layer to misalign with the target image , leading to a low correlation . Our registration free approach is less sensitive to such misalignments . The above results showed that the trained classifiers achieve high accuracy on held out data of known markers . We further applied the trained layer predictors to the full mouse genome ( genes in the ABA database ) . All predictions , including lists of genes that are localized to specific layers are available online at http://chechiklab . biu . ac . il/~lior/cerebellum . html . Out of genes that are expressed in the cerebellum , genes are predicted to be primarily expressed in the Purkinje layer , in the granular layer , in molecular layer and in the white matter . We validated the predictions by manually scanning the top predicted genes ( and the bottom predicted genes - Table 2 ) , visualizing their measured expression patterns , and comparing them to the patterns expected at that layer . Out of the top 250 genes predicted to be localized to the Purkinje layer we correctly classified . Similarly , of the top 250 granular layer prediction were accurate . The precision was worse for localization of the molecular layer: All prediction had a molecular expression , but out of the also had a granular expression . Finally , 10 out of 16 predicted white matter were positive . It should be clarified however , that many of the genes that exhibited localized expression in one cerebellar layer , are also expressed in other regions of the brain , sometimes very widely . Also , despite the fact that most of the training images in the molecular class show expression in the molecular layer and also in the Purkinje layer , our classifier was able to identify genes that show expression only in the molecular layer . Applying the white-matter classifier and the molecular layer classifier to the full genome yielded very few positively scored genes . This could be attributed to the small number of positive samples in the training set for these classes . Indeed , when we manually examined one thousand of the genes in the database we only found one gene that was exclusively localized to the white matter ( and one gene localized to the molecular layer ) . In comparison , there were many more genes localized to the granular or the Purkinje layers . To find out if our classifiers can be generalized , and can detect genes on images that are different in their spatial expression organization . We applied the four classifiers that were previously trained on the sagittal sections to all the images available in coronal sections , covering 4000 genes . Unlike sagittal section images , many coronal images contain parts of the brain from outside the cerebellum and their layer organization is quite different from the sagittal images . Despite these large differences between the training set and the test set , the Purkinje layer classifier generalized well and detected genes that are primarily expressed in the Purkinje layer in 95% of the top 100 . The other layer detectors did not generalized as well . Results are available at http://chechiklab . biu . ac . il/~lior/cerebellum . html The above results show that at least 450 genes , which are more than 3 . 4% of genes that are expressed in the cerebellum , are primarily expressed in one layer ( mostly the Purkinje and granular layers ) . There could be many reasons for this highly structured expression pattern . For example , localized genes may reflect unique cell-type dependent biological processes , like shaping the cell morphology or controlling the connectivity between specific neuron types . Alternatively , localized expression may also reflect properties that are not necessarily cell-type specific , like processes that depend on cell size , since Purkinje cells are exceptionally large . We therefore turned to characterize the properties of localized genes , by testing their functional annotations and comparing them with the transcriptome of Purkinje-deficient mice .
We described a machine vision approach for localizing genes to specific layers in the cerebellum . Using a small number of known cell-type markers , we trained classifiers based on visual features in ISH images of these genes and used the classifiers to detect other genes that exhibit similar localization patterns . The area under the ROC curve ( AUC ) of all four classifiers , trained for Purkinje , granular , molecular and white-matter layers , was higher than 0 . 94 on held-out data . Furthermore , when the predictions are evaluated on the full genome using human inspection , the Purkinje and granular classifiers achieved 98% accuracy over their top 250 ranked predicted genes . Two factors contributed to the high classification accuracy of this approach . First , we extracted features from ISH images at multiple resolutions , capturing both texture and coarser structure in their features . Second , we combine multiple image predictions to a single gene-level decision . Together , these two factors reduced the classification error by over 50 percent compared to a naive classifier . The fraction of genes whose expression is localized is surprisingly high: About 3 . 4% of the genes that are expressed in the cerebellum exhibit an expression pattern that was strongly localized to one layer . Functional enrichment suggest that part of this effect is due to the unusual shape and size of the Purkinje cells , leading to high expression of lipid metabolism genes . Interestingly , by comparing the genes localized to the Purkinje layer , with genes detected in Purkinje deficient mice [9] , we find that many Purkinje-layer genes are not necessarily expressed in Purkinje cells . This result , together with the fact that Purkinje-layer genes are associated with astrocytes , suggests that the transcriptome of Bergmann astrocytes , which reside in the Purkinje layer , has a wider range of specifically expressed genes than previously suspected . The full list of Bergmann glia specific genes is provided in the Supporting Information . This list could be used to further understand the unique properties of Bergman Glia cells . The large fraction of genes which exhibit localized pattern of expression hints to a high level of functional specialization across different cell types in the brain . It suggests that the average transcriptome of a neural tissue is actually a very heterogeneous mix of genes , some of which are expressed in unique cell types . This is in agreement with microarray analysis of specific cortical layers in the Rhesus monkeys [5] , where the variability of the transcriptome across layers is significant . The approach described in this paper can be used in conjunction with other approaches to improve our understanding of how the transcriptome changes between different types of neurons and glia cells . For instance , the transcriptome of transgenic mice that lack Purkinje neurons [9] can be used to further delineate the transcriptome of Bergmann and Purkinje cells , both a part of the Purkinje layer . Our approach was applied to the cerebellum where the layers have a clear and pronounced structure . Other brain regions , including the dentate gyrus , the CA areas in the hippocampus or the anterior olfactory nucleus also contain laminar structures and could be analyzed in a similar way . Furthermore , it will be interesting to extend this approach to learn more refined discriminations . For example , in many brain areas astrocytes and neurons have different spatial distributions and sizes , suggesting that it may be feasible to train detectors that are sensitive to these differences . This could help further characterize the genetic profiles of many specific cell types across the brain .
We used gene expression images measured using in situ hybridization ( ISH ) . The images were collected by the Allen Brain Institute and published online as the Allen Brain Atlas ( ABA ) [11] available at http://mouse . brain-map . org . To measure the expression of a target gene , ISH uses fluorescently labeled DNA sequences that are complementary to the target gene RNA . These DNA probes are cloned and applied to each brain slice . The complementary probes hybridize to the target RNA sequence inside the cells , while the non bound probes are washed away . This fluorescent labeling captures the spatial pattern of expression of a target gene across the brain . The quality of this approach was quantified in [30] , showing mostly agreement with microarray data . ISH measurements were gathered for the adult mouse full genome , covering genes . For each gene , mice brains were dissected into 100 m thick slices in sagittal and coronal sections , and the slices were fluorescently labeled and imaged . The outcome of this process is a set of images for each gene showing the gene expression pattern across the whole mouse brain . We collected a set of positive samples for four classes: Purkinje layer , granular layer , white matter and molecular layer classes . The positive samples were collected from three sources: First , we included known markers for each of the four layers . For example , we selected Calb1 as a marker of the Purkinje layer , and Plp1 for the white matter . The molecular class contained genes that are expressed in the molecular layer and also possibly in the Purkinje layers . Second , we sifted through images of 1000 random genes ( ordered by gene name ) and manually selected images with spatial patterns that fit the four classes . Finally , since the white matter and the molecular class had only few genes , we computed the spatial correlation of expression between the positive samples selected from the first two sources and the rest of the genome using ABA NeuroBlast [22] . We added genes whose expression in the hindbrain was highly correlated ( -value ) with the positive samples . Overall , the number of positive genes in each of our classes was , , and for Purkinje , granular , molecular and white matter . We also collected a set of 300 randomly selected genes ( 632 images ) to act as a negative set for each class . For each gene we then collected all its ISH images covering the area of 600–1200 microns medial to the most lateral cut . The number of images per gene varied considerably: in Purkinje genes , in granular , in molecular , and in white matter . All together , a total of 433 genes and 1112 images were used in the labeled training set . Also , 13361 genes and 31321 unlabeled images were used in genome-wide analysis . We used masked gene expression images available from ABA as RGB images . We transformed the RGB triplet at every pixel into a scalar intensity value using the heat color scale . Images that were completely black ( no expression detected ) were excluded . This happens , for example , when the sampled gene is not active in the cerebellum or when gene activity was too low to be detected by ISH . We downsampled every image at multiple resolution to capture structures at multiple scales . We used downsampling factors of . As feature vector we used local binary patterns ( LBP , [12] ) . At every scale the LBP representation computes an 8-bits signature at every pixel of the image ( Figure 9B ) , by comparing the pixel intensity to the intensities of its 8 circular neighbors ( Figure 9D ) , yielding a value of 0 for a lower intensity neighbor and otherwise ( Figure 9E ) . LBP signatures are then collected across the full image and their histogram is computed , yielding a 256-features vector ( Figure 9F ) . The feature vectors of each resolution are then concatenated into a single feature vector ( Figure 9G ) representing the image in different scales . In LBP , the distance from the center to the surrounding pixels could be tuned . Here we used a circle of radius centered at each pixel ( known as LBP ( 8 , 2 ) ) which achieved superior performance in early experiments . A common variant of LBP , called uniform LBP [12] , avoids collecting all possible binary patterns separately . Instead , it merges bins that correspond to sequences ( going around the center ) with more than two bit flips . While in some applications uniform LBP may improve runtime and classification accuracy [12] , this was not the case in our experiments , probably because ISH images have very different statistics than real world images . The total number of features per image at a specific resolution was therefore 257 ( LBP features and the mean image intensity ) . Features were scaled to the range [0 , 1] by dividing by the maximal value in the histogram . We also tested classifiers that uses SIFT features [13] . The Purkinje layer detector showed lower performance , the granular layer detector and the white matter detector performance did not change much and molecular layer detector was improved . We chose to use LBP for their ease of implementation and interpretation ( Results using SIFT as features are not shown ) . For image classifiers , we trained a support vector machine ( SVM ) to discriminate images of each layer from images of other layers . Given a new ( test ) image that is not used in training , each classifier can provide a “soft” decision score ( Figure 10 C ) , based on the distance of the sample from the separating hyperplane ( the margin ) . To classify images , we trained an SVM using libSVM [31] with a radial basis function ( RBF ) kernel . We choose SVM because its inherent regularization handles well learning with a relatively small number of samples per class . We used two layers of five-fold cross-validation , one to tune the classifier parameters and the second to tune the hyper parameters . When splitting images into the train and test sets , all images of a gene were either in the train or the test set , but never in both , to avoid overfitting . We used grid search to select the best regularization hyper parameter , and best RBF hyper parameter . Optimal AUC was usually found to be near the middle of the regularization range and near for the RBF hyper parameter . Our dataset is highly imbalanced with many more negatives than positives for each class . To take this bias into account , we assigned different costs to false positive and false negative errors during training . This was done by setting the c+ and c− parameters in SVM , based on the relative sizes of the positive and negative sets . Also , we evaluate performance using the area under the ROC curve ( AUC ) a measure that is invariant to this bias . Combining image scores into a single gene score is a special case of what is known as multi-instance learning ( MIL ) . In general , MIL deals with the case were labels are assigned to a “bag” of samples , which in our case are all the images of a common gene . MIL also commonly appears in tasks like visual object recognition , where features are collected over multiple patches in a single image . The features of the instances are then pooled together using a summary statistic like the mean or maximal . We tested three different ways to combine image scores into a decision over genes . First , we used the mean scores of all images that correspond to a certain gene . We call this approach Mean image score . Second , we collected the LBP features of all images that correspond to a gene , computed their average histogram , and trained a classifier using this single histogram as a feature vector . We call this approach Mean LBP features . Third , instead of using only a single order statistic ( max , median or mean ) , we combined multiple order statistics of the images scores . Using the soft decision scores from all images , we trained a second classifier . This approach is referred as Two level classifier . We also tested a fourth approach as a baseline , treating each image independently , and making a prediction at the level of single images . This can also be viewed as classification when each gene has a single image only . For the gene-level classifier we trained a linear SVM which receives as inputs the confidence scores ( Figure 10C ) of all corresponding images ( their distance from the separating hyperplane ) . Since each gene has a different number of corresponding images , image scores were pooled using order and moment statistics ( Figure 10D ) . Specifically , we used the mean , the median and the k-top images scores ( total of 5 features ) . We found that best performance was achieved for . Whenever a gene had less than 3 corresponding images the lowest available value was duplicated to fill the missing values . We used again two layers of 5-fold cross-validation for tuning the SVM regularization hyper-parameter . NeuroBlast [22] - http://mouse . brain-map . org/ ranks in-situ images according to their spatial correlation to an image of a specific gene ( the seed ) . We focused on correlation in the cerebellum region . For seeds we used a layer known marker ( Plp1 , Calb1 , Neurond1 and Pvalb for the white matter , Purkinje , granular , molecular layers respectively ) . For each gene from our manual labeled list we collected the correlation scores of its images and computed their mean correlation score . These gene scores were used to calculate the AUC for each layer . GO enrichment was tested using elim [32] , which takes into account local dependencies in the hierarchical structure of the Gene Ontology trees , and then by applying the fisher exact test to determine statistical significance of the results ( compared to hypergeometric distribution ) . -values were corrected for multiple comparisons using FDR [33] . | The way gene expression is spatially distributed across the brain reflects the function and micro-structure of neural tissues . Measuring these patterns is hard because brain tissues are composed of many types of neurons and glia cells , and average gene expression across a region mixes transcripts from many different cells . We present here an approach to identify genes that are primarily expressed in specific brain layers or cell types , based on analyzing high resolution in-situ hybridization images . By learning the spatial patterns of a few known cell markers , we annotate the expression patterns of hundreds of new genes , and predict the layers and cell types they are expressed in . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"systems",
"biology",
"gene",
"expression",
"molecular",
"genetics",
"biology",
"computational",
"biology"
] | 2012 | Localizing Genes to Cerebellar Layers by Classifying ISH Images |
Neurocysticercosis is a common helminthic infection of the central nervous system and an important cause of adult-onset epilepsy in endemic countries . However , few studies have examined associations between neurologic symptoms , serology and radiographic findings on a community-level . We conducted a population-based study of resident’s ≥2 years old in a highly endemic village in Peru ( pop . 454 ) . We applied a 14 -question neurologic screening tool and evaluated serum for antibodies against Taenia solium cysticercosis using enzyme-linked immunoelectrotransfer blot ( LLGP-EITB ) . We invited all residents ≥18 years old to have non-contrast computerized tomography ( CT ) of the head . Of the 385 residents who provided serum samples , 142 ( 36 . 9% ) were seropositive . Of the 256 residents who underwent CT scan , 48 ( 18 . 8% ) had brain calcifications consistent with NCC; 8/48 ( 17 . 0% ) reported a history of headache and/or seizures . Exposure to T . solium is very common in this endemic community where 1 out of 5 residents had brain calcifications . However , the vast majority of people with calcifications were asymptomatic . This study reports a high prevalence of NCC infection in an endemic community in Peru and confirms that a large proportion of apparently asymptomatic residents have brain calcifications that could provoke seizures in the future .
Neurocysticercosis ( NCC ) is a common helminthic infection of the central nervous system and the cause of late-onset epilepsy in many lower and middle-income countries [1 , 2] . This chronic neurological condition is the most serious health consequence of the lifecycle of Taenia solium , the parasite which causes the disease . Humans acquire NCC by ingesting tapeworm eggs shed in the feces of someone infected with an adult intestinal tapeworm . Once ingested the eggs release oncospheres that penetrate the intestinal wall and disseminate to form cysts throughout the body including the brain . As these cysts degenerate they can provoke an inflammatory process that may produce seizures [3] . A persistent calcified lesion may result and become foci for chronic seizure activity [4 , 5] . Not all NCC infections are symptomatic and it remains unclear why some people develop seizures while others do not . Neuroimaging by either computerized tomography ( CT ) or magnetic resonance imaging ( MRI ) is required for diagnosis of NCC , yet these tools are often unavailable in areas where T . solium is endemic . Nonetheless there have been several communities-based studies in Latin-American countries which have evaluated the association between epilepsy or headache and NCC [6–10] . Others studies have evaluated the association between epilepsy or headache and positive serology for cysticercosis [11–12] . Only two studies provide a direct measure of the prevalence of NCC; they both find that a high proportion of asymptomatic or serologically negative persons had NCC [7 , 10] . We conducted a cross sectional study in northern Peru using CT scan , serology and symptoms survey to provide an estimate of the background prevalence of NCC in endemic areas .
In 2005 , we conducted a cross-sectional study in the rural village of Rica Playa ( n = 454 ) , Tumbes , Peru , a region where cysticercosis is endemic . This village was chosen by convenience as it was the site of an ongoing study of porcine cysticercosis . The population in this region is mostly mestizo , a mixture of Spaniard and Amerindian . It is common for residents of this area to raise pigs for consumption and sale by allowing the animals to roam and forage . This practice exposes pigs to human feces , as open defecation is common . We conducted a door-to-door survey collecting household level information on the presence of latrine , water source , number of inhabitants and animal husbandry practices . All residents aged 2 years and older were invited to provide a 5ml blood sample to be evaluated for antibodies against T . solium , and to answer a 14-question neurologic survey to screen for a history of seizures and headaches ( appendix ) ; the survey was completed by a parent or legal guardian if the participant was less than 12 years old . This survey was developed in Ecuador and later validated in Peru [11 , 13–15] . We offered in-home clinical evaluation by a neurologist to all participants who screened positive for a history of seizures or severe headaches . The International League Against Epilepsy classification of 1993 and the 2nd Edition of The International Classification of Headache Disorders guidelines were applied to diagnose the disorders [16 , 17] . All residents aged 18 years and older were also invited to receive a non-contrast computerized tomography ( CT scan ) of the head to evaluate for lesions consistent with neurocysticercosis . Participants who agreed to receive a CT scan were transported to the clinical facilities at the Center for Global Health Tumbes , where the exam was performed using a helicoid model ( Siemens AG , Germany ) . The CT images were read independently by two radiologists who were blind to the symptoms and serology results of each participant . Women of reproductive age had a urine pregnancy test before receiving the CT scan . There were no exclusion criteria other than age . Sera were separated using a centrifuge and samples were stored at -20°C until processing at the Instituto de Ciencias Neurológicas , Lima . Samples were analyzed by enzyme-linked immunoelectrotransfer blot for presence of antibodies against T . solium cysts using lentil-lectin purified glycoprotein antigens ( LLGP-EITB ) as previously described [18] . The LLGP-EITB assay uses a semi-purified fraction of homogenized T . solium cysts containing 7 glycoprotein antigens named after the kDa molecular weights of the corresponding reactive bands ( GP50 , GP42 , GP24 , GP21 , GP18 , GP14 , GP13 ) . Reactions to any of these 7 glycoprotein antigens are considered positive . This assay is reported to be 100% specific to exposure to T . solium cysticercosis in humans , although it does not distinguish active infection from exposure ( see supplemental table ) [18] . The sensitivity is reported to be 98% when more than one viable cysts are present , but it is substantially lower for single viable cysts or when only calcified cysts are present [19 , 20] . We analyzed data using STATA SE12 ( StataCorp; College Station , TX ) . We used two-sided Fisher’s exact test to evaluate differences in proportions . We constructed binomial family general linear models with a logit link to evaluate associations between individual variables and odds of both seropositivity and presence of brain calcifications . Variables significant at the level of p<0 . 25 were retained in the multivariable models in which adjusted odds ratios were estimated . We then used negative binomial family general linear models with log link to estimate rate ratios and 95% confidence intervals for variables associated with the number of brain calcifications . Cluster-robust standard errors were used in all models to account for intrahousehold clustering and model fit was evaluated using Akaike Information Criteria . Geospatial analysis was conducted in ArcGIS version 10 . 2 . 1 with data projected from WGS84 to UTM 17S . 5 households were removed from this analysis because they were outside the town center . We calculated Moran’s I and Getis-Ord General G statistics to evaluate the spatial distribution of calcifications ( number of calcifications per household ) and seropositivity ( number of household residents with ≥3 reactive bands on LLGP-EITB ) using squared inverse spatial weighting and 200 meter distance thresholds . P-values were determined using the randomization null hypothesis . The study protocol and consent forms were reviewed and approved by the institutional review board of the Universidad Peruana Cayetano Heredia . We provided a written informed consent form and a detailed oral explanation to all potential participants . Informed consent was documented by the participant's signature for adults ( 18 years or older ) or by signature of a parent or guardian for children .
Of the 454 total residents in Rica Playa , 442 were eligible for the study based on age ≥2 years old . Fig 1 and Table 1 shows eligibility , participation and results of the various screening activities . The median age of participants was 28 years ( range 2–95 years ) and 232 ( 52 . 5% ) were male . The distribution of participants by serology and head CT results are shown in Table 2 . Of the 403 people who participated in the neurologic survey , 43 ( 10 . 7% ) reported a history of seizures and 33 ( 8 . 2% ) reported a history of severe headache . Six people ( 1 . 5% ) reported history of both seizure and headache . Of the 385 individuals who provided a blood sample , 36 . 9% ( n = 142; 95% CI: 32 . 2 to 41 . 8 ) were seropositive for antibodies against cysticercosis . However , the majority ( 113/142 ) of seropositive participants denied any history of seizures or severe headache ( 79 . 6%; 95% CI: 72 . 1 to 85 . 5 ) . Results of bivariable and multivariate analyses are shown in Table 3 . After controlling for other variables , age and pig ownership remained statistically associated with seropositivity . For every 1-yr increase in age , there was a 3% increase in the odds of seropositivity ( OR 1 . 03; 95% CI 1 . 02 to 1 . 04 ) ( Fig 2 ) . Among adults aged 18 years and older , the seroprevalence was 45 . 4% ( 119/262 ) ( 95% CI 39 . 4 to 51 . 5% ) . Pig ownership nearly doubled the odds of seropositivity ( OR 1 . 87; 95% CI 1 . 07 to 3 . 25 ) . The prevalence of brain calcifications on CT scan among adults aged 18 years and older was 18 . 8% ( 48/256 ) ( 95% CI 14 . 4 to 24 . 0% ) . However , the majority of participants with calcification denied any history of seizure or severe headache ( 40/48 , 83 . 3%; 95% CI 69 . 5 to 91 . 7 ) . Of the 48 people with calcifications , 32 ( 66 . 7% ) had a single calcification , 14 ( 29 . 2% ) had between 2–10 , and 2 ( 4 . 2% ) had 11 or more . One participant had a single cystic parenchymal lesion in addition to having calcifications . There was no difference in the proportion of participants with calcifications among those who reported headache and/or seizure ( 8/48 , 17 . 0% ) versus those who were asymptomatic ( 40/209; 19 . 1% , p = 0 . 8 ) . Although CT scan and antibody positivity were significantly associated in this population ( McNemar χ2 , p<0 . 001 ) , this association was far from perfect . Only 28 of 47 CT positive individuals ( 59 . 6% ) were positive on LLGP-EITB , and 85 out of 202 CT negative individuals ( 42 . 1% ) were LLGP-EITB positive . Age , seropositivity and lack of a household latrine were significantly associated with the presence of brain calcifications after controlling for other factors ( Table 4 ) . For every 1-yr increase in age the odds of having brain calcifications increased by 3% . The number of calcifications present also increased by 3% with each 1-yr age increase . Brain calcifications were twice as likely among seropositive people compared to those who were seronegative . However , latrines were protective against having calcifications , with the odds of having brain calcifications reduced in half if there was a latrine at the household . There was no evidence of geospatial clustering of calcifications or seropositivity ( Fig 3A and 3B ) . Participation in follow-up clinical evaluation with the study physician was low which limited our ability to construct models based on clinical diagnosis of headache and seizure . Of the 70 people who reported a history of headache or seizure only 38 ( 54 . 2% ) agreed to be evaluated by the physician; 25 ( 65 . 8% ) reported a history of headache , 17 ( 44 . 7% ) a history of seizure , and 4 ( 10 . 5% ) a history of both headache and seizure . Of the 38 , 4 were confirmed as having epilepsy , 3 as having single non-febrile seizure events , and 4 as having severe headaches . Six of the 7 participants with seizure history provided a blood sample and only one was seropositive; three had CT scan and none had brain calcifications . All 4 of the participants with headache participated in blood sampling and one was seropositive; three underwent CT scan and two had calcifications .
This study found widespread infection with T . solium in an endemic village in northern Peru where nearly one in five adults had NCC with calcified cysts in the brain . This is one of the only studies of NCC in which neuroimaging was offered in the general population without prior screening , and therefore provides a direct estimate ( 18 . 8% ) of the prevalence of NCC among adults . Children were not imaged in this study due to the potential risks associated with CT and cumulative radiation . Most infections were asymptomatic at the time of imaging . Only 17% of those people who had brain calcifications ( 8/48 ) reported ever having experienced seizures or severe headaches , the main clinical sequel of NCC . Because calcifications are chronic , it is possible that some of these people will progress to having symptoms in the future . Intermittent peri-lesional edema around calcifications is associated with seizures but the cause and timing of the edema is not well understood [4 , 5] . However , we cannot evaluate the risk that asymptomatic people with brain calcifications will go on to develop seizures in this cross sectionals study . A longitudinal study is needed to estimate the risk of symptom progression and to understand the factors involved in progression or protection . The fact that in endemic communities most individuals with NCC have asymptomatic calcified brain lesions has been pointed out before [6–10] . All of the NCC cases had calcifications on CT scan; only a single case had a viable cyst . A single calcified lesion was the most common presentation , occurring in two-thirds of the NCC cases . This predominance of single calcifications is typical for NCC in Latin America and has been noted in multiple other studies [6–9 , 13] . All of these studies , as well as our own , relied on CT scan only , however , which has a lower sensitivity for detecting cysts compared to MRI . It is likely that all of these studies failed to detect some cysts , particularly small parenchymal cysts , and those occurring in the base of the brain or in the extraparenchymal spaces . The only population-based imaging study of NCC to use MRI found cysts in 15 . 1% of a high-risk , selected asymptomatic population ( 90/595 ) , although calcifications again predominated ( 58/90 , 64% ) [10] . It is not clear how much of this difference is due to imaging modality compared to host-parasite differences . The 36 . 9% seroprevalence of antibodies against T . solium cysticercosis in this study is among the highest reported in Latin America and is consistent with the high prevalence of NCC . Given the rapid rate of seroconversion of the LLGP-EITB from positive to negative , this suggests a highly endemic state with frequent ongoing exposure to T . solium eggs in the study community [21 , 22] . The lack of clustering of positive serology or brain calcifications in our study also suggests widespread exposure to the parasite . We noted NCC was twice as likely among people who were seropositive compared to those who were not . Increasing age was the primary risk factor for both seropositivity and NCC , suggesting that continued exposure to the endemic environment increased the risk of acquiring NCC . When compared against CT findings on the population level , the LLGP-EITB has a sensitivity of 59 . 6% and a specificity of 57 . 9% . This is consistent with our findings that the main disease presentation in this study community was asymptomatic calcifications and that only a single participant had a live brain cyst . In clinical settings , the sensitivity of the LLGP-EITB for patients with multiple live brain cysts is of 98% and specificity 100% , with stronger antibody responses ( 4–7 bands ) increasing the likelihood of have active NCC lesions . The presence of a latrine was protective against NCC though not against positive serology . At first glance , this may appear paradoxical . However , this could reflect broad low-level exposure to tapeworm eggs within the overall community with risk of infection concentrated in areas with poorest sanitation . There was no evidence , however , of spatial clustering of calcifications or seropositivity within the community . This study had several limitations in addition to those previously mentioned . This was a single small study in northern Peru in a community with extremely high endemic transmission . The results may not be generalizable to other regions where underlying conditions are different . Although clinical evaluation and verification of symptoms was planned , nearly half of those who reported symptoms refused evaluation by the neurologist for religious reasons despite our efforts to obtain support from local church leaders . We therefore rely on self-report of symptoms in the analysis , for which the positive predictive value is known to be low . In conclusion , this study reports a high prevalence of NCC infection in an endemic community in Peru and confirms that a large proportion of apparently asymptomatic residents have brain calcifications that could provoke seizures in the future . Long-term follow-up of these individuals could provide an estimate of the risk of symptom development . Effective control interventions are needed in T . solium endemic regions around the world to reduce the incidence of disease . | Neurocysticercosis is a parasitic infection of the brain and a common cause of epilepsy in many countries in Latin America , Asia and Africa . In this study , we applied a combination of head CT , serology and symptoms screening in a rural village in northern Peru . We found that the infection was very common in this community , where nearly one in five adults had calcified lesions in the brain . While the majority of these people reported never had experienced seizures , brain calcifications are known to result in chronic intermittent seizures in some . Follow-up studies are needed to help understand how many people with calcifications will go on to experience seizures in their lifetime , and to better predict those who are at greatest risk so that preventive intervention can be offered . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"diagnostic",
"radiology",
"pathology",
"and",
"laboratory",
"medicine",
"tropical",
"diseases",
"geographical",
"locations",
"neuroscience",
"parasitic",
"diseases",
"peru",
"headaches",
"physiological",
"processes",
"signs",
"and",
"symptoms",
"neglected",
"tropical",
"diseases",
"neuroimaging",
"research",
"and",
"analysis",
"methods",
"epilepsy",
"imaging",
"techniques",
"serology",
"south",
"america",
"tomography",
"computed",
"axial",
"tomography",
"people",
"and",
"places",
"helminth",
"infections",
"radiology",
"and",
"imaging",
"diagnostic",
"medicine",
"physiology",
"cysticercosis",
"neurology",
"biology",
"and",
"life",
"sciences",
"calcification"
] | 2016 | High Prevalence of Asymptomatic Neurocysticercosis in an Endemic Rural Community in Peru |
Holometabolous insects undergo a radical anatomical re-organisation during metamorphosis . This poses a developmental challenge: the host must replace the larval gut but at the same time retain symbiotic gut microbes and avoid infection by opportunistic pathogens . By manipulating host immunity and bacterial competitive ability , we study how the host Galleria mellonella and the symbiotic bacterium Enterococcus mundtii interact to manage the composition of the microbiota during metamorphosis . Disenabling one or both symbiotic partners alters the composition of the gut microbiota , which incurs fitness costs: adult hosts with a gut microbiota dominated by pathogens such as Serratia and Staphylococcus die early . Our results reveal an interaction that guarantees the safe passage of the symbiont through metamorphosis and benefits the resulting adult host . Host-symbiont “conspiracies” as described here are almost certainly widespread in holometobolous insects including many disease vectors .
The vast majority of animal species are insects [1 , 2] . Most species of insects are holometabolous , including important vectors of infectious disesases ( sandflies , mosquitoes ) and model organisms ( Drosophila melanogaster , Galleria mellonella ) with distinct larval and adult stages separated by metamorphosis , which entails dramatic remodeling of external and internal anatomy [3 , 4] . The evolutionary advantage of metamorphosis is usually explained by the adaptive decoupling hypothesis [5]: traits in larvae and adults are genetically decoupled , facilitating adaptation to life-stage specific selection [6] . Anatomical re-organization of the body , however , poses a significant problem during the replacement of the gut , as the gut hosts a microbiota . Either the organism must eradicate and subsequently re-establish the microbiota from the environment , or it must maintain its microbiota while preventing opportunistic microbes from entering the hemocoel and causing infections . Early studies clearly demonstrated the presence and maintenance of bacteria in the gut during metamorphosis in Lepidoptera and Diptera [7–9] , and more recent work has described the same phenomenon in Coleoptera [10] , Diptera [11] , Lepidoptera [12] , and Hymenoptera [13] . Two competing mechanisms have been proposed to explain the composition of the retained gut microbiota . One explanation holds that bacterial competition drives the composition of the adult gut microbiota [14 , 15] . Alternatively , the host immune system plays an important role in shaping the gut microbiota [16 , 17] . However despite continued interest in insect gut immunity [18] , the interaction between host immunity and bacterial competition during complete metamorphosis has remained unstudied . Here we exploit an ancient [19] , facultative and prevalent [20–26] symbiosis between Lepidoptera and enterococci to reconcile these approaches by studying the role of host immunity and bacterial competition during metamorphosis within a single system . The gut microbiota of Lepidoptera is limited to a handful of bacterial species that varies with habitat and diet but often is dominated by enterococci that persist through metamorphosis [12] . In the lepidopteran gut , enterococci interact with pathogens through ( i ) competitive exclusion ( ii ) attenuation by direct antagonism or ( iii ) eliciting protective host immune responses and provide lepidopterans including Galleria mellonella with protection against one of the most virulent entomopathogens , Bacillus thuringiensis [21 , 27–29] ( reviewed in [30] ) . In Lepidoptera , the contents of the gut lumen and the peritrophic matrix are purged at the onset of metamorphosis . Basal midgut stem cells proliferate and differentiate to form a continuous layer beneath the larval gut epithelium where lysozyme accumulates in apical vacuoles [16] . Following ecdysis of the larval epithelium , the vacuole contents are released into the gut lumen producing a burst of antibacterial activity that is presumed to prevent septicemic infection [16] . The detached larval epithelium undergoes autophagy and apoptosis and degenerates as the 'yellow body' [31] . Bacteria that resist mechanical and immunological exclusion by the host during pupation , compete intensely for colonization of the pupal gut as has been demonstrated in flies [14] . In the model lepidopteran Galleria mellonella , Enterococcus mundtii ( syn . Streptococcus faecalis Andrewes and Horder ) is passed from female to offspring via the surface of the egg [25] . In vitro observations of lepidopteran gut microbes imply that E . mundtii has the highest abundance , in adults it is often the only detectable microbe in the gut , and that this is mediated by synergy between lysozyme and a broad-spectrum bacteriocin [28] . As is common in many Lepidoptera [32] , G . mellonella adults lack functional mouthparts and therefore additional microbes cannot be acquired during adult life . Using the Galleria-Enterococcus symbiosis we tested the hypothesis that host and symbiont interact to determine the adult gut microbiota . We manipulated host gut immunity and bacterial competitive ability during metamorphosis in a full factorial fashion . Based on these findings we studied fitness consequences of altered microbiotas for adult hosts .
Previous studies show that lysozyme is important in host-microbe interactions in the pre-pupae of another lepidopteran [16] , and in G . mellonella a synergistic interaction between C-type lysozyme and antimicrobial peptides was recently demonstrated [33] . Based on the expression of a C-type lysozyme ( Swiss-Prot accession P82174 ) in the gut during metamorphosis ( S1 Fig ) and the reported synergism of G . mellonella lysozyme we hypothesized that lysozyme mediates changes in the microbiota during metamorphosis . We knocked down lysozyme expression using RNAi in insect hosts that were colonized by either E . mundtii strain G2-mun+ , which produces the broad-spectrum bacteriocin mundticin [34] , or by E . mundtii strain G2-mun- , which is unable to express the mundticin-encoding gene munA . To test the impact of these manipulations we cured the gut microbiota from final-instar larvae using antibiotics and re-inoculated these individuals with either E . mundtii G2-mun+ or G2-mun- , and reared them on conventional non-sterile diet until pupation . Using a combination of 16S rRNA gene amplicon sequencing , qPCR , and conventional bacterial culturing we monitored the composition of the gut microbiota during the larval-pupal molt as well as after adult eclosion . The microbiota of the wild-type host during pupation was increasingly dominated by Enterococcus , whereas Serratia and Staphylococcus were undetectable in the adult stage by culturing , 16S rRNA gene amplicon sequencing ( S2 Fig , S1 Table ) , and 16S rRNA gene qPCR ( Fig 1 ) . Host lysozyme-knockdown resulted in a significant increase in Enterobacteriaceae and persistence into the adult stage ( T = -25 . 145 , df = 456 , p = <0 . 0001 ) , which appear to be entirely composed of a Serratia sp . When the host is instead associated with E . mundtii G2-mun- ( which does not produce the bacteriocin munditicin ) , Staphylococcus becomes highly abundant after pupation ( T = -96 . 48 , df = 456 , p <0 . 0001 , Fig 1 ) and Enterococcus are reduced by two orders of magnitude . When both host and symbiont are disenabled , Enterobacteriaceae ( Serratia sp . ) dominates ( T = -28 . 655 , df = 456 , p < 0 . 0001 ) and again Enterococcus are strongly reduced on reaching the adult stage ( T = 10 . 290 , df = 448 , p < 0 . 0001 , Fig 1 ) . We found that the gut microbiota composition significantly changes when either the symbiont and/or host and symbiont were both disenabled ( Fig 1 ) . Based on these results we therefore inoculated mature larvae , that were first cleared of their microbiota using antibiotics ( ‘re-inoculated larvae’ ) , with a defined microbiota comprising either wild-type Enterococcus mundtii , Staphylococcus sp . ( reflecting the results when the symbiont is disenabled ) or Serratia sp . ( as found when both host and symbiont are disenabled ) , which were isolated from G . mellonella larvae . This made it possible to investigate fitness costs of a defined microbiota without the confounding effects of the manipulation of the host immune system by RNAi or changes to symbiont competitive ability by manipulating mundticin expression . Survival of the resulting adults was monitored after eclosion . Independent of host sex , G . mellonella individuals with E . mundtii survived significantly longer than those with a Staphylococcus- ( Z = -4 . 72 , p = <0 . 0001 ) , or Serratia- ( Z = -2 . 97 , p = 0 . 003 ) dominated microbiota ( Fig 2 , S2 Table ) . There was no difference in survival between G . mellonella adults derived from larvae that were either cured of their microbiota and maintained axenically , or which were re-inoculated with E . mundtii ( S3 Fig ) . This supports the conclusion that the main benefit of E . mundtii mutualism is the interaction with other members of the gut microbiota . Our study shows that host and symbiont interact to maintain a ‘healthy’ gut microbiota through complete metamorphosis . Given the protection that E . mundtii confers to the lepidopteran host [28 , 30] the selective advantage for the host is clear . The transmission of the gut microbiota between individuals is usually considered as mixed-mode transmission , combining vertical and horizontal transmission [35] . For the bacterial symbiont , passage through metamorphosis constitutes an important component of vertical transmission . Complete sterilization of the gut by the host would spell disaster for the symbiont . The transition of the symbiont through metamorphosis is crucial to guarantee vertical transmission to the host offspring . In social species , this can be overcome through exposure to feces of nest mates [36] . But most holometabolous insects are solitary and therefore this mechanism of re-acquisition of symbionts from conspecifics after metamorphosis is impractical . Some species of ants and weevils have resolved the transition of the microbiota through metamorphosis with bacteriocytes , crypts , and other specialized structures that harbour symbionts throughout the life cycle [37 , 38] , this is not the case in Lepidoptera . Our study also sheds light on an important aspect of the evolution of complete metamorphosis , a key innovation of holometabolous insects [39] that resulted in their extraordinary diversity . An understanding of the benefits and costs of complete metamorphosis is essential to explain the evolutionary success of holometabolous insects . Ecological and evolutionary models of metamorphosis are sufficient to explain the evolution of complex life cycles [5] , but not of the pupal stage that defines complete metamorphosis . One adaptive explanation that has been proposed , but barely tested , is the decoupling of growth and differentiation into consecutive stages of the larva and the pupa [40] . The evolution of such a novel complex trait as the pupal stage also creates significant problems . The pupa is a sessile and hence vulnerable stage in the insect life cycle . Some parasitoids specialize on pupal hosts and display adaptations that exploit host endocrinology during metamorphosis [41] . Here , by contrast , we have identified and described a ubiquitous problem encountered by most holometabolous insect during ontogeny: the passage of symbiontic bacteria through pupation . Our data show how the host and symbiont interact to achieve this passage . Moreover , the data also suggest that the risk of opportunistic infection during the destruction of the larval gut is countered by the host through up-regulation of immune function . The destruction of the gut , and hence the abundance of danger signals , in combination with microbe-derived immune elicitors result in a strong immune response [42] as found here .
Enterococcus mundtii G2 was isolated from a long-term laboratory population of Galleria mellonella where insects were reared on a natural diet of honeycomb [43] . To cure E . mundtii G2 of its munditicin-encoding plasmid , a single colony was picked and grown in BHI broth at 42°C for 5 24-h serial passages . The resultant strain was transformed by the method of Dunny et al . [44] with either pRK1 or pRK62 which both contain the entire mun locus with and without a munA promoter , respectively [34] . Both strains express the mundticin ABC transporter protein and mundticin immunity protein ( MunB and MunC respectively ) [34] , and enterococci are intrinsically resistant to lysozyme [45] . The resultant strains are referred to as mun+ and mun- . The wild-type mun locus comprises munA , which encodes the bacteriocin mundticin; munB encoding a mundticin-translocating ABC transporter; and munC encoding a mundticin immunity protein . munBC expression is under independent transcriptional control from munA and is driven by a promoter located between munA and munB and downstream of the munA terminator [34] . To enumerate gut bacteria , insects were dissected and their guts were homogenized with 5-mm steel bead using a TissueLyser ( Qiagen ) at 20 Hz for 10 s . Homogenates were serially diluted in sterile saline and plated onto 1/10 strength TSA ( Oxoid ) . Bacterial colonies were categorized by morphotype and representatives were subjected to colony PCR with universal primers 27F and 1492r . Sanger sequencing of PCR products was performed by MWG Biotech ( Ebersberg , Germany ) or GATC ( Konstanz , Germany ) . For high-throughput amplicon sequencing , total DNA was recovered from gut homogenates by bead milling and CTAB extraction [46] and 24 pools of DNA were constructed representing each combination of treatment and developmental stage using 100 ng of purified DNA from each individual . Pools were subjected to PCR with barcoded versions of the universal primers 27f and 519r . Roche multiplex identifiers were incorporated between the sequences of adaptor A and 519r to give the structure: 5'-Adaptor_A-sequencing_key-multiplex_identifier-519r-3' . PCR consisted of an initial denaturation step of 2 min at 94°C and 25 cycles of of 30 s at 94°C , 20 s at 52°C , and 60 s at 65°C . PCR products were checked by gel electrophoresis , purified with AMPure beads , and sequenced on a 454 titanium GS FLX at 24-plex per quarter pico-titer plate . Amplicon sequence data were processed using QIIME version 1 . 6 [47] . Sequences were assigned to operational taxonomic units according to a 97% identity threshold using uclust [48] . Data were deposited in the NCBI SRA under accession PRJNA268795 . Based on the high-throughput 16S rRNA gene amplicon sequencing , taxon-specific 16S rRNA gene primers were used to quantify the three dominant taxa for the genera Enterococcus [49] , Staphylococcus [50] , and the family Enterobacteriaceae [49] in each individual . Dilution plating and high-throughput 16S rRNA amplicon sequencing showed the presence of three bacterial genera: Enterococcus , Staphylococcus , and Serratia however since Serratia-specific 16S rRNA gene primers could not be designed , family-specific Enterobacteriaceae primers were used to generate individual-based qPCR measurements . Standard curves were prepared using samples derived from axenic guts spiked with known quantities of either Enterococcus mundtii G2 , Staphylococcus sp , or Serratia sp . CFU . qPCR was performed on an ABI StepOne using KAPA SYBR FAST ABI Prism mastermix ( Peqlab ) . The resulting data were log transformed and analysed using linear models in R 3 . 1 . 3 . G . mellonella larvae were reared in the dark at 30°C on a grain-honey diet described previously [29] . Hoffman's tobacco hornworm diet was used to manipulate the G . mellonella gut microbiota . Sterile artificial diet was produced using an autoclave and heat-labile components such as Vanderzants vitamin mixture ( Sigma-Aldrich ) and antibiotics were dissolved in water and filter-sterilized before combing with molten diet at 60°C . To remove the gut microbiota , diet was amended with 100 μg ml-1 streptomycin and tetracycline ( Sigma-Aldrich ) . Mature final-instar larvae were starved for 4 h before being transferred to sterile antibiotic-amended diet for 24 h . Removal of the microbiota was confirmed by dissecting and plating the guts of 30 randomly-chosen larvae onto TSA plates . To associate larvae with a specific bacterial strain , sterile diet ( without antibiotics ) was amended with an aliquot of an overnight culture to a final concentration of 103 CFU ml-1 . Larvae were removed from sterile antibiotic-amended diet , starved for 4 h , and transferred to bacteria-amended diet for 16 h . As is common in many Lepidoptera [32] , G . mellonella adults do not possess functional mouthparts therefore oral infection of adults is not possible . Larvae were subsequently starved for 4h before being returned to a conventional grain-honey diet . The segregational stability of the plasmids pRK1 and pRK62 in E . mundtii G2 was determined according to Simon and Chopin [51] in both non-selective MRS broth ( Oxoid ) as well as in insect hosts . To quantify stability in broth , an overnight culture was diluted in non-selective MRS broth , grown to late exponential phase and plated onto non-selective MRS agar . 384 colonies were arrayed in duplicate onto erythromycin-selective and non-selective MRS agar . To quantify stability in insects , mature larvae were mono-associated as described above with E . mundtii G2 carrying either pRK1 or pRK62 , and returned to conventional grain-honey diet to complete larval and pupal development . The frequency of vector loss was < 2 . 8 x 10−3 both in broth culture and insect hosts . In the case of broth culture , this stability is comparable to the parent vector pIL253 [51] . Upon eclosion , 10 adults carrying either mun+ or mun- strains were dissected and their guts were plated onto non-selective MRS agar ( S4 Fig ) . 384 colonies were tested for erythromycin-sensitivity as described above . 46 randomly selected EmR colonies from each larva were screened for the presence of the plasmid by colony PCR using T7 promoter-specific primers . Complete detachment of the larval gut epithelium occurs prior to ecdysis of the larval cuticle . Therefore the staging system of Kühn and Piepho [52] , as adapted by Uwo et al . [31] , was used to specify the stages of midgut metamorphosis in larvae and pupae ( see Uwo et al . [31] and Ellis et al . [53] for illustration ) . Stage I is a wandering larva that has ceased feeding and started spinning . Stemmatal pigments have not started to migrate and the midgut is empty . Stage II , is the spinning larva and pigments have started to migrate from the stemmata . Stage III , a l ater spinning larva , where the pigments have left the stemmata but are still in contact with the cuticle . The larval gut epithelium has completely detached and floats freely in the lumen . Stage IV defines a mature spinning larva and the pigments have sunk beneath the cuticle but are still visible . Stage V is the prepupa that has ceased spinning and stemmatal pigments are not visible . The midgut is laterally flattened and the detached larval gut has formed the yellow body which undergoes apoptosis . The new pupa is classed as stage VI; the cuticle has not sclerotized and is completely white . Stage VII describes a sclerotized pupa approximately 24 h after the larval-pupal molt . The midgut is cylindrical and surrounded by an extra-epithelial layer . The migration of stemmatal pigments was monitored under a stereo microscope . An internal region of the cDNA sequence encoding a C-type lysozyme , previously designated lysozyme GALME ( Swiss-Prot accession P82174 ) [54] , was amplified with T7-tailed primers Gm_Lys_T7_F1 ( 5'-TAATACGACTCACTATAGGGAGAGCAAGCCGAATAAAAATGGA-3' ) and Gm_Lys_T7_R1 ( 5'-TAATACGACTCACTATAGGGAGATATCTGGCAGCGGCTTATTT-3' ) and used as template to synthesize dsRNA using a MEGAscript T7 Kit ( Ambion ) . In order to knockdown lysozyme GALME expression , 500 ng of purified dsRNA was injected into the hemocoel of mature final-instar larvae . RNAi efficacy was monitored throughout the larval-pupal molt by performing relative qPCR on cDNA derived from dissected guts using the primers Gm_Lys_qPCR_F1 ( 5'-ACTTTTACGAGATGCGGACTG-3' ) and Gm_Lys_qPCR_R1 ( 5'-TCTCATTCTCAACAAGGCACAC-3' ) , which target a region upstream of the region chosen as template for dsRNA synthesis , as well as S7e_forward and S7e_reverse which target the gene encoding ribosomal protein S7e [55] , which was analyzed as a reference . Relative expression was calculated using the relative Ct method . cDNA was synthesized using a cDNA-Synthesis Kit H Plus ( Peqlab ) from 100 ng of total RNA from a pool of RNA from 3 individual insects . qPCR was performed using a peqGOLD Hot Start-Mix kit ( Peqlab ) and a StepOne real-time thermocycler ( Applied Biosystems ) according to the manufacturer’s instructions . Mature pupae were weighed and segregated individually in plastic cups covered with muslin cloth at 30°C . Newly eclosed adults were sexed according to the forewing margin [53] and survival was recorded every 24 h . The data were analysed in R with an accelerated failure time model using the survival package [56] . The Bayesian information criterion was used to select the final model . | The majority of animals are holometabolous insects and change dramatically through development . They undergo a dramatic transformation from a larval stage , adapted to feed , to an adult separated by a pupal stage . During this pupal stage the majority of the organs are renewed including the gut . This creates a risky situation that we study here: when the gut is renewed insects risk losing beneficial microbiota while simultaneously being at risk of opportunistic infection . Here , by manipulating host and symbiont we show how host and symbiont succeed in jointly controlling opportunistic pathogens . If one or both of the partners are compromised , opportunistic pathogens dominate the gut microbiota resulting in increased mortality . These findings may be broadly applicable to insects with complete metamorphosis , including many disease vectors . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Host and Symbiont Jointly Control Gut Microbiota during Complete Metamorphosis |
Schistosomiasis is a major endemic disease that affects hundreds of millions worldwide . Since the treatment and control of this parasitic disease rely on a single drug , praziquantel , it is imperative that new effective drugs are developed . Here , we report that phytol , a diterpene alcohol from chlorophyll widely used as a food additive and in medicinal fields , possesses promising antischistosomal properties in vitro and in a mouse model of schistosomiasis mansoni . In vitro , phytol reduced the motor activity of worms , caused their death and confocal laser scanning microscopy analysis showed extensive tegumental alterations in a concentration-dependent manner ( 50 to 100 µg/mL ) . Additionally , phytol at sublethal doses ( 25 µg/mL ) reduced the number of Schistosoma mansoni eggs . In vivo , a single dose of phytol ( 40 mg/kg ) administered orally to mice infected with adult S . mansoni resulted in total and female worm burden reductions of 51 . 2% and 70 . 3% , respectively . Moreover , phytol reduced the number of eggs in faeces ( 76 . 6% ) and the frequency of immature eggs ( oogram pattern ) was significantly reduced . The oogram also showed increases in the proportion of dead eggs . Confocal microcopy studies revealed tegumental damage in adult S . mansoni recovered from mice , especially in female worms . The significant reduction in parasite burden by this chlorophyll molecule validates phytol as a promising drug and offers the potential of a new direction for chemotherapy of human schistosomiasis . Phytol is a common food additive and nonmutagenic , with satisfactory safety . Thus , phytol has potential as a safe and cost-effective addition to antischistosomal therapy .
Schistosomiasis still constitutes one of the biggest health problems in the world . This disease of poverty has proved difficult to control for centuries and consequently , it still affects hundreds of millions of people . Recent articles document infection of approximately 200 million people in more than 70 developing countries , with approximately 800 million , mostly children at risk of infection [1] . Additionally , the disease burden is estimated to exceed 70 million disability-adjusted life-years [2] . The causative agents of schistosomiasis are parasitic flatworms of the genus Schistosoma . Three species ( Schistosoma mansoni , Schistosoma haematobium and Schistosoma japonicum ) account for the majority of human infections . The major aetiological agent of human schistosomiasis is S . mansoni and intestinal schistosomiasis caused by this species is present in Africa , the Middle East , the Caribbean , and South America . Typically , the morbidity associated with schistosomiasis results from the immunological reactions launched in response to parasite egg deposition in the liver and other host tissues [3] . Despite the public health importance of schistosomiasis and the risk that the disease might further spread and intensify , schistosomiasis control programmes are based are based mainly on chemotherapy , which is limited to the anthelmintic drug praziquantel [4] . However , due to the widespread and intensive use of praziquantel , there is increasing concern about the development of drug-resistant strains [5] , [6] . For this reason , the search for new schistosomicidal agents is a priority . Plants have always been used as a common source of medicine , both for traditional remedies and in industrialised products [7] , [8] . Chlorophylls , found in all green vegetables , constitute an important source of an isoprenoid component , phytol ( 3 , 7 , 11 , 15-tetramethyl-2-hexadecen-1-ol ) [9] . It is an acyclic monounsaturated diterpene alcohol , present in vitamin K , vitamin E , and other tocopherols . Phytol is an aromatic ingredient used in many fragrance compounds and it may be found in cosmetic and non-cosmetic products [10] . In medicinal fields , phytol has shown antinociceptive and antioxidant activities [11] as well as anti-inflammatory and antiallergic effects [12] . Recent studies have revealed that phytol is an excellent immunostimulant , superior to a number of commercial adjuvants in terms of long-term memory induction and activation of both innate and acquired immunity [13] . Additionally , phytol and its derivatives have no cumulative inflammatory or toxic effects even in immuno-compromised mice [14] . Phytol has also shown antimicrobial activity against Mycobacterium tuberculosis [15] , [16] and Staphylococcus aureus [17] . Drugs of natural origin have already been used to treat parasitic diseases . In this regard , the search for antischistosomal compounds from natural sources , mainly from plants , has been intensified [18] , [19] . We have been specifically interested in phytol since it has well-characterised mechanisms of toxicity , is structurally simple , easily available , and cost-effective . Additionally , phytol is a common food additive and , thus , should be well tolerated by the body [9] , [10] , [14] . In this paper , we describe the in vitro and in vivo schistosomicidal activity of phytol against Schistosoma mansoni for the first time . As a benchmark , praziquantel was also used in vitro . As a first step , in vitro antischistosomal studies were performed . Subsequently , a trial was designed to test the schistosomicidal activity of phytol in experimental schistosomiasis caused by S . mansoni in a mouse model . We also demonstrated and described the ability of phytol to induce severe membrane damage in schistosomes through the use of confocal laser scanning microscopy . Furthermore , the effects of phytol on pairing and egg production by adult worms were also examined .
Phytol ( Fig . 1 ) was purchased from Sigma-Aldrich ( St . Louis , MO , USA ) and praziquantel tablets were purchased from Merck ( São Paulo , SP , Brazil ) . For in vitro studies , drugs were dissolved in dimethyl sulfoxide ( DMSO , Sigma-Aldrich ) to obtain stock solutions of 4 mg/mL . For in vivo studies , phytol was suspended in 3 . 7 mL of phosphate buffered saline ( PBS ) and orally administered at final concentration of 40 mg/kg . The Belo Horizonte strain of Schistosoma mansoni was used in all experiments . The parasite life-cycle is maintained in the laboratory by routine passage through a rodent host and intermediate snail host Biomphalaria glabrata [19] . Infections of rodent host with S . mansoni were initiated by subcutaneous injection of approximately 150 cercariae . Cercariae were harvested from infected snails by exposure to light for 3 h , following standard procedures of our laboratory [19] . For in vivo studies , 3-week-old Balb/c mice were used . Mice were infected with 70 cercariae of S . mansoni by tail immersion and kept under environmentally controlled conditions ( temperature , 25°C; humidity , 70% ) with free access to water and rodent diet [20] . The present study was approved by the Ethics Committee at Universidade Federal do Piauí , PI , Brazil ( approval number 013/11 ) and Universidade Estadual de Campinas , SP , Brazil ( approval number 2753-1 ) . All the animals were handled in strict accordance with good animal practice as defined by the Universidade Federal do Piauí and Universidade Estadual de Campinas guidelines for animal husbandry , according to with the Brazilian legislation ( Comissão de Ética de Uso de Animais , CEUA , 11 , 794/2008 ) . To observe morphological changes in the tegument of adult parasites after in vitro and in vivo assays , schistosomes were monitored using a confocal laser scanning microscope following standard procedures presented elsewhere [19] . Briefly , at the end of the drug treatment period ( 120 h ) or in the case of death , the parasites were fixed in a formalin-acetic acid-alcohol solution ( FAA ) and analysed under a confocal microscope ( Laser Scanning Microscope , LSM 510 META , Carl Zeiss , Standorf Göttingen , Vertrieb , Germany ) . Autofluorescence was excited with a 488-nm line from an Argon laser , and emitted light was collected with 505 nm [30] , [31] . For assessment of changes in the tegument of parasites , three-dimensional images obtained from confocal laser microscopy were used for a quantitative method . In this quantitative analysis , areas of the tegument of male worms are assessed , and the numbers of tubercles were counted according to standard procedures [19] . Briefly , during the microscopic analysis of the three-dimensional images captured using LSM Image Browser software ( Zeiss ) , areas of the tegument of parasite are assessed , and the numbers of intact tubercles on the dorsal surface of male helminths were counted in a 20 , 000 µm2 area . Statistical tests were performed with GRAPHPAD PRISM ( version 5 . 0 ) software . Dunnet's test was used to analyze the statistical significance of differences between mean experimental and control values . Significant differences were also determined by applying Tukey's test for multiple comparisons . A P value of <0 . 05 was considered significant .
Adult S . mansoni worms ( 56-day-old ) were cultured in RPMI 1640 medium in the presence of phytol . The parasites were maintained for 120 h and monitored every 24 h to evaluate their general condition: motor activity , changes in pairing , egg production , alteration in the tegument , and mortality rate . Since phytol had antischistosomal effects in vitro , it was further investigated in vivo . Therefore , the efficacy of phytol was tested against the adult parasite life stage in an experimental mammalian host . Single 40 mg/kg oral dose of phytol was administered to S . mansoni-infected mice at 56 days postinfection . During this period , male and female worms had matured and paired , and eggs were found in the liver , intestine , and faeces .
Phytol is widespread in nature , especially because it occurs ubiquitously as a component of chlorophyll [9] . It is considered a common food additive and information about oral bioavailability of phytol in mice revealed that this drug is well absorbed ( 30–66% of the administered dose ) [32] . Moreover , comprehensive toxicological data are available . For example , the acute oral LD50 of phytol in rats was reported to be greater than 10 , 000 mg/kg and it was also not considered mutagenic [10] . This study has highlighted S . mansoni as a possible new target for phytol . Initially , we examined its antischistosomal activity on adult worms in vitro and the results encouraged us to examine its efficacy in mice harbouring adult S . mansoni . In vitro assays demonstrated that phytol affected parasite motility , viability , and egg production and it induced severe tegumental damage in schistosomes . Additionally , various parasitological criteria indicated the in vivo antischistosomal effects of phytol: it caused significant reductions in worm load , faeces egg load , and the frequency of egg developmental stages . To the best of our knowledge , we have , for the first time , evaluated the activity of phytol against the laboratory model S . mansoni in vitro and in vivo . In general , our in vitro experiments on adult schistosomes confirmed the promising in vivo results . Indeed , the in vitro bioassay revealed that phytol acted preferentially against female rather than male worms . Likewise , the effect on worm burden of a single 40 mg/kg oral dose of phytol administered to mice harbouring a 56-day-old adult S . mansoni infection clearly showed that females were more susceptible than males . A similar variation in drug susceptibility between male and female schistosomes both in vitro and in vivo , has been observed with several antischistosomal drugs . For example , Mitsui et al . , 2009 [33] reported that female worms of S . mansoni were often more susceptible than males to artenusate in vitro . Keiser et al . ( 2009 ) [34] described that female adult worms were more affected by mefloquine than male adults when the drug was administered orally to mice infected with adult S . mansoni . A similar finding was previously reported by Botros et al . ( 2003 ) [35] when testing the activity of the acyclic nucleotide analogue 9- ( S ) -[3-hydroxy-2- ( phosphonomethoxy ) propyl]adenine [ ( S ) -HPMPA] against experimental schistosomiasis mansoni . Unlike other recently described schistosomicides such as the cysteine protease inhibitor K11777 [36] or the oxadiazoles [37] , which thus far have only been tested intraperitoneally and in multiple doses , phytol at a single oral dose resulted in worm burden reductions . Although our results were moderate with respect to total worm burden reduction ( 51 . 2% ) , high female worm burden reductions ( 70 . 3% ) were observed in S . mansoni-infected mice treated with phytol . Besides , in contrast to the effects of the recognised antischistosomal drug praziquantel , which kills adult schistosomes ( male and female ) , the killing effect of phytol is weaker . In this respect , in vitro , all adult worm pairs were separated into individual male and female worms after 24 h of incubation with phytol at concentrations of 25 µg/mL and above . Male and female worms were unable to embrace and mate and remained separated , and 100% of the female and male worms were dead after 24 h of incubation with phytol at concentrations of 50 and 100 µg/mL . A single oral dose of phytol administered to mice did not cause a significant reduction in the load of unpaired male worms . Nevertheless , treatment with phytol resulted in the death of most of the S . mansoni unpaired females ( no unpaired female was recovered from five mice; one unpaired female was recovered from four mice; and four unpaired females were recovered from one mouse ) . The reduction of coupled worms and total load of worms may be due to the decrease in the number of female schistosomes . Since the tegument of schistosomes is an important target for antischistosomal drugs , alterations in the surface topography of schistosome worms were used by several investigators for the evaluation of antischistosomal drug activities in vitro and in vivo [e . g . 25] , [38]–[42] . In our in vitro assays , confocal laser scanning microscopy revealed that phytol induced severe tegumental damage in both male and female schistosomes . Additionally , quantitative analysis showed that phytol caused changes on the tubercles of S . mansoni male worms in a dose-dependent manner . Comparable results were obtained by previous works using other antischistosomal compounds , such as piplartine [22] , dermaseptin [26] , and ( + ) -limonene epoxide [43] . Based on our in vitro analysis , the anatomical disturbance differed between the reference drug ( praziquantel ) and phytol . Praziquantel caused severe muscle contractions and the worms became partially curved or swirled . In contrast , phytol caused worm paralysis but not muscle contraction . Also , the present observations of alteration in the surface architecture of S . mansoni male worms as a result of treatment with phytol are not similar to the tegumental alterations seen in vitro . In this sense , at the confocal microscopic level , the male parasites recovered from mice 48 h after phytol treatment did not show significant morphological alterations , although blebbing was visible on the tegument of male worms . Blebbing is an indicator of stress and has been observed in previous studies evaluating antischistosomal drugs such as carvacryl acetate [23] , mefloquine [38] , miltefosine [39] , artesunate and praziquantel [44] , [45] . The discrepancy between the effect of the drug and the onset of action of phytol in vitro and in vivo might be related to the lower phytol concentrations present in the liver and mesenteric veins in mice compared with the in vitro model . Moreover , these differences between in vitro and in vivo results may be explained by the fact that in vitro , the parasite is in a direct contact with the drug and , thus , it is not in direct contact with the host's microenvironment . Pharmacokinetic studies , measuring drug concentrations in the body and the target organs , might aid in the elucidation of these differences observed in vitro and in vivo . Furthermore , in contrast to adult male schistosomes , which have some blebs on the tegument , a single oral dose of 40 mg/kg phytol resulted in extensive tegumental damage of female worms . These results confirm , at least partially , that phytol is orally bioavailable [10] , but possibly the oral dose must be increased to achieve tegumental damage in male worms . We speculate that there is either sex-specific interference of the drug with the target , or that there are different targets for phytol in females compared with males . Nonetheless , further research is needed to provide a better understanding of the schistosomicidal action of phytol . Tegumental damage may not always result in death [46] , but the morphological alterations observed in this study could be a mechanism through which phytol kills the worms . The damage to the tegument along the worm's body would have impaired the functioning of the tegument and also destroyed the defence system of the worm , and so it could easily be attacked by the host's immune system . Further studies are necessary to elucidate the multiple mechanisms of action of phytol , which seem to be involved in the killing of schistosomes . It might also be useful to investigate whether phytol acts synergistically with the host immune response , similar to the chemotherapeutic effect of praziquantel , which has been shown to be dependent on the host antibody response [47] , [48] . On the other hand , it is possible that phytol has a direct killing effect without the absolute need for antibody , which occurs with praziquantel [49] . Finally , to see whether phytol affects the sexual fitness of adult worms , we evaluated the number of eggs in vitro and development stages ( oogram pattern ) and faeces egg load in S . mansoni-infected mice . Reductions in worm recovery and egg density in treated mice and in vitro were observed; this is considered by several authors as strong evidence of the efficiency of antischistosomal drugs [e . g . ] , [ 19] , [39] , [40] , [42] , [50 , 51] . The reduction of egg load in the tissues and faeces in treated mice may be attributed to the reduction in worm burden as a result of phytol treatment , the low productivity of the female already present , and the active destruction by the host's tissue reaction of the few eggs produced . In vitro , phytol caused a 75% reduction in egg production compared with untreated worms , although it is known that in vitro egg production is spontaneously reduced after a few days in culture [24] . However , more importantly , the inhibition of oviposition was irreversible , as found by examination of the worms following washing and incubation in drug-free RPMI medium , whose effect has been reported in previous studies with other antischistosomal compounds such as epiisopiloturine [25] , piplartine and dermaseptin [24] . In vivo , significant alterations in oogram patterns and faeces egg load were found and , thus , phytol affected the fecundity of the worms and/or the viability of the eggs . As described by Sanderson et al . ( 2002 ) [52] for in vitro and in vivo studies on the bioactivity of a ginger extract , it is not known whether these anti-fecundity effects were the result of generalised cytotoxic damage or more specific inhibition of reproductive process by phytol . It is known , furthermore , that inhibitors of cholesterol synthesis such as lovastatin ( mevinolin ) can reduce egg laying by S . mansoni females . For example , Vanderwaa et al . ( 1989 ) [53] has demonstrated with lovastatin that egg production by S . mansoni , in vitro and in vivo , is associated with the enzyme hydroxymethylglutaryl-coenzyme A ( HMG-CoA ) reductase , and that cholesterol precursors , mevalonate and farnesol , stimulate egg production by the female parasite and can reverse mevinolin-induced inhibition of egg production . Subsequently , Chen et al . ( 1990 ) [54] demonstrated that mevalonate and/or its metabolite not only plays a vital role in schistosome egg production but is also vital for survival of the parasite . Importantly , recent investigations have demonstrated that phytol is a cholesterol-lowering agent [55] . Accordingly , we speculated that the reduction in oviposition by phytol may be associated with the inhibition of HMG-CoA reductase . However , the underlying mechanism ( s ) of the effects remains to be fully elucidated . Presumably , it may also be due to a direct assault on female worms , thus diminishing their numbers or their ability to lay eggs , although a direct ovicidal action cannot be excluded [35] . Results obtained from preliminary morphological investigations ( no data shown ) indicate that phytol exerts a rapid action on schistosomes , resulting in marked alterations of the reproductive system of the worms . The main targets of the action of phytol are the female worms in terms of either the load of female worms or their ability to lay eggs . Egg production is contingent on worm maturation , pairing , and the support of the metabolic needs of the female . Phytol clearly disrupted this development process by directly killing female worms or inhibition of oviposition . In schistosomiasis , reductions in worm burden are associated with reduced pathology , and there is no concern about relapse because schistosome parasites do not multiply in the mammalian host . Moreover , reduction in egg burden can reduce egg shedding and the potential for parasite and disease propagation . In conclusion , the present results suggest that phytol has antischistosomal activities and provide a basis for subsequent experimental and clinical trials . The low toxicity and high bioactivity and tolerance by mammals support the potential of phytol as a new lead compound for human schistosomiasis . However , the effect of phytol in both in vitro and in vivo studies was evaluated using S . mansoni adult worms; thus , further studies are needed to evaluate the efficacy of phytol in different therapeutic regimens ( e . g . , multiple oral doses ) and to evaluate the efficacy of this drug against different life-cycle stages ( e . g . , schistosomula and juvenile worms ) as well as other Schistosoma species . Additionally , the detailed mechanism of action of phytol on schistosomes remains to be investigated . | Schistosomiasis is an infectious parasitic disease caused by helminths from the genus Schistosoma , which affects hundreds of millions of people , mainly the poor . Despite schistosomiasis being one of the most prevalent and debilitating neglected tropical diseases , the treatment and control of this disease relies on a single drug , praziquantel . However , there is increasing concern about the development of drug resistance . For this reason , the search for new schistosomicidal agents is a priority . Here , we report that phytol , a molecule from chlorophyll widely used as a food additive and in medicinal fields , shows promising antischistosomal properties against adult Schistosoma mansoni in vitro and in laboratory studies with mice harbouring adult S . mansoni . Phytol is a common food additive and is nonmutagenic , with satisfactory safety . Thus , phytol has potential as a safe and cost-effective addition to antischistosomal therapy . Further studies are needed because our results might have public health relevance . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2014 | Phytol, a Diterpene Alcohol from Chlorophyll, as a Drug against Neglected Tropical Disease Schistosomiasis Mansoni |
In vitro , dendritic cells ( DCs ) bind and transfer intact , infectious HIV to CD4 T cells without first becoming infected , a process known as trans-infection . trans-infection is accomplished by recruitment of HIV and its receptors to the site of DC–T cell contact and transfer of virions at a structure known as the infectious synapse . In this study , we used fluorescent microscopy to track individual HIV particles trafficking in DCs during virus uptake and trans-infection . Mature DCs rapidly concentrated HIV into an apparently intracellular compartment that lacked markers characteristic of early endosomes , lysosomes , or antigen-processing vesicles . Live cell microscopy demonstrated that the HIV-containing compartment was rapidly polarized toward the infectious synapse after contact with a T cell; however , the bulk of the concentrated virus remained in the DCs after T cell engagement . Individual virions were observed emerging from the compartment and fusing with the T cell membrane at the infectious synapse . The compartmentalized HIV , although engulfed by the cytoplasm , was fully accessible to HIV envelope-specific inhibitors and other membrane-impermeable probes that were delivered to the cell surface . These results demonstrate that HIV resides in an invaginated domain within DCs that is both contiguous with the plasma membrane and distinct from endocytic vesicles . We conclude that HIV virions are routed through this specialized compartment , which allows individual particles to be delivered to T cells during trans-infection .
Cell-to-cell transmission of viral infections is an important mechanism that enables HIV to establish systemic infections in the face of a strong immune response . Dendritic cells ( DCs ) play a critical role in the establishment and persistence of viral infection in HIV/AIDS . DCs efficiently bind , degrade and present HIV to T cells , initiating a potent immune response [1] , [2] . However , a portion of the bound virus can be transferred to T cells as intact , infectious particles through a process known as trans-infection . DCs greatly enhance infection of T cells by binding and concentrating HIV at sites of T cell contact . Recruitment of CD4 and chemokine co-receptors CCR5 and CXCR4 to the contact site on the T cell surface provides a receptor-rich environment for HIV entry . This structure is referred to as the infectious synapse , due to its similarity to the immunological synapse [3] . Because DCs can bind and sequester HIV without becoming infected , they can potentially harbor infectious virus despite ongoing antiviral therapy . Additionally , the intimate contacts between DCs and T cells could result in transmission that is resistant to some therapies . Hence , a better understanding of how DCs effect trans-infection might yield better strategies for control of HIV infections . The prevailing hypothesis to explain HIV trans-infection has been that DCs store HIV in an endocytic compartment that is subsequently delivered to the infectious synapse after engagement of a CD4 T cell . Early studies suggested that HIV trans-infection is routed through a non-degradative , endosomal compartment that is protected from proteolytic cleavage , implying that intact HIV is sequestered within the cytoplasm and re-exposed at the DC surface prior to trans-infection [4] . Others have reported that trans-infection by MDDCs is resistant to a neutralizing antibody , suggesting that HIV might be transmitted from internal stores [5] . More recently , intracellular infectious HIV was shown to be concentrated within a non-acidic compartment rich in tetraspanin proteins , suggestive of a multivesicular body ( MVB ) localization [6] , [7] , and similar structures appear to mediate HIV transmission from productively infected dendritic cells [8] . MVBs act as sorting compartments that either send receptors back to the cell surface in recycling endosomes or pass the lumenal contents to the degradative lysosomal pathway ( for a review see [9] ) . In some cells , microvesicles known as exosomes can be released by fusion of the MVB at the plasma membrane , a potential route for delivery of internalized HIV . Indeed , DCs release infectious HIV tightly associated with exosomes , supporting the notion that the virus traverses a late endosome/MVB pathway during trans-infection [10] . The exosome hypothesis predicts that the intracellular HIV would be protected from immune attack and from membrane impermeant inhibitors such as neutralizing antibodies or other peptides . A recent study , however , demonstrated that virtually all trans-infected virus could be neutralized by soluble CD4 ( sCD4 ) , a potent inhibitory protein , when it was delivered to the surface of DCs prior to trans-infection [11] . In those experiments , DCs were exposed to the sCD4 at 4°C to prevent endocytosis and the inhibitor was washed away before co-culture with target cells . Under those conditions , all infectious transfer was abolished , indicating that only surface-accessible HIV was productively transferred into target cells . The authors therefore concluded that virions transmitted in trans from MDDCs to T cells principally originated from the surface of DCs , and that the intracellular HIV observed in other studies was not the source of trans-infected HIV and was likely destined for lysosomal degradation . To reconcile the microscopic observations that DCs concentrate HIV in a distinct intracellular body [6] , [7] , [8] with the results of Cavrois et al . , we decided to test whether HIV is sequestered within a surface-accessible , apparently intracellular compartment . Here we show that LPS activated monocyte derived dendritic cells ( MDDCs ) and blood myeloid dendritic cells ( myDCs ) both concentrate HIV into a distinct compartment that remains readily accessible to a variety of membrane-impermeable probes , indicating that the apparently intracellular compartment remains contiguous with the exterior of the cell . Moreover , a fixable fluid-phase marker accumulated within this region , indicating that the internalized HIV is contained within an invaginated domain possessing a lumenal volume , and is not simply concentrated at the surface of the cell . Live cell analysis demonstrated that the compartmentalized HIV was rapidly re-localized to the infectious synapse and that individual particles were released from the compartment and rapidly entered the T cell , albeit at relatively infrequent intervals . We hypothesize that this pocket-like structure is formed in order to sequester virions off the surface of the DC and that subsequent presentation to T cells results in re-emergence of viral particles from the pocket at the infectious synapse , where HIV entry receptors are enriched in the target CD4 T cell .
We and others have reported that HIV is efficiently concentrated at the infectious synapse in conjugates formed between LPS matured monocyte-derived dendritic cells ( MDDCs ) and CD4 T cells [3] , [6] . Prior to engaging T cells the DCs efficiently concentrate HIV into a single intracellular , CD81-positive compartment [6] , [7] . Figure 1 demonstrates that HIV accumulation in mature MDDCs results from the accumulation of virions into a single subcellular region over time . When MDDC adhered to poly-L-Lysine coated coverslips were exposed to GFP-Vpr labeled HIV , the virus was distributed evenly throughout most of the cells immediately after exposure ( Figure 1A ) . After an additional ninety minutes on the coverslips , most of the HIV signal was confined to a single subcellular region , suggesting that the virus was actively concentrated within the cells . Notably , the HIV appeared to concentrate in Actin-rich pseudopods that formed as the DCs crawled along the poly-L-Lysine substrate ( Figure 1B ) . Three-dimensional reconstruction of the image data demonstrated that the concentrated HIV was sequestered in an intracellular compartment and not at the cell surface . In the experiment shown , 12% of the cells that were fixed immediately after exposure contained a fraction of HIV concentrated into a single region . After an additional 90 minutes in culture , 57% of the cells contained greater than 80% of the total cellular signal within a single subcellular region . Further incubation resulted in progressively more cells with HIV confined to a single subcellular compartment . When MDDCs were exposed to HIV in suspension cultures rather than bound to coverslips , the virus was compartmentalized more efficiently , so that the majority of cells harbored concentrated HIV immediately after exposure and greater than 90% of cells concentrated HIV after an additional hour in culture in a typical experiment . We hypothesize that the increased cell to cell contact under these conditions favors the concentration of HIV , similar to what has been described in the formation of the infectious synapse between DCs and CD4 T cells [3] . To determine whether the cytoskeleton was required for HIV concentration , we treated MDDCs with inhibitors of actin and microtubule polymerization during HIV exposure ( Figure 1C–1K ) . Latrunculin B , a potent inhibitor of actin polymerization and microfilament formation , effectively blocked the HIV concentration . In the experiment shown , some of the MDDCs bound very little virus ( Figure 1F , top right cell ) , probably due to drug toxicity . The majority of cells , however , still efficiently bound HIV , but no concentration of the virus was observed . When the Latrunculin B was subsequently washed away , HIV concentration was restored along with reconstitution of the actin cytoskeleton ( not shown ) . Disruption of the microtubule network with nocodazole treatment , by contrast , had little effect on virus binding or concentration , even though perinuclear CD63-positive endosomes were redistributed throughout the cytoplasm , indicating that the drug disrupted microtube-dependent endosomal trafficking ( Figure 1I–1K ) . These results indicate that concentration of HIV in mature MDDCs is an actin-dependent , microtubule independent process . To identify the HIV containing intracellular compartment more precisely , we stained GFP-HIV pulsed MDDCs with a panel of monoclonal antibodies comprising a variety of cell surface and intracellular markers ( Figure 2 ) . We identified a number of proteins that co-localized with the concentrated HIV . CD81 , a member of the tetraspanin family of integral membrane proteins , was highly concentrated along with the HIV , as has been reported previously [6] , [7] . High resolution imaging showed that CD81 and HIV were concentrated into a compact , apparently intracellular structure ( Figure 2A–2C ) . Before exposure to HIV , CD81 was distributed throughout the DCs , primarily at the cell surface . After exposure , a significant proportion of surface CD81 signal co-localized to the HIV containing compartment . Importantly , when GFP-HIV was bound to coverslips , CD81 mAb only marginally stained individual virions , indicating that the strong signal detected in DCs was not due to CD81 protein present on virion particles ( not shown ) . The tetraspanin CD63 was also associated with the compartmentalized HIV , however the majority of cellular CD63 was found in perinuclear punctate structures that were CD81-negative and stained brightly with LAMP1 antibodies , consistent with lysosomal localization ( Figure 2B and 2D ) . Cell-free GFP-HIV bound to coverslips was also recognized by the anti-CD63 antibody , indicating that staining of the compartmentalized HIV arose from the virion-associated CD63 . The CD81/HIV compartment did not co-stain with LAMP1 or other lysosomal markers and was morphologically distinct from those endosomal vesicles . The staining pattern of a third tetraspanin , CD9 , was identical to the CD81 localization , however in this case the anti-CD9 antibody strongly stained both isolated virion particles as well as the surface of DCs , suggesting that the signal in the compartment likely arose from both cellular and viral sources ( unpublished data ) . HLA Class II and the T cell co-stimulatory protein CD86 are expressed at high levels on the cell surface ( Figure 2E ) ; however , focusing on the interior of the cell revealed that these markers were also present in the HIV-containing compartment ( Figure 2F–2H ) . We observed similar staining patterns using antibodies directed at DC-SIGN ( CD209 ) , one of the C-type Lectin receptors known to bind and trans-infect HIV . By contrast , using a panel of antibodies that define a variety of endosomal vesicles , the HIV compartment did not contain any of the classical markers of early endosomes ( EEA1 ) , recycling endosomes ( transferrin receptor ) , lysosomes ( LAMP1 ) or HLA Class II processing vesicles ( HLA-DM; unpublished data ) . Individual virions outside of the CD81/HIV compartment , on the other hand , were sometimes found associated with endosomal markers , especially CD63-positive lysosomes , suggesting that some of the viral particles trafficked through conventional endocytic pathways , as seen in other studies [6] . Together , the staining data suggests that the concentrated HIV resides in an apparently intracellular structure that contains other cell surface proteins but does not co-localize with standard endosomal markers . Figure 2I–2K shows an example of an MDDC harboring compartmentalized HIV 2 days after virus exposure . The HIV that remained in these cells was consistently confined to a single CD81-positive region . Over a 4-day period , we observed a progressive loss in the number of HIV positive MDDC as well as the amount of signal in each cell . This suggests that the HIV was slowly degraded in the compartment or that it trafficked out of the CD81 compartment and was subsequently degraded or released from the cells . The number of MDDCs harboring HIV as well as the number of particles per cell dropped progressively and the virus was nearly undetectable after four days in culture , directly correlating with our ability to detect trans-infection of target cells over time . Figure S1 demonstrates that unstimulated , immature MDDCs do not efficiently sequester HIV into a similar compartment in short term cultures . One hour after HIV exposure , when greater than 90% of HIV was sequestered in LPS stimulated MDDCs , no apparent concentration of HIV particles occurred within the immature MDDCs ( Figire S1A–S1C ) . Over time , some of the viral particles co-localized with CD81 ( Figure S1D–S1F ) , and overnight culture resulted in complete concentration within a compacted , CD81-positive compartment , similar to that seen in mature MDDCs ( Figure S1G and S1H ) . Importantly , the amount of HIV remaining in the immature cells declined rapidly , so that the majority of cells contained little or no virus 24 hours after HIV exposure . This is consistent with other reports demonstrating that HIV is rapidly degraded in immature DCs [12] , [13] and is likely a consequence of efficient endocytosis and antigen degradation found in immature , but not in mature MDDCs [14] . We conclude , therefore , that immature MDDCs can sequester intact , infectious HIV into a compartment similar to that found in mature MDDCs . In the short term , however , the virus appears to be either destined for lysosomal degradation or retained on the cell surface . To verify that the concentration of HIV particles was not an artifact of in vitro culture of MDDCs , we tested myeloid DCs purified directly from peripheral blood mononuclear cells ( PBMC ) . Myeloid DCs ( myDCs ) are found in blood , skin , and mucosal tissues and have been associated with HIV capture and sexual transmission . Circulating myDCs enter the tissues in response to activating stimuli and differentiate into Langerhans , dermal , and interstitial DCs ( reviewed in [15] ) . Figure 3 demonstrates that trans-infection by myDCs is greatly enhanced by LPS stimulation and HIV is sequestered in a CD81-positive subcellular compartment . As with MDDCs , trans-infection by immature myDCs was much lower ( Figure 3A ) and rapid HIV sequestration was not observed ( not shown ) . The sequestration and trans-infection of HIV by this important dendritic cell population supports the notion that HIV trafficking within MDDCs reflects that of primary myeloid dendritic cells . Additionally , activated monocyte derived Langerhans cells [16] and intraepithelial vaginal Langerhans cells [17] concentrate intact HIV in a similar fashion , suggesting that sequestration can occur in these dendritic cell subtypes during natural infections . HIV transmission at the infectious synapse has been observed in numerous immunofluorescent and EM studies [3] , [6] , [7] , [18] , [19] . A limitation of these studies was that only static images of cells were obtained , raising the possibility that the bulk of internalized virus was ultimately destined for degradation . The alternative hypothesis that transmission occurs only from surface-bound HIV , and not from internalized virus particles , was based on virological studies; however , that study did not include direct imaging analyses [11] . To reconcile whether the apparently intracellular HIV could be transmitted at the infectious synapse , we imaged DC interactions with CD4-positive Jurkat T cells in real time . For live cell analysis , target Jurkat T cells were marked either by expression of low levels of GFP ( Figure 4A–4C ) or with a fluorescent dye ( Figure 4D–4F ) in order to unambiguously identify DC:T cell interactions . In addition to GFP-Vpr , which labels the virion core [20] , the HIV was labeled with S15-RFP , a myristoylated fusion protein that associates with the inner leaflet of the plasma membrane . The RFP therefore marks HIV particles with intact lipid envelopes prior to CD4-dependent viral fusion [21] . The RFP signal is predicted to remain associated with the virus after binding to the DC prior to trans-infection and lost after CD4-dependent entry into the target cells . To visualize the cellular distribution of HIV , the time-lapse movies were compiled from 3-D renderings of the whole cell volumes , demonstrating that there were no individual virions found outside of the compartment in the DCs at the beginning of the acquisition . In the examples shown , the virus compartment was localized near the cell interface within a few minutes after initial contact , however little viral transmission into the Jurkat T cells occurred even after extended interaction . In the first example , no viral transfer is evident until about 50 minutes after initial contact , at which time two particles were delivered in rapid succession from the DC compartment into the Jurkat cell ( Figure 4A–4C and Video S1 ) . Importantly , after entering the T cell , both HIV particles lost the RFP membrane marker , suggesting that virus fusion had occurred . The loss of RFP signal is particularly evident in the first particle transmitted at the infectious synapse because it was possible to track the particle before and after loss of the marker ( Figure 4B and 4C ) . Although the loss of the RFP marker does not identify productive infection of the target cell , it is a strong indicator of CD4-dependent entry , a prerequisite for productive trans-infection . During the course of the time-lapse experiment shown in Figure 4A–4C , several individual viral particles were observed trafficking within the DC outside of the compartment . Because the images are whole-cell reconstructions and the particles were not observed prior to cell contact , these particles likely were released from the compartment into the DC . None of these viral particles were transferred to any of the four Jurkat T cells that interacted with the DC over the course of the experiment . Instead , the particles rapidly disappeared from view , suggesting that they were either degraded in the cell or reintegrated into the HIV compartment . Figure 4D and Video S2 show another typical transmission event , in that instance mediated by a myeloid DC . Although the virus compartment was polarized toward the interface throughout the 40-minute acquisition , only a single transmission event occurred . As predicted , after entry into the Jurkat cell , the RFP signal was no longer detected , suggesting that the virus had fused into the Jurkat cell at the infectious synapse shortly after emerging from the dendritic cell compartment ( Figure 4F ) . In fourteen independent experiments performed with mature MDDCs , no more than one or two transmission events were observed in a typical 30- to 60-minute time-lapse . Often no transmission events at all were recorded even after sustained polarization of the HIV at the cell interface . Similar transmission frequencies were observed in time-lapse experiments using mature myeloid DCs . This relatively inefficient transmission is in contrast to surface-mediated transmission , which appears to result in both a more rapid and efficient transfer . Video S3 demonstrates the transmission of at least 4 HIV particles in a 35-minute movie of a DC that was exposed to GFP-HIV at 4°C and imaged prior to virus internalization . It is apparent from this movie that surface-bound HIV has increased access to the Jurkat cell surface and is transferred at a substantially greater rate than the HIV from internal stores . As noted , however , when DCs are exposed to HIV at 37°C , little virus remains on the cell surface immediately after exposure to the virus , so that under these conditions it is unlikely that surface transmission of HIV is the predominant source of trans-infection . Our fixed cell analysis suggested that the concentrated HIV was confined to a single compact structure in the DCs . Live cell analysis , however , demonstrated that the HIV compartment could undergo substantial deformation . As shown in Figure S2 and Video S4 , the HIV compartment was polarized toward the cell interface shortly after contact and remained there throughout the interaction . Eight minutes into the movie , the compartment split into two separate structures and some individual virions were released into the cell . The compartment remained separated for four minutes and then re-formed into a single region for the remainder of the time-lapse . This data suggests that the HIV containing compartment is a highly dynamic structure that is capable of releasing and rapidly re-acquiring virion particles . Cavrois et al . , have demonstrated that trans-infection by DCs can be completely abolished by incubating HIV-bound DCs with a membrane-impermeant inhibitor , soluble CD4 , that is applied under conditions that prevent access of the inhibitor to endocytosed HIV [11] . Because the inhibitor was applied at 4°C and washed away before co-culture with target T cells , the authors reasoned that only surface-bound HIV could be inhibited and internalized HIV should resist inhibition . Since the inhibitor abolished trans-infection under these conditions , they concluded that only surface-bound , and not endocytosed HIV was productively transferred during trans-infection . The authors hypothesized that the internalized HIV was destined for lysosomal degradation and not routed back to the infectious synapse for transmission , as had been suggested by previous reports [3] , [4] , [6] , [7] , [10] . The Cavrois experiments presented us with a paradox: How could trans-infection occur efficiently if over 90% of HIV associated with DCs is destined for degradation , and how can HIV remain on the surface of DCs for prolonged periods of time when our data clearly demonstrated rapid internalization of the majority of HIV ? As we have shown , HIV is rapidly internalized and remains sequestered within DCs for up to 2 days without undergoing degradation . We considered it unlikely that the compartment was a degradative one and reasoned that surface-applied inhibitors might access the compartmentalized HIV even when endocytosis is prevented by incubating at 4°C . We therefore performed inhibitor experiments similar to those described [11] . Figure 5 shows a representative experiment in which DCs were sequentially exposed to differentially tagged reporter viruses under conditions in which the virus was concentrated within the DCs . We tested whether exposure to sCD4 at 4°C inhibited trans-infection when added before , after , or in between exposure to the two reporter HIVs . When sCD4 was added before exposure to either reporter it had no effect on trans-infection , as expected . Addition of the inhibitor after exposure to both HIVs resulted in complete inhibition of trans-infection , and when the sCD4 was added between exposures to the respective reporters , only the pre-bound HIV was inhibited . These results , although performed using distinct experimental systems , are functionally identical to those reported by Cavrois , et al . , and support the conclusion that only HIV that is accessible to the surface applied sCD4 inhibitor is productively transmitted to the target cells . We next examined the localization of soluble CD4 when it was applied under the same conditions as the inhibitor experiments to determine whether internalized HIV was accessed by the surface applied inhibitor . We used a CD4-IgG fusion protein ( Pro 542 , a CD4-human IgG2b chimeric protein kindly provided by Progenics , Inc . ) to enable detection by immunofluorescence . When CD4-IgG was applied at 4°C to DCs harboring sequestered HIV , the ligand strongly stained the CD81-positive HIV compartment ( Figure 6A–6C ) . Close examination of the co-localized signals revealed that the CD4-IgG stained the distinct , CD81-positive structure indicative of the compartmentalized HIV . CD4-IgG also stained a number of punctate structures outside of the HIV compartment , possibly due to binding of the human IgG fusion to Fc Receptors or other ligands on the DC surface . Nevertheless , the compartmentalized HIV was specifically stained by surface-applied CD4-IgG and not by nonspecific control human IgG ( not shown ) . To confirm that the compartment was accessible to another surface-applied inhibitor , we tested the HIV-specific neutralizing antibody 2G12 , a human IgG1 monoclonal antibody ( mAb ) that recognizes the HIV envelope glycoprotein outside of the CD4 binding site [22] . Like CD4-IgG , surface-applied 2G12 strongly stained the CD81-positive HIV compartment ( Figure 6D–6F ) . This antibody also stained some punctate structures on the DC surface , however the 2G12-specific signal within the HIV compartment was the brightest signal in the cell and co-localized with the CD81 signal . In these experiments , greater than 90% of HIV-containing compartments were strongly stained with the envelope-specific probes , and human IgG1 control antibodies stained only the punctate , surface structures and not the internalized HIV . When the cells were fixed with formaldehyde before application of the antibodies , less than 20% of the HIV-containing compartments stained with the surface-applied probes , whereas inclusion of detergent to solubilize cell membranes resulted in strong staining of all of the compartments . These results indicate that the internalized HIV remains accessible to the surface applied envelope-specific inhibitors , suggesting that HIV is concentrated in a non-endocytic , plasma membrane derived structure that is contiguous with the outside of the cell . Furthermore , the sensitivity to fixatives suggests that the HIV resides within a structure that can be sealed off by formaldehyde fixation , and is not simply a collection of virions confined to a single region on the cell surface . To determine whether the HIV compartment was accessible to a surface-applied fluid phase marker , we exposed HIV pulsed DCs to aldehyde fixable , Texas-Red labeled dextran at 4°C ( Figure 7A–7C ) . This approach has been used to identify the exposure of intracellular compartments delivered to the cell surface during acinar cell exocytic events [23] . Remarkably , the dextran strongly labeled the compartmentalized HIV in the DCs ( Figure 7A ) , indicating that the internalized HIV was contained within a surface-accessible lumenal structure , and not in an endocytic vesicle . Close examination demonstrated the same compact structure seen with co-localizing surface proteins in Figure 2 . In all cells harboring HIV , the only strong dextran signals in the cells were associated with the sequestered virus . Prior to HIV exposure , less than one percent of mature MDDCs contained similar staining structures , strongly suggesting that HIV binding induced the formation of the compartments . The accumulation of the dextran signal within the HIV compartment compared to the dim staining of the cell surface suggests that the compartment is an invaginated plasma membrane domain with a lumenal volume and not simply concentrated virion particles at the cell surface . MDDCs , therefore , appear to sequester HIV into an intracellular , plasma-membrane derived pocket that remains physically connected with the outside of the cell . To confirm that the antibodies labeled only surface accessible and not endocytic vesicles when applied at 4°C , we probed HIV pulsed MDDCs with an anti-CD63 mAb . We took advantage of our earlier observation that anti-CD63 can recognize both virion-associated and endosomal CD63 in fixed cells ( Figure 2 ) . When anti-CD63 was used as a probe for surface-exposed HIV , we observed strong staining of the HIV compartment but not of the CD63-positive , intracellular lysosomes ( Figure 7 ) . Removal of cell membranes with detergent after fixation and re-probing with the same anti-CD63 antibody tagged with a different fluorescent label resulted in strong staining of the lysosomes , indicating that these structures were not accessed by the surface-applied anti-CD63 ( Figure 7D–7F ) . In addition , when DCs were fed fluorescently labeled hen egg lysozyme ( HEL ) along with GFP-HIV at 37°C , HEL concentrated in the CD63-positive lysosomal compartment and was not accessed by surface-applied anti-CD63 ( unpublished data ) . Together these results indicate that the surface applied antibody probes did not access internal , endocytic compartments . We wished to again verify that our observations in MDDCs were reproduced in primary dendritic cells purified from PBMCs . Figure S3 demonstrates that compartmentalized HIV in mature myeloid DCs also remains accessible to surface applied probes . As in MDDCs , greater than 90% of the myDCs contained sequestered HIV that was stained by the surface-applied , envelope-specific mAb 2G12 . We observed identical results with the other surface-applied probes ( sCD4 , CD63 and CD81 ) as well as the fluid phase marker Dextran ( unpublished data ) . Figure 8 demonstrates that 24 hours after virus exposure the HIV compartment remained accessible to surface applied anti-CD81 and anti-envelope monoclonal antibodies . Video S5 shows the entire z-stack of the CD81 staining profile , revealing that the convoluted structure of the compartment is contiguous with the cell surface . In three independent experiments , more than 60% of the compartments stained brightly with the anti-CD81 antibody after 24 hours in culture , and only about 20% were not detectably stained . Similarly , the 2G12 anti-HIV envelope antibody accessed the sequestered HIV in approximately 80% of the cells after a day in culture . We observed similar staining patterns after 6 hours in culture , at which time at least 80% of the compartments were stained by the surface applied anti-CD81 antibody . We noted that after extended incubation , a small fraction of the HIV compartments did not stain with the surface applied probes , suggesting that some of the compartments may arise from endocytic fusion of the internalized structures . Alternatively , this subpopulation of structures may maintain connections to the surface that are too restrictive to allow diffusion of the relatively large protein probes . Our results favor the constitutive maintenance of a surface-accessible compartment that decreases in intensity over time , possibly as a result of some of the surface-contiguous compartments undergoing endocytotic fusion , while leaving the majority of the HIV sequestered and surface-accessible throughout the culture period . The data presented here strongly suggests that myeloid-derived DCs , whether differentiated in vitro or purified directly from blood , sequester HIV within an intracellular , tetraspanin rich domain that retains connection with the surface of the cell and that intimate contact with CD4 T cells results in HIV delivery from the compartment into the target T cell at the infectious synapse .
In this study we have demonstrated that LPS-activated DCs concentrate HIV into a single intracellular compartment along with a subset of cell surface proteins , most strikingly CD81 , as has been reported elsewhere [6] , [7] , [8] , [19] . Surprisingly , the compartment remained accessible to surface-applied ligands , indicating that the concentrated HIV did not reside in an endocytic compartment but instead was sequestered in an invaginated , plasma membrane derived pocket-like structure . Accessibility of HIV to surface-applied probes was maintained throughout extended culture , suggesting that the pocket-like structure was not an intermediate vesicle destined for the endocytic pathway . Live cell analysis showed that after contact with CD4 T cells , the HIV compartment was rapidly polarized to the cell–cell junctions , and transmission of viral particles occurred in discreet , relatively infrequent events . During prolonged T cell contact , the compartment appeared to be quite dynamic , releasing and re-incorporating individual virions and at times even splitting into two distinct structures and reforming as one , suggesting that the compartment was not an vesicle with a single limiting membrane but instead might consist of multiple compacted membrane domains that can stretch and re-form in the cell . Figure 9A and 9B summarizes two competing models of trans-infection that have been presented in the literature . The exosome model ( Figure 9A ) proposes that HIV is sequestered into a subcellular compartment in mature DCs that is endocytic in origin [4] , [6] and likely to be a late endosomal/multivesicular body structure [7] , [19] . In support of this model , HIV is released from DCs in association with exosomal structures formed in the MVB mediated pathway of exocytosis [10] . This model proposes that trans-infection occurs by re-exposure of the contents of the HIV containing MVB/late endosome at the cell surface , an event requiring fusion of the MVB and plasma membranes . The exosome model was recently challenged by Cavrois et al . , who used viral infectivity assays to demonstrate that trans-infection occurred primarily by surface-accessible HIV . In their central experiment , HIV bound at 4°C prior to co-culture was entirely inhibited by a soluble CD4 ( sCD4 ) protein , and when the DCs were shifted to 37°C before sCD4 addition , the inhibitor still blocked infection . Cavrois et al . reasoned that sCD4 should not have access to the internalized HIV and therefore that the internalized virus does not substantially contribute to productive transfer of infection . They therefore proposed that virus particles bound to the external plasma membrane are the primary source of trans-infection , and that the internalized virus observed by others likely was bound for lysosomal degradation ( Figure 9B ) . The results presented here reconcile these two models by demonstrating that the intracellular , apparently endocytosed HIV remains fully accessible to a surface-applied inhibitor ( Figure 9C ) . In this model , HIV is taken up by DCs and sequestered in the cytoplasm by invagination of the plasma membrane to form a pocket-like , intracellular compartment that remains contiguous with the cell surface . Individual virus particles can escape from the pocket-like structure and infect target cells at the infectious synapse without the need for exocytic delivery . Membrane invagination is likely to engage the endocytic machinery; however , fusion and endocytic maturation is arrested , resulting in a membrane enclosed intracellular structure that is not subject to endosomal degradation . Although we have been able to confirm the virus neutralization results of Cavrois et al . , the imaging-based approaches presented here are inconsistent with their conclusion that only the minority of virus particles that remain on the cell surface are responsible for trans-infection . Virus particles that remain on the cell surface are likely to remain infectious , and undoubtedly can contribute to trans-infection if the DC encounters a T cell before sequestration of that virus . However , we have demonstrated that the sequestered virus is also able to infect T cells after re-emerging from the compartment at the infectious synapse . We hypothesize that HIV is sequestered within the DCs in order to prevent surface-mediated transfer , however a small amount of egress from the compartment results in efficient infection of the target cells and results in higher levels of infection than that generated by an equivalent amount of cell free HIV . Consistent with this idea , Cavrois et al . demonstrated a progressive loss of infectious transfer over time after shifting HIV-loaded DCs from 4°C to 37°C [11] . They concluded that internalization of the HIV at 37°C resulted in endocytosis and degradation of the virus . We have observed similar loss of infectivity concomitant with sequestration into the invaginated compartment over time . Under those conditions , however , we did not observe substantial loss of the fluorescent HIV signal and the virus remained accessible to surface applied probes ( data not shown ) . We therefore believe that what Cavrois et al . were observing was the loss of infection arising from sequestration and not endocytic degradation of the virus . Similar intracellular structures have been recently described in HIV infected macrophages , which concentrate virions in CD81-positive , plasma membrane-derived intracellular invaginations during productive infection [24] , [25] . EM analysis revealed that the intracellular HIV-containing compartment , previously identified as a multivesicular body , consisted of a network of invaginated structures contiguous with the plasma membrane that were induced following HIV infection . The structures were accessible by membrane impermeant probes applied at 4°C , similar to the experiments presented in this study [24] . Those reports resolved the conflicting models of virus assembly sites in macrophages and identified a previously unknown mechanism for physical sequestration of viral particles in a non-endocytic , surface exposed cellular compartment . Interestingly , a recent report demonstrated that sequestered HIV was rapidly translocated to the virological synapse formed between infected macrophages and uninfected T cells [26] . We propose that dendritic cells use similar mechanisms to sequester HIV when it is taken up from the extracellular milleau and concentrated into the invaginated plasma-membrane derived structure described in this study . After sequestration , the virus can remain intact for an extended period of time and either traffic into the cell for endocytic degradation or transfer to other cells to share antigenic processing and presentation functions . Because dendritic cells constantly interact and stimulate CD4 T cells , even infrequent transmission of intact HIV particles during cellular communication events can result in efficient dissemination of HIV from the very cells that are designed to control its infections .
Hos-CD4 ( human osteosarcoma cell line stably expressing human CD4 ) and HEK293T cells were maintained in DMEM supplemented with 10% FBS . Jurkat , Jurkat LTR-GFP ( kindly provided by Olaf Kutsch ) and LuSIV ( CEM T cells transduced with an HIV LTR-Luciferase reporter ) ( AIDS Reference and Reagent Program ) cells were maintained in RPMI supplemented with 10% FBS . Monocyte-derived DCs were generated as described previously [27] . Briefly , CD14-positive monocytes were purified from PBMC by magnetic bead separation ( Miltenyi Biotec ) and cultured in RPMI ( Life Technologies ) supplemented with 10% FBS ( Hyclone ) , 100 ng/ml IL-4 and 50 ng/ml GM-CSF ( Gentaur Biosciences ) for 5–7 days to generate immature DC . In a typical experiment , greater than 90% of the cells were CD14- HLA-DR+ DC-SIGN+ and 80%–90% displayed an immature phenotype as determined by low or no expression of CD80 and CD86 . Activated DCs were generated by the addition of LPS ( 100 ng/ml , Sigma ) for 12–24 h . Maturation was assessed by upregulation of CD86 and HLA-DR . Myeloid DCs were purified from PBMC using CD1c ( BDCA-1 ) + dendritic cell magnetic bead selection kit according to the manufacturer ( Miltenyi Biotech ) . Myeloid DCs were maintained overnight in GM-CSF ( 5 ng/ml ) and activated with LPS ( 100 ng/ml ) . For immunofluorescent studies , monoclonal antibodies ( mAbs ) specific for DC-SIGN , CXCR4 , CCR5 ( R&D Systems ) , CD4 ( Sigma ) , CD9 , CD63 , CD80 , HLA-DM , HLA- ( DR , DP , DQ ) , LAMP-1 ( Pharmingen ) were diluted to predetermined concentrations in PBS+10% normal donkey serum+ . 1% Triton X-100 . Cy3- or Cy5-labeled Donkey anti-mouse antibodies ( Jackson ImmunoResearch ) were used as secondary reagents . Anti-HIV env antibody 2G12 ( AIDS Reference and Reagent Program ) and Pro-542 ( sCD4-hIgG ) ( kindly provided by Norbert Shulke , Progenics , Inc . ) were detected using anti-human IgG secondary reagents ( Molecular Probes ) . For multi-color staining , mAbs were pre-labeled with appropriate Zenon reagents ( Molecular Probes ) and added after secondary antibody labeling . Flow cytometric analysis was performed using direct labeled antibodies to the specified antigen with appropriate isotype controls ( BD Biosciences ) . Actin cytoskeleton was stained with fluorescent-phalloidin ( Molecular Probes ) and nuclei were stained with Hoechst dye ( Sigma ) . Nocodazole ( Sigma , 5 µM ) or Latrunculin B ( BioMol , 2 . 5 µM ) were added to MDDCs 15 min . prior to HIV exposure to disrupt cytoskeletal structures and maintained in the cultures throughout the experiment . Inhibition of HIV trans-infection was performed as described using recombinant sCD4 ( 10 µg/ml ) ( AIDS Reference and Reagent Program ) or the Progenics sCD4-hIgG ( 8 µg/ml ) ( Pro 542 , Progenics , Inc . ) fusion with similar results ( data not shown ) . GFP-Vpr labeled HIV was produced by calcium phosphate co-transfection of HEK293T cells with an eGFP-Vpr expression construct , HIV env deficient proviral clone pLAI∂env and HXB2 envelope glycoprotein expression construct as previously described [20] . GFP-Vpr/S15-RFP was generated by including S15-mCherry , a myrystoylated fusion protein that associates with lipid bilayers in transfected cells and marks the HIV lipid envelope [21] . Transfected cells were washed 16 hours post-transfection , media was replaced again 8 hours later and supernatants containing labeled virus was collected the next morning , approximately 40 hours post-transfection . Cleared supernatant was passed through a . 45 μ filter and frozen at −80°C . Stocks were assayed for infectivity and p24 concentration , and incorporation of GFP-Vpr was assessed by co-staining with Gag antibodies [20] . GFP-Vpr/S15-RFP was assessed by co-localization of GFP and RFP with Gag staining , and optimized so that greater than 95% of the GFP-positive particles were also RFP-positive . Single-round infectious , HIV luciferase stocks were generated by transfection of HEK293T cells with the env-deficient proviral vector plasmid NL-Luc-E-R- containing a firefly luciferase reporter gene or NL-Ren-E-R- containing renilla luciferase reporter ( kindly provided by Dr . Nathaniel Landau ) [28] along with an HIV-1 HXB2 envelope glycoprotein expression construct . MDDCs ( 106/ml ) were incubated with HxB2 pseudotyped Luciferase or Renilla stocks ( 37°C , 2 h ) , washed twice and resuspended in culture medium . DCs ( 5×103 ) were then co-cultured with Hos-CD4 target cells ( 2×104 ) in 96-well plates and assayed 40 hours later using the Brite-Luc or Dual-Luc luciferase assay reagents ( Promega ) and reading the plates on a multi-well format luminometer ( BioRad ) . Freshly thawed aliquots of HIV-Luciferase were included as normalization standards . Alternatively , MDDC or myDCs were incubated with HXB2 pseudotyped GFP-Vpr HIV , washed and co-cultured with LuSIV ( HIV LTR-Luciferase ) indicator cells for 40 hours and assayed as above . For inhibitor studies , DCs were incubated at the appropriate times with sCD4 ( AIDS Reference and Reagent Program ) at 4°C for 1 h , washed twice at 4°C and incubated further as indicated in the text . DCs were allowed to adhere to poly L-Lysine–treated coverslips , rinsed with PBS and fixed with 4% EM grade formaldehyde ( Polysciences ) in PBS . Antibodies were added in SB ( PBS , 10% normal donkey serum [Jackson ImmunoResearch] ) or SBTx ( SB+0 . 1% Triton X-100 ) to remove cellular membranes for staining intracellular antigens for 20 mn at RT . Coverslips were rinsed extensively and stained with donkey anti-mouse secondary antibodies ( Jackson ImmunoResearch ) in SB . For live cell staining , cells were incubated at 4°C with the indicated probes for 30 to 60 mn , washed twice with cold PBS and fixed onto poly-L-lysine coverslips . Probes were then detected with the appropriate fluorescent reagents . Coverslips were mounted onto glass slides using Gel Mount ( Biomedia ) containing an anti-fade reagent . Dried slides were imaged on a Deltavision RT epifluorescent microscope system fitted with an automated stage ( Applied Precision , Inc ) and images were captured in z-series on a CCD digital camera . Out-of-focus light was digitally removed using the Softworks deconvolution software ( Applied Precision , Inc ) . 3-D volume projections were generated using the Softworx analysis program . Images were exported as . tif files and figures were composed using Adobe Photoshop CS ( Adobe , Inc ) . CD4: P01730 , CD209 ( DC-SIGN ) : Q9NNX6 , CD86: P42081 , CD63: P08962 , CD9: P21926 , CD80: P33681 , CD81: P60033 , HLA-DR: P04229 , HLA-DP: P20036 , HLA-DQ: P01907 , HLA-DM: P28067 , EEA1: Q15075 , Transferrin Receptor: P02786 , LAMP-1: P11279 , ICAM-1: P05362 , LFA-1: P20701 , CXCR4: P61073 , CCR5: P51681 , HIV gp120: O70902 . | Dendritic cells ( DCs ) patrol mucosal areas of the body , where they engulf invading pathogens and transport them to immune tissues . There the DCs degrade the microbes and present antigenic peptides to T lymphocytes to elicit specific immune responses . HIV-1 has appropriated this feature of the immune system to better establish and maintain infection of its primary target–CD4-positive T cells . DCs efficiently bind and degrade HIV , however a portion of the virus remains intact and can be transmitted into CD4 T cells , a process called trans-infection . DC maturation by various stimuli dramatically increases their capacity to trans-infect . Here we report that mature DCs concentrate infectious HIV into a pocket-like compartment that resides within the cell but remains physically connected to the cell surface . This structure is distinct from the intracellular degradative compartments that are used for microbial processing and presentation . The intact viral particles are retained within this compartment for extended periods , and individual particles can emerge and infect T cells at the cellular interface . We hypothesize that DCs form this compartment to sequester HIV off of the cell surface , however escape of virions from the pocket results in efficient infection of T cells during immune presentation events . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/immunodeficiency",
"viruses",
"cell",
"biology"
] | 2008 | HIV Traffics through a Specialized, Surface-Accessible Intracellular Compartment during trans-Infection of T Cells by Mature Dendritic Cells |
Prions , characterized by self-propagating protease-resistant prion protein ( PrP ) conformations , are agents causing prion disease . Recent studies generated several such self-propagating protease-resistant recombinant PrP ( rPrP-res ) conformers . While some cause prion disease , others fail to induce any pathology . Here we showed that although distinctly different , the pathogenic and non-pathogenic rPrP-res conformers were similarly recognized by a group of conformational antibodies against prions and shared a similar guanidine hydrochloride denaturation profile , suggesting a similar overall architecture . Interestingly , two independently generated non-pathogenic rPrP-res were almost identical , indicating that the particular rPrP-res resulted from cofactor-guided PrP misfolding , rather than stochastic PrP aggregation . Consistent with the notion that cofactors influence rPrP-res conformation , the propagation of all rPrP-res formed with phosphatidylglycerol/RNA was cofactor-dependent , which is different from rPrP-res generated with a single cofactor , phosphatidylethanolamine . Unexpectedly , despite the dramatic difference in disease-causing capability , RT-QuIC assays detected large increases in seeding activity in both pathogenic and non-pathogenic rPrP-res inoculated mice , indicating that the non-pathogenic rPrP-res is not completely inert in vivo . Together , our study supported a role of cofactors in guiding PrP misfolding , indicated that relatively small structural features determine rPrP-res’ pathogenicity , and revealed that the in vivo seeding ability of rPrP-res does not necessarily result in pathogenicity .
Transmissible spongiform encephalopathies ( TSEs ) , also known as prion diseases , are a group of fatal neurodegenerative disorders affecting both humans and other mammals[1] . A central pathogenic event in prion disease is conformational conversion of the host-encoded prion protein ( PrPC ) , a normal , protease-sensitive , cell-surface localized glycoprotein , to a misfolded and protease-resistant pathogenic conformer , PrPSc[1–5] . As an unorthodox infectious agent , PrPSc replicates itself by imprinting its distinctive infectious conformation on host PrPC molecules[6] . The molecular mechanisms underlying the in vivo PrPC-to-PrPSc conversion are largely unknown . Recent in vitro studies have revealed that bacterially expressed recombinant PrP ( rPrP ) can be converted into pathogenic conformations in a test tube and those pathogenic forms cause bona fide prion disease in animals[7–13] . Even though some in vitro rPrP conversions in the absence of any additives have produced prion infectivity[11–13] , generation of rPrP pathogenic conformers with a proper , i . e . scrapie-like , proteinase K ( PK ) -resistant pattern and a high titer of prion infectivity have so far been achieved only in the presence of cofactors[7–10] . The serial protein misfolding cyclic amplification ( sPMCA ) is one of the methods commonly used to study prion conversion in vitro[14 , 15] . During sPMCA , a mixture of PrPC-containing normal brain homogenate plus a small amount of PrPSc-containing diseased brain homogenate is subject to successive cycles of sonication and incubation , allowing simultaneous propagation of the PrPSc conformers and prion infectivity[16] . The enormous amplification power makes sPMCA a sensitive tool for detecting minute amounts of PrPSc , and this methodology has been successfully used for the diagnosis of prion disease[17 , 18] . The sPMCA can also be performed without any seed , allowing de novo generation of PrPSc . Using the latter approach , we have shown that , in the presence of total RNA isolated from mouse liver plus synthetic phospholipid POPG ( 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho- ( 1′-rac-glycerol ) , recombinant murine prion protein ( rPrP ) purified from E . coli can be converted into the highly infectious and PK-resistant conformer rPrP-resRNA[7 , 19] , which causes prion disease in wild-type animals and has the same pathogenic properties as naturally occurring prions[8] . A similar conversion system , containing rPrP , total mouse liver RNA , and POPG , was used at the NIH Rocky Mountain Laboratory to produce an rPrP-res conformer ( rPrP-resNIH ) de novo[20] . Interestingly , despite its ability to propagate indefinitely by sPMCA , rPrP-resNIH does not cause disease in vivo[20] . In sPMCA reactions seeded by native murine prions or rPrP-resRNA , phosphatidylethanolamine ( PE ) was found to allow rPrP conversion into the PK-resistant and highly pathogenic conformer rPrP-resPE as the only cofactor[10] . Omitting the PE cofactor resulted in either the halting of rPrP-resPE propagation or the generation of a cofactor-independent , “protein-only” , PK-resistant conformer rPrP-resprotein-only that failed to induce any pathology in mice[21] . The non-pathogenic rPrP-resprotein-only has a smaller PK-resistant core than rPrP-resPE , but nonetheless can be propagated indefinitely by sPMCA[21] . The fact that the non-pathogenic rPrP-resprotein-only could be propagated in the absence of any cofactor raised a series of interesting questions . Is the lack of infectivity in certain non-pathogenic rPrP-res conformers due to the absence of cofactors ? Are cofactors required for the propagation of other non-pathogenic rPrP-res conformers ? Do all non-pathogenic rPrP-res conformers share the same structure ? And how significant is the difference between the structures of the non-pathogenic and pathogenic rPrP-res conformers ? Here we performed detailed comparisons between the highly pathogenic rPrP-resRNA and two independently formed , non-pathogenic rPrP-res conformers in terms of their propagation , PK-resistance patterns , conformational differences , capability of infecting cultured cells , and seeding of rPrP amyloid fibril growth . Our results provided novel insights into the relationship among cofactors , self-propagating conformations , and bona fide prion infectivity .
We previously reported that in the presence of total mouse liver RNA and synthetic POPG , purified and fully folded rPrP can be converted into highly pathogenic recombinant prion ( rPrP-resRNA ) in an unseeded sPMCA reaction[7] . Using the same protocol , we found that another rPrP-res form , rPrP-resRNA-low , could also be generated de novo ( S1 Fig ) . The rPrP-resRNA-low has a smaller PK-resistant core ( about 15 kDa , vs . ~16 kDa for rPrP-resRNA ) . Similar to the pathogenic rPrP-resRNA , rPrP-resRNA-low can propagate indefinitely by sPMCA ( Fig 1A ) . To determine whether rPrP-resRNA-low is pathogenic in vivo , we intracerebrally inoculated wild-type C57BL/6 mice with rPrP-resRNA or rPrP-resRNA-low . Consistent with our previous report[7] , mice inoculated with rPrP-resRNA developed prion disease after a relatively synchronized incubation period and presented histopathological changes in the brain typical of prion disease ( Table 1 ) . In animals inoculated with rPrP-resRNA-low , multiple animal bioassays ( including secondary transmission in wild-type C57BL/6 mice or intracerebral inoculation in Tga20 transgenic mice that overexpress wild-type PrPC ( Table 1 ) ) failed to show any signs of prion disease , or any pathology . Brain homogenates from rPrP-resRNA-low mice sacrificed at 477–583 dpi ( days post injection ) were subjected to PK digestion and no PK-resistant PrP was detected in those mice ( Fig 1B ) . The lack of pathology was also confirmed by histopathological analyses ( S2–S5 Figs ) . Compared to the traditional rodent bioassay , the Elispot-based cultured-cell prion infection assay is a sensitive , more rapid and economical assay[22 , 23] . Because of the restrictive sensitivity of cultured cells to prion strains , we chose CAD5 cells , which are known to be susceptible to a broad spectrum of murine prion strains[22 , 23] . When naïve CAD5 cells were infected with either rPrP-resRNA or rPrP-resRNA-low , the Elispot data matched very well with traditional animal bioassay results[8] , showing a significant amount of prion infectivity from rPrP-resRNA , but none from rPrP-resRNA-low ( Fig 1C ) . Lysates of the CAD5 cells used in the Elispot assay were further verified by classic PK digestion and western blots , confirming that rPrP-resRNA efficiently converted endogenous PrPC into PrPSc , but rPrP-resRNA-low failed to infect CAD5 cells ( S6 Fig ) . Together , these results confirmed that the self-propagating rPrP-resRNA-low does not cause any pathology in vivo , nor does it contain any detectable prion infectivity in cell culture assay . Moreover , these data suggest that the co-existence of a self-propagating PK-resistant rPrP conformation and cofactors does not automatically produce prion infectivity . Since the non-pathogenic rPrP-resprotein-only is able to replicate without any cofactor[21] , we tested whether the propagation of rPrP-resRNA-low was cofactor-dependent . The sPMCA was carried out either with substrate lacking any cofactor and containing only rPrP or with the complete substrate , i . e . , rPrP plus cofactors . In the presence of cofactors , both pathogenic rPrP-resRNA and non-pathogenic rPrP-resRNA-low were propagated efficiently , but in the absence of cofactors , neither of the conformers sustained their propagation ( Fig 2A ) . The requirement of cofactors for rPrP-resRNA-low propagation suggests that even though rPrP-resRNA-low shares with rPrP-resprotein-only the properties of being non-pathogenic and a smaller PK-resistant core , it is different from the cofactor-independent rPrP-resprotein-only . Since the lack of PK-resistant PrP does not always correlate with loss of prion infectivity[24] , we determined whether the failure of rPrP conversion correlated with a loss of infectivity by the Elispot infection assay . The rPrP-resRNA was used to seed sPMCA reactions with either complete substrate or substrate lacking the cofactor for 6 rounds to ensure that no residual infectivity was carried over from the infectious rPrP-resRNA seed . As expected , the rPrP-res conformer was only generated in the presence of cofactors , but not in the cofactor-free reactions ( Fig 2B ) . The 6th-round sPMCA products were used to infect naïve CAD5 cells , and the Elispot assay revealed that the prion infectivity had been propagated along with rPrP-resRNA in regular sPMCA , but no prion infectivity was detected in sPMCA performed in the absence of cofactors ( Fig 2C and S7 Fig ) . These results support an intimate association between the self-propagating rPrP-res conformations and prion infectivity . Although rPrP-resRNA and rPrP-resRNA-low were propagated under exactly the same conditions—i . e . , using the same batch of rPrP , the same batch of cofactors , and the same sPMCA parameters—these two rPrP-res conformers differed drastically in their biological activities . This difference led us to ask whether these two conformers differed in any of their other properties besides the obvious size difference in their PK-resistant cores . We found that both rPrP-resRNA and rPrP-resRNA-low appeared in the pellet fraction after ultracentrifugation ( Fig 3A ) , suggesting that both were aggregated . Analysis of these aggregates by atomic force microscopy ( AFM ) revealed the presence of relatively short fibrillar structures that clumped together forming large aggregates ( Fig 3B , R and R-low ) , and became more apparent when large clumps were partially dispersed by sonication ( Fig 3B , R ( sonicated ) ) . No obvious morphological differences could be detected by AFM between rPrP-resRNA and rPrP-resRNA-low aggregates . It should be noted that fibrillar clumps were accompanied by nonfibrillar particles of varied morphologies ( in some fields imaged by AFM only the latter structures were seen ) . However , similar particles were also present in control sPMCA samples containing cofactors in the absence of rPrP ( Fig 3B , control ) , making it difficult to conclude whether such nonfibrillar particles correspond to rPrP-containing structures or those formed by cofactors only . Western blots probed with anti-PrP antibodies , i . e . , monoclonal 6D11 ( recognizing an epitope of 93–109 of PrP ) and polyclonal M20 ( recognizing PrP90-230 ) , showed that the PK-resistant core of rPrP-resRNA-low was a C-terminal fragment similar to that of rPrP-resRNA ( Fig 4A ) . Notably , the size difference between the cores remained relatively constant following digestions with increasing amounts of PK ( Fig 4B , POM1 ) , indicating that both rPrP-res forms have relatively stable PK-resistant cores . The monoclonal 3F10 antibody , recognizing an epitope encompassing residues 137–151 of PrP[25] , detected more PK-resistant fragments on the same blot ( Fig 4B , 3F10 ) . The banding patterns were consistent and distinct for rPrP-resRNA and rPrP-resRNA-low , particularly the stronger small bands of rPrP-resRNA-low at the forefront of the gel ( Fig 4B , 3F10 , arrow ) , confirming the structural difference between those two rPrP-res conformers . To further probe the conformational differences between rPrP-resRNA and rPrP-resRNA-low , we performed an immunoprecipitation assay with a panel of four conformational antibodies that were raised to specifically capture PrPSc molecules[26 , 27] . Interestingly , none of the antibodies was able to unambiguously differentiate rPrP-resRNA from rPrP-resRNA-low ( Fig 5A ) , suggesting that despite the obvious difference in the size of their PK-resistant cores , the pathogenic and non-pathogenic rPrP-res forms share a similar overall architecture . This conclusion was further supported by the guanidine hydrochloride ( GdnHCl ) denaturation assay ( Fig 5B ) . In this assay , increased concentrations of GdnHCl gradually solubilize aggregated rPrP-res , allowing a differentiation of different prion strains[28] . Notably , when rPrP-resRNA and rPrP-resRNA-low were exposed to increased concentrations of GdnHCl , the [GdnHCl]1/2 for the two were 2 . 22 ± 0 . 13 and 2 . 15 ± 0 . 35 , respectively . Moreover , the curves of insoluble rPrP were very similar and no statistical difference could be detected at any GdnHCl concentration ( Fig 5B ) . Thus , both the conformational antibody binding and GdnHCl denaturation assay suggest that rPrP-resRNA from rPrP-resRNA-low share a similar overall architecture . Because the pathogenic rPrP-resRNA and non-pathogenic rPrP-resRNA-low were generated in the same lab , we expanded our comparison to another non-pathogenic conformer , rPrP-resNIH , which was generated de novo independently at the NIH Rocky Mountain Laboratory[20] . The banding patterns of rPrP-resRNA-low and rPrP-resNIH detected by the POM1 and 3F10 antibodies were almost identical , highlighted by the stronger small bands detected by the 3F10 antibody ( Fig 6A , arrow ) . The sPMCA results showed that rPrP-resNIH was able to propagate in a separate laboratory under the same conditions used to generate rPrP-resRNA and rPrP-resRNA-low ( Fig 6B ) . More importantly , the propagation of rPrP-resNIH also depended on the presence of the cofactor molecules ( Fig 6C ) . The Elispot assay using CAD5 cells confirmed that both rPrP-resRNA-low and rPrP-resNIH lacked the capability of converting endogenous PrPC ( S8 Fig ) . The GdnHCl denaturation assay of rPrP-resNIH revealed that the curve of insoluble PrP was similar to that of rPrP-resRNA or rPrP-resRNA-low ( [GdnHCl]1/2 for rPrP-resNIH was 2 . 18 ± 0 . 29 ) and no statistical difference could be detected at any GdnHCl concentration among three sets of data ( Fig 6D and S1 Appendix ) . Collectively , our results revealed that despite being generated de novo in two independent laboratories , the non-pathogenic rPrP-resRNA-low and rPrP-resNIH resemble each other , suggesting that they represent the same non-pathogenic , self-propagating rPrP-res structure . Thus , the de novo rPrP-res formation is likely via a distinct PrP misfolding pathway guided by POPG/RNA cofactors . One characteristic of native prions is their ability to seed rPrP amyloid fibril formation[29–31] . Using the semi-denaturing rPrP amyloid fibril formation assay that monitors fibril growth with Thioflavin T ( ThT ) fluorescence[32] , we found that all three rPrP-res conformers—pathogenic rPrP-resRNA , non-pathogenic rPrP-resRNA-low , and non-pathogenic rPrP-resNIH—seeded amyloid fibril formation ( Fig 7 ) . The lag phases of reactions seeded by rPrP-resRNA-low and rPrP-resNIH were similar , in both cases , significantly shorter than that of rPrP-resRNA-seeded reactions ( Fig 7A ) . Furthermore , the lag phases of all rPrP-res-seeded reactions were significantly longer than reactions seeded by preformed rPrP amyloid fibrils . The amyloid fibrils seeded by all three rPrP-res conformers were morphologically indistinguishable from each other or from those seeded by the preformed rPrP amyloid fibrils ( Fig 7B ) . In addition , the amyloid fibrils seeded by either pathogenic or non-pathogenic rPrP-res conformers showed no infectivity in the Elispot cell infection assay ( S9 Fig ) . The real-time quaking induced conversion assay ( RT-QuIC ) is a newly developed prion seeding assay that is also based on the ability of prions to seed rPrP amyloid fibril growth . The RT-QuIC , using a non-denaturing , near-neutral pH reaction system different than the semi-denaturation system for rPrP amyloid fibril growth used in the above experiments , is highly sensitive and has been successfully used to diagnose prion disease in humans and animals[31 , 33–35] . Using this assay , we compared the in vitro and in vivo seeding activity of the pathogenic rPrP-resRNA and non-pathogenic rPrP-resRNA-low . Both rPrP-resRNA and rPrP-resRNA-low were able to seed rPrP amyloid fibril growth in the RT-QuIC assay ( Fig 8A ) . Since prion strain differences can be observed in the immunoblot banding profile of PK-digested RT-QuIC products[36] , we subjected the rPrP-resRNA- or rPrP-resRNA-low-seeded RT-QuIC products to PK-digestion and western blot with the R20 antibody ( recognizing residue 218–231 of hamster PrP ) . The PK-resistant banding patterns were almost identical except for a small PK-resistant band ( Fig 8B , Left panel , indicated by an arrow ) , which is much more prominent in samples seeded by the pathogenic rPrP-resRNA . This result is consistent with the notion that rPrP-resRNA and rPrP-resRNA-low share a similar overall architecture , but have distinct small structural features . Brain homogenates were prepared from three rPrP-resRNA-inoculated mice at terminal stage ( rPrP-resRNA-BH ) and three rPrP-resRNA-low-inoculated mice ( Table 1 ) . As expected , prion seeding activity was detected in rPrP-resRNA-BH ( Fig 8C , upper panel ) . Surprisingly , positive RT-QuIC results were also obtained with brain homogenates prepared from all three mice inoculated with non-pathogenic rPrP-resRNA-low ( rPrP-resRNA-low-BH ) , which were without any signs of neurological dysfunction ( Fig 8C , lower panel ) . To quantitate the total seeding activity , we performed end-point RT-QuIC quantitation[37] of the original inocula and the inoculated brain homogenates ( Fig 8D ) . The log SD50 of the rPrP-resRNA-low inoculum was 4 . 95 /μl , which gave a total SD50 of 106 . 25 in the 20 μl inoculum ( volume used to inject one mouse ) . Assuming that the total weight of a mouse brain is around 500 mg , the total SD50 in the brain of Br354 mouse was around 108 . 4 . The more than two orders of magnitude increase suggests that some amplification of the seeding activity occurred in the non-pathogenic rPrP-resRNA-low-inoculated brain . Consistent with this interpretation , seeding activity was detected in each of 5 mice receiving secondary transmission from a mouse ( Br355 ) that was inoculated with the non-pathogenic rPrP-resRNA-low ( S10 Fig ) . Quantitative RT-QuIC comparisons of the Br355-derived inoculum and the brains of the second passage mice indicated ~1 , 700-fold increases in seeding activity in each case . The brain homogenate seeded RT-QuIC products were also subject to PK-digestion and western blot analysis ( Fig 8B , right panel ) . All of the PK-resistant bands detected in the RT-QuIC products seeded with rPrP-resRNA were detected in the RT-QuIC products seeded by rPrP-resRNA-BH ( Fig 8B ) , indicating a faithful in vivo propagation of the rPrP-resRNA conformation . Although the non-pathogenic rPrP-resRNA-low-BH seeded RT-QuIC products also retained most of the PK-resistant bands , extra PK-resistant bands were also detected ( Fig 8B , right panel , asterisk represents an example ) , which may reflect an adaptation of the non-pathogenic rPrP-resRNA-low to the in vivo environment . Together , these findings provided further support for the overall similarity between two distinct rPrP-res conformers . More importantly , the end-point analysis provided the first evidence that despite the drastic difference in pathogenicity , both rPrP-res conformers are active in vivo .
Our study revealed that although rPrP-resRNA-low appears active and leads to the replication of prion seeding activity in vivo , it does not automatically result in pathogenic changes or the development of prion disease , at least for two consecutive passages in wild-type mice . The pathogenic and non-pathogenic rPrP-res conformers show many similarities in their overall architecture , suggesting that relatively small structural differences determine distinct biological properties of these rPrP-res aggregates . Moreover , our results support a critical role of cofactor in guiding the de novo rPrP-res formation , and suggest that different cofactors guide PrP misfolding in distinct manners , resulting in different rPrP-res conformers . An interesting finding of this study is the ability of rPrP-resRNA-low to cause the replication of seeding activity in vivo . This finding is reminiscent of previous reports that GSS patients’ brain homogenates containing only PrP amyloid fibrils , brain homogenates from diseased transgenic mice overexpressing P101L PrP , or synthetically generated rPrP amyloid fibrils are able to seed PrP amyloid plaque formation , but completely fail to cause any pathological changes of prion disease in P101L knock-in mice[38–40] . However , it has to be noted that we did not observe any PrP amyloid plaques in rPrP-resRNA-low inoculated mouse brains . Together with the facts that wild-type mice were used in our analyses and that amyloid fibrils were likely only a part of rPrP-res ( fibrils were only detected in certain fields with highly concentrated , PK-digested rPrP-res preparations ) , the rPrP-resRNA-low likely propagates in vivo in a manner different from amyloid fibril seeding in P101L knock-in mice . Nevertheless , both lines of studies support that the in vivo PrP seeding does not necessarily lead to disease pathology . The most likely reason for the difference in pathogenicity is the structural difference between rPrP-resRNA and rPrP-resRNA-low . Interestingly , our results revealed a similar architecture ( and thus likely similar overall folding motif ) of the pathogenic rPrP-resRNA and non-pathogenic rPrP-resRNA-low , which likely resulted from the strong influence of RNA and POPG cofactors on rPrP structure[41–43] that guides rPrP conversion . It appears that relatively minor structural differences between rPrP-res conformers are sufficient to result in large differences in in vivo pathogenicity . Understanding the nature of these structural differences requires studies employing higher resolution biophysical methods , and such future studies are of fundamental importance to understanding the molecular basis for prion infectivity . The role of cofactor molecules in generating infectious prions is supported by many in vitro conversion studies[7 , 10 , 21 , 44–46] , but the mechanisms are yet to be established . The highly pathogenic rPrP-resRNA and rPrP-resPE were formed with two different sets of cofactors , RNA+POPG[7] or PE only[10] , respectively , and have displayed distinct prion strain properties in mice[21] . When PE was omitted from the sPMCA reaction , the propagation of rPrP-resPE either stopped or led to the emergence of rPrP-resprotein-only , which has a smaller PK-resistant core and no detectable in vivo pathogenicity[21] . Adding PE back to rPrP-resprotein-only-seeded sPMCA reactions did not restore the rPrP-resPE conformer[21] . The cofactor-dependence for rPrP-resPE propagation supports a role of cofactor in rPrP conversion , but leaves open the question whether cofactors are mandatory for prion pathogenicity in vivo . Our study demonstrates that cofactors are essential for the propagation of pathogenic rPrP-resRNA and non-pathogenic rPrP-resRNA-low or rPrP-resNIH . For the pathogenic rPrP-resRNA , omitting cofactors not only abolished the rPrP-resRNA propagation but also eliminated the ability of the sPMCA products to convert endogenous PrPC in CAD5 cells . Since exactly the same cofactors were required for the formation of the pathogenic rPrP-resRNA , and the non-pathogenic rPrP-resRNA-low or rPrP-resNIH , our results demonstrated that it is not the cofactors , but rather the distinctive structural features of rPrP-resRNA that determine the pathogenicity . In this study , no “protein-only” , self-propagating , rPrP-res conformer was formed in the absence of cofactors in either rPrP-resRNA- or rPrP-resRNA-low-seeded sPMCA reactions , which is different from those seeded by rPrP-resPE[21] . Many factors may account for this difference , but the difference between the two sets of cofactors is likely to be the key . Full-length rPrP has a high isoelectric point ( pI > 9 ) and binds to negatively charged RNA and POPG[41 , 42] . We and other groups have shown that the binding of rPrP to anionic lipids or RNA results in significant rPrP conformational changes[41–43 , 47] . PE , on the other hand , is a neutral phospholipid that has little or no in vitro interaction with rPrP[47] . The sonication in sPMCA may foster a unique rPrP-PE interaction leading to the formation of PE-dependent rPrP-resPE . Thus , both rPrP-resPE and rPrP-resRNA can be propagated in sPMCA , but likely through different pathways , which is in agreement with their different strain properties in vivo[21] . Consistent with the idea that different cofactors used in sPMCA may lead to different rPrP conformations , the non-pathogenic rPrP-res forms derived from two sPMCA reactions , rPrP-resRNA-low and rPrP-resprotein-only , have drastic differences in cofactor dependence in their propagation , which strongly indicates a difference in their structures . Even though native prions are generally not present with fibrillar structure in vivo , diseased brain homogenates do have the ability to seed rPrP amyloid fiber formation[29–31 , 48] , and this property has been successfully developed into an ultrasensitive diagnostic tool[33] . Our data showed that , akin to brain-derived prions , all three cofactor-dependent rPrP-res conformers studied are able to seed rPrP amyloid fibril growth , supporting the structural similarity among those conformers . The non-pathogenic forms ( i . e . rPrP-resRNA-low and rPrP-resNIH ) have apparently stronger seeding capacity than the pathogenic rPrP-resRNA in the amyloid fibril formation assay , which may reflect the fact that the non-pathogenic rPrP-resRNA-low maintains more seeding competent conformation in the presence of 2M GdnHCl , a semi-denaturing buffer system used for growing rPrP amyloid fibrils[32] . However , using the non-denaturing conditions of RT-QuIC , the seeding activity of the non-pathogenic form was about a log lower than the pathogenic form . This again suggests differences in the conformation and seeding capabilities of these two conformers . In summary , our current study provides evidence that ( i ) cofactors are able to guide rPrP misfolding to result in different rPrP-res conformers , ( ii ) unique structural features of the rPrP-res determine the pathogenicity , and ( iii ) the in vivo rPrP-res seeding activity is not necessarily equal to disease pathogenicity . These novel insights help to elucidate the molecular basis for prion infectivity .
Recombinant murine PrP 23–230 purification and sPMCA experiments were performed as previously described[7 , 19 , 45 , 49] . For seeded sPMCA , 10 μL of rPrP-resRNA seed was added to the substrate and the mixture was subjected to 1 round of PMCA . After each round , 10 μL of the PMCA product was transferred to a new tube containing 90 μL of substrate for another round . For unseeded PMCA , the same protocol was followed except that 10 μL of PBS instead of rPrP-resRNA was added to the first tube of substrate . For the cofactor-free sPMCA , mouse liver total RNA and synthetic phospholipid POPG were omitted during substrate preparation . To detect the generation of rPrP-res , 10 μL of PMCA product was incubated with 10 μL PK ( 100 μg/mL unless stated otherwise ) for 30 min at 37°C followed by the addition of 2 mM PMSF . The PK-digested samples were subjected to SDS-PAGE and western blotting . All the PK-resistant PrP fragments were detected using POM1 primary anti-PrP antibody[50] unless stated otherwise . The Elispot cell infection assay was adapted from previous studies[22 , 23] with minor adjustments . Briefly , 200 μL of PMCA products at round 6 were collected and centrifuged at 100 , 000 x g for 1 h at 4°C . The pellets were then washed twice , with centrifugation at 100 , 000 x g for 1 h at 4°C after each wash . After the final wash , the pellets were resuspended in 200 μL of CAD5 growth media ( OPTI-MEM , 5% BGS , and 1% penicillin and streptomycin ) and sonicated for 30 sec with 50% output using a Misonic Sonicator ( XL2020 ) . Then each sample was serially diluted 10 , 100 , and 1 , 000 times , and 60 μL of undiluted and diluted samples were used to infect CAD5 cells . After two 1:10 splits , 20 , 000 CAD5 cells/well were transferred to the Millipore 96-well Elispot plates ( MSIPN4W ) and subjected to the Elispot assay . The images were taken by S6 Micro Analyzer ( CTL Analyzers , LLC ) and processed by the ImmunoSpot software ( CTL Analyzers , LLC ) . The graph was generated using GraphPad Prism ( GraphPad Software , Inc . ) . To validate the Elispot data , the remaining infected cells were lysed and subjected to PK digestion ( 100 μg/mL PK , 37°C , 30 min ) and SDS-PAGE . The PK-resistant PrP fragments were detected by western blots using POM1 anti-PrP antibody . The mouse bioassays were performed as previously described[7 , 19 , 45 , 49] . In brief , 20 μL of purified rPrP-res was inoculated into a mouse intracerebrally . Second-round transmission , animal monitoring , biochemical analyses , and histopathological analyses were performed as previously described[7 , 19 , 45 , 49] . This study was carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocols were approved by the Institutional Animal Care and Use Committees of the Van Andel Research Institute ( Assurance Number A4383-01 ) . Protein-G DynalBeads ( 100 μL; Life Technologies ) were washed twice with 250 μL of coating buffer ( 0 . 1% BSA in PBS ) , incubated in 250 μL of coating buffer at 4°C overnight , and then resuspended in 300 μL of coating buffer for use . The rPrP-res-seeded PMCA products containing 100 ng of PrP were incubated with no antibody or with 2 . 5 μg of conformational anti-PrPSc antibodies in 250 μL of incubation buffer ( 10 mM Tris , 150 mM NaCl , 0 . 28% Triton X-100 , pH 7 . 5 ) at 4°C overnight , then mixed with 30 μL of resuspended coated beads and incubated at room temperature for 4 h , followed by washing 3 times with 100 μL of incubation buffer . The beads were then resuspended in 20 μL of SDS-PAGE sample buffer , boiled for 10 min , and subjected to SDS-PAGE and western blotting for detection of PrP using the anti-PrP M-20 polyclonal antibody . The conformational stability assay was performed as previously described[28] . Briefly , aliquot of rPrP-resRNA , rPrP-resRNA-low or rPrP-resNIH was mixed with an equal volume of GdnHCl solutions to reach final concentrations of 0 , 0 . 5 , 1 . 0 , 1 . 5 , 2 . 0 , 2 . 5 , 3 . 0 , 3 . 5 , and 4 . 0 M and kept at 37°C for 1 h , followed by centrifugation at 20 , 000 x g for 1 h at 22°C . Supernatants were removed and pellets were resuspended in SDS-PAGE sample buffer and subjected to SDS-PAGE and western blotting for detecting PrP using the anti-PrP POM1 antibody . Western blotting images were obtained with Fujifilm LAS-4000 imaging system and banding intensity was quantified with ImageJ . The denaturation curves for each rPrP-res conformer were generated by fitting the insoluble PrP ( % ) as a function of GdnHCl concentrations using a Sigmoidal , 4 parameter logistic equation in GraphPad Prism . Amyloid fibril formation was performed as previously described[51] . For unseeded growth , 0 . 5 mg/mL of rPrP was incubated in 2 M guanidine hydrochloride ( GdnHCl ) , 100 mM potassium phosphate buffer , pH 6 . 5 , and 20 μM Thioflavin T ( ThT ) . The reaction volume was 200 μL per well in 96-well plates ( Corning , Lot No . 065514030 ) . In seeded reactions , 1 μL of preformed mouse rPrP fibrils ( 0 . 5 mg/mL ) or 5 μL of treated rPrP-res was added to each well . The plate was incubated at 37°C with continuous shaking on a microplate reader ( SYNERGY2 , BioTek ) . The fibril kinetics was monitored by measuring ThT fluorescence intensity every 15 min using 440-nm excitation and 480-nm emission . The amyloid-formation kinetics was generated using GraphPad Prism ( GraphPad Software , Inc . ) . The lag time was determined when the ThT fluorescence reached threefold above the baseline[52] . The sPMCA products of each rPrP-res conformer were digested with Benzonase and PK and purified as previously described[20] to seed amyloid fibril formation . Atomic force microscopy ( AFM ) images were collected on a Multimode 8 AFM fitted with the Nanoscope V controller ( Bruker Co . , USA ) . Images were acquired in ScanAsyst mode using silicon tips . Samples were absorbed on freshly cleaved mica and then rinsed with nanopure water and dried with compressed air . Images were analyzed using Scanning Probe Image Processor ( SPIP ) software ( version 6 . 5 . 2 , Image Metrology A/S , Lyngby , Denmark ) or NanoScope Analysis 1 . 5 software ( Bruker Co . , USA ) . For imaging sPMCA products , rPrP-resRNA or rPrP-resRNA-low was treated with Benzonase ( 200 U/mL , 1 mM MgCl2 ) plus α-amylase ( Sigma , 5 units/mL ) at 37°C overnight , followed by PK digestion ( 25 μg/mL ) at 37°C for 30 minutes . The treated samples were centrifuged at 100 , 000 x g for 1 h at 4°C and the pellets were washed once in 10 mM Potassium Phosphate buffer ( pH 7 . 4 ) . The control sample , containing all the cofactors but no rPrP , went through the same treatments except the PK-digestion . The final pellets were resuspended in appropriate volumes of ddH2O for imaging . For imaging rPrP amyloid fibrils , samples were centrifuged at 100 , 000 x g for 1 h at 4°C . The pellets were washed twice with ddH2O and resuspended in appropriate volumes of ddH2O for imaging . Means are presented with their standard deviations and compared by one-way analysis of variance ( ANOVA ) followed by Tukey’s multiple comparisons test , two-way ANOVA analyses followed by Tukey’s multiple comparisons test , or by two-tailed unpaired multiple t test with Holm-Sidak’s correction . Statistical analysis was performed with GraphPad Prism 6 . 05 . | Many neurodegenerative disorders , including Alzheimer’s disease , Parkinson’s disease and Prion disease , are caused by misfolded proteins that can self-propagate in vivo and in vitro . Misfolded self-replicating recombinant prion protein ( PrP ) conformers have been generated in vitro with defined cofactors , some of which are highly infectious and cause bona fide prion diseases , while others completely fail to induce any pathology . Here we compare these misfolded recombinant PrP conformers and show that the non-pathogenic misfolded recombinant PrP is not completely inert in vivo . We also found that the pathogenic and non-pathogenic recombinant PrP conformers share a similar overall architecture . Importantly , our study clearly shows that in vivo seeded spread of misfolded conformation does not necessarily lead to pathogenic change or cause disease . These findings not only are important for understanding the molecular basis for prion infectivity , but also may have important implications for the “prion-like” spread of misfolded proteins in other neurodegenerative diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"animal",
"diseases",
"enzyme-linked",
"immunoassays",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"enzymology",
"animal",
"prion",
"diseases",
"immunoprecipitation",
"immunologic",
"techniques",
"zoology",
"enzyme",
"chemistry",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"zoonoses",
"proteins",
"immunoassays",
"pathogenesis",
"precipitation",
"techniques",
"biochemistry",
"biology",
"and",
"life",
"sciences",
"cofactors",
"(biochemistry)",
"amyloid",
"proteins",
"prion",
"diseases"
] | 2017 | Self-propagating, protease-resistant, recombinant prion protein conformers with or without in vivo pathogenicity |
The regulation of eukaryotic chromatin relies on interactions between many epigenetic factors , including histone modifications , DNA methylation , and the incorporation of histone variants . H2A . Z , one of the most conserved but enigmatic histone variants that is enriched at the transcriptional start sites of genes , has been implicated in a variety of chromosomal processes . Recently , we reported a genome-wide anticorrelation between H2A . Z and DNA methylation , an epigenetic hallmark of heterochromatin that has also been found in the bodies of active genes in plants and animals . Here , we investigate the basis of this anticorrelation using a novel h2a . z loss-of-function line in Arabidopsis thaliana . Through genome-wide bisulfite sequencing , we demonstrate that loss of H2A . Z in Arabidopsis has only a minor effect on the level or profile of DNA methylation in genes , and we propose that the global anticorrelation between DNA methylation and H2A . Z is primarily caused by the exclusion of H2A . Z from methylated DNA . RNA sequencing and genomic mapping of H2A . Z show that H2A . Z enrichment across gene bodies , rather than at the TSS , is correlated with lower transcription levels and higher measures of gene responsiveness . Loss of H2A . Z causes misregulation of many genes that are disproportionately associated with response to environmental and developmental stimuli . We propose that H2A . Z deposition in gene bodies promotes variability in levels and patterns of gene expression , and that a major function of genic DNA methylation is to exclude H2A . Z from constitutively expressed genes .
In addition to packaging the DNA to fit within the cell , histones function to control the structure and accessibility of the chromatin environment by altering the biochemical properties of the nucleosome or through the recruitment of distinct binding partners . These actions promote changes in transcription that regulate the proper timing of developmental decisions and appropriate responses to the external environment . One such method of histone-mediated control comes from the exchange of the canonical histones with non-allelic histone variants , which alter the fundamental structure and stability of the nucleosome [1]–[4] . H2A . Z is one of the most enigmatic of these histone variants , as well as the most well-conserved , with a single origin at the root of eukaryotic evolution [1] . H2A . Z has been implicated in a number of apparently disparate and even contrary chromosomal processes , including heterochromatic silencing , gene activation , transcriptional memory , cell-cycle progression and thermal-sensory response [5]–[9] . A common aspect of H2A . Z biology is its enrichment within the few nucleosomes surrounding transcription start sites ( TSS ) , which has been demonstrated by genome-wide localization experiments in protozoa , fungi , animals , and plants [10]–[19] . The conserved H2A . Z distribution pattern at the TSS in many species has lead to considerable effort to understand the effect of H2A . Z on transcription . H2A . Z enrichment at promoters in yeast is simultaneously required for transcription and inversely correlated with transcription level [11] , [20] , [21] . Studies in animals have reported that H2A . Z exhibits a positive correlation with transcription [12] , [22] , [23] , although some have found that this relationship is only true up to a point , after which the association becomes negative [15] , [24] . In plants , the relationship between H2A . Z at the TSS and transcription appears to be roughly parabolic , with the highest and lowest expressed genes having the least H2A . Z enrichment [16] . In the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe , H2A . Z regulates genes that respond to changes in the environment [21] , [25] , [26] , and loss-of-function mutants fail to react appropriately to external cues [27] , [28] . Arabidopsis thaliana plants lacking PIE1 ( AT3G12810 ) , the plant homolog of the SWR1 catalytic subunit of protein complexes responsible for the deposition of H2A . Z in yeast and mammals [29]–[36] , exhibit misregulation of many genes involved in the innate immune response [37] . Recent work has shown that Arabidopsis plants with a mutated ARP6 , which encodes a component of the PIE1 complex , inappropriately express temperature response genes , leading to the proposal that H2A . Z may act specifically as a thermosensor in plants [8] . The genomic distribution and biological functions of DNA methylation , another well-conserved feature of chromatin , are in many aspects strikingly different from those of H2A . Z . DNA methylation in the form of 5-methylcytosine is present in all vertebrates examined to date , as well as in many invertebrates , fungi , and plants [24] , [38] , [39] . The primary function of eukaryotic DNA methylation has long been considered to be the silencing of the sequences it decorates , particularly transposable elements [40] , although the recent discovery of gene body methylation in plants and animals , the functional significance of which is still unknown , has complicated this view [24] , [38] , [41]–[45] . Whereas H2A . Z is enriched near the TSS of most genes , TSS-proximate DNA methylation is strongly associated with transcriptional repression in plants and vertebrates [46] . Recently , we reported a strong , genome-wide anticorrelation between H2A . Z and DNA methylation in Arabidopsis , including in bodies of active genes [16] . Results from similar studies in vertebrates suggest that this anticorrelation is a conserved feature of eukaryotes [24] , [47] , [48] . In Arabidopsis , we showed that changes in DNA methylation caused by a mutation in the DNA methyltransferase MET1 induced reciprocal alterations in H2A . Z deposition , demonstrating that DNA methylation antagonizes H2A . Z recruitment [16] . We also used a null mutation in PIE1 ( pie1–5 ) to examine the effect of disrupted H2A . Z function on DNA methylation . By coupling methylated DNA immunoprecipitation ( MeDIP ) to microarray analysis , we found a low magnitude but genome-wide DNA methylation increase in genes that suggested a mutual antagonism between H2A . Z and DNA methylation [16] . There is now considerable evidence that the PIE1 complex deposits H2A . Z into chromatin in Arabidopsis , though whether it has H2A . Z-independent functions , as has been shown for other eukaryotic SWR1 homologs , remains unclear [29] , [49] , [50] . Other Arabidopsis chromatin remodelers are probably also able to deposit H2A . Z , as does the yeast INO80 complex [51] , because H2A . Z is incorporated into nucleosomes at low levels in pie1 and swr1 mutants [29] , [34] , [52] . Given that the sets of genes that are misregulated in H2A . Z and SWR1-related mutants only partially overlap in both S . cerevisiae and Arabidopsis [29] , [37] , we sought to use an H2A . Z-deficient plant line , as opposed to SWR1-related mutants , for further analysis of H2A . Z function . Here , we describe the characterization of an H2A . Z loss-of-function line in Arabidopsis thaliana . We find that loss of H2A . Z in Arabidopsis has little effect on the level or profile of DNA methylation in genes , and propose that the global anticorrelation between DNA methylation and H2A . Z is primarily caused by the exclusion of H2A . Z from methylated DNA . We show that the level of H2A . Z enrichment in gene bodies is generally correlated with gene responsiveness and that lack of H2A . Z causes misregulation of genes that respond to a variety of stimuli . We propose that H2A . Z deposition in gene bodies promotes gene responsiveness , but may destabilize constitutive expression , and that a major function of gene body DNA methylation is to exclude H2A . Z from constitutively expressed genes .
Three of the thirteen Arabidopsis H2A genes , HTA8 ( AT2G38810 ) , HTA9 ( AT1G52740 ) , and HTA11 ( AT3G54560 ) , have been classified as encoding H2A . Z based on phylogenetic analyses [53] , [54] , and distribution patterns and genetic studies suggests that these proteins are largely functionally redundant [16] , [33] , [34] . Recently published work has demonstrated that a double mutant of hta9-1 and hta11-1 produced plants with phenotypes similar to those found in null pie1–5 mutants [37] . To generate a line devoid of H2A . Z , we crossed hta9-1 and hta11-1 plants with a line bearing an insertion in HTA8 , hta8-1 ( Figure 1A ) . Contrasting with recent evidence that individual knockouts of the two vertebrate H2A . Z isoforms exhibit different phenotypes [55] , we did not observe morphological abnormalities in any of the three single mutant lines . The resulting triple mutant line , which we will refer to as h2a . z , is both viable and phenotypically distinguishable from WT ( Figure 1B ) . Transcripts of HTA8 and HTA11 were not detectable in the h2a . z mutant by RT-PCR , but low levels of HTA9 RNA were present ( ∼26% of wild-type; Figure 1C and 1D ) in h2a . z plants but not in hta9-1 single mutants , suggesting that the intronic T-DNA insertion in HTA9 is spliced out in a fraction of transcripts , as confirmed by sequencing of the cDNA ( Figure S1 ) . To test whether this low level of expression was the result of a genetic rearrangement at the HTA9 locus that occurred in our crosses , we recreated the h2a . z line using hta9-1 plants lacking HTA9 transcript ( Figure 1D ) . The h2a . z progeny from the independent cross produced similar phenotypes to the original h2a . z line and similar RT-PCR results for HTA9 , suggesting upregulation of HTA9 in the triple mutant . A fourth gene , HTA4 ( AT4G13570 ) , is the closest H2A family member to the three H2A . Z genes and has been categorized as H2A . Z-like [54] , but all publically available data indicate that HTA4 is not expressed at significant levels in any WT tissue . To ensure that HTA4 is not upregulated as a result of the drop in H2A . Z levels in our h2a . z line , we tested the expression of HTA4 by RT-PCR ( Figure 1E ) , and did not detect HTA4 RNA in h2a . z or in WT . Taken together , our data indicate that the h2a . z line has less than ten percent of wild-type H2A . Z transcript levels . Despite reduced fertility ( Figure 1F ) , h2a . z plants are viable and produce offspring , differing markedly from the lethality of strong H2A . Z mutations in other multicellular organisms [15] , [56] , [57] , [58] , [59] . We measured the number of leaves present when the plant produced its first flower buds in h2a . z and WT ( Figure 2A ) . In short days ( SD ) , the h2a . z line flowered significantly earlier than WT , with 23 . 2+/−1 . 1 leaves vs . 49 . 7+/−1 . 5 leaves ( P-value<0 . 0001 , two sample T-test ) . In long days ( LD ) , the difference in flowering time between h2a . z and WT was less pronounced , with 8 . 3+/−0 . 2 leaves and 10 . 6+/−0 . 2 leaves , respectively ( P-value<0 . 0001 ) , but the difference in rosette size and plant stature was greater in LD than SD ( Figure S2 ) . Of the first ten flowers , 22+/−3 . 1% in LD and 76+/−4 . 6% in SD exhibited extra petals ( between 5 and 8 ) in the h2a . z mutant line , compared to 1 . 5+/−0 . 6% ( LD ) and 2+/−0 . 8% ( SD ) in WT ( Figure 2B and 2C ) . The h2a . z mutant also exhibited short , thickened siliques , a phenomenon potentially related to decreased fertility . The h2a . z siliques averaged 4 . 8+/−0 . 1 and 5 . 6+/−0 . 1 mm in length under LD and SD conditions , compared to 10 . 6+/−0 . 1 and 11+/−0 . 2 mm for WT ( Figure 2D–2F ) . The h2a . z phenotypes described above , as well as increased leaf serration and petiole length in SD ( Figure 2G ) , are similar to those previously published for hta9-1; hta11-1 and pie1–5 mutants [32] , [37] , [60] . The h2a . z line exhibited several phenotypes not previously reported for pie1–5 or hta9-1;hta11-1 . First , while both pie1–5 and h2a . z have reduced stature , pie1–5 plants are more severely dwarfed and have a bushy , extensively branched architecture , whereas h2a . z plants are spindly and have trouble remaining upright ( Figure 2H ) . This aspect of the h2a . z phenotype might be caused by contributions from the WS ecotype of hta8-1 ( all other lines are in the Col ecotype ) , but this is unlikely because the WT siblings from the same cross do not show these traits . Additionally , many of the siliques in the h2a . z mutant exhibited a strong asymmetric curvature , most likely due to the improper fusion of its carpels ( Figure 2F ) . Other novel phenotypes occurred only rarely , affecting multiple aerial plant tissues including leaf and stem structures , but were most prevalent among floral organs ( Figure S3 ) . The most striking examples were the inappropriate emergence of petals and stamens directly from the stem , and flowers with improperly fused carpels , leading to severely compromised reproductive structures . A mutation in yeast swr1 ( pie1 ) ameliorates many of the phenotypes observed with the htz1 ( h2a . z ) single mutant , as well as the severe phenotypes of the double mutant between htz1 and set3 [61] , [62] . The cause of the htz1 phenotypes was proposed to be chromatin disruption by the SWR1 complex in the absence of its proper substrate , a hypothesis supported by SWR1-dependent accumulation of DNA damage in the absence of htz1 . To test whether simultaneous removal of the PIE1 chromatin remodeler and H2A . Z would reduce the severity of phenotypes seen in h2a . z plants , we crossed the h2a . z mutant line to pie1–5 . Contrary to the results from yeast , the phenotype of the Arabidopsis double mutant is more severe than that of either parent – progeny exhibit early developmental arrest , dying shortly after germination ( Figure 2I ) . Taken together with the phenotypic disparity , our results suggest that H2A . Z and PIE1 have non-redundant functions in Arabidopsis . Because h2a . z is not a complete loss of function line , the stronger phenotype of h2a . z; pie1 plants might be caused by a further reduction of H2A . Z incorporation into chromatin , but nevertheless demonstrates that pie1–5 does not entirely abolish H2A . Z function . While we cannot rule out the possibility that all pie1 phenotypes are associated with H2A . Z , we consider this unlikely because of the stronger effect of pie1 on plant architecture compared to h2a . z ( Figure 2H ) . To test our hypothesis that H2A . Z protects genes from DNA methylation , we generated genome-wide methylation profiles for the h2a . z mutant and WT using shotgun bisulfite sequencing . Because plants have DNA methylation in three different sequence contexts , CG , CHG , and CHH ( H = A , T or C ) , which are largely controlled by distinct families of methyltransferases and have different genome-wide distributions [24] , [38] , it is advantageous to use an assay that has single base-resolution to distinguish between these contexts . Two biological replicates each of h2a . z and WT were generated for each of three different tissue types that represent different stages along a developmental continuum: 14 day-old whole seedlings , 6 week-old rosette leaves , and 6 week-old cauline leaves . One biological replicate was taken from the original h2a . z mutant line , and the second from the additional h2a . z line generated from independent crosses with the same T-DNA insertional alleles . Analysis of the average methylation levels across all genes revealed that a loss of H2A . Z in Arabidopsis has little effect on the global patterns of DNA methylation in CG , CHG or CHH contexts ( Figure 3A and Figure S4 ) . For comparison , we generated bisulfite sequencing data for two biological replicates each of pie1 and sibling WT seedlings , and one replicate of h2a . z;pie1 seedlings . As with the results for the h2a . z mutant , the pie1 and h2a . z;pie1 mutants showed only subtle changes compared with WT in the global patterns of genic DNA methylation ( Figure S5 ) . Previously , we used locus-specific bisulfite sequencing to validate our microarray results at five candidate genes scored as hypermethylated in the pie1 mutant; all five showed modest but consistent gains in CG methylation [16] . Similar analyses performed here on both our h2a . z and pie1 mutants demonstrate an overall consistency between current and previous pie1 data . They also show that the hypermethylation at these loci in pie1 is less consistently present in h2a . z ( Figure S6 ) . Statistical analyses of the methylation differences between the h2a . z , pie1 , and h2a . z;pie1 mutants and their respective WT controls suggest that there are subtle increases in genic methylation as a result of H2A . Z loss ( Figure S7 ) . These results are consistent with our published data , showing a small but statistically significant increase of genic methylation in the pie1 mutant [16] . However , the quantitative data generated here demonstrate that this increase is of a very low magnitude and is unlikely to substantially contribute to the global anticorrelation between H2A . Z and DNA methylation . Unexpectedly , the h2a . z mutant exhibited tissue-specific DNA methylation changes in transposable elements ( TEs; Figure 3B and Figure S8 ) . CG methylation was marginally increased over wild-type in four of the six replicates , with the most consistent change in seedlings , whereas CHG methylation decreased more heavily in the older tissues , though there is considerable variation between replicates ( Figure 3B and Figure S8 ) . CHH methylation was substantially reduced specifically in cauline leaves ( Figure 3B and Figure S8 ) . Kernel density estimations of these changes demonstrate that the majority of transposons show a modest change in methylation , rather than a larger effect in a small subset of TEs ( Figure S9 ) . Analyses of DNA methylation in pie1 and h2a . z;pie1 seedlings show that , like h2a . z seedlings , these lines exhibit increased CG methylation in TEs ( Figure S10 ) . Curiously , the h2a . z;pie1 seedlings exhibit decreases in CHG and CHH TE methylation that are not seen in seedlings of pie1 or h2a . z , but which are reminiscent of the decreases in h2a . z plants later during development ( cauline and rosette leaves; Figure 3B–3C and Figure S10 ) . Our data indicate that whereas a loss of H2A . Z does not substantially change DNA methylation within genes , lack of H2A . Z affects TE methylation in all three sequence contexts in a development-specific manner . We hypothesized that a larger effect of H2A . Z on DNA methylation may be undetectable in our h2a . z mutant due to the carefully targeted nature of routine DNA methylation maintenance . By this logic , H2A . Z's role may be to protect against random and spurious accumulation of DNA methylation at the TSS over evolutionary timescales , rather than to act as a barrier to regular DNA methylation processes . To test this hypothesis , we performed crosses of h2a . z and pie1 to two methylation mutants , ibm1–6 and met1–6 , in which normal methylation targeting to genes is perturbed . We expected that if H2A . Z were acting to prevent methylation from accumulating at the TSS , there would be greater increases in methylation in these double mutants than in the parental lines . IBM1 ( AT3G07610 ) encodes a H3 lysine 9 demethylase , MET1 ( AT5G49160 ) encodes the primary CG DNA methyltransferase , and both ibm1 and met1 mutations cause increased CHG methylation in gene bodies [43] , [44] , [63] , [64] , [65] , [66] , [67] . Single mutant plants are viable and fertile ( Figure 3D ) , but h2a . z;ibm1 , h2a . z;met1 , pie1;ibm1 , and pie1;met1 double mutants die shortly after germination and exhibit severe developmental abnormalities , including the production of undifferentiated callus-like material , under-sized root systems , and premature flowering ( Figure 3E ) . Bisulfite sequencing of h2a . z;ibm1 , h2a . z;met1 , and pie1;ibm1 seedlings revealed that a loss of H2A . Z does not strongly alter the genic methylation profile in any context from that seen in the parental backgrounds ( Figures S11 , S12 , S13 ) . Statistical analyses of the CG methylation differences between the h2a . z;ibm1 , pie1;ibm1 , and the ibm1 control suggest that the loss of H2A . Z in these double mutant lines leads to a subtle increase in genic methylation , as we found in the h2a . z and pie1 single mutants ( Figure S14 ) . Once again , however , the magnitude of this change is extremely small . The defining characteristic of ibm1 mutants is a major increase in genic CHG methylation [63] , [64] , [68] . The h2a . z;ibm1 and pie1;ibm1 double mutant lines were generated such that h2a . z;ibm1 seedlings were newly homozygous for ibm1 ( first generation ) , whereas pie1;ibm1 seedlings came from first generation ibm1 homozygous parents ( second generation ) . The h2a . z;ibm1 seedlings in their first generation of ibm1 homozygosity have higher levels of CHG methylation than first generation ibm1 seedlings , and pie1;ibm1 seedlings in their second generation of ibm1 homozygosity have lower levels of CHG methylation than second generation ibm1 seedlings ( Figure S12 ) . Both first generation datasets , h2a . z;ibm1 and ibm1 , show similar levels of CHH hypermethylation to one another; likewise , the second generation pie1;ibm1 and ibm1 data exhibit similar CHH hypermethylation levels ( Figure S13 ) . Importantly , the control data show that genic CHG and CHH methylation is unstable in ibm1 , increasing greatly in the second generation ( Figure S12 ) , making interpretation of changes in h2a . z;ibm1 and pie1;ibm1 CHG methylation difficult . Whereas there is little difference between the double mutant lines and their parental lines in TE CG methylation ( Figure S15 ) , we found CHG hypomethylation in the double mutants as compared to their respective parental lines ( Figure 3F and Figure S16 ) . Additionally , while CHH methylation is unaltered in h2a . z and pie1 seedlings , there is a significant reduction of TE CHH methylation in h2a . z;ibm1 , h2a . z;met1 , and pie1;ibm1 seedlings compared to the ibm1 and met1 single mutants , which is similar to the reduction seen in h2a . z;pie1 seedlings ( Figure 3C and Figure S17 ) . Taken together , our results suggest that while H2A . Z may play a modest role in the regulation of DNA methylation in TEs , the genome-wide anticorrelation between H2A . Z and DNA methylation is due to DNA methylation preventing the incorporation of H2A . Z . Given the published work linking H2A . Z with regulation of several types of genes that respond to the environment [8] , [21] , [26] , [27] , [28] , [37] , [69] , we sought to examine H2A . Z enrichment with respect to gene responsiveness . To do so , we generated a genome-wide map of H2A . Z using our published tagged H2A . Z Arabidopsis line [16] by coupling affinity purification of H2A . Z-bound DNA with high-throughput sequencing . Metaanalyses of the new dataset demonstrate a strong peak of H2A . Z at the 5′ end and a smaller peak at the 3′ end of most genes , with varying levels of H2A . Z distributed within gene bodies ( Figure 4A–4C ) . Our new data are consistent with our published microarray results , though the resolution provided by high-throughput sequencing is significantly better ( Figure S18 ) . To determine if a loss of H2A . Z had a preferential effect on genic methylation in the subset of genes that were enriched for H2A . Z in WT , as suggested by our published results in pie1 , we compared the h2a . z and WT bisulfite sequencing datasets for those genes with the most and least H2A . Z across the TSS and the gene-body ( Figure S19 ) . Although there are major methylation differences between these four groups of genes , the profiles of h2a . z and WT within each group were virtually indistinguishable from one another . However , the subtle increase in genic methylation we detected in h2a . z , pie1 , and h2a . z;pie1 plants was stronger in H2A . Z enriched genes ( Figure S7 ) , consistent with our published results [16] . In support of earlier data [16] , [24] , we found a negative correlation between H2A . Z enrichment in gene bodies and WT transcript levels ( Spearman's rho = −0 . 4039 , P-value<0 . 0001 ) . Genes with the most gene body H2A . Z ( n = 4 , 081 , Table S1 ) have median WT expression more than six-fold lower than that of genes with the lowest H2A . Z within their bodies ( n = 3 , 920 , Table S2 ) ( Figure 4D ) . In comparison , levels of H2A . Z enrichment near the TSS showed a different trend: genes with the most and least H2A . Z at the TSS had lower levels of expression than those with intermediate levels of H2A . Z ( Figure 4D ) , as we showed earlier for both Arabidopsis and pufferfish [16] , [24] . We also discovered a positive correlation between enrichment of H2A . Z across gene bodies and gene responsiveness – the degree to which a gene is differentially expressed among different tissue types or experimental conditions ( including hormone , nutrient , and chemical treatments , as well as biotic or abiotic stimulus ) , with higher response scores associated with greater differential expression [70] . H2A . Z body-enriched genes ( n = 4 , 081 ) have a six-fold higher median gene responsiveness score than that of genes with the lowest H2A . Z levels across their bodies ( n = 3 , 920 ) ( Figure 4E ) . Levels of H2A . Z at the TSS are considerably less correlated with response score than levels of H2A . Z in the body ( Spearman's rho = 0 . 0748 and 0 . 3325 , P-values<0 . 0001 , respectively ) , and highly responsive genes have more body H2A . Z than genes with low responsiveness ( Figure S20 ) . The least and most responsive genes [70] , defined as housekeeping genes ( n = 371 ) and hypervariable genes ( n = 117 ) [70] , are depleted for and enriched in H2A . Z across the gene body , respectively ( Figure 4A and Figure S20 ) . These results suggest that H2A . Z deposition in the gene body may facilitate rapid activation or inactivation of genes . To determine which genes are misregulated upon loss of H2A . Z , we profiled the transcriptomes of the h2a . z mutant and WT in 4-week old rosette leaves with three replicates each of RNA sequencing . 1 , 800 genes were upregulated and 544 genes were downregulated in h2a . z with a P-value cut-off of 0 . 001 . This is consistent with transcriptome analyses of hta9;hta11 and pie1 , which showed three-fold and two-fold more genes upregulated than downregulated , respectively [37] . The genes exhibiting up and downregulation in the h2a . z mutant show statistically significant overlap with lists of up and downregulated genes in pie1 and hta9;hta11 [37] , despite differences in tissues type , developmental stage , growth conditions , and transcriptional profiling platform used to generate these data ( Figure S21 ) . Gene Ontology analysis of the misregulated genes in h2a . z revealed enrichment of categories related to immune response ( P-value = 8 . 6×10−9 ) and temperature response ( P-value = 4 . 8×10−8 ) , consistent with previous studies of pie1 and arp4 mutants [8] , [37] ( Table S3 ) . Strikingly , all of the most-enriched categories ( P-value<1×10−5 ) are specifically response-related , and include many previously unreported GO-terms involved in the perception of a variety of external cues ( Figure 5A ) . Many of these GO terms are also overrepresented in the smaller subset of genes upregulated in at least two of the h2a . z , pie1 , and hta9;hta11 mutant datasets ( Figure 5A ) . Consistent with our Gene Ontology analysis ( Tables S3 , S4 ) , we discovered a relationship between the degree of misregulation in the h2a . z mutant and the responsiveness score of a gene ( Figure 5B ) . Genes exhibiting greater than 4-fold upregulation ( n = 938 ) had a 2 . 5-fold higher median responsiveness score than that of the least upregulated genes ( less than 1 . 4-fold up or downregulated , n = 9 , 300 ) . The relationship between downregulation and response score , on the other hand , was roughly parabolic , with the most downregulated and least downregulated genes showing the lowest levels of responsiveness , and genes with intermediate levels of downregulation ( 2 to 4-fold ) showing the greatest responsiveness ( Figure 5B ) . Hypervariable genes are generally strongly upregulated in h2a . z plants , despite a lack of change in DNA methylation ( Figure S20 ) , whereas the expression of housekeeping genes is largely unchanged ( Figure 5C ) . Because H2A . Z is enriched in bodies of response genes , we investigated whether changes in transcriptional regulation in the h2a . z mutant correlated with specific H2A . Z enrichment patterns in WT . As expected , we found a positive relationship between misregulation in the h2a . z line and H2A . Z gene body enrichment ( Figure 5D ) ( Spearman's rho = 0 . 2634 for downregulated genes and 0 . 2540 for upregulated genes , P-value<0 . 0001 ) . Genes with the greatest misregulation ( greater than four-fold up or downregulated , n = 1 , 258 ) have more than a 36-fold higher median H2A . Z-body enrichment score than that of genes with the lowest levels of change in transcription between h2a . z and WT ( less than 1 . 4-fold up or downregulated , n = 9 , 300 ) . Taken together , our data demonstrate that loss of H2A . Z leads to a general transcriptional misregulation of response genes that are enriched for H2A . Z within the gene body in wild type , including genes that respond to developmental , biotic , and abiotic stimuli ( Figure 5E ) . Our results also suggest that one function of gene body methylation , which is strongly anticorrelated with gene responsiveness in plants and animals [70] , [71] , [72] , is the exclusion of H2A . Z from the bodies of constitutively expressed genes .
Whereas the fungi S . pombe and S . cerevisiae can tolerate mutations in H2A . Z [27] , [73] , H2A . Z is essential in many species , including Tetrahymena thermophila , Drosophila melanogastor , Xenopus laevis , Caenorhabditis elegans and mice [15] , [56] , [58] , [74] , [75] . Consequently , many studies of H2A . Z function outside of yeast have utilized mutants in components of the chromatin remodelers that deposit H2A . Z to emulate H2A . Z loss-of-function [8] , [34] , [37] , [69] . The substantial overlap between the phenotypes of Arabidopsis pie1 and h2a . z mutants suggests that PIE1 is the primary remodeler responsible for H2A . Z deposition . However , h2a . z;pie1 double mutants exhibit early developmental arrest not seen in either of the single mutant lines , indicating that H2A . Z may be deposited in the absence of the PIE1 complex , potentially by the Arabidopsis homolog of INO80 [76] , which can deposit H2A . Z in yeast [51] . The PIE1 complex might also have H2A . Z-indpendent roles , as has been hypothesized for the PIE1/SWR1 orthologs in animals [49] , [50] . Indeed , a recent study showed that H2A . Z deposition by p400 and SRCAP , the human orthologs of SWR1 , could not account for all the regulatory roles of these complexes [50] . These results emphasize that phenotypes caused by mutations in chromatin remodeling complexes must be interpreted with caution . DNA methylation and H2A . Z are tightly anticorrelated in plants and animals [16] , [24] , [47] , [48] , and we have shown that DNA methylation quantitatively excludes H2A . Z from chromatin [16] . Here , we demonstrate that H2A . Z does not have a large influence on DNA methylation in genes , even when genic DNA methylation is in flux , but that loss of H2A . Z does cause a small increase in genic methylation , particularly in H2A . Z-enriched genes , consistent with our earlier results [16] . The magnitude of these changes is unlikely to substantially contribute to the genome-wide anticorrelation between DNA methylation and H2A . Z , indicating that exclusion of H2A . Z from methylated DNA is the cause ( Figure 6 ) . The bisulfite sequencing data also reveal global decreases in CHG and CHH TE methylation in the h2a . z mutant . Changes in TE methylation could be a direct result of H2A . Z loss , or may be caused by a variety of indirect effects . Given the depletion of H2A . Z from methylated transposons and the substantial transcriptional and developmental changes in h2a . z plants , we consider indirect explanations to be more probable . For example , the approximately two-fold downregulation of the DNA methyltransferase CMT3 , which catalyzes CHG methylation [77] , might be partly responsible for the decreased CHG methylation . ( Table S5 ) . The significance of H2A . Z enrichment near transcriptional start sites has been a major focus of research [22] , [23] , [78] , [79] , [80] , but a distinct function for H2A . Z in gene bodies has been recently hypothesized [81] . Consistent with this idea , we previously showed that H2A . Z abundance within gene bodies correlates negatively with transcription in Arabidopsis and the pufferfish Tetraodon nigroviridis , whereas H2A . Z near the TSS is most enriched in moderately transcribed genes in both organisms [16] , [24] . Human studies also show that gene body H2A . Z correlates with silencing [12] and that H2A . Z is depleted from the bodies of actively transcribed genes [23] . The presence of this relationship in plants and animals implies that it is an ancient property of eukaryotes . Interestingly , recent studies in yeast have shown that mutation of the IN080 complex causes loss of H2A . Z near the TSS and gain of H2A . Z across the coding region [51] , suggesting that competing nucleosome remodelers may shape the genic patterns of H2A . Z . Here , we show that H2A . Z within gene bodies is correlated with gene responsiveness , consistent with recent yeast data demonstrating that H2A . Z is enriched across coding sequences of genes that are differentially transcribed after environmental stress [26] . Loss of H2A . Z leads to misregulation of Arabidopsis genes with high responsiveness scores , which measure differential expression across both tissue types and environmental conditions . Furthermore , this misregulation occurs despite a lack of change in the DNA methylation profiles of these genes in the h2a . z mutant . Our results are consistent with evidence from many other species , where loss of H2A . Z leads to misregulation of various inducible genes , including environmental response genes in yeast [21] , [26] , [78] and developmentally regulated and tissue-specific genes in animals [13] , [15] , [18] , [82] , [83] . The phenotypes of h2a . z mutants , including altered flowering time , floral homeotic transformations and silique deformation , also strongly imply that developmental regulators are misregulated . We also demonstrate that genes that show little change in transcription in our h2a . z mutant plants tend to have H2A . Z depleted from the gene body , whereas those genes with either strong up- or downregulation tend to have much more gene-body H2A . Z . Taken together , these results indicate that H2A . Z within transcribed sequences is necessary for proper regulation of responsive genes but may antagonize constitutive and high-level expression , and that this relationship is both ancient and well-conserved across many eukaryotic lineages . The presence of DNA methylation within the bodies of animal and plant genes has been known for some time [84] , [85] . Recent genome-wide bisulfite sequencing in various eukaryotic species has revealed that gene body methylation is an ancient and widely conserved feature of eukaryotic chromatin predating the divergence of animals and plants [24] , [38] , [43] , [44] , [45] , [71] , [86] , [87] . In animals and plants , gene body methylation exists almost exclusively within the CG context and follows a consistent pattern , with depletion of DNA methylation from the 5′ and 3′ ends of genes . Taken together with the finding that many species of invertebrates have DNA methylation primarily or exclusively within gene bodies [24] , [87] , [88] , these results strongly suggest that genic methylation plays an important and conserved function in at least some eukaryotic lineages [89] . Despite the prevalence of gene body methylation in diverse eukaryotes , its function remains mysterious [90] . A potential clue comes from the correlation between genic methylation and transcription . Gene body methylation is highest in moderately transcribed genes in plants and animals , with the lowest levels of genic methylation at either transcriptional extreme [24] , [45] , [71] . Additionally , there is an unexplained negative linear correlation between genic methylation and gene responsiveness in Arabidopsis and the honeybee Apis mellifera [70] , [71] , [91] . High levels of body methylation tend to be found in slowly evolving genes with vital housekeeping functions in honeybee , silkworm ( Bombyx mori ) , sea squirt ( Ciona intestinalis ) , sea anemone ( Nematostella vectensis ) , poplar ( Populus tricharpa ) , and Arabidopsis [71] , [88] , [92] , [93] . These results indicate that DNA methylation of the transcribed region may be important for proper regulation of constitutively expressed genes . Here , we show that the genome-wide anticorrelation between DNA methylation and H2A . Z is established by the exclusion of H2A . Z from methylated DNA . Because gene body DNA methylation and H2A . Z show opposing correlations with gene responsiveness , and the anticorrelation between DNA methylation and H2A . Z is ancient , we propose that a basal function of genic DNA methylation is the stabilization of constitutive expression patterns within housekeeping genes by antagonizing H2A . Z deposition ( Figure 6 ) . As H2A . Z has been linked to the regulation of inducible genes in many organisms , including species such as S . cerevisae and C . elegans that lack DNA methylation [8] , [13] , [15] , [18] , [21] , [26] , [69] , [78] , [82] , [94] , and DNA methylation can exclude H2A . Z but not vice versa , we believe that the presence or absence of H2A . Z in the gene body is a better candidate for direct gene regulation than DNA methylation . The functional significance of DNA methylation of constitutive genes may be primarily to prevent incorporation of H2A . Z .
The Arabidopsis T-DNA lines hta9-1 ( SALK_054814 ) , hta11-1 ( SALK_017235 ) , ibm1–6 ( SALK_006042 ) , and pie1–5 ( SALK_096434 ) were obtained from the SALK collection ( Col-0 ecotype ) ( http://signal . salk . edu/ ) . The Arabidopsis T-DNA line hta8-1 ( FLAG_593B04 ) was obtained from the INRA ( http://www-ijpb . versailles . inra . fr/ ) collection ( WS ecotype ) . Sequencing of the 5′ promoter region of HTA8 confirmed the T-DNA insertion site for hta8-1 at position 16 , 220 , 917 on Chr2 ( NC_003071 . 1 ) , 8 bp downstream of the 5′ end of gene model AT2G38810 . 2 . The Arabidopsis EMS mutant met1–6 is described in [95] ( Col-0 ecotype ) . The h2a . z mutant was generated from crosses of an hta9-1; hta-11 double mutant with the hta8-1 line . In all experiments , the WT control line for each mutant was generated from the nearest WT sibling of the mutant ( i . e . the WT control for the h2a . z mutant is an F2 progeny of wild-type genotype at all three H2A . Z loci , derived from an F1 parent that is heterozygous for hta9-1 , hta11-1 , and hta8-1 ) . In instances where we felt it was necessary to distinguish which WT control we refer to ( i . e . Figure S6 ) , we use the non-mutant gene identifier ( in all capital letters ) preceding the “WT” ( i . e . , the WT line associated with the pie1 mutant is “PIE1 WT” ) . For bisulfite sequencing of seedling tissues , seeds were planted on 1× Murashige and Skoog Media with micronutrients and 1 . 5% Sucrose ( Caisson Laboratories ) and grown under 16 h light/8 h dark for 14 days in a growth chamber . For bisulfite sequencing of rosette and cauline leaf tissue , seeds were planted on soil and grown in greenhouse conditions with LD 16 h light/8 h dark . For phenotype analysis of the h2a . z mutant , seeds were planted on soil and grown in greenhouse conditions with either 16 h light/8 h dark ( LD ) or 8 h light/16 h dark ( SD ) . Genotyping of SALK and INRA T-DNA lines was carried out by PCR with primers listed in Table S6 . Genotyping of the met1–6 line was carried out by dCAPS-PCR with primers listed in Table S6 and subsequent digestion with BglII . Expression analyses for the h2a . z and hta9-1 mutant lines and for the WT control were performed on total RNA extracted from 4 week post germination rosette leaves grown on soil in LD conditions using the RNeasy Plant Extraction Kit ( Qiagen ) with the optional on-column DNAse treatment . RT-PCR reactions were carried out on cDNA generated using 1 ug total RNA and the Superscript III Kit ( Invitrogen ) using gene specific primers listed in Table S6 . qPCR was carried out on similarly generated cDNA using EvaGreen Detection chemistry on an ABI 7500 FAST Real-Time PCR System with primers in exons flanking the single intron in HTA9 . The gene UBQ5 ( AT3G62250 ) was used as in internal control . Three biological replicates , each with three technical replicates , were averaged . Approximately 100–500 ng genomic DNA was isolated from either seedling , rosette or cauline leaf tissues . Seedling tissue was obtained from 14 days post germination seedlings grown on Murashige and Skoog media in LD ( 16 h light/8 h dark ) . Mature rosette leaves and mature cauline leaves were obtained from 4 week post germination mature plants grown on soil in LD ( 16 h light/8 h dark ) . In general , multiple biological replicates were generated for each mutant and WT line; a complete list of all generated libraries is available in Table S7 . WT datasets for each mutant were generated from plants derived from recent relatives of the relevant mutant . Bisulfite conversion and Illumina library construction and sequencing were performed as described in [96] . We used single ends ( SE ) Illumina sequencing for bisulfite sequencing on the GAII and HiSeq platforms and sequence alignments were performed using Bowtie [97] and the TAIR8 Genome Annotation ( http://www . arabidopsis . org/ ) as in [24] . The average percent methylation plots were generated as described in [96] and [24] . For locus-specific bisulfite sequencing ( referred to as BS-PCR ) , data were generated exactly as described previously [16] . Bisulfite-converted genomic DNA from 14 day seedlings was PCR amplified using primers from each of four genotypes , including h2a . z , pie1 , H2A . Z WT , and PIE1 WT . Approximately 10–12 clones were sequenced from each genotype ( except for At3g22340 , in which 6 clones were sequenced for pie1 ) and percent methylation was determined at the same cytosine sites used for this calculation in our previous publication . Alternatively , percent methylation scores were also calculated by extracting the reads associated with each locus from the relevant whole genome bisulfite sequencing datasets ( referred to as BS-Seq ) . Average percent methylation levels were calculated at the same cytosine sites described above for BS-PCR from available seedling replicates for each genotype ( 2 for pie1 , 3 for PIE1 WT , 2 for h2a . z , 2 for H2A . Z WT ) . Approximately 30 ug total RNA was isolated from 4 week post germination mature rosette leaves using the RNeasy Plant Extraction Kit ( Qiagen ) with the optional on-column DNAse treatment . mRNA was purified from total RNA by two treatments of poly-A enrichment using the Oligotex kit ( Qiagen #72022 ) , followed by a rRNA removal step using the RiboMinus Plant Kit for RNA sequencing ( Invitrogen #A1083702 ) . Illumina library construction and RNA sequencing were performed as described in [24] . We used single ends ( SE ) Illumina sequencing for RNA sequencing on the GAII platform and sequence alignments were performed using Bowtie and the TAIR8 Genome Annotation and cDNA Annotation ( http://www . arabidopsis . org/ ) as in [24] . H2A . Z-containing nucleosomes were chromatin affinity purified ( ChAP ) from 4 week post germination Arabidopsis roots of our H2A . Z-BLRP transgenic lines grown in LD conditions as in [16] . Illumina libraries were constructed for IP and input DNA samples and sequenced on the HiSeq 2000 platform , generating 50 bp reads . Sequence alignments were performed using Bowtie and the TAIR8 genome annotation as in [24] . Nucleosomal midpoints were estimated based on an average 150-bp nucleosome length by adding 75 bp to the start position of each read . Differences between IP and input over each single-base window were generated to give an overall genome-wide map of H2A . Z-enrichment . For differential expression analysis of the RNA sequencing datasets , a strategy was employed to account for expression differences between WS and Col ecotypes . In brief , we used the recently published list of 144 , 879 SNPs between the WS and Col ecotypes [98] to obtain reads per kilobase of exon model per million reads ( RPKM ) scores for each gene in h2a . z and WT from either the WS or Col backgrounds . First , using Bowtie with no tolerance for mismatches , reads from each of the three h2a . z and WT RNA sequencing datasets were mapped to small 75 bp scaffolds containing either the WS or Col SNP around each SNP locus that mapped within an exon of a gene greater than 200 bp in length and with at least 10 mapped reads . We removed all SNPs that were less than one read-length ( 36 bp ) from the end of the exon , which left approximately 5 , 000 SNPs across the genome . The number of reads mapping to the WS and Col scaffolds were compared at each SNP locus and used to determine whether the region was homozygous for WS , Col or heterozygous for the two ecotypes in each dataset . For SNPs at heterozygous loci , a Read Count Contribution from each WS or Col genome was determined by dividing the number of reads mapping to either WS or Col genome by the total reads mapping to the SNP scaffold for each ecotype . As SNPs within a given heterozygous region generally exhibited similar ratios of WS to Col mapped reads , a rolling 20-window ( where the windows are the 5 , 000 SNPs ) smoothing function was applied to these read count contribution values . Next , the six RNA sequencing datasets were mapped to the TAIR cDNA scaffold , and each cDNA model was assigned a score equal to the number of mapped RPKM . For both the h2a . z and WT datasets , the normalized read counts of the three replicates were partitioned into reads contributed by WS and by Col using the smoothed read count contribution value obtained from the nearest SNP . In this way , approximate WS and Col read count scores were determined for each gene in both h2a . z and WT . To test for statistical significance of the difference between the h2a . z and WT , we repeated the above partitioning process using read counts normalized to the size of the smallest library , rather than per million of reads . This alternate normalization less drastically underestimates the number of reads per locus , which is important as the statistical significance is dependent on the number of reads . We calculated the probability that a gene's expression deviates from expectation using a Fisher's two-tailed exact test of h2a . z vs . WT scores for each ecotype . Genes were determined to be differentially expressed if for either ecotype they exhibited a two-fold change in expression between h2a . z and WT and had a P-value<0 . 001 , or if for both ecotypes they exhibited a two-fold change in expression and had p-values<0 . 005 . Gene Ontology analysis was performed on the up- and downregulated gene lists using the GO FAT Ontology on the DAVID web server ( http://david . abcc . ncifcrf . gov ) [99] , [100] and categories with P-values<1×10−5 were considered enriched . Sequences are deposited in Gene Expression Omnibus ( GEO ) with accession number GSE39045 . | Eukaryotes package their DNA to fit within the nucleus using well-conserved proteins , called histones , that form the building blocks of nucleosomes , the fundamental units of chromatin . Histone variants are specialized versions of these proteins that change the chromatin landscape by altering the biochemical properties and interacting partners of the nucleosome . H2A . Z , a conserved eukaryotic histone variant , is preferentially enriched at the beginnings of genes , though the significance of this enrichment remains unknown . We and others have shown that H2A . Z is conspicuously absent from methylated DNA across the genome in plants and animals . Typically considered a mark of epigenetic silencing , DNA methylation has more recently been discovered in the bodies of many genes . Here , we present evidence that the genome-wide anticorrelation between DNA methylation and H2A . Z enrichment in Arabidopsis is the result of DNA methylation acting to prevent H2A . Z incorporation . We demonstrate that the presence of H2A . Z within gene bodies is correlated with lower transcription levels and higher variability in expression patterns across tissue types and environmental conditions , and we propose that a major function of gene-body DNA methylation is to exclude H2A . Z from the bodies of highly and constitutively expressed genes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genome-wide",
"association",
"studies",
"genome",
"expression",
"analysis",
"functional",
"genomics",
"retrotransposons",
"plant",
"growth",
"and",
"development",
"plant",
"biology",
"dna",
"transcription",
"gene",
"function",
"histone",
"modification",
"genome",
"sequencing",
"developmental",
"biology",
"model",
"organisms",
"epigenetics",
"chromatin",
"arabidopsis",
"thaliana",
"transposons",
"chromosome",
"biology",
"gene",
"expression",
"plant",
"genetics",
"biology",
"dna",
"modification",
"molecular",
"biology",
"plant",
"and",
"algal",
"models",
"cell",
"biology",
"genetics",
"dna",
"transposons",
"genomics",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] | 2012 | Deposition of Histone Variant H2A.Z within Gene Bodies Regulates Responsive Genes |
Polycomb-group ( PcG ) and Trithorax-group proteins together form a maintenance machinery that is responsible for stable heritable states of gene activity . While the best-studied target genes are the Hox genes of the Antennapedia and Bithorax complexes , a large number of key developmental genes are also Polycomb ( Pc ) targets , indicating a widespread role for this maintenance machinery in cell fate determination . We have studied the linkage between the binding of PcG proteins and the developmental regulation of gene expression using whole-genome mapping to identify sites bound by the PcG proteins , Pc and Pleiohomeotic ( Pho ) , in the Drosophila embryo and in a more restricted tissue , the imaginal discs of the third thoracic segment . Our data provide support for the idea that Pho is a general component of the maintenance machinery , since the majority of Pc targets are also associated with Pho binding . We find , in general , considerable developmental stability of Pc and Pho binding at target genes and observe that Pc/Pho binding can be associated with both expressed and inactive genes . In particular , at the Hox complexes , both active and inactive genes have significant Pc and Pho binding . However , in comparison to inactive genes , the active Hox genes show reduced and altered binding profiles . During development , Pc target genes are not simply constantly associated with Pc/Pho binding , and we identify sets of genes with clear differential binding between embryo and imaginal disc . Using existing datasets , we show that for specific fate-determining genes of the haemocyte lineage , the active state is characterised by lack of Pc binding . Overall , our analysis suggests a dynamic relationship between Pc/Pho binding and gene transcription . Pc/Pho binding does not preclude transcription , but levels of Pc/Pho binding change during development , and loss of Pc/Pho binding can be associated with both stable gene activity and inactivity .
As the cells of the embryo progress along developmental pathways they make fate decisions , becoming committed to particular lineages and ultimately to a specific differentiated cell state . Although cell fate decisions may be triggered by transient signals , the resultant cell states are generally stable and are maintained through time and cell division . A long-standing paradigm for understanding the mechanisms underlying the stability of cell fate decisions has been the maintenance of Hox gene expression through gene silencing by Polycomb-group ( PcG ) genes in Drosophila ( reviewed in [1] ) . Hox gene expression domains , initiated in the early embryo through active transcriptional regulation by the transiently-expressed products of the segmentation genes , are thereafter maintained throughout the rest of development and adult life by the maintenance machinery of the PcG and Trithorax-group ( TrxG ) genes . The products of the PcG genes build the Polycomb Repressive Complexes ( PRC1 and PRC2 ) that are required for gene silencing , whereas the TrxG genes are required for the maintenance of gene activation ( reviewed in [2] ) . In this paradigm , the balance between gene repression and activation is set once and thereafter stably remembered . A more dynamic view of the role of PcG silencing has recently been emerging , largely from work with embryonic stem cells , where several PcG genes have been shown to be required for both embryonic and adult stem cell maintenance ( reviewed in [3] ) . Genome-wide analysis of the targets of PRC1 and PRC2 complex components reveals that a large number of genes with roles in cell fate decisions and cell differentiation are bound by PcG gene products in stem cells [4] , [5] . Many of these genes are repressed by PcG proteins since loss or down-regulation of PcG genes results in their derepression . Upon stem cell differentiation many repressed genes become activated and some concomitantly lose binding of PcG complexes . In stem cells many developmental genes exhibit a “poised” bivalent chromatin organisation , carrying both repressive and active chromatin modifications [6]–[8] . The repressive H3K27me3 histone modification , dependent on the PRC2 complex , is lost from many genes on differentiation . Thus PcG silencing appears to maintain the stem cell state via repression of cytodifferentiation genes; this repression is not permanent and can be lifted upon receipt of differentiation signals . When the human embryonic teratocarcinoma cell line NT2/D1 is induced to undergo neural differentiation by exposure to retinoic acid , two different scenarios are observed for PcG regulation of target genes [9] . For PcG target genes activated during neuronal differentiation ( e . g . the neuronal transcription factor ZIC1 and the neurofilament light chain gene , NEFL ) , PcG proteins are associated with these genes prior to activation but are lost upon differentiation . In contrast , for PcG target genes repressed during differentiation ( e . g . the pro-neural transcription factors OLIG2 and NEUROG2 ) , PcG proteins are already associated with these genes in undifferentiated cells , even though the genes are expressed , and the Polycomb complexes remain after differentiation when expression is switched off . Thus it appears that , at some genes , the association of Polycomb complexes with target genes can change dramatically upon differentiation , but the presence of Polycomb complexes does not always accord with transcriptional repression . PcG target genes have been identified in Drosophila by genome-wide mapping of PcG protein binding in tissue culture cells [10] , [11] and by more limited genomic mapping ( across 10 Mb of Drosophila euchromatin ) with different developmental stages in vivo [12] . In this latter study , examples of target genes with clear developmental changes in PcG protein association were identified , suggesting that the chromosome association profile of Polycomb complexes in Drosophila may be more dynamic than previously thought . Here we extend these studies , presenting a genome-wide analysis of PcG proteins in Drosophila embryos and in imaginal discs from the third thoracic segment . We examine the binding profiles of Polycomb ( Pc ) , the canonical member of the PRC1 complex , and of Pleiohomeotic ( Pho ) a DNA-binding protein proposed to recruit the PRC2 complex [13] . Analysis of tissue derived specifically from the third thoracic ( T3 ) segment allows us to examine Pc and Pho association with Hox genes that are known to be either active or inactive in this segment . Comparing binding profiles between the embryo and third larval instar imaginal discs also enables us to examine the dynamics of PcG binding during development of specific tissues . Finally , we compare our in vivo developmental analysis with a previously described genome-wide analysis of Pc binding in Drosophila tissue culture cells [10] identifying further examples of differential Pc binding .
We performed genome-wide mapping of binding sites for Pc and Pho in chromatin from two in vivo sources; Drosophila embryos and imaginal discs . We studied Pc as a representative of the four core PRC1 components , Pc , Polyhomeotic , Posterior sex combs and dRing [14] . We investigated Pho since this is a sequence-specific DNA-binding protein known to be associated with several PcG Response Elements ( PREs ) . Pho binding sites have been shown to be required for PcG-mediated silencing at these PREs [15]–[19] and although Pho is not a component of purified PRC complexes , it interacts biochemically with both PRC1 and PRC2 [13] , [20] , [21] . Pho co-localises with PRC1 proteins at many sites on polytene chromosomes [16] and , by ChIP ( Chromatin Immunoprecipitation ) analysis , it is associated with PRC1 binding sites in Hox genes [22] , [23] . The 0–16 hr embryonic chromatin provides a base-line for our analysis identifying a set of in vivo targets in a mixture of developmental time-points and tissues . In contrast , the imaginal disc chromatin provides a more focussed sampling of targets within a single tissue ( epidermal imaginal ) , at a particular developmental time ( wandering third larval instar ) and at a specific position along the body axis ( T3 segment ) . At a gross level , comparison of the binding profiles , e . g . across chromosome 3R as illustrated in Figure 1 , reveals considerable similarity , suggesting that Pc and Pho are generally bound at the same locations and that their binding sites appear relatively constant with little change between embryo and imaginal disc . To analyse these data in more detail , we defined upper and lower binding thresholds for each profile , allowing us to categorise binding over each Drosophila gene as positive , intermediate or negative ( see Table S1 ) . As well as counting binding events directly over transcription units , intergenic events were separately ascribed to the nearest transcript . Validation of the ChIP-array data by ChIP followed by specific PCR confirmed that thresholds were appropriate ( Figure S1 ) . Using conservative thresholds , we find 386 genes with Pc binding over the transcription unit . 179 ( 46% ) of these are also associated with Pho binding , which rises to 229 ( 59% ) when we include intergenic Pho binding , supporting the idea that Pho has a general role as a DNA-binding protein targeting the assembly of Polycomb complexes . Interestingly , we find a substantial number of genes ( 212 ) that exhibit Pho binding in the absence of Pc ( Figure 2 ) . The majority ( 85% ) of these Pho-only binding sites are specific to embryo chromatin . Examination of the GO classifications that are enriched in this subset reveals a markedly different profile from the set of genes that bind both Pc and Pho ( Figure 2C ) . In particular , we find significant overrepresentation ( p<0 . 05 ) of genes involved in oogenesis , mitosis and mRNA splicing . In addition , we also note that several genes whose products are involved in chromatin regulation ( e . g . brahma , Polycomblike , Cp190 and su ( Hw ) ) are associated with Pho but not Pc . These observations indicate a role for Pho in the embryo that is independent from its association with Pc . As illustrated in Figure 2D the local profiles of Pc and Pho binding are very different . Pc is often associated with a broad binding domain extending over tens of kilobases whereas Pho binding is characterised by much narrower isolated peaks . Since the sharp Pho peaks identify relatively short regions associated with Pho binding we searched for enriched sequence motifs underlying these peaks . In addition to Pho , several other DNA-binding proteins , including GAGA factor and Zeste , are associated with PREs [24]–[29] . We were interested to see if Pho-bound sequences exhibited the canonical Pho binding motif , as well as putative binding sites for these other factors . We learned motif dictionaries from the central areas of 150 strong Pc-associated Pho peaks using various search parameters . The searches identified more than 70 partially redundant sequence motifs and we selected 18 of these for further analysis , based on their length , information content and/or similarity to known binding sites of Pho , GAGA and Zeste ( Figure 3 , see also Table S2 and Dataset S1 ) . Binding sites of additional factors known to be involved in Pc recruitment ( e . g . Grainy head ) could not be identified . We found Pho-type motifs comprising a core GCCAT sequence with a more or less pronounced tail of four Ts . In addition , we found a novel TGGCC motif that may be related to the Pho-type as it has a GCC core ( and GCCA on the reverse strand ) but which lacks the tail of Ts . We also found a frequently occurring GTT repeat and a CGCACT sequence motif . The GAGA- and Zeste-type motifs differ in that Zeste-like motifs have a pronounced “GAG” , however we recognise that this classification is somewhat arbitrary . The selected motifs were significantly over-represented when we compared their occurrence in all Pho peaks to random sets of sequences that were not occupied by Pho ( Figure 3B ) . While longer motifs with positions of low information content are mostly over-represented when allowing for mismatches , short sequence motifs are only over-represented when considering perfect matches or small sub-optimal bit scores . This over-representation approach also enabled us to derive informed cutoff values for further analyses; for each motif we identified the sub-optimal bit score for which the motif shows the strongest evidence for over-representation . Using these cutoff values , we determined the presence of the different motif types in all 628 embryonic Pho peaks ( Figure 3C ) . The short motifs ( clustered to the right of the diagram ) are well represented in the Pho peak sequences; 85% of the sequences contain Pho_6 , 63% contain GAGA_6 and 64% contain Zeste_7 . Longer Pho and TGGCC motifs occur in 51% of the peaks and have an interesting antagonistic clustering to the longer GAGA and Zeste-type motifs; i . e . peaks containing longer Pho/TGGCC-type motifs ( region A in Figure 3C ) cluster separately from peaks containing longer GAGA/Zeste-type ( region B ) . Overall , the general association of Pho , GAGA and Zeste binding motifs with Pho binding is consistent with the clustering of these motifs previously used to predict PREs in the Drosophila genome [29] and we add novel enriched sequences that may improve such approaches . However , we emphasise that there is considerable variability in the motif occurrence at Pho peaks as we illustrate for representative peaks in Figure S2 . We were interested to determine if there was any qualitative difference in motif composition between Pc-associated and Pho-only peaks . We compared the number of peaks containing particular motifs for both these peak classes and tested for significance using chi-square statistics . Interestingly , we find significant differences with TGGCC_7 ( 34 . 7% vs 53 . 6% , p<3×10−6 ) and Zeste_7 ( 31% vs 46 . 8% , p<8×10−5 ) under-represented in the Pho-peaks that are not associated with Pc . In contrast , the Pho_12a ( 43 . 9% vs 26 . 2% , p<3×10−6 ) and Pho_14b ( 35 . 2% vs 20 . 4% , p<3×10−5 ) motifs are over-represented in the Pho-only group . This observation highlights the potential importance of a long Pho motif at the Pho-only sites . There is no compositional bias for the longer GAGA- and Zeste-type motifs . What differentiates a silenced from an active Hox gene ? Although the PcG and TrxG genes have antagonistic effects on gene expression they can nevertheless be present at the same gene . PcG proteins and TrxG proteins were found to co-localise at targets on salivary gland polytene chromosomes [30] , [31] and at PREs in the Bithorax Complex ( BX-C ) [32] . In addition , Pc-binding does not correlate with gene expression in S2 cells [33] . Recently , Papp and Mueller have analysed the binding of PcG and TrxG at the Ubx gene in active and repressed states in vivo [22] . Sampling the occupancy of these complexes at 17 sites across 115 kb of the Ubx region , encompassing the transcription unit and regulatory sequences , they found that PcG proteins of both the PRC1 and PRC2 complexes as well as Trx protein are bound to Ubx PREs in both the ON and the OFF states . Similarly , Trx and PcG were found to co-localise at binding sites in both active and inactive Hox genes in tissue-culture cells [23] . Our ChIP-array data allows a more extensive assessment of Pc and Pho occupancy across the Drosophila Hox complexes in vivo . Chromatin derived from embryos represents a mix of active and inactive states for the different Hox genes , however , for the T3 imaginal discs we can compare silenced and active Hox genes . Focusing on the BX-C: Ubx is active in the T3 discs , where it is required in the haltere disc to specify haltere in contrast to wing development , and in the T3 leg disc to specify T3 characteristics . In contrast , both abdominal-A ( abd-A ) and Abdominal-B ( Abd-B ) are silenced in T3 discs . The Pc binding profile shows an extensive domain of binding that covers the approximately 300 kb BX-C region ( Figure 4A ) . Characterised PREs tend to be represented as regions of relatively higher binding within the overall domain . As with other regions of the genome , the Pho binding profile is very different with much sharper , more isolated peaks several of which coincide with characterised PREs . In chromatin from the T3 imaginal discs both Pc and Pho are associated , as expected , with the silenced genes , abd-A and Abd-B . However , we also find significant association with the active Ubx gene . The T3 disc Pc profile over Ubx is similar to the embryo chromatin profile with an extensive domain and significant binding at both the bx and bxd PREs as well as a peak close to the start of Ubx transcription . The Pho profile in T3 discs also shows clear peaks at these PREs and binding close to the Ubx 5′ end . These data show clear evidence of Pc and Pho association with an active gene and confirm and extend the results of Papp and Mueller [22] . There are , however , differences between the embryo and T3 disc profiles . For example , several strong Pho binding peaks in embryo chromatin are only weakly represented in the T3 disc chromatin . In addition , there is a generally lower level of Pc and Pho binding across the active Ubx gene in comparison to the inactive abd-A and Abd-B genes . The average enrichments ( log2 binding ratios ) across the three transcription units in disc chromatin for Pc are: Ubx 0 . 33 , abd-A 1 . 03 and Abd-B 1 . 01 and in the case of Pho: Ubx 0 . 07 , abd-A 0 . 31 and Abd-B 0 . 34 . Significant Pc and Pho binding associated with an active Hox gene is also found in the Antennapedia Hox cluster ( ANT-C ) . This cluster contains the Hox genes lab , pb , Dfd , Scr and Antp , and all these genes are associated with widespread Pc binding and distinct Pho peaks in the embryo ( Figure 4B ) . It is striking that peaks in the Pc distribution as well as strong Pho peaks are found close to the 5′-ends of lab , pb , Dfd and Scr . The longer Antp gene is covered by a domain of Pc binding and is associated with several Pho peaks . As with Ubx , Antp is active in T3 discs since it is expressed from the labial segment posteriorly . Indeed , Antp may be a better gene than Ubx for the analysis of the active state in T3 because it is expressed in both the ectoderm and mesoderm of the T3 segment [34] , [35] , whereas Ubx is only active in the ectodermal imaginal cells of the T3 disc and may be silenced in the small population of mesodermal adepithelial cells [36] . Despite this difference we find a similar situation with Antp as we observe with Ubx . Although it is active , Antp is nevertheless associated with a significant domain of Pc binding , which encompasses the Antp transcription unit , as well as strong peaks of Pho binding close to the 5′ and 3′ ends of the gene . Other Pho peaks over Antp that are prominent in embryo chromatin are less apparent in T3 discs . As shown in Figure 4 we find many more Pho binding peaks across the Hox complexes than there are characterised PREs . As all these Pho sites may not be functionally equivalent we examined the motif composition in the 36 Pho peaks in the BX-C . We find a high variability of motif counts but characterised PREs do not appear as a distinct motif-rich group ( Figure S3 ) . While our analysis of the Hox clusters demonstrates significant Pc and Pho binding associated with both active and inactive genes , a genome-wide comparison of the embryo and T3 imaginal disc profiles shows that PcG proteins are not constitutively associated with target genes . The binding profiles of embryo and T3 disc chromatin are similar , with 65% ( 252/386 ) of the genes significantly associated with Pc in the embryo also above threshold in the disc chromatin . This rises to 81% ( 314/386 ) if we include the genes with intermediate binding in discs . When we include the data from the genome-wide S2 tissue culture study [10] we also find considerable overlap . For genes with direct binding over transcription units there is a 42% ( 161/386 ) overlap across all three data sets rising to 58% ( 224/386 ) if we include intermediate binding in discs and S2 cells . A gene-by-gene comparison is provided in Table S1 . Comparison with other previously published datasets also reveals considerable overlap . For the analysis of Pc targets by the DamID method in Kc tissue culture cells [11] we find that our set of Pc targets in the embryo ( 386 targets ) contains 136 targets from the Kc cell data . As the genome coverage of the Kc cell analysis is approximately 60% , this extrapolates to an estimated 60% overlap . This comparison is detailed in Table S1 . There is also good correspondence with the in vivo data from Negre et al . [12] where , for example , 5 out of the 7 targets they identify in the 3 Mb Adh region in embryo chromatin are also present in our set of Pc targets in the embryo . A detailed comparison is presented in Figure S4 . For examining differential Pc binding , we focus on the most comparable datasets , the two chromatin samples in our dataset and the Schwartz et al . S2 data [10] , as these three datasets are genome-wide and use the same Affymetrix array platform . Despite the overall similarity comparing the embryo , T3 disc and S2 cell chromatin samples , there are clear differences in Pc occupancy for some genes indicating potential sites of differential Pc activity . To reduce the level of artificially selected differential Pc targets resulting from automatic selection of peaks in the high-throughput analysis , we visually screened the binding profiles of all differentially bound regions and restricted our further analysis to differential gene sets that show significant enrichment of GO categories . We identify three robust sets of differentially occupied genes . We find 49 genes that are bound by Pc in the embryo but are not Pc-associated in imaginal discs , 107 genes that are bound by Pc in the embryo but not in S2 cells and 119 genes that are bound by Pc in imaginal disc but not in S2 cells . By examining the genes in these differentially-bound sets we anticipated that we might identify cell fate-determining genes for cell fate decisions in particular developmental pathways . For example , genes bound by Pc in the embryo but not in imaginal discs might represent genes released from Pc silencing on the pathway of disc development and hence might represent key cell-fate determining genes for that pathway . However , the GO analysis of these gene sets ( summarised in Figure 5 ( also see Table S3 ) ) presents a striking observation . The Pc target genes that are unoccupied by Pc in a particular tissue appear to have little to do with cell fate decisions that are relevant for that tissue . For example , the genes occupied by Pc in the embryo but not in the imaginal discs are enriched in genes involved in fate decisions in neuroblasts of the central nervous system . Similarly the genes that are occupied in embryos but not in S2 cells , which are mesodermally–derived cells of the haemocyte lineage , are genes required for ectodermal and neural fate decisions . The genes that are occupied in imaginal discs but not in S2 cells are relevant for fate decisions occurring in discs but not in S2 cells ( e . g . sensory organ development , eye development , ectoderm development ) . We further investigated the group of 49 genes which are associated with Pc in the embryo but not in T3 discs . Figure 6A lists these genes and shows their pattern of binding of Pc and Pho across the five data sets . Representative binding profiles are shown in Figure 6B . As the plot in Figure 6A demonstrates , approximately half of the Pc targets also show Pho binding in the embryo ( 49% ) and , as with Pc , Pho binding is absent in the T3 discs . For this gene set , target occupancy in the S2 cells is similar to that observed in imaginal discs with only a few targets ( 14% ) showing Pc binding . The set of 49 genes specifically bound in the embryo contains several well-characterised genes; notably run , hb and tll that are involved in early embryonic patterning and in neuroblast specification as well as two genes , ind and vnd involved in the specification of the nervous system and in neuroblast fate . As mentioned above , this class of genes has a strong GO signature and is highly enriched in transcription factors ( Figure 5 ) . Some individual classes of transcription factor are particularly strongly enriched . For example , 3 out of the 19 forkhead domain proteins present in the Drosophila genome are represented ( hypergeometric probability 3 . 5E-05 ) and 3 putative hormone-receptor C4-zinc finger genes of the 21 in the genome ( 4 . 7E-05 ) . The GO analysis and the individual gene functions suggest that this set of genes may be involved in early embryonic fate decisions but not in fate decisions that are relevant for the imaginal disc cells , where these targets are unoccupied . To explore this we asked whether these genes are actually expressed in imaginal discs . All of the six genes tested for expression by RT-PCR are expressed the embryo but show little or no expression in imaginal discs ( Figure 6C ) . Thus , as with the Hox genes , we find Pc occupancy is not linked to expression state in a simple fashion . Drosophila S2 cells are an embryo-derived cell line that appear to be related to haemocytes since they are phagocytic and express haemocyte markers [37] . We were interested to relate the Pc binding profile in these cells to the genes involved in cell fate decisions in the haemocyte lineage ( reviewed in [38] ) . The embryonic haemocytes are derived from a head mesoderm primordium defined by the GATA transcription factor , Serpent ( Srp ) , and differentiate into crystal cells or plasmatocytes . Lozenge ( Lz ) , a Runx family transcription factor , is required for crystal cell development whereas U-shaped ( Ush ) antagonises crystal cell development and Glial cells missing ( Gcm ) promotes plasmatocyte development . The closely related Gcm2 acts redundantly with Gcm in plasmatocyte differentiation . Full maturation of plasmatocytes requires the PDGF/VEGF Receptor ( Pvr ) . S2 cells express srp together with the plasmatocyte markers ush and pvr and do not express the crystal cell marker lz ( FLIGHT database: http://flight . licr . org/ , [37] ) . The expression status of gcm and gcm2 is less clear; they are not scored as expressed in S2 cells in the FLIGHT database but are reported to be detectable by RT-PCR [37] . The key cell fate-determining genes in the haemocyte lineage , srp , lz , gcm , gcm2 and ush , are all Pc targets . Figure 7 compares the Pc and Pho occupancy at srp , ush and lz in S2 cells with the occupancy in embryos and imaginal discs . Strikingly , the cell fate genes associated with the plasmatocyte fate , srp and ush , show strongly reduced Pc occupancy in S2 cells compared to embryos and imaginal discs whereas the crystal cell determining gene , lz , shows clear Pc binding . The comparative binding at srp is dramatic as there is a highly specific reduction in Pc binding in a specific domain over the srp gene in S2 cells , whereas the neighbouring gene GATAe is strongly associated with Pc binding . Overall , this analysis of Pc binding at cell fate-determining genes in the haematocyte lineage shows clear differential binding in S2 cells that correlates with gene expression and the requirement for gene activity in the plasmatocyte pathway . The Pc maintenance machinery functions to stably propagate states of gene activity through cell division and , for the Hox genes , stable expression patterns are preserved throughout development . We were interested to examine if this is also true for other Pc target genes . If Pc targets in general are stably expressed once activated , then differentiated cells may express the set of Pc target genes that have been activated along the developmental pathway they have followed . We used the FlyAtlas data set ( http://www . flyatlas . org/ ) of gene expression profiles from selected adult and larval tissues to examine the pattern of deployment of Pc target genes in specific tissues [39] . Out of the 386 Pc target genes we identified with embryo chromatin , we obtained tissue-specific expression data for 373 genes from FlyAtlas . The cluster analysis of the expression data is presented in Figure 8 ( and Figure S5 ) . We find that the data for this small sample of genes out of the 18 , 770 transcripts in the data set nevertheless clusters according to tissue type . For example , the two neural samples , brain and thoracic/abdominal ganglia cluster together , as do the crop and hindgut samples representing ectodermal-derived gut ensheathed in visceral mesoderm . Thus the Pc target gene expression profile provides a tissue-type signature . The cluster diagram , in addition to the block of genes that are present in all samples , also provides several blocks of genes whose expression is related to particular tissues . For example , the block illustrated in Figure 8B includes genes expressed in foregut ( crop ) and in hindgut and contains the two key genes bin and bap that are required for the specification of the visceral mesoderm in mid-embryogenesis . This analysis indicates that not only the Hox genes but also other Pc target genes remain stably expressed through to adulthood , providing a basis for the stable specification of cell type .
The PcG target genes identified by several genome-wide binding studies represent an assembly of key regulators that generate cellular diversity and patterning in the developing organism [4] , [5] , [10] . Coupled with the ability of the PcG/TrxG maintenance machinery to stably transmit states of gene expression through cell division , this provides a system for the stable inheritance of cell fate decisions and for the stability of differentiated cell states . In this paper we have examined the linkage between the PcG machinery and cell fate decisions by comparing the binding sites of PcG machinery components in different tissues . We have generated genomic binding profiles for Pc and Pho from whole Drosophila embryos and from the specific tissue of the imaginal discs of the third thoracic segment . We combined our data from in vivo sources with the data from Drosophila S2 tissue culture cells [10] allowing comparison of PcG occupancy in chromatin from different tissues . In general we find considerable overlap of target sites in the three data sets . However Pc and Pho binding to target sites is not simply constitutive and we find clear examples of alterations in Pho and Pc binding at specific target sites in particular tissues . We find a substantial number of genes associated with Pho binding but not Pc , suggesting a function for Pho separate from its role in PcG-mediated gene silencing . Pho has been found to be associated with two distinct protein complexes , Pho-dINO80 and PhoRC [40] . PhoRC is implicated in PcG-mediated gene silencing and it contains dSfmbt , a PcG protein required for Hox gene repression . PhoRC , but not the Pho-dINO80 complex is bound at PREs . The role of the Pho-dINO80 complex is unknown but it is a candidate for mediating Pho function at the Pc-independent Pho targets . Null pho mutants lacking any maternal contribution exhibit severe pleiotropic phenotypes and one pho allele shows a specific female sterility phenotype [15] . In this respect it is interesting to note that the Pho target genes we identify are overrepresented for oogenesis , mitosis and splicing functions . Of the 212 Pho-only targets , 60% are enriched for ovary expression and 66% are absent in testis according to FlyAtlas ( http://www . flyatlas . org/ ) . This suggests that pho may regulate a set of specific functions during oogenesis and we suggest that Pho continues to be associated with these targets in the embryo . A more general role for Pho , separate from PhoRC function , is also suggested by clonal analysis of pho and dSfmbt mutations in imaginal discs [40] . Mutant clones lacking pho ( together with pleiohomeotic like which functions largely redundantly with pho ) show not only loss of Hox gene silencing but also growth defects that result in the elimination of the clones from the disc epithelium . Clones lacking dSfmbt lose Hox silencing but do not show growth defects . Recently , Pho was found to be bound at active genes and is strongly recruited at sites with high transcription ( chromosome puffs ) on salivary gland polytene chromosomes . Based on the kinetics of Pho binding at heat-shock loci a role for Pho in the repression of previously active genes was proposed [23] . Examination of Pc and Pho binding in the BX-C in T3 imaginal discs provides a clear test case for the linkage between occupancy and gene expression since Ubx is expressed but abd-A and Abd-B are silenced . We find significant Pc and Pho binding associated with both the expressed and the silenced genes . This provides a clear demonstration that silencing is not simply established by the presence of PcG proteins at a target site and supports previous observations of a lack of correlation between PcG binding and gene silencing [22] , [23] , [33] . Although the Ubx gene is associated with significant Pc and Pho binding , there is overall less binding over Ubx in comparison with the two silenced genes . Also , the Pho binding profile in the embryo , representing a mixture of gene activity states , is markedly different from the T3 disc profile; the T3 profile shows prominent peaks at the bx and bxd PREs but the other peaks seen with embryo chromatin are less prominent . Similar effects are also seen at the Antp gene , which is also active in T3 discs . Reduction and rearrangement , rather than absence , of PcG protein at active genes suggests a dynamic interaction between silencing and gene transcription . Indeed , Pc complexes have been shown to be highly dynamic with rapid exchange of PcG proteins on chromatin [41] . Alteration of the Pho binding profile at Hox genes in cell lines with differential Hox expression has also been reported [23] with the striking observation of spreading of the Pho binding at active loci . Such dramatic Pho spreading is , however , not apparent in our data . We identified a set of 49 Pc target genes that were bound by Pc in the embryo but not in the T3 imaginal discs . We had expected that such a class might contain genes required for fate decisions on the pathway to T3 imaginal disc cell differentiation and were surprised to find that this gene set was enriched in genes required for early cell fate decisions in the nervous system . We examined the expression of several of these genes and found little or no expression in the T3 discs , reinforcing the idea that Pc binding does not simply correlate with silencing of gene expression . In the case of Ubx and Antp we find that expressed genes have significant Pc binding and , with the set of genes that show Pc association in embryos but not in imaginal discs , we find inactive genes that lack Pc . This is reminiscent of an observation with the Pc target hedgehog ( hh ) which has an identified PRE and is silenced by PcG in the embryo and imaginal discs [42] , [43]; in salivary gland polytene chromosomes , Chanas and Maschat found no PcG binding at the hh gene despite the fact that hh is not expressed in this tissue [43] . A similar observation was made for CycA [44] . In this case we note that although hh is a clear Pc target in S2 cells and in our in vivo analysis , CycA is not [10] . In all of these cases of differential Pc binding it is possible that the particular genes are inactive due to the absence of appropriate transcription factors to drive expression in particular tissues . This contrasts with the situation in the Hox genes where Pc is continuously required to maintain silencing against a background presence of gene activators [45]–[47] . If Pc complexes are only recruited to genes where they are required to counteract gene activation , this would provide an economical way to deploy the silencing machinery . It would also imply the existence of a mechanism that enables the PcG-machinery to identify genes that are capable of being activated . A possible basis for this mechanism could be the targeting of Polycomb complexes by non-coding RNAs; PcG proteins are recruited by non-coding RNAs in mammalian X chromosome inactivation [48] and recent studies implicate non-coding RNA in Hox gene repression [49] , [50] . Alternatively , the lack of Pc associated with non-expressed genes may indicate that these genes are repressed through non-PcG dependent mechanisms . In the case of hh in the salivary glands , there is some support for this since attempts to activate hh by supplying activators were unsuccessful [43] . In addition , a study on histone modifications and cell lineage provided evidence for a class of genes that lose both the PcG-dependent H3K27me3 mark associated with silencing and the TrxG–dependent H3K4me3 mark associated with activation on lineage progression [8] . Loss of both marks was found to be associated with gene inactivity . In the case of the haemocyte lineage cell fate-determining genes required for plasmatocyte development , these genes are expressed in S2 cells and are found to be selectively unoccupied by Pc . This is what would be expected for a non-silenced active gene and fits with the idea that Pc is lost from PRE/TREs following switching to the active state . However it raises the question of why Ubx or Antp , as genes expressed in T3 imaginal disc cells , are still associated with significant Pc binding whereas a Pc target gene such as srp appears to lack Pc binding in haemocyte lineage cells . S2 cells are tissue culture cells whose gene regulatory systems may have deviated considerably from the endogenous state and it is therefore possible that the observed Pc status represents a tissue culture artefact . However , another possibility is that it relates to plasticity . Imaginal disc cells are relatively undifferentiated precursor cells that only differentiate fully during metamorphosis . S2 cells , on the other hand , may represent a more committed cell state . In this respect it is interesting to compare the Pc profiles observed at the BX-C in S2 cells and T3 imaginal discs ( Figure 4A ) . S2 cells express high levels of Abd-B but very low levels of Ubx and abd-A . Pc binding reflects this gene expression and in particular shows no binding over an Abd-B domain that includes the four active Abd-B promoters [10] . This contrasts with the situation in T3 imaginal discs where the active Ubx gene is associated with significant Pc binding . However , it should be noted that the state of Hox gene expression in S2 cells is rather curious since these cells are thought to derive from the head mesoderm , an area of the embryo that does not express any of the genes of the BX-C . Despite this caveat , the differences in the Pc binding at active genes in S2 versus imaginal disc cells may reflect the plasticity of the undifferentiated imaginal disc cells compared to the loss of plasticity in the S2 cells . In general , we have identified two situations where Pc target genes are not bound by Pc proteins , a set of inactive genes in imaginal discs and a set of active genes in S2 cells and the common feature may be that these both represent terminal stable gene states . In these situations , loss of Pc binding may be associated with loss of plasticity and may indicate final cell commitment . Our analysis of T3 imaginal discs enabled us to investigate the Pc occupancy of genes in this specific tissue but does not immediately reveal the developmental history of these cells in terms of which cell fate switches had been turned on and which had been turned off along the developmental pathway leading to T3 imaginal epidermal specification . The Pc target genes which exhibited no Pc binding in the T3 imaginal discs did not obviously suggest a set of fate-determining genes for T3 disc specification . In the relatively undifferentiated imaginal disc cells it is apparent that Pc occupancy by itself does not differentiate a silenced from an active state and so to map the fate switching history of a cell we will either need to find markers that provide a clearer readout of the state of gene activation or else we will have to look at more differentiated cells where the PcG system has stabilised . Our analysis of a limited set of adult tissues , where gene expression data is available , provides support for stable activation of cell fate decision genes , suggesting that examining the expression of Pc target genes in differentiated cell types can provide information on the key developmental genes that are activated on a specific developmental pathway . Although the T3 imaginal discs represent a tissue sample of limited cell fate diversity they are nevertheless a complex mixture of cells with different states of gene activity . Many of the known key genes in imaginal disc development e . g . vg , Dll , hh and tsh are active in only a subset of disc cells and therefore the Pc and Pho binding profiles we observe may represent a mixture of active and silenced states . Further analysis examining more restricted tissues will be required to investigate to what extent the Pc target genes provide a stable “genetic address” [51] specifying cell differentiation .
The wild type strain used was OregonR . The Pc-GFP transgenic fly line was generated by Dietzel et al . [52] . The antibodies used were affinity purified rabbit anti-GFP [53] , rabbit anti-Pho [18] and affinity-purified anti-Pc [54] . Chromatin from embryos aged between 0 to 16 h after egg laying was purified as described previously [55] . For the preparation of chromatin from T3 imaginal discs ( haltere and third leg ) late 3rd instar larvae were dissected in ice-cold Schneider's Medium . Dissected discs were washed with PBS , fixed in PBS/1 . 5% formaldehyde for 20 min and washed with PBS . Batches of material were snap-frozen in liquid N2 and stored at −80°C . Chromatin was prepared from a minimum of 100 discs . For Pc target analysis the specific reaction used chromatin from the Pc-GFP fly line immunopurified using anti-GFP , and the control reaction used wild type chromatin immunopurified using anti-GFP . The Pc-GFP protein binds to the same polytene chromosome loci as wild-type Pc [41] and we validated a selection of targets by ChIP using anti-Pc antiserum ( Figure S1 ) . For Pho analysis wild type chromatin was used with anti-Pho for the specific reaction and pre-immune antiserum for the control . For validation reactions anti-Pc and anti-Pho were used for the ChIP and enrichment was assayed using PCR with specific primers as described previously [55] . The primer sequences are given in Table S4 . Three biological replicates were used for each condition and enrichment profiles were generated by comparison of specific and control ChIP DNA samples . In order to identify regions bound by Pho or Pc , 10–20 ng of ChIP and control DNA samples were amplified using a random-primed PCR method according to Affymetrix recommendations ( Affymetrix Chromatin Immunoprecipitation Assay Protocol; http://www . affymetrix . com/support/technical/manuals . affx ) . Purified DNAs were then fragmented , TdT labeled , and hybridized to the Affymetrix Drosophila genome Tiling Array 1 . 0 ( reverse part no . 520 , 054 ) as described previously [56] . ChIP–array data have been deposited in the GEO database under accession code GSE11006 . Affymetrix CEL files were converted into chromosomal enrichment profiles using the TiMAT2 package ( http://bdtnp . lbl . gov/TiMAT/TiMAT2/ ) . Probe mapping information ( “bpmap” ) to D . melanogaster genome release 4 was obtained from David Nix . Each CEL file was visualised for manual inspection and artefacts were masked using CelMasker . Normalisation was subsequently performed with CelProcessor using default parameters . Enrichment profiles were generated using ScanChip , outputting windowed enrichment signals and Wilcoxon Rank Sum scores . The . sgr files are provided in Datasets S2–5 ) . We classified enrichment events into positive , intermediate and negative based on visual inspection in the Integrated Genome Browser ( http://www . affymetrix . com/support/ developer/tools/download_igb . affx ) . We found that our manual classification could be automated using basic descriptive statistics . Positive bound regions ( “peaks” ) were characterised by enrichment values greater than an experiment-specific cutoff as well as a Wilcoxon Rank Sum score greater than 55 . Intermediate regions were score-independent but showed an enrichment value greater than 50% of the experiment-specific cutoff . Negative regions accounted for all regions that did not fulfil these criteria . The experiment-specific cutoffs were empirically determined as the signal average plus three standard deviations for Pc ( log2 enrichment ratio of 0 . 39 for the embryo and 0 . 77 for the T3 disc material ) or five standard deviations for Pho ( log2 enrichment ratio of 0 . 62 for the embryo and 0 . 80 for the T3 disc material ) . For the S2 Pc data of [10] , we followed a similar classification with positive regions having enrichments greater than the signal average plus three standard deviations and negative regions showing ratios of less than 50% of this value . We assigned each binding event to a target gene , based on complete or partial overlap with a gene model . Binding events that did not overlap with a gene model were assigned to the nearest gene . In most cases for Pc , this concerned bound regions that represented extensions of larger domains overlapping with the gene . We selected the 150 strongest Pho peaks that overlapped with Pc binding and generated two different search sets comprising 200 nt or 700 nt of sequence around the centre of the peaks . We used NestedMICA [57] to search for statistically overrepresented sequence motifs in the search set . A first round of searches was performed with NestedMICA v0 . 72 , specifying expected motif length and usage frequency . A second search was performed with NestedMICA v0 . 8 using default usage frequency and dynamic motif length . Both searches aimed to identify 10–15 overrepresented motifs . Candidate motifs were visually inspected in MotifExplorer and a set of promising candidates resembling Pho- , GAGA- or Zeste-like motifs as well as some with high information content were chosen for further analysis . Statistical overrepresentation of motifs was determined by comparing the set of all Pho peak sequences to 1 , 000 sets of random sequences of the same length , representing regions of the Drosophila genome that are not bound by Pc . A Z-score was derived , incorporating the number of occurrences in real peaks and the numbers observed for the 1 , 000 random sets . All downstream analyses were performed with custom-made Perl scripts . Clustering and visualisation was performed with Genesis v1 . 6 [58] . Binding sites in the sequence context were visualised in BioSAVE [59] . Enrichment of Gene Ontology terms was determined with the GeneMerge 1 . 2 software tool , comparing enrichment in specific lists with all Drosophila genes . Gene Association files used were March 2007 release of the Gene Ontology . The enrichments quoted in the text are corrected for multiple testing by applying a modified Bonferroni method within the Gene Merge algorithm . Enrichments with e-scores better than 0 . 05 are called significant . Tissue expression analysis used the data from FlyAtlas [39] with clustering and visualisation using Cluster [60] and Java Treeview [61] . OregonR embryos ( 0–20 hr ) were dechorionated with bleach , then divided into aliquots , placed directly in Trizol and stored at –80°C . Homogenisation and RNA extraction were carried out according to the following protocol: http://www . flychip . org . uk/protocols/gene_expression/standard_extraction . php . T3 leg and haltere discs were dissected from wandering 3rd instar OregonR larvae in PBS . Each pair was transferred in a small drop of PBS directly into 100 µl Trizol and frozen immediately . For RNA extraction , 7 disc pairs were randomly pooled for each of 3 samples and RNA extracted as above . RNA samples were treated with RQ1 DNase to remove any genomic DNA . cDNA synthesis was performed by combining 10 µg DNase treated RNA with 500 ng anchored oligo ( dT ) 23 primer ( Sigma; Cat . No . 04387 ) , 1 µl of 10 mM dNTPs , DEPC treated H2O to 13 µl . The reaction was heated to 65°C for 5 min then chilled on ice for 1 min . 4 µl of 5x First Strand Buffer ( Invitrogen ) , 1 µl 0 . 1 M DTT ( Invitrogen ) , 1 µl RNAsin ( Promega; Cat . No . 18064-014 ) and 1 µl Superscript III Reverse Transcriptase ( Invitrogen; Cat . No . 18080-044 ) were added . Reactions were incubated at 50°C for 60 min and inactivated at 70°C for 15 min . 0 . 5 µl of the resulting cDNA was used in PCR reactions with the following primers: hb-F ggcctcttcgttcacatgg , hb-R agcggcttaattggcttatg , ind-F aacgattatgccgattccag , ind-R gattgaaggtgggactttcg , vnd-F cgacgagatgtcctcgtacc , vnd-R ctcttgtaatcgccggaaag , fd59A-F ttcagtcaccgcacaagaag , fd59A-R gtccagaagttgccctttcc , run-F agtccttcacgctgaccatc , run-R gtagtccgcatagccgtagg , tll-F tacaacagcgtgcgtctttc , tll-R ttgtccaccacacagagtcc , Rp49_F catacaggcccaagatcg , Rp49_R tgggcatcagatactgtcc . The Flybase ( http://flybase . bio . indiana . edu ) accession numbers of the genes and gene products discussed in this paper are: abdominal-A ( abd-A ) , FBgn0000014; Abdominal-B ( Abd-B ) , FBgn0000015; Antennapedia ( Antp ) , FBgn0000095; bagpipe ( bap ) , FBgn0004862; biniou ( bin ) , FBgn0045759; brahma ( brm ) , FBgn0000212; Centrosomal protein 190kD ( Cp190 ) , FBgn0000283; Cyclin A ( CycA ) , FBgn0000404; Deformed ( Dfd ) , FBgn0000439; Distal-less ( Dll ) , FBgn0000157; dRing ( Sce ) , FBgn0003330; GAGA factor ( Trl ) , FBgn0013263; GATAe , FBgn0038391; gcm2 , FBgn0019809; glial cells missing ( gcm ) , FBgn0014179; grainy head ( grh ) , FBgn0004586; hedgehog ( hh ) , FBgn0004644; hunchback ( hb ) , FBgn0001180; intermediate neuroblasts defective ( ind ) , FBgn0025776; labial ( lab ) , FBgn0002522; lozenge ( lz ) , FBgn0002576; PDGF- and VEGF-receptor related ( Pvr ) , FBgn0032006; pleiohomeotic ( pho ) , FBgn0002521; pleiohomeotic like ( phol ) , FBgn0035997; Polycomb ( Pc ) , FBgn0003042; polyhomeotic distal ( ph-d ) , FBgn0004860; polyhomeotic proximal ( ph-p ) , FBgn0004861; Posterior sex combs ( Psc ) , FBgn0005624; proboscipedia ( pb ) , FBgn0051481; runt ( run ) , FBgn0003300; Scm-related gene containing four mbt domains ( Sfmbt ) , FBgn0032475; serpent ( srp ) , FBgn0003507; Sex combs reduced ( Scr ) , FBgn0003339; suppressor of Hairy wing ( su ( Hw ) ) , FBgn0003567; tailless ( tll ) , FBgn0003720; teashirt ( tsh ) , FBgn0003866; trithorax ( trx ) , FBgn0003862; Ultrabithorax ( Ubx ) , FBgn0003944; u-shaped ( ush ) , FBgn0003963; ventral nervous system defective ( vnd ) , FBgn0003986; vestigial ( vg ) , FBgn0003975; and zeste ( z ) , FBgn0004050 . | Cells make fate decisions as they progressively differentiate into specific cell types during development . The stability of these decisions is important and is achieved , in part , by changes to the chromatin that packages DNA in the nucleus . A key set of protein complexes that together constitute the Polycomb-group/Trithorax-group ( PcG/TrxG ) machinery is involved in chromatin modification and is known to operate at a large number of genes involved in developmental decisions . The PcG proteins establish stable gene repression , whereas the TrxG counteract the PcG to enable gene activation . How this PcG/TrxG balance works is not understood . By mapping PcG protein binding to chromatin in vivo , we show , in general , a relatively constant association of PcG protein at target genes during development . However , we also find changes in binding at specific genes . While some of these changes are consistent with a loss of PcG proteins associated with gene expression , we also find examples where PcG proteins are present at active genes and not present at inactive genes . Our analysis supports the idea that simply the presence of PcG proteins at a target gene does not necessarily result in gene repression and suggests a more dynamic balance between PcG protein binding and gene expression . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"developmental",
"biology/molecular",
"development",
"genetics",
"and",
"genomics/epigenetics",
"molecular",
"biology",
"genetics",
"and",
"genomics",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
] | 2008 | Stability and Dynamics of Polycomb Target Sites in Drosophila Development |
Food shortage was associated with leprosy in two recent studies investigating the relation between socioeconomic factors and leprosy . Inadequate intake of nutrients due to food shortage may affect the immune system and influence the progression of infection to clinical leprosy . We aimed to identify possible differences in dietary intake between recently diagnosed leprosy patients and control subjects . In a leprosy endemic area of Bangladesh , newly diagnosed leprosy patients and control subjects were interviewed about their socioeconomic situation , health and diet . Dietary intakes were recorded with a 24-hour recall , from which a Dietary Diversity Score ( DDS ) was calculated . Body Mass Index ( BMI ) was calculated and Household Food Insecurity Access Scale ( HFIAS ) was filled out for every participant . Using logistic regression , a univariate , block wise multivariate , and an integrated analysis were carried out . 52 leprosy cases and 100 control subjects were included . Food shortage was more common , dietary diversity was lower and household food insecurity was higher in the patient group . Patients consumed significantly less items from the DDS food groups ‘Meat and fish’ and ‘Other fruits and vegetables . ’ Lower food expenditure per capita , lower BMI , lower DDS and absence of household food stocks are the main factors associated with an increased risk of having leprosy . Low income families have only little money to spend on food and consequently have a low intake of highly nutritious non-rice foods such as meat , fish , milk , eggs , fruits and vegetables . Development of clinical leprosy could be explained by deficiencies of the nutrients that these foods normally provide .
Leprosy , an infectious disease caused by Mycobacterium leprae , affects skin and nerves and can lead to deformities of the hands , feet and face . The disease remains a public health problem in underdeveloped areas in the world , and is therefore known as a disease of the poor . Our understanding of risk factors for the transmission of M . leprae and the development of leprosy disease is not complete . One of the reasons is the long incubation period , which is on average 2–5 years . It is therefore difficult to investigate causal relationships between circumstances at the time of infection and the onset of clinical symptoms years later . The most important known determinant for contracting leprosy is being a household contact of a leprosy patient , which carries a five to eight times higher risk of contracting leprosy [1 , 2] . The specific risk factors that determine the risk for contacts include the Ridley-Jopling leprosy classification of the index patient , physical distance to the patient and age of the contact [3] . However , in endemic regions the majority of new leprosy patients are not close contacts of a known leprosy case [4 , 5] . Another possible risk factor is poverty , although not all poor countries have high leprosy prevalence rates . It is even the case that Brazil , an economically emerging country , has one of the highest new case detection rates in the world [6] . It remains unclear which aspects of poverty are associated with leprosy susceptibility and the progression to clinically detectable disease . Two recent case-control studies investigating socioeconomic factors in relation to leprosy found that food shortage was associated with leprosy . The setting of one of these studies was a poor , high leprosy endemic area in Brazil [7] . Among other factors , having experienced food shortage at any time in life was related to leprosy . The other study was set in two leprosy endemic districts in Bangladesh . In this study , food shortage in the past year was the only factor significantly associated with the clinical manifestation of leprosy [8] . The definition of food shortage used in the Bangladesh study was: ‘the period in which a family had to reduce the number of meals a day , or reduce the intake of foods other than rice’ . This is most likely to occur when rice prices are high , household food stocks depleted and income is low due to lack of labor opportunities . Multiple studies in Bangladesh documented that this situation typically arises in the period between September and the end of November , just before the major harvest Aman in December [9–12] . Food shortage worsens the often already inadequate intake of micro- and macronutrients . Nutritional deficiencies impair the immune system and thus the defense of the body against infections [13 , 14] . The risk of contracting subclinical M . leprae infection is not necessarily increased by food shortage , but it could facilitate the progression from infection to the clinical presentation of leprosy . The findings of the two above-mentioned studies raise many questions regarding the exact mechanisms behind the relationship between food shortage and leprosy . Literature on this subject is scarce , and to further examine this relationship we designed a case-control study in rural Bangladesh during an expected period of food shortage . The aim was to assess possible differences in dietary intake between recently diagnosed leprosy patients and control subjects without the disease that could lead to hypotheses on immunological mechanisms underlying the clinical development of leprosy .
This case-control study was conducted in October and November 2013 in northwest Bangladesh , in the mainly rural and agricultural Nilphamari and Rangpur districts . These are among the poorest regions in Bangladesh [15] , and leprosy is still endemic in this area . In 2012 , 512 new leprosy cases were found in these districts , which have a total population of four million ( data from the Rural Health Program , Nilphamari ) . Data of all patients diagnosed with leprosy in the first half of 2013 were gathered from the Rural Health Program database in Nilphamari , which is run by The Leprosy Mission International , Bangladesh ( TLMIB ) . Our aim was to include an equal number of men and women , to have an even age distribution and take in only one person ( case or control ) per household . Furthermore , we only included patients aged between 18 and 50 years and with help of field staff we pre-selected patients with a low risk of stigma possibly caused by a home visit . Of the 180 patients , 92 were eligible for this study , 64 were outside the age bracket criterion and 24 were excluded to avoid the risk of stigma of a home visit . Controls were selected from a random cluster sample of the population , originally composed for the COLEP study [16] . Out of the 13 sub-districts in the area , 20 clusters were formed , each containing 1000 randomly selected people [17] . We selected three clusters that could be reached within approximately one hour by motorcycle from the TLMIB Leprosy Center in Nilphamari; two clusters in Nilphamari district , of which one rural and one suburban , and one rural cluster in Rangpur district . From each cluster , 34 controls were randomly selected using a computerized sampling method , with an even distribution of men and women . Controls were excluded if they or a household member had ever been diagnosed with leprosy and if they were under 18 or over 50 years of age . When a control subject was not available at the time of our first visit , we made two more attempts . If the control subject was still not available the third time , a neighbor of similar age was invited to participate . Ethical approval for the study protocol was given by the institutional review board of TLMI Bangladesh , Nilphamari . Informed written consent was obtained from all participants . Data on patients and controls were collected by means of a structured questionnaire , a 24-hour dietary recall and anthropometric measures . The questionnaires for cases and controls were developed in English , translated separately by two translators to Bengali and each of them translated their colleague’s version back to English . The translations were discussed and the questionnaire was optimized . The questionnaire was pre-tested on patients and controls and adjusted where necessary . The questions of the Household Food Insecurity Access Scale ( HFIAS ) were kindly provided in Bengali by the International Centre for Diarrheal Disease Research , Bangladesh ( ICDDR , B ) . Data were collected during household visits by two staff members of the TLMIB Nilphamari Training Center , both were trained field workers fluent in Bengali and English . The questionnaire focused on demographic , socioeconomic and health data of the subjects and their households . The questions dealt with the occupation of the income generator , household size ( defined as the number of people eating in the house ) , average income and income variation , self-classification on a poverty scale ( very poor to rich ) , land ownership , food expenditure , any health problems other than leprosy in the past year and the presence of a BCG scar . If a patient’s income had changed since the diagnosis of leprosy , the pre-diagnosis income was used in the analyses . Income and food expenditure were then calculated per capita . Second , the HFIAS was administered [18] . This validated tool monitors problems with food access , dietary modifications and concerns about food insecurity over the past four weeks . Finally , subjects were asked questions about their history of food shortage , their household food stocks , and details of their coping mechanisms such as reducing the number or variety of meals . For the sake of comparability , these questions and the definition of food shortage were based on the study of Feenstra et al . [8] . Dietary intakes were assessed by a 24-hour recall , following the Food and Agriculture Organization ( FAO ) guidelines for measuring individual dietary diversity [19] . Subjects were asked to list the foods consumed during the previous day , starting from waking up in the morning . Details of all meals and snacks , consumed inside and outside the house during the full day , were recorded in chronological order . To be as accurate as possible , subjects were prompted about drinks , snacks and food consumed in and outside the house . Recipes of mixed meals were obtained to ascertain that all ingredients were recorded . The 24-hour recalls were carried out on weekdays , with exception of atypical holidays . Therefore , no interviews were held in the week after Eid al-Adha ( Festival of Sacrifice ) . Also , two focus group discussions were held with women to gather information on commonly used ingredients and preparation methods . Weight of subjects was measured using a portable scale and assessed to the nearest 0 . 5 kg . Subjects were asked to remove shoes . Height was measured with a measuring tape while the subject was standing barefooted with his/her back straight against a wall . Body mass index ( BMI ) was calculated as weight ( kg ) / height2 ( m ) . Subjects were identified as underweight when their BMI was lower than 18 . 5 kg/m2 . From the 24-hour recalls , the Dietary Diversity Score ( DDS ) was calculated [19] . The DDS is a simple count of the food groups consumed by the subject , and is increasingly used to measure dietary quality [20 , 21] . Nine food groups were included in this study: ‘Starchy staples’ , ‘Dark green leafy vegetables’ , ‘Other vitamin A rich fruits and vegetables’ , ‘Other fruits and vegetables’ , ‘Organ meat’ , ‘Meat and fish’ , ‘Eggs’ , ‘Legumes , nuts and seeds’ and ‘Milk and milk products’ . Vitamin A rich fruits and vegetables were defined as containing a minimum of 60 RAE/100 g ( RAE stands for retinol activity equivalents ) [19] . The condiments garlic and chilies were not counted , because the amount consumed per person was likely to be very low . The possible score ranged from 0 to 9 . A score ≥5 was considered as adequately diverse [22] . Before statistical analysis , a framework was built using four blocks comprised of several related variables: Demographic factors ( age , sex , religion , district , and household size ) ; socioeconomic factors ( income , food expenditure , poverty classification , occupation , and land ownership ) ; health factors ( diseased in the last year , BCG scar , and BMI ) ; and diet-related factors ( HFIAS , DDS , food shortage in the past year , food shortage at any time in life , and household food stocks ) . Statistical analyses were performed using SPSS ( version 22 , SPSS Inc . , Chicago , IL ) . All analyses were done using logistic regression , with case/control as dependent variable . Income and food expenditure were log transformed to normalize their distribution . To reduce the effect of matching , age and sex were continuously adjusted for in the univariate , multivariate per block and integrated analyses . Univariate analysis was carried out first , and the variables significantly ( p<0 . 10 ) associated with leprosy were included in a multivariate backward stepwise logistic regression per block . The variables that remained statistically significant ( p ≤0 . 05 ) in these multivariate analyses were considered as the main result . Finally , for the integrated analysis , the significant variables of each block ( p<0 . 10 in the Wald Chi Square test ) were combined and again backward stepwise logistic regression was carried out , this time using a p-value of <0 . 05 as statistically significantly contributing to the model .
Both religion and household size were significantly related to leprosy in the demographic block ( p<0 . 10 ) . Hindus were two times more likely to be in the patient group . A larger household was associated with a lower risk on leprosy . All variables in this block showed a significant association with leprosy in the univariate analysis . A higher income , land ownership , working as a farmer or a businessman and being part of a low/middle or middle income classified household lead to a decreased risk of leprosy . In the multivariate analysis of this socioeconomic block , step by step income per capita , land ownership , and self-classification were removed . The factors that remained significant were food expenditure per capita ( p<0 . 05 ) and occupation ( p<0 . 10 ) . BCG vaccination coverage was almost similar in both groups , and therefore did not show a relation to leprosy . BMI was the only significant factor in the uni- and multivariate analysis ( p<0 . 05 ) ; a lower BMI increased the risk of leprosy . In the univariate analysis , higher HFIAS , food shortage experienced in the past year and at any time in life were significantly associated with an increased risk of leprosy , while higher DDS and household food stocks reduced the chance of having leprosy . In the multivariate analysis of this block , only DDS and household food stocks remained significant ( p<0 . 05 ) . In addition to the analyses per block , we considered relationships between the blocks . Therefore , all significant factors from each of the four blocks were analyzed together in a final integrated analysis , presented in Table 4 . After stepwise elimination of the least significant factors , two factors remained significantly associated with leprosy: food expenditure ( log ) and household size ( p ≤0 . 05 ) .
Traditionally , a Bengali diet consists mainly of rice . Previous studies found that people from Bangladesh get between 74% and 86% of their energy intake from rice [12 , 22 , 24] . This suggests that consumption of nutritious non-rice foods is relatively low , which may explain the low DDSs found in our study . These studies demonstrated that the amount of rice a person consumes remains stable during the year , independent of rice price and season [12 , 25] . As a result , in the period September-November , when rice prices are high and income is low , expenditures on highly nutritious , generally more expensive , food products are likely to be lower . Consequently , dietary diversity scores are lower during these periods . To our knowledge , this is the first published study investigating the DDS in leprosy patients , and therefore there is no data to compare our results with . However , dietary diversity studies carried out among Bangladeshi women found a mean DDS of 3 . 6 ±1 . 1 and 3 . 4 ±1 . 1 , which are very close to our findings of 3 . 2 ±1 . 1 and 3 . 8 ±1 . 4 for patient and controls , respectively [22 , 26] . For the DDS , the condiments chilies and garlic were not counted , in spite of the fact that they are used in almost all dishes according to the women participating in our focus group discussions . Still , the amounts consumed per person were likely to be very low , and therefore the contribution to the diet and dietary diversity should be negligible [22] . Food expenditure was another important factor associated with leprosy . However , per capita food expenditure and per capita income were highly correlated ( Spearman’s correlation coefficient: 0 . 81 , p<0 . 001 ) and additional analyses demonstrated that income and food expenditure are interchangeable in the block- and integrated analysis . The association with leprosy could therefore be just through poverty in general , which is a risk factor often associated with leprosy [27] . Two studies in Bangladesh have linked food expenditure and income to dietary diversity , a variable that was statistically significantly related to leprosy in our study [24 , 28] . In the study of Thorne-Lyman et al . , household dietary diversity increased with increasing food expenditure , and primarily the intakes of animal source foods and fruits increased strongly with higher food expenditures . Our control population had significantly higher food expenditures , and accordingly had higher intakes of these food groups . The second variable that remained statistically significant in the integrated analysis was household size . A larger household size gives a lower risk on leprosy . In the study of Feenstra et al . , mean household size was larger in the control group as well , although this was not statistically significant . In theory , however , a larger household could increase the risk of leprosy , since it increases the chance of transmission . Nevertheless , in an Indonesia-based study an increased risk was found only for households larger than seven people [29] , while in our study no more than 9 of 152 households counted more than seven people; two ( 3 . 8% ) in the patient group and seven ( 7% ) in the control group . The exact role of malnutrition on susceptibility to leprosy and the development to a clinical stage remains unclear [30 , 31] . A recent review on micronutrients and the immune response in leprosy emphasizes this knowledge gap , as only few studies in this field have been carried out and most of the evidence is derived from other diseases , mainly tuberculosis . Apart from this , previous studies are based either on blood analyses , or focused on diets of ( cured ) leprosy patients after they developed clinical leprosy . In both cases , it is difficult to determine if leprosy is a cause or a consequence of nutritional deficiencies [32–36] . Mycobacterium leprae is an intracellular micro-organism , thus a cell-mediated immune response is important in the defense of the human host [37] . Protein-energy malnutrition , as well as inadequate intake of vitamins and/or minerals are linked to a reduced cell-mediated immunity [38] . With lower intakes of several food groups during food shortage periods , deficiencies may have put leprosy patients in our study at risk for a reduced cell-mediated immunity . Another interesting theory that could possibly apply to leprosy is that of immune reconstitution , a well-known phenomenon in HIV after anti-retroviral therapy [39 , 40] . When the immune response is restored after a period of suppression , the immune system will start to respond to infections present in the body . We hypothesize that , when food intake is improved after a long period of food shortage and nutrient deficiencies , the body may start to respond to M . leprae , causing the development of clinical disease . This study has some limitations . First , data were collected referring to the period after the diagnosis of leprosy , which makes it hard to determine a causal relationship . However , the interviews were held shortly after diagnosis ( maximum of nine months ) and we specifically asked for changes in the patients’ diet and income after diagnosis , to be able to correct for this difference . Second , a cross-sectional design was employed because we aimed to collect data during a food shortage period . We assumed that the persons experiencing food shortage this year have also experienced this in the previous years and that their diets did not change over time . Ideally , a longitudinal study , collecting detailed data on diet and health , and taking blood samples to determine nutrient absorption , should be carried out to compare long-term data of the persons who developed leprosy with data of persons who did not . However , this will be a lengthy and expensive process . A third limitation is that most of the data were self-reported through questionnaires , introducing recall and response biases . Especially for very poor people with an unstable income their average income is difficult to estimate . By asking the same questions to cases and controls we tried to limit the effect on our results . In addition to the 24-hour recall , biomarkers for micro- and macronutrients in blood , urine and/or feces can be analyzed to assess dietary intake more objectively [41] . However , we decided not to use this method because collection and analyses of biomarkers is laborious and costly , especially because a high number of nutrients need to be analyzed since the key nutrients are unknown . Fourth , the DDS might have been over or underestimated as it was based on one 24h-recall , which may not be representative of the usual diet . We tried to get the most reliable information possible by avoiding a recall for atypical days such as religious holidays . Furthermore , in developing countries diets tend to have a low day-to-day variability , thus one 24h-recall may be enough to get a good idea of the usual diet . In addition , when comparing population groups , a single dietary recall should give an accurate estimation of the intake of a whole group [42] . The DDS informs about the last 24 hours , while we extrapolated this to the diet before leprosy was diagnosed . Only few patients indicated that their food intake was different from that of last year , before diagnosis , however . Data of these patients were kept in the analyses , because there was little difference in the numbers of people who consumed more ( 4 patients ) and who consumed less ( 5 patients ) than in the period before they were diagnosed . In conclusion , this study adds to the current knowledge on food shortage , nutrition and leprosy . We found that DDS and household food stocks are the most important diet-related factors associated with leprosy in Bangladesh . Furthermore , BMI and food expenditure per capita have a strong association with leprosy in our study . People living in poverty have less money available to spend on food . This results in a low consumption of animal source foods , fruits and vegetables . Deficiencies of the nutrients that these types of foods provide could result in an impaired immune response , which may be an explanation for the development of clinical leprosy . It is evident that little research has been carried out on the association between leprosy and nutrition , and that the immunological pathway leading to the clinical development of leprosy and the influence of nutrition should be studied further . Our results can be a starting point to elucidate the relation between nutrition and leprosy . | Even though leprosy is one of the oldest diseases known to mankind , there is still a lot unknown about its transmission and why some people develop the disease and others do not . Leprosy is often seen as a disease of the poor , but which aspects of poverty are associated with leprosy are still under study . Recently though , food shortage has been identified as a risk factor for leprosy in two socioeconomic studies . In our study , lead in a poor area of Bangladesh , we further investigated this link by interviewing recently diagnosed leprosy patients during a food shortage period . We found that compared to a control population , leprosy patients have less money to spend on food , have less household food stocks and have a less diverse diet . The patient group had a lower consumption of highly nutritious foods such as meat , fish , eggs , milk , fruits and vegetables . An inadequate diet for a longer period of time leads to nutrient deficiencies . The body’s immune system , however , needs proteins , vitamins and minerals to effectively fight off infections . We conclude that people who are living in poverty and who are not able to get an adequate , diverse diet have a higher chance of developing leprosy . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Diet-Related Risk Factors for Leprosy: A Case-Control Study |
Principal components analysis , PCA , is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background . However , while the method is often used to inform about historical demographic processes , little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes . Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes . The result provides a framework for interpreting PCA projections in terms of underlying processes , including migration , geographical isolation , and admixture . I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples . Using examples from human genetics , I discuss the application of these results to empirical data and the implications for inference .
The distribution of genetic variation across geographical location and ethnic background provides a rich source of information about the historical demographic events and processes experienced by a species . However , while colonization , isolation , migration and admixture all lead to a structuring of genetic variation , in which groups of individuals show greater or lesser relatedness to other groups , making inferences about the nature and timing of such processes is notoriously difficult . There are three key problems . First , there are many different processes that one might want to consider as explanations for patterns of structure in empirical data and efficient inference , even under simple models can be difficult . Second , different processes can lead to similar patterns of structure . For example , equilibrium models of restricted migration can give similar patterns of differentiation to non-equilibrium models of population splitting events ( at least in terms of some data summaries such as Wright's ) . Third , any species is likely to have experienced many different demographic events and processes in its history and their superposition leads to complex patterns of genetic variability . Consequently , while there is a long history of estimating parameters of demographic models from patterns of genetic variation , such models are often highly simplistic and restricted to a subset of possible explanations . An alternative approach to directly fitting models is to use dimension-reduction and data summary techniques to identify key components of the structure within the data in a model-free manner . Perhaps the most widely used technique , and the most important from a historical perspective , is principal components analysis ( PCA ) . Technical descriptions of PCA can be found elsewhere , however , its key feature is that it can be used to project samples onto a series of orthogonal axes , each of which is made up of a linear combination of allelic or genotypic values across SNPs or other types of variant . These axes are chosen such that the projection of samples along the first axis ( or first principal component ) explains the greatest possible variance in the data among all possible axes . Likewise , projection of samples onto the second axis maximizes the variance for all possibles axes perpendicular to the first and so on for the subsequent components . Typically , the positions of samples along the first two or three axes are presented , although methods for obtaining the statistical significance of any given axis have been developed [1] . Beyond being non-parametric , PCA has many attractive properties including computational speed , the ability to identify structure caused by diverse processes and its ability to group or separate samples in a striking visual manner; for example , see [2] . PCA has also become widespread in the analysis of disease-association studies where the inclusion of the locations of samples on a limited number of axes as covariates can be used in an attempt to control for population stratification [3] . Although PCA is explicitly a non-parametric data summary , it is nevertheless attractive to attempt to use the projections to make inferences about underlying events and processes . For example , dispersion of sample projections along a line is thought to be diagnostic of the samples being admixed between the two populations at the ends of the line , though these need not always be present [1] , while correlations between principal components and geographical axes have been interpreted as evidence for waves of migration [4] , [5] . However , while simulation studies have shown that such patterns do occur when the inferred process has acted [1] , [6] , they can also be caused by other processes or even statistical artefacts . For example , clines in principal components result not just from waves of expansion , but also recurrent bottlenecks , admixture and equilibrium models of spatial structure [6]–[11] . In this paper I develop a framework for understanding how PCA relates to underlying processes and events . I show that the expected location of samples on the principal components can , for single nucleotide polymorphism ( SNP ) data , be predicted directly from the pairwise coalescence times between samples . Because it is often relatively easy to obtain analytical or numerical solutions to expected coalescence times under explicit population genetics models , it is also possible to obtain expressions for the PCA projections of samples under diverse scenarios , including island models , models with isolation and founder events and historical admixture . The result also highlights some key limitations of PCA . For example , it follows that PCA cannot be used to distinguish between models that lead to the same mean coalescence times ( for example models with migration or isolation ) . Furthermore , PCA projections are strongly influenced by uneven sampling . Using examples from human genetics I discuss the implications of these results for making inferences from PCA of genetic variation data .
In this section I provide a brief summary of how PCA is carried out and describe the key result concerning the relationship between PCA and average coalescence time . In what follows I assume that haploid individuals have been sequenced with complete accuracy ( diploid samples and the influence of SNP ascertainment will be discussed later ) . The only polymorphisms present are biallelic SNPs that are the result of a single historical mutation . Let be the allelic state for individual i at locus s ( here I assume that the ancestral allele is defined as 0 and the derived allele as 1 , however the following also applies for any coding , for example where the minor allele is coded as 1 ) . After removing monomorphic sites the data , , consist of an binary matrix ( is the number of SNPs ) . In PCA , the first step is to zero-centre the data , so as to create a new matrix , X , where ( 1 ) At this stage , the data rows are often normalized so as to have equal variance , however , it is assumed that this is not the case ( in practice normalization has little effect for SNP data , though will tend to up-weight the influence of rare variants ) . Each individual sample can be thought of as representing a point in L-dimensional space , where each dimension ( or axis ) represents a single SNP . The goal of PCA is to find a new set of orthogonal axes ( the principal components ) , each of which is made up from a linear combination of the original axes , such that the projection of the original data onto these new axes leads to an efficient summary of the structure of the data . More formally , PCA defines a stretch and rotation transformation , expressed through the matrix , such that application of to the original data ( ) leads to transformed data with the following properties . The principal components can be obtained directly by finding the eigenvectors of the covariance matrix ( 2 ) such that the ith principal component ( the ith row of , ) is the ith eigenvector of . However , because ( of dimension ) can be very large for genome-wide SNP data sets , it can be more convenient to use singular value decomposition ( SVD ) to find the principal components and individual projections . SVD , which exists for any real matrix ( where ) rewrites the original data in terms of three other matrices ( 3 ) where is an orthogonal matrix ( i . e . the dot-product between any two columns is zero ) of dimension , is a diagonal matrix of dimension and is another orthogonal matrix of dimension . This is achieved by setting , the ith column of , to be the ith eigenvector of the matrix ( 4 ) , the ith diagonal entry of to be the square root of the corresponding eigenvalue and , the ith column of , to be the vector ( 5 ) PCA and SVD are , through construction , intimately related . Specifically , the projection of samples along the ith principal component is given by ( note this is the ith row of and the ith column of ) and the ith principal component is . For typical population genetics data sets , eigenvalue analysis of the matrix ( of dimension ) is computationally simpler than analysis of the matrix ( typically hundreds or thousands of samples have been genotyped at hundreds of thousands or millions of SNPs ) . The above construction results in the projection of samples on the PCs being influenced by the number of SNPs ( e . g . repeating the analysis on a data set in which every SNP is included twice will lead to projections that are a factor larger than previously ) . To correct for this , consider a slightly different definition of the matrix : ( 6 ) which is equivalent to dividing the data matrix by the square-root of the number of SNPs . It is worth noting that may either be a random variable as in the case of sequencing , or a fixed variable , as in the case of genotyping . Here , it will be treated as a fixed variable , though in practice this is of little importance . is a stochastic matrix . However , it is possible to learn about the key structural features of by considering its expectation . From above , it follows that ( 7a ) ( 7b ) ( 7c ) Assuming that sites are identical in distribution ( though not necessarily independent ) the subscript can be dropped to give ( 8 ) where the terms such as indicate the expectation ( for sample ) is averaged over all individuals in the sample ( note this includes self ) ; i . e . . Because is either 0 or 1 , the four terms in Equation 8 can be thought of as: In the case of a low mutation rate , where polymorphic sites are the result of a single historical mutation , expressions can be obtained for the above quantities in terms of features of the genealogical tree [12]–[14] . Figure 1 shows how the probability of two samples both carrying a mutation depends on their time to a common ancestor relative to the time to the common ancestor of the whole sample . Let be the expected coalescence time for samples and , be the expected time to the most recent common ancestor of the sample , and be the expected total branch length in the tree . The probability that two samples share a derived mutation ( conditional on the site being segregating ) is given by ( 9a ) ( 9b ) ( 9c ) By writing similar expressions for the other terms in Equation 8 it follows that ( 10 ) where and . Note that these expressions include coalescence with self where the coalescence time is always zero; i . e . . In short , the expectation of the matrix whose eigenvectors give the projections of samples on the principal components can be written in terms of the mean coalescence times for pairs of samples . It is worth noting that ( and the related quantities ) can be interpreted either as the expected coalescence time under some model or else the average realized coalescent time across the genome . The difference between these quantities can be important in some settings , such as admixture models ( see below ) . For diploid individuals the genotypic value for an individual at a given SNP is typically given by the sum of the allelic values; i . e . , where the superscripts indicate the two alleles . By following the same argument as above it can be shown that for genotype data ( 11 ) where the superscripts again indicate the relevant allele in each individual . In the following I will assume that data consist of haplotypes , however Equation 11 makes it clear that essentially identical results will hold for genotype data . The implication of Equation 10 is that if the structure of pairwise coalescence times in a given data set can be understood , then the projection of the samples on the principal components can be predicted directly . Two illustrate this idea consider the simple model of a population split shown in Figure 2A . Under this model the expected coalescence time for pairs of samples within either population is 1 ( in units of generations ) and the expected coalescence time for pairs of samples from different populations is , where is the age of the population split ( also in units of generations ) . Suppose of the total sample size , a fraction are from population A . Define and , it follows that for large , has a simple block structure; ( 12 ) where the first rows and columns represent the samples from population A ( here , for example , three samples from A and two from B are shown ) . What will the leading eigenvalue and associated eigenvector be for a matrix with this kind of block structure ? Although it is simple to obtain eigenvectors numerically , it is also worth having some intuition about what they represent . Through the construction of SVD it follows that the leading eigenvector , and eigenvector , , are those that , through Equation 3 , provide the best approximation to the original data in terms of least-squares error . Equivalently , the matrix is the best least-squares approximation to . Intuitively , the original data is well approximated by the average allele frequency in each population and the the block structure of can be recovered by clustering samples from the two populations either side of the origin in . More formally , it can be shown that ( 13a ) ( 13b ) Assuming that , the projection of the samples on the first principal component is given by the vector ( 14 ) Note that the sign of the projections is arbitrary . This result implies that the Euclidean distance between samples from the two populations on the first principal component will be and their position relative to the origin is determined by the relative sample size , with the larger sample lying closer to the origin . Figure 2B shows the expected projection of samples . These results refer explicitly to the expected value of . However , it is also important to know whether stochasticity resulting from the finite size of the genome has a significant effect on the results . Theoretical work on the nature and size of the first principal component in random matrices [15] , [16] has identified a critical signal to noise ratio below which the true structure of the signal cannot be recovered . In the context of a two-population model this equates to being greater than [1] . For example , with a sample size of 100 and , the threshold is 100 SNPs . Simulations were carried out for different numbers of independent SNPs ( Figure 2C ) . As expected , for 10 or 100 SNPs PCA fails to separate samples from the two populations , while for 1 , 000 SNPs or more samples from the two populations are distinct on the first PC and centre around the theoretical expectation . A direct consequence of Equation 10 is that PCA predominantly reflects structure in the expected ( or mean realized coalescent ) time . Consequently , any two demographic models that give the same structure of expected coalescence times will also give the same projections . To illustrate this result , consider a fully general model with two homogeneous populations where the expected coalescence time for two samples from population A is , the expected coalescence time for two samples from population B is and the expected coalescence time for one sample from each population is . Define , and . It can be shown that ( 15 ) Again , only three samples from population A and two from population B are shown . For large , the leading eigenvalue and corresponding eigenvector of the above matrix are respectively ( 16a ) ( 16b ) Consequently , the projection of the samples on the first principal component is given by the vector ( 17 ) Comparison of Equations 14 and 17 shows that the Euclidean distance between samples from the two populations on the first PC is a function of the difference between cross-population and within-population coalescence times and that the positioning of the populations relative to the origin simply reflects their relative sample size ( as for the simpler two-population model ) . Consequently , any two models that give the same value of will give the same expected projections of samples on the first PC . One connection that is worth exploring further is the link between the results shown here and those of Slatkin [12] concerning . Slatkin showed that ( 18 ) where is the average coalescence time for pairs of samples from the same population and is the average coalescence time across all pairs of samples . In the notation used above it can be shown that ( 19 ) Now consider the PCA projection . The variance along the first axis is . The total variance in the sample is . Consequently , the fraction of the total variance explained by the first PC is equal to . Given that is defined as the fraction of the total variance that is explained by between-population differences this result is not surprising . Nevertheless , the result demonstrates a simple relationship between the Euclidean distance of populations in PCA space and , at least in the case of two populations . As has been shown previously [11] , PCA projections can be strongly influenced by uneven sampling from a series of populations . The results described here provide an explanation . First , from Equation 10 it can be seen that the the matrix is influenced by the relative sample size from each population through the components . For instance , even if all populations are equally divergent from each other , those for which there are fewer samples will have larger values of because relatively more pairwise comparisons are between populations . Second , even if the entries of were not influenced by the relative sample size , its eigenvectors will be , simply because relative sample size will influence the structure of the genetic variance in the sample ( see Figure 2 ) . The influence of uneven sample size can be to bias the projection of samples on the first few PCs in unexpected ways , for example , where there is spatial structure to genetic variation . Consider a lattice arrangement of populations with equal migration between neighbouring populations . For this arrangement it is possible to obtain analytical expressions for the expected coalescence time for pairs of samples from the different populations ( results not shown ) and hence the matrix ( up to an unknown scaling factor ) and subsequently the projection of samples on the first few PCs under different assumptions about sample size and migration rate . If sample sizes from the different populations are equal , the spatial arrangement of the populations on the first two PCs mimics the structure of the migration matrix ( Figure 3A ) . However , sample sizes differ between populations the effect is to distort the projection space ( Figure 3B and 3C ) . This distortion of PC-space relative to the structure of the migration matrix is problematic for interpreting the location of samples on PCs . Sub-sampling from populations to achieve more equal representation , as in [2] , is the only way to avoid this problem . The principal components identified through PCA can be used to project not just those samples from which the PCs were obtained , but also additional samples . The appeal of such analyses is that it enables the analysis of structural features identified in one data set to be transferred to another . For example , where data from two source populations and a set of possibly admixed samples are available , projection of the admixed samples onto the axes defined by the source populations can identify the extent of mixed ancestry . The advantage of this approach rather than simply performing PCA on all samples together is that other structural features within the admixed samples ( e . g . admixture from a third population or relatedness ) will have little influence on the projection . In the light of the above results showing how the PCA projection of samples can be interpreted in terms of coalescence times , it is interesting to ask how the the projection of additional samples onto the same axes also relates to coalescence times . Consider the case of the general two-population model where the positions of the samples on the first PC are for samples from population A and for samples from population B . The first PC can be obtained as in Equation 5 . For a given SNP , , the expected loading for the first PC , , is therefore ( 20a ) ( 20b ) ( 20c ) where is the number of samples carrying the derived allele in population A . By writing and , such that and are the frequencies of the derived alleles in populations A and B respectively , it follows that ( 21 ) The expected location of an additional sample , , on the first PC is therefore ( 22a ) ( 22b ) ( 22c ) where ( note this does not include the additional sample ) . Again , the subscript has been dropped by assuming that sites are identical in distribution . By noting that , where the expectation is over those samples from population A , it follows that similar arguments to those above can be made to relate the quantities in Equation 22 to coalescent times . Define as the average coalescent time between the additional sample and all samples from population A and to be the equivalent for population B , it can be shown that ( 23 ) An important implication of Equation 23 is that if the additional sample is the result of an admixture event between the two populations with a fraction of its genome coming from population A then it follows that the location of the sample on the first PC is ( 24 ) In words , the admixture proportion of the individual can be directly inferred from their relative position along the first PC from the two source populations . There are three important points to note when applying this result . First , only if the admixture event was very recent are the source populations likely to be available . Rather samples may be available for descendants of these source populations . Consequently , the average divergence between the population A part of an individuals genome and other samples from population A might typically be greater than for two samples taken directly from population A . However , this effect is likely to be very similar for the two source populations and , given Equation 23 , these effects largely cancel out . The second point to note is that if samples are admixed between more than two populations , the result generalises so that an individual whose genome is derived from several source populations will have a projected position ( along each significant PC ) defined by the weighted sum of the positions of its source populations . Informally , the result arises because of the linearity in Equation 22 . Those parts of the genome with ancestry from a given population will have a PC projection that matches samples taken directly from the source population . If there is mixed ancestry , the effect is simply to average the PC projections . Finally , it is important to note that projection of non-admixed individuals can also lead to their location being intermediate between the two original populations . For example , samples from a third population that either diverged from population A since the split with population B or that come from a population that diverged before the A/B split will ( in both cases ) be projected between the locations of samples from populations A and B . It may , however , be possible to distinguish between such cases by carrying out PCA on all data combined . As has already been shown through simulation [1] , PCA carried out on samples that are the result of admixture events can identify admixed samples as lying along the axes between the two or more source populations , even if one or more of the source populations are absent . The results above shed some light onto when such analyses are expected to work and when they will fail . Consider a sample of individuals who are the result of an historical admixture event between two populations A and B . In order to define the matrix for this sample it is necessary to know which part of their genome is derived from each of the source populations . Let be a series of indicator functions for each of the SNPs in individual that takes value 1 if that part of the individual's genome was derived from population A and 0 if it was derived from population B . The value can be obtained by comparing the value of and at each position and adding up the relevant contribution from each of , and . Note that here the achieved ancestry proportions are being used rather than their expectation under some model ( which might be the same for all samples ) . Given these considerations there are two situations under which none of the structure between the two source populations is expected to be reflected in the matrix . First , all individuals could have the same vector , which could occur if the admixture event were ancient and involved relatively few individuals such that the source population at every point in the genome were fixed ( note this does not mean that there is no variation , simply that all individuals at this location have an ancestry from the same population ) . Second , individuals have different ancestry vectors , but the average value is the same for all individuals and the admixture chunks have been sufficiently broken up through historical recombination such that everyone is equally related to everyone else . Again , this scenario could occur if the admixture result were ancient . Note that all individuals having the same average ancestry proportions is , by itself , not sufficient to create this problem . To examine the rate at which admixture signal is lost , an admixed population was simulated forward in time and the projections of samples on the first PC were followed , along with the correlation between PC projection and individual ancestry . As shown in Figure 4 , in which the population is chosen to have parameters comparable to humans , the initially strong correlation between ancestry proportion and location on the first PC is rapidly lost such that after only 15 generations there is essentially no signal remaining , even though locally within the genome admixture chunks are still very clear ( i . e . there is still admixture LD ) after 50 generations .
The primary result of this paper is that the locations of samples on the principal components identified from genome-wide data on genetic variation can be predicted from an understanding of the average coalescent time for pairs of samples . This gives a direct route to understanding the influence various demographic scenarios can have on the relationships between samples identified from PCA and how PCA can be used to make inference about processes of interest such as admixture . However , the results also demonstrate the way in which sampling schemes can influence PC projections and how similar projections can arise from very different demographic scenarios . Consequently , using these results to motivate inference from PCA about underlying demographic process may prove difficult . There are , however , situations in which PCA can be used to infer demographic parameters directly . For example , in cases of simple two- or three-way admixture , where populations close to the source populations can be identified and sampled from , estimation of admixture proportions can be achieved from projecting samples onto the PCs identified from the source populations . To illustrate this , Figure 5 shows the inferred ancestry proportions for a set of haplotypes ( estimated from trio data ) in 20 African Americans collected as part of the HapMap3 project . In this analysis , haplotypes ( also inferred from trios ) from the European ancestry population in Utah ( CEU ) and the Yoruba in Nigeria ( YRI ) are used to represent the source populations ( note , as discussed above , the requirement is not that these are the source populations , simply that they are closely related to the source populations ) . By analysing each chromosome separately it can be shown that while each individual's average ancestry proportion across the genome is fairly constant ( typically 70–90% African ) , there is considerable variation at the level of individual chromosomes , with some chromosomes appearing essentially European ( for some individuals ) and others essentially African ( no chromosome shows an overall tendency to come from one population ) . Such information could be informative about processes such as the level of assortative mating and the rate of ongoing admixture . One important issue in the application of these ideas to the analysis of empirical data is the extent to which SNP ascertainment will influence outcome . SNP discovery in a small panel will typically lead to the under-representation of rare SNPs in the genotyped data and , depending on the geographical distribution of the samples used for discovery , can also lead to biases in the representation of variation from different areas . The quantities in Equation 8 are therefore conditional not just on segregation in the genotyped sample , but also on segregation within the SNP discovery panel . Consider the joint genealogy of the genotyped and discovery samples shown in Figure 6A . The probability that a pair of samples , and share a derived mutation ( in the genotyped samples ) that also lies on the subtree of the discovery samples , is ( 25 ) where is the first time at which the common ancestor of the samples and is also a common ancestor of at least one of the discovery panel samples ( ) , is the time to the more recent of the discovery or sample MRCAs and is the total time of the intersection between the discovery and genotyped samples' genealogies ( Figure 6A ) . It follows that the equivalent expression for Equation 10 with SNP ascertainment will typically be larger than without SNP ascertainment because whereas the differences in the numerators will largely cancel each other out . Consequently , it is expected that , except for very strongly biased SNP discovery ( e . g . a sample of two from one of a series of very divergent populations ) , that PCA projections from genotype data will be similar to PCA projections from resequencing data , but will typically be larger in magnitude ( if the matrix is normalized by the number of SNPs ) by a factor ; a result confirmed by simulation ( Figure 6B and 6C ) . For the example shown , this result holds even under the most extreme ascertainment scheme of two discovery samples from a single population . In short , SNP ascertainment will tend to have a simple and predictable effect on PC projections that has little influence on the relative placing of samples . Finally , it is worth pointing out that because PCA effectively summarizes structure in the matrix of average pairwise coalescent times , but in a manner that is influenced by sample composition , more direct inferences can potentially be made from the matrix of pairwise differences ( which are trivially related to pairwise coalescent times ) . This is not to say that eigenvalue analysis of the pairwise distance matrix will correct for the effects of biased sampling demonstrated in Figure 3 . However , while readily-available alternatives to PCA , such as multidimensional scaling , seem to have properties similar to PCA , it is possible to envisage non-parametric methods for analysing the matrix of pairwise differences that identify structure without being influenced by sample size .
Coalescent simulations were carried out using scripts written by the author in the R language ( www . r-project . org ) and available on request . Principal component analysis of simulated data was carried out using the R function eigen . Phased haplotypes from the International HapMap Project ( HapMap3 release 2 ) were used in the analysis of the CEU , YRI and ASW population ( see ftp://ftp . hapmap . org/hapmap/phasing/2009-02_phaseIII/HapMap3_r2/ ) . | Genetic variation in natural populations typically demonstrates structure arising from diverse processes including geographical isolation , founder events , migration , and admixture . One technique commonly used to uncover such structure is principal components analysis , which identifies the primary axes of variation in data and projects the samples onto these axes in a graphically appealing and intuitive manner . However , as the method is non-parametric , it can be hard to relate PCA to underlying process . Here , I show that the underlying genealogical history of the samples can be related directly to the PC projection . The result is useful because it is straightforward to predict the effects of different demographic processes on the sample genealogy . However , the result also reveals the limitations of PCA , in that multiple processes can give the same projections , it is strongly influenced by uneven sampling , and it discards important information in the spatial structure of genetic variation along chromosomes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"evolutionary",
"biology/human",
"evolution",
"genetics",
"and",
"genomics/population",
"genetics"
] | 2009 | A Genealogical Interpretation of Principal Components Analysis |
Buruli Ulcer ( BU ) is a neglected , necrotizing skin disease caused by Mycobacterium ulcerans . Currently , there is no vaccine against M . ulcerans infection . Although the World Health Organization recommends a combination of rifampicin and streptomycin for the treatment of BU , clinical management of advanced stages is still based on the surgical resection of infected skin . The use of bacteriophages for the control of bacterial infections has been considered as an alternative or to be used in association with antibiotherapy . Additionally , the mycobacteriophage D29 has previously been shown to display lytic activity against M . ulcerans isolates . We used the mouse footpad model of M . ulcerans infection to evaluate the therapeutic efficacy of treatment with mycobacteriophage D29 . Analyses of macroscopic lesions , bacterial burdens , histology and cytokine production were performed in both M . ulcerans-infected footpads and draining lymph nodes ( DLN ) . We have demonstrated that a single subcutaneous injection of the mycobacteriophage D29 , administered 33 days after bacterial challenge , was sufficient to decrease pathology and to prevent ulceration . This protection resulted in a significant reduction of M . ulcerans numbers accompanied by an increase of cytokine levels ( including IFN-γ ) , both in footpads and DLN . Additionally , mycobacteriophage D29 treatment induced a cellular infiltrate of a lymphocytic/macrophagic profile . Our observations demonstrate the potential of phage therapy against M . ulcerans infection , paving the way for future studies aiming at the development of novel phage-related therapeutic approaches against BU .
Buruli Ulcer ( BU ) , caused by Mycobacterium ulcerans , is an emerging , devastating skin disease reported in more than 30 countries , mainly in West Africa [1] , [2] . BU is characterized by different clinical forms , including nonulcerative subcutaneous nodules , papules , edema , and plaques that can progress to necrotic ulcerative forms . The pathogenesis of BU is associated with mycolactone , a lipidic exotoxin presenting cytotoxic and immunosuppressive properties [3]–[7] . Prevention is difficult as little is known about disease transmission , although it has been shown that M . ulcerans is an environmental pathogen [8]–[10] , and no vaccine is available . Since 2004 , the World Health Organization ( WHO ) recommends the treatment of BU with a combination of rifampicin and streptomycin ( RS ) [11] . Nevertheless , this treatment presents several limitations: ( i ) it does not resolve extensive lesions ( as a result , surgery is the only alternative ) [12]; ( ii ) the long period of administration of streptomycin by muscular injection demands skilled personnel; ( iii ) it is associated with adverse side effects [13] , [14] leading to poor compliance; and ( iv ) importantly , it may lead to the occurrence of paradoxical reactions associated with the worsening of the lesion and/or the appearance of new lesions [14]–[18] . Bacteriophages ( phages ) have been proposed to treat human bacterial infections since their discovery in the early 20th century [19] . Several well controlled studies in both animal models and human infections have successfully applied phage therapy to several types of bacterial infections , demonstrating its potential as an antibacterial therapy in vivo [20]–[30] Additionally , in the UK , the first phase II clinical trial performed under European regulations on phage treatment of chronic otitis has open the door for novel phage-based human applications [31] . Phage therapy presents several potential advantages for the treatment of BU patients , namely phages present lytic activity against extracellular bacteria which predominate in advanced lesions; phages may be used for the treatment of ulcerative lesions where the necrotic infection site would be accessible; and phages may be administered topically [28] . In the present study , following the screening of the lytic activity of several mycobacteriophages , the therapeutic effect of the selected mycobacteriophage D29 was evaluated against M . ulcerans in the mouse footpad model of infection . The progression of macroscopic/microscopic pathology and bacterial load , as well as the cytokine profile , in both the footpad and the draining lymph node ( DLN ) , were evaluated after mycobacteriophage D29 administration .
Mycobacteriophages , kindly provided by Dr . Graham F . Hatfull from the Pittsburgh Bacteriophage Institute and Department of Biological Sciences , University of Pitsburgh , were screened against M . ulcerans isolates . In order to select mycobacteriophages active against M . ulcerans strains , we first selected representative isolates of M . ulcerans from endemic BU areas , based on their genetic and phenotypic characteristics , including the type of mycolactone produced [3] , [32] , [33] and their virulence in mice [7] , [34] ( see Table 1 ) . The strains were obtained from the collection of the Institute of Tropical Medicine ( ITM ) , Antwerp , Belgium . This host-range determination was done by adapting a spot-test technique described elsewhere [35] , [36] . Briefly , M . ulcerans was grown to an OD600 of 1 . 0 and clumps were dispersed by passing the bacterial suspension several times through a 25-gauge needle . The suspension was plated on Middlebrook 7H9 agar medium ( Becton , Dickinson and Company ) . For each mycobacteriophage , serial dilutions were prepared in phage buffer ( MPB ) ( 10 mM Tris , pH 7 . 5 , 1 mM MgSO4 , 70 mM NaCl ) and were plated onto the M . ulcerans lawn and the spots were allowed to dry completely . Plates were incubated at 32°C for approximately 6–8 weeks . A total of 120 ( per experience ) eight-week-old female BALB/c mice were obtained from Charles River ( Barcelona , Spain ) and were housed under specific-pathogen-free conditions with food and water ad libitum . M . ulcerans 1615 is a mycolactone A/B producing strain isolated in Malaysia from an ulcerative case [7] . The isolate was grown on Middlebrook 7H9 agar medium at 32°C for approximately 6–8 weeks . For the preparation of inoculum , M . ulcerans was recovered , diluted in phosphate-buffered saline ( PBS ) and vortexed using glass beads . The number of acid-fast bacilli ( AFB ) in inocula were determined as described previously using Ziehl-Neelsen ( ZN ) staining [37] . Mice were infected in the left hind footpad with 0 . 03 ml of M . ulcerans suspension containing 5 . 5 log10 AFB . D29 particles were propagated in Mycobacterium smegmatis mc2155 ( ATCC ) , as described elsewhere [36] . In brief , approximately 105 phage particles and 250 µl of M . smegmatis mc2155 ( ATCC ) ( OD600 of 1 . 0 ) were plated on Middlebrook 7H9 overlays ( 0 . 6% agar ) and incubated at 37°C overnight . Phage particles were extracted with 3 ml of MPB and harvested filtering through a 0 . 2 µm pore-size filter . Phages were concentrated through polyethylene glycol ( PEG ) precipitation and purified using a CsCl equilibrium density gradient centrifugation . Phage titers ( PFU/ml ) were determined by serial dilution and plaque assays by the soft overlay technique with some modifications [35] . Briefly , phage dilutions were spotted onto Middlebrook 7H9 overlays ( 0 . 6% agar ) with M . smegmatis mc2155 ( ATCC ) and incubated at 37°C overnight . The treatment was initiated at day 33 post-infection , when the footpad of mice were swollen to 3 . 0 mm , and was performed by subcutaneous injection in the infected footpad with a single dose of mycobacteriophage D29 containing 8 log10 PFU . MPB was given to control ( non-treated ) mice . Footpad swelling was monitored throughout the experiment , as an index of lesion development , by using a caliper to measure the diameter of the frontal area of the footpad . For ethical reasons , the non-treated mice were sacrificed after the emergence of ulceration at day 68 post-infection , and no further parameters were evaluated for this group . M . ulcerans growth and phage proliferation were evaluated in footpad tissues and in the DLN . Briefly , footpad tissue specimens were minced , resuspended in PBS ( Sigma ) and vortexed with glass beads to obtain homogenized suspensions . DLN were homogenized , the cell numbers were counted and then suspensions were lysed with saponin 0 . 1% . Serial dilutions of the footpad and DLN homogenates were plated on Middlebrook 7H9 agar medium . M . ulcerans numbers were counted after 6 to 8 weeks of incubation at 32°C and expressed as colony forming units ( CFU/ml ) . Homogenized samples were also centrifuged for 10 min at 5000 rpm , supernatant was used for phage determination by the soft overlay technique [35] and expressed as plaque forming units ( PFU/ml ) . Phage dissemination was also investigated by detecting phages in the spleen and sera of mice . The levels of the cytokines tumor necrosis factor ( TNF ) , interleukin ( IL ) -6 , gamma interferon ( IFN-γ ) and IL-10 in the supernatant of homogenized suspensions from DLN and footpad tissues of control-infected and mycobacteriophage D29 treated mice were quantified by using a Quantikine Murine ELISA kit ( eBioscience Inc ) , according to the manufacturer's instructions . Mouse footpads and DLN were harvested , fixed in 10% phosphate-buffered formalin and embedded in paraffin . Light microscopy studies were performed on tissue sections stained with hematoxylin and eosin ( HE ) or Ziehl-Neelsen ( ZN ) . Images were obtained with an Olympus BX61 microscope . Differences between the means of experimental groups were analyzed with the two-tailed Student t test . Differences with a P value of ≤0 . 05 were considered significant . This study was approved by the Portuguese national authority for animal experimentation Direção Geral de Veterinária ( ID: DGV 594 from 1st June 2010 ) . Animals were kept and handled in accordance with the guidelines for the care and handling of laboratory animals in the Directive 2010/63/EU of the European Parliament and of the Council .
We first tested the lytic activity of different mycobacteriophages against several M . ulcerans isolates . The results for the plaque formation on the tested M . ulcerans strains are given in Table 2 . We observed that some phages were more strain-specific , such as the phages Adjutor , Kostya and Brujita , and others presented a more narrow lytic host range spectrum ( L5 , Chah and Phaedrus ) . A cluster of three phages , namely D29 , Bxz2 and Tweety , displayed the broadest lytic host range spectrum and highest lytic activity against representative strains of M . ulcerans . In line with a previous report [36] , D29 phage showed the broadest lytic host range spectrum amongst the tested mycobacteriophages , affecting M . ulcerans isolates with genetic heterogeneity , variable phenotypic characteristics and from different geographic origins ( Table 1 ) . Based on these results , we selected mycobacteriophage D29 for in vivo therapeutic studies against infection with M . ulcerans 1615 , a well characterized and stable strain that presents a mycolactone profile identical to that of African strains [32] . To investigate the efficacy of mycobacteriophage D29 treatment for the control of M . ulcerans , we used a footpad mouse model of infection [34] , [38] , [39] . Mice were subcutaneously infected in footpads with 5 . 5 log10 AFB of M . ulcerans strain 1615 . At day 33 post-infection , when footpad swelling had reached 3 . 0 mm ( Figure 1A ) , mice were subcutaneously injected in the footpad with a single dose of mycobacteriophage D29 ( 8 log10 PFU ) or with the vehicle MPB as a control . In both control-infected and mycobacteriophage D29 treated mice we observed an initial footpad swelling ( Figure 1A ) . However , at day 68 post-infection , footpads of non-treated mice started showing signs of ulceration , while in mycobacteriophage D29 treated mice the progression of swelling halted after day 91 post-infection ( day 58 post-treatment ) ( Figure 1A ) . Furthermore , in mycobacteriophage D29 treated mice , we observed a progressive reduction of footpad swelling , until initial treatment values , recorded by day 150 post-infection . Moreover , signs of ulceration were continuously absent during the period of experimental infection ( Figure 1A ) . The administration of mycobacteriophage D29 or vehicle MPB alone did not induce significant swelling of the footpad ( data not shown ) . Regarding M . ulcerans growth in infected footpads of non-treated mice , we observed a significant bacterial proliferation over the course of experimental infection ( P<0 . 01 ) ( Figure 1B ) . On the other hand , in footpads of mycobacteriophage D29 treated mice , we observed a significant reduction in CFU counts ( P<0 . 001 ) at day 68 post-infection ( day 35 post-treatment ) , following the administration of a single dose of mycobacteriophage D29 on day 33 post-infection ( Figure 1B ) . As previously described [38]–[40] , we found that M . ulcerans disseminates to the DLN after footpad infection ( Figure 1C ) , probably due to continuous lymphatic dissemination of bacteria either freely or shuttled within phagocytes . Here we show a significant reduction in CFU counts ( P<0 . 05 ) in the DLN of mycobacteriophage D29 treated mice , as compared with non-treated counterparts , at day 68 post-infection ( day 35 post-treatment ) ( Figure 1C ) , correlating with the reduction of M . ulcerans numbers in the footpads . It is well known that bacteriophages can disseminate from the administration site and reach several organs such as lymph nodes , spleen and liver , which are the primary sites involved in phage clearance [41] , [42] . In order to investigate the possible dissemination of mycobacteriophage D29 , we determined phage titres in the footpad , DLN , spleen and blood after its inoculation in M . ulcerans infected footpads . As shown in Figure 2 , mycobacteriophage D29 numbers significantly decreased ( P<0 . 001 ) in infected footpads from 2 to 24 h post-treatment and no phages could be detected after this time point ( Figure 2 ) . Phage numbers were also detected in the DLN , as early as 2 h after the administration in infected footpads , time point at which maximum phage counts were obtained ( Figure 2 ) . After 24 h , we observed a significant decrease ( P<0 . 001 ) in phage titers in the DLN , but phages were still present until day 15 post-treatment ( Figure 2 ) . No phages could be detected in the DLN by the end of the experimental period of infection ( day 35 post-treatment ) ( Figure 2 ) . D29 phages were also detected in the spleen ( 2 . 2 log10±0 . 25 ) and in the serum ( 2 . 3 log10±0 . 17 ) of mycobacteriophage D29 treated mice as early as 2 h post-treatment but were no longer detectable until the end of the experimental period . To characterize the profile of the immune response in M . ulcerans-infected tissues and to determine how phage treatment influences the host response , we carried out a comparative analysis of cytokine kinetics in DLN and footpads . Regarding the production of the pro-inflammatory cytokine tumor necrosis factor ( TNF ) in the DLN , at the emergence of ulceration , protein levels were no longer detectable in non-treated mice . In comparison , in mycobacteriophage D29 treated mice , significant levels of TNF were detectable at day 68 post-infection ( day 35 post-treatment ) ( Figure 3A ) . Treatment with mycobacteriophage D29 also resulted in a significant increase of TNF levels in footpads of M . ulcerans infected mice ( P<0 . 01 ) at day 35 post-treatment ( day 68 post-infection ) , as compared with non-treated mice ( Figure 3B ) . Protein levels of IL-6 were detected in DLN and footpads of M . ulcerans infected mice at day 33 post-infection ( Figure 3C and D ) . At day 68 post-infection ( 35 days post-treatment ) , higher levels of IL-6 were detected in footpads of infected non-treated mice ( P<0 . 01 ) , as compared with mycobacteriophage D29 treated mice ( P<0 . 05 ) ( Figure 3D ) . As shown in Figure 3E and F , treatment with mycobacteriophage D29 resulted in a significant increase in the levels of IFN-γ in both the DLN and footpads ( P<0 . 05 ) , at day 35 post-treatment ( day 68 post-infection ) as compared with non-treated mice ( Figure 3E and F ) . The production of the anti-inflammatory cytokine IL-10 was also increased in both DLN and footpads of mycobacteriophage D29 treated mice ( Figure 3G and H ) , as compared to non-treated mice at day 68 post-infection . Histopathological analysis showed that at day 68 post-infection necrotic lesions ( Figure 4A ) were well established in the footpad tissue , as previously described in M . ulcerans progressing lesions from both humans and mice [34] , [43] . Necrotic tissue was surrounded by an inflammatory infiltrate composed mainly by macrophages ( Figure 4B ) . These necrotic areas , as expected , contained clumps of extracellular bacilli correlating with the emergence of footpad ulceration ( Figure 4C ) . At the same time point ( day 35 after treatment ) in mycobacteriophage D29 treated mice , we observed an abundant cellular infiltration ( Figure 4D ) with a predominance of lymphocytes and macrophages ( Figure 4E ) . We also observed bacilli , but they mainly co-localized with cells ( Figure 4F and G ) . In addition , the maintenance of these inflammatory infiltrates ( Figure 4H ) mainly composed by mononuclear cells ( Figure 4I ) , was observed 5 months after the end of mycobacteriophage D29 treatment . Although some bacilli were observed in the remaining necrotic areas ( Figure 4J ) , as well at the periphery ( Figure 4K ) , they were poorly stained by ZN . To determine the effect of D29 phage inoculation , a group of mice was injected only with the phage . The histological analysis shows no significant alterations in subcutaneous tissues of non-infected mice inoculated with mycobacteriophage D29 , at least until the end of the experimental period ( day 150 after treatment ) ( data not shown ) . Analysis of histopathology at day 68 post-infection showed that , in non-treated animals , the structure of the DLN was damaged , with absence of organized germinal centers leading to the destruction of the lymphoid tissue ( Figure 5A ) , as recently reported in experimental M . ulcerans infection [38] . On the other hand , in D29 phage-treated mice the structure of the DLN was maintained with mild alterations ( Figure 5B ) . Previous studies from our laboratory showed that the initial increase of cell numbers in the DLN , upon footpad infection by M . ulcerans , is followed by a rapid decrease , correlating with the destruction of lymphoid tissue [38] , [39] . Confirming previous results , here we observed a significant peak in the total cells ( P<0 . 05 ) at day 33 post-infection , followed by a sharp decrease observed at day 68 post-infection ( Figure 5C ) . We now show that mycobacteriophage D29 treatment induced a significant increase in the total number of cells in the DLN ( P<0 . 05 ) at day 68 post-infection ( day 35 after treatment ) .
The RS regimen for BU , recommended by the WHO [11] , is effective for small lesions but presents several limitations and adverse side effects . Additionally , the RS regimen presents a variation in efficacy for advanced ulcerative stages of the disease , for which the adjunction of surgical resection of the infected skin followed by skin graft is often required [44] . The use of bacteriophages in targeting bacteria , even antibiotic resistant ones , has been regarded as an alternative method to control bacterial infections in both animals and humans [20]–[31] , [45] . In fact , some studies have applied phage therapy to prevent and treat bacterial human diseases , such as the use of a novel , biodegradable preparation capable of releasing bacteriophages and ciprofloxacin ( PhagoBioderm™ ) , successfully used for the treatment of patients with severe radiation burns infected with multidrug-resistant Staphylococcus aureus [28] . In addition , early studies suggest that phage therapy may have potential for the treatment of mycobacterial diseases . Indeed , a reduction of lesions in the spleen , lungs and livers has been reported in experimentally infected guinea pigs with disseminated tuberculosis following therapy with phage DS-6A [46] . Previous reports suggest the potential use of mycobacteriophage D29 for the detection of M . ulcerans or for the assessment of drug resistance among mycobacterial isolates [36] , [46] . In this study , we have demonstrated for the first time the potential of phage therapy against M . ulcerans infection . Indeed , we have shown in the mouse footpad model that a single subcutaneous injection of the lytic mycobacteriophage D29 can effectively decrease the proliferation of the mycolactone-producing M . ulcerans 1615 . Importantly , mycobacteriophage D29 also showed lytic activity against several other M . ulcerans isolates in vitro , indicating that its activity in vivo may not be limited to M . ulcerans 1615 . As described , intravenous injection of phages enables a fast and directed introduction of phages in blood circulation and their spread through the organism [42] . Additionally , it has been described in mice that phages can also reach several organs , including lungs , kidney , spleen , liver and brain within 24 h after administration by other routes , including oral and traqueal routes [42] . Based on these observations , we studied the dissemination of mycobacteriophage D29 after subcutaneous injection in infected footpads . We show that mycobacteriophage D29 could only be detected in the blood and spleen of mice at 2 h post-injection , while in the footpad phages were detected until 24 h after injection . On the other hand , phages could be found in the DLN for longer periods of time , remaining viable for at least 15 days . The rapid elimination of phages from the circulation and their retention in the DLN as observed in our study , may be responsible for reducing the number of phages to a level that prevents complete bacterial clearance in infected footpads . One possible approach to solve this rapid phage clearance , observed in both the footpads and the blood , may be through the administration of a long-lived circulating phage strain , as described in the case of other infection models [22] , [47] , [48] . Although using a high phage dose could also result in a decrease of phage clearance , studies have shown that this approach may result in bacterial death without phage replication [47] , [49] , [50] and also lead to a drop in the phage titer , effectively diminishing the dose of active phages . Additionally , phage replication only occurs when the bacterial density is above a certain threshold [51] . This threshold is reached in the course of systemic infections [22] , [48] , [52] , but may be compromised in the case of necrotic lesions , such as those induced by M . ulcerans infections . As described in phage treatment of a local S . aureus infection , even with multiple subcutaneous doses of 109 PFU/mouse , phages significantly reduced but did not eliminate the bacterial load in abscesses induced by bacteria [22] . A possible concern about phage therapy is the emergence of phage-resistant bacteria [22] , [24] , [48] , [53] . Although in this study we do not provide data related to the emergence of M . ulcerans phage-resistance , it has been described , in experimental models of other bacterial diseases , namely with Pseudomonas aeruginosa , Escherichia coli and S . aureus , that phage resistance is a rare event [22] , [24] , [48] , even more so than antibiotic resistance [22] , [54] . Even though we cannot rule out that some phage resistance can occur , the use , in this study , of a single phage treatment dose greatly reduces this hypothesis . To characterize the type of immune response associated with the administration of mycobacteriophage D29 and , particularly , how phage treatment influences the host immune response against M . ulcerans , we carried out a comparative analysis of cytokine kinetics in footpads and DLN , where the initiation of the adaptive immune response occurs [38] . It is known that the differentiation/proliferation of mycobacteria-specific lymphocytes can occur in the DLN , early after M . ulcerans infection , and that effector T cells are recruited to the site of infection [38] , where they mediate partial protection by enhancing IFN-γ-induced macrophage antimicrobial mechanisms . In agreement , we detected IFN-γ in the DLN , however this host response is not sufficient to inhibit the proliferation of virulent M . ulcerans , as increasing concentrations of mycolactone impair the effector activity of macrophages [6] . Interestingly , we observed that mycobacteriophage D29 treatment results in a significant increase in the total number of cells in the DLN , as well as in an increase of IFN-γ levels , correlating with a decrease in the number of viable bacteria , both in footpads and DLN , measured at day 68 post-infection ( day 35 post-treatment ) . Collectively , these results suggest that the dissemination and prolonged permanence of phages in the DLN may prevent local M . ulcerans proliferation and the associated accumulation of mycolactone , therefore preventing DLN destruction . As previously described [38]–[40] and confirmed in this study , the tissue destruction of the DLN was associated with bacterial colonization , which is consistent with the spreading of M . ulcerans from the site of infection via afferent lymphatic drainage [55] , [56] On the other hand , the increased immune activation induced in the DLN of treated mice may explain an immune-mediated control of bacterial proliferation in the footpad , despite the lack of phages at the primary site of infection . In fact , as previously described , IFN-γ , and TNF play a protective role in immunity against M . ulcerans experimental infections , contributing to control bacterial proliferation [6] , [7] , [57] . Accordingly , we show here that mycobacteriophage D29 treatment was associated with increased levels of both IFN-γ and TNF in M . ulcerans-infected footpads [6] , [7] , correlating with a predominance of a mononuclear infiltrate and prevention of ulceration at 150 days post-infection . Additionally , our histological data show that bacilli are still present in footpad tissues , albeit with an altered morphology and poorly stained with ZN . Although this observation may indicate that , as described [39] , M . ulcerans bacilli underwent degradation after bacterial killing , a possible relapse of M . ulcerans infection after the 150 day period of experimental infection was not checked in this study . Here we show that IL-6 concentration was markedly lower in footpads of mycobacteriophage D29 treated mice at day 68 post-infection ( day 35 after treatment ) as compared to non-treated mice , confirming that footpad tissue damage is less severe in D29 phage treated footpads . Although production of IL-10 could be detected in skin lesions of patients with BU [58] , [59] the exact role of this cytokine in the progression of M . ulcerans infection has to be further analyzed . In this experimental setting , it is possible that the increased levels of IL-10 in mycobacteriophage D29 treated mice are modulating the activity of the pro-inflammatory cytokines . In summary , our results show that administration of the lytic mycobacteriophage D29: ( i ) is an effective approach for reducing M . ulcerans-induced pathology in the mouse model of infection; ( ii ) reduced M . ulcerans numbers in the footpad and the DLN , associated with increased IFN-γ and TNF levels and; ( iii ) is not associated with detectable side effects over a minimum delay of 150 day observation period . To our knowledge , this is the first study on mycobacteriophage therapy against M . ulcerans in vivo infection . It should be pointed out that mice were treated at an advanced stage of M . ulcerans infection , which is relevant for human infection since BU patients often seek medical treatment in advanced stages of the disease . More detailed studies examining the effects of phage dosage , routes and timing of administration , as well as on pharmacokinetics , will be needed to determine if phage therapy will provide a consistent alternative/supplement for the treatment of BU . Although the development of a therapeutic regimen using phages will involve a commitment to fulfill the scientific requirements of current pharmaceutical agencies , our encouraging results justifies further investigation on the potential of phages for the management of this mycobacteriosis . Moreover , mycobacteriophage D29 represents an ideal agent from a regulatory standpoint in that it has been fully characterized genetically [60] and is able to be used on a stand-alone basis . Another approach could be based on the therapeutic use of lysins bacteriophage proteins produced at the end of a lytic life cycle , designed to attack peptidoglycan in order to allow the release of the new synthesized phage particles [61] . | Buruli Ulcer ( BU ) , caused by Mycobacterium ulcerans , is a necrotizing disease of the skin , subcutaneous tissue and bone . Standard treatment of BU patients consists of a combination of the antibiotics rifampicin and streptomycin for 8 weeks . However , in advanced stages of the disease , surgical resection of the destroyed skin is still required . The use of bacterial viruses ( bacteriophages ) for the control of bacterial infections has been considered as an alternative or a supplement to antibiotic chemotherapy . By using a mouse model of M . ulcerans footpad infection , we show that mice treated with a single subcutaneous injection of the mycobacteriophage D29 present decreased footpad pathology associated with a reduction of the bacterial burden . In addition , D29 treatment induced increased levels of IFN-γ and TNF in M . ulcerans-infected footpads , correlating with a predominance of a mononuclear infiltrate . These findings suggest the potential use of phage therapy in BU , as a novel therapeutic approach against this disease , particularly in advanced stages where bacteria are found primarily in an extracellular location in the subcutaneous tissue , and thus immediately accessible by lytic phages . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"buruli",
"ulcer",
"cytokines",
"microbial",
"control",
"immunology",
"neglected",
"tropical",
"diseases",
"microbiology",
"biology",
"immune",
"system"
] | 2013 | Phage Therapy Is Effective against Infection by Mycobacterium ulcerans in a Murine Footpad Model |
Scrub typhus , a bacterial infection caused by Orientia tsutsugamushi , is increasingly recognized as an important cause of fever in Asia , with an estimated one million infections occurring each year . Limited access to health care and the disease’s non-specific symptoms mean that many patients are undiagnosed and untreated , but the mortality from untreated scrub typhus is unknown . This review systematically summarizes the literature on the untreated mortality from scrub typhus and disease outcomes . A literature search was performed to identify patient series containing untreated patients . Patients were included if they were symptomatic and had a clinical or laboratory diagnosis of scrub typhus and excluded if they were treated with antibiotics . The primary outcome was mortality from untreated scrub typhus and secondary outcomes were total days of fever , clinical symptoms , and laboratory results . A total of 76 studies containing 89 patient series and 19 , 644 patients were included in the final analysis . The median mortality of all patient series was 6 . 0% with a wide range ( min-max ) of 0–70% . Many studies used clinical diagnosis alone and had incomplete data on secondary outcomes . Mortality varied by location and increased with age and in patients with myocarditis , delirium , pneumonitis , or signs of hemorrhage , but not according to sex or the presence of an eschar or meningitis . Duration of fever was shown to be long ( median 14 . 4 days Range ( 9–19 ) ) . Results show that the untreated mortality from scrub typhus appears lower than previously reported estimates . More data are required to clarify mortality according to location and host factors , clinical syndromes including myocarditis and central nervous system disease , and in vulnerable mother-child populations . Increased surveillance and improved access to diagnostic tests are required to accurately estimate the untreated mortality of scrub typhus . This information would facilitate reliable quantification of DALYs and guide empirical treatment strategies .
Scrub typhus is caused by infection with the intracellular bacteria Orientia tsutsugamushi , which is transmitted by the bite of larval trombiculid mites . The disease is known to occur throughout Asia , but recent evidence suggest that its range may be larger , with case reports in Africa [1 , 2] , Chile [3] and a new related species , O . chuto , described in the Middle East [4] . In Southeastern Asia , it is thought that up to 1 million cases occur per year [5 , 6] , and a significant proportion of hospital admissions for acute undifferentiated fever have been shown to be attributable to scrub typhus [5 , 7] . The disease is most common in rural areas , where there is limited access to healthcare , diagnostics and treatment , and is difficult to differentiate from other infections , such as leptospirosis and dengue , on a clinical basis alone . Eschar , a diagnostic clue , is not always present , while access to rapid tests is not widespread and the tests are limited by low sensitivity , especially early in the disease course [8] . Laboratory diagnosis with the gold standard Indirect Immunofluorescence Assay ( IFA ) is expensive and impractical in rural areas . Effective treatment with doxycycline or azithromycin is available , but evidence of resistance to doxycycline in Northern Thailand [9] raises concern of undiscovered resistance elsewhere [10] . Due to these factors , many scrub typhus infections remain undiagnosed or untreated , but the outcomes from untreated infections are unknown . Current estimates of the untreated mortality from scrub typhus are unclear , with those from before the age of antibiotic therapy as high as 40–45% [11 , 12] . Estimating the true mortality , however , is challenging as disease severity is thought to vary according to regional strains [13 , 14] , infectious dose [15 , 16] , patient age , and comorbidities [17] . This review aims to estimate the untreated mortality from symptomatic scrub typhus through a comprehensive review of literature . Such understanding will improve our current understanding of the untreated mortality from scrub typhus and help estimate the burden of disease .
Literature was reviewed for patient series containing untreated patients with scrub typhus . Included patients were of any age or sex and had fever and clinical symptoms consistent with scrub typhus , with or without a confirmed laboratory diagnosis through culture , inoculation of laboratory animals , serological tests , or the Polymerase Chain Reaction ( PCR ) . Patients were defined as untreated if they had not been treated with antibiotics or convalescent serum , and had not been vaccinated against scrub typhus . Patients admitted to hospital for supportive treatment , including intravenous fluids , or Intensive Care Units ( ICU ) , were included in the analysis . Clinical diagnosis was defined per individual patient series and included patients with no eschar , but patients with evidence suggestive of an alternative diagnosis were excluded . The primary outcome of the analysis was mortality from untreated scrub typhus . Secondary outcomes were total days of fever , clinical symptoms , and laboratory results where available . All study designs describing the untreated mortality of scrub typhus were included . Patient series with fewer than 10 patients were excluded to reduce selection bias . All published papers regardless of year of publication were included in the search . Journals in European languages ( English , French , German , Dutch ) were included but those in other languages were excluded . This review followed the PRISMA statement for systematic reviews ( S1 Checklist ) . Articles were identified through electronic resources , through scanning of reference lists of relevant articles , and from library index catalogues . The electronic search was performed using Ovid MEDLINE ( 1946—Present ) , Embase Classic ( 1947 –Present ) , and Global Health ( 1910 –Present ) on 28th July 2014 . The electronic databases were searched using “scrub typhus or Orientia tsutsugamushi or Rickettsia tsutsugamushi or Orientia tsu* or Akamushi disease or Japanese river fever or mite typhus or tropical typhus or tsutsugamushi disease” and a second search for “mortality or death” . ( S1–S3 Figs ) . Duplicate search results were removed using Mendeley ( 2008–14 Mendeley Ltd , Version 1 . 12 . 1 ) . Authors were not contacted regarding further information due to the age of many of the articles , no unpublished literature was obtained , and abstracts were not included if a full article was unobtainable . AJT reviewed abstracts and titles from all search results to assess eligibility and if there was doubt as to the relevance of the article from the abstract and title alone , the full article was obtained and then conferred with the other authors . AJT extracted data for geographical location , year of study , study design , number of patients , patient demographics , clinical symptoms and signs , laboratory results , diagnostic test and mortality , noting missing data . Patient series were extracted separately if an article contained more than one patient series . Each patient series was reviewed with respect to other articles and duplicates excluded . Untreated patients in partially treated series were extracted when possible , but excluded if the paper did not specifically express outcomes for untreated patients . Articles not in English were translated using Google translate where necessary . A standardized form was created to assess bias in patient series ( S1 Table ) . Four criteria were assessed on a 3-point grade scale: patient selection , diagnostic test , patient information and outcomes . Diagnosis was considered Grade I if O . tsutsugamushi were cultured or there was a 4-fold rise in titre on IFA , Grade II if there was a single high titre on IFA , or Weil-Felix ( OXK ) test was positive for all included patients , or Grade III if there was a clinical diagnosis alone or no laboratory diagnosis for all included patients . The primary outcome of the review was the median mortality ( range ) across all patient series and termed the “median series mortality” . Secondary outcomes were measured as the median value across patient series unless otherwise stated . The chi-squared test was used to compare overall secondary outcomes when appropriate . Data was mapped using an image from NASA—Visible Earth .
Details of included articles are displayed in S4 Table . Articles were published between 1878 and 2008 and size of patient series ranged from 10 to 1 , 522 patients . Sixty-seven articles were in English , 4 in German , 3 in Dutch and 2 in French . Nine patient series were prospective cohort series , 2 controlled trials , 75 retrospective series , and 3 summaries of case reports . All studies were hospital based with twenty-eight undertaken in the Indian Subcontinent ( India , Myanmar , Sri Lanka and Pakistan ) , 18 in New Guinea , 11 in Japan , 11 in Malaysia or Singapore , 5 in Australia , 5 in Taiwan , 4 in Indonesia ( excluding New Guinea ) , 2 in Cambodia or Vietnam , 1 in the Philippines and 1 in Korea ( Fig 2 ) . Three further patient series were infected using inoculation of experimental strains of Orientia tsutsugamushi . Individual patient series were assessed for methodological quality using a data extraction sheet designed for this review . A summary of bias within each study is displayed in S5 Table and more detailed information is contained in S6 Table . Many of the patient series had a high level of bias with significant missing data . There was a high risk of bias in diagnosis in 75 . 3% ( 67/89 ) of studies , due to reliance on clinical diagnosis alone in these papers , while only 4 . 5% ( 5/89 ) papers had a confirmed diagnosis through culture , paired serology or inoculation of O . tsutsugamushi . Patient mortality data were available for 89/89 patient series for a total of 19 , 644 patients . Median series mortality was 6 . 0% with a wide range across series ( 0–70% ) . Overall 12 . 7% ( 2 , 488/19 , 644 ) of patients died ( Fig 3 and Table 1 ) . Information on individual secondary outcomes was available in as few as 3 of 89 reports and up to 65 of 89 studies for each outcome . The majority of included studies reported a high median incidence of headache ( 100% ( 71 . 0–100 ) ) and lymphadenopathy ( 84 . 7% ( 20 . 0–100% ) ) , while conjunctival congestion ( 69 . 3% ( 10 . 2–100% ) ) , myalgia ( 56 . 1% ( 2 . 2–100% ) ) and cough ( 50% ( 5 . 1–100% ) ) were reported in a significant proportion of patients . Haemorrhagic symptoms ranged from epistaxis to more severe bleeds and the definition of pneumonitis varied between studies , often being diagnosed on clinical grounds . Pneumonia , as confirmed radiologically , was defined separately . Definition of myocarditis varied between studies and was often on a clinical basis alone . No included patients were documented as receiving treatment on ICU , mechanical ventilation , or vasopressor support . Median series mortality was higher in Japan at 31 . 6% ( 12–70% ) than in other regions , where median patient series mortality was below 10% ( 0–30% ) ( Fig 4 ) . Data on mortality by year of patient series were present for all patient series ( 89/89 ) with a wide range in mortality over time but no overall discernable trend in mortality according to year . Mortality varied according to study design with median series mortality 12 . 1% ( 5 . 3–18 . 8% ) in 2 controlled trials , 6 . 25% ( 0–23 . 5% ) in 9 prospective case series , 5 . 7% ( 0–45 . 8% ) in 75 retrospective case series , and 60% ( 19–70% ) in 3 patient series summarising case reports . Median series mortality in 5 patient series with a Grade I diagnosis ( mouse inoculation with O . tsutsugamushi , in vitro isolation of O . tsutsugamushi or use of paired IFA sera ) was 0% ( 0–14 . 3% ) , in 17 series with a Grade II diagnosis ( single high titre OXK ) 3 . 0% ( 0–23 . 5% ) , and in 67 studies with a Grade III diagnosis ( clinical diagnosis alone for some patients ) 8 . 3% ( 0–70% ) . No studies used PCR to diagnose infection . Data on mortality by age were included in 15/89 patient series for 3 , 879 patients ( Table 2 ) and showed a trend towards increasing mortality with age . Overall mortality in patients under 30 was 10 . 7% ( 241/2261 ) , compared to overall mortality of 21 . 3% ( 344/1618 ) in all patients over 30 . Information on mortality by sex was available in 60/89 series for male patients and 14/89 series for female patients . Median series mortality was 4 . 9% ( range 0–30% ) with overall mortality 6 . 9% ( 851/12 , 273 deaths ) in males compared to median series mortality 1 . 4% ( range 0–32 . 2% ) and overall mortality 23 . 5% ( 181/771 deaths ) in female patients . The majority of included patients in this review were male and sample sizes for series including female patients were often small , with many series reporting less than 10 patients . One series of 519 female patients [18] reported 179 deaths ( 34 . 5% ) and had a large influence on the overall female mortality . Data on patient mortality according to patient symptoms or signs were not available from all studies due to limitations in data collection and description in many studies . Available data ( Table 3 ) showed that the presence of myocarditis , haemorrhagic symptoms , delirium and pulmonary symptoms were associated with increased overall mortality , but the presence of eschar or meningitis were not .
Results of this study show that mortality from scrub typhus varies greatly , but is lower than commonly reported estimates . Morbidity from the disease however , is significant , due to the prolonged duration of fever in scrub typhus ( median 14 . 4 days ( range 9–19 ) ) . Further work is required to clarify mortality according to location and host factors , and patient symptoms including myocarditis , central nervous system disease and in vulnerable mother-child populations . Results of this study suggest that further investigation into differences in strain virulence by region are required to reliably quantify DALYs , measure the disease burden from scrub typhus , and to guide empirical treatment strategies . More widespread access to medical care , coupled with the increased use of affordable and accurate rapid tests , is required to improve diagnosis and treatment of these easily treatable infections . | Scrub typhus is a common cause of fever in rural Asia where there is limited access to healthcare , diagnostics , and treatment . It is thought that up to 1 million cases occur per year , but the disease is difficult to differentiate clinically from other infections , such as leptospirosis and dengue , meaning that many infections go undiagnosed and untreated . This review systematically summarizes the literature on the untreated mortality of scrub typhus and describes the outcome from untreated disease . Many articles had incomplete data and relied on a clinical diagnosis of disease , but results showed that scrub typhus is associated with a lower mortality than previously described , at approximately 6% , ranging from 0–70% . Further information is required to clarify the mortality according to location , host factors , and clinical syndromes . More widespread access to medical care , coupled with the increased use of affordable and accurate rapid tests , would improve the diagnosis and treatment of scrub typhus . Increased surveillance and investigation into differences in strain virulence by region are required to reliably quantify DALYs and the disease burden from scrub typhus , and to guide empirical treatment strategies . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | A Systematic Review of Mortality from Untreated Scrub Typhus (Orientia tsutsugamushi) |
The mechanisms that ensure fertilization of egg by a sperm are not fully understood . In all teleosts , a channel called the ‘micropyle’ is the only route of entry for sperm to enter and fertilize the egg . The micropyle forms by penetration of the vitelline envelope by a single specialized follicle cell , the micropylar cell . The mechanisms underlying micropylar cell specification and micropyle formation are poorly understood . Here , we show that an effector of the Hippo signaling pathway , the Transcriptional co-activator with a PDZ-binding domain ( Taz ) , plays crucial roles in micropyle formation and fertilization in zebrafish ( Danio rerio ) . Genome editing mutants affecting taz can grow to adults . However , eggs from homozygous taz females are not fertilized even though oocytes in mutant females are histologically normal with intact animal-vegetal polarity , complete meiosis and proper ovulation . We find that taz mutant eggs have no micropyle . Taz protein is specifically enriched in mid-oogenesis in the micropylar cell located at the animal pole of wild type oocyte , where it might regulate the cytoskeleton . Taz protein and micropylar cells are not detected in taz mutant ovaries . Our work identifies a novel role for the Hippo/Taz pathway in micropylar cell specification in zebrafish , and uncovers the molecular basis of micropyle formation in teleosts .
In vertebrates , fertilization occurs by two major strategies . Amniotes such as reptiles , birds and mammals , undergo copulation and internal insemination to ensure gamete fusion . The acrosome reaction is necessary for sperm to penetrate the zona pellucida , a protective egg envelope , and entry of sperm can occur at any position in the egg surface [1–3] . By contrast , most teleosts ( bony fish ) undergo external fertilization . Without a recognizable acrosome reaction , sperm entry in teleosts relies entirely upon a specialized funnel-like structure , the micropyle , in the chorion , an acellular coat of the egg [4–6] . Morphological and physiological studies of the micropyle in a variety of different teleost species suggest that channel formation results from the transformation of a special micropylar cell in mid-oogenesis [7–12] . The micropylar cell is morphologically distinct from other follicle cells surrounding the oocyte . Positioned over the oocyte animal pole , the micropylar cell is bigger in size and appears like an inverted cone in shape , in contrast to the flattened appearance of follicle cells , sometimes called ‘mushroom’-like [11–13] . The unique shape of the micropylar cell is gradually achieved during oogenesis . A cytoplasmic extension from the micropylar cell expands and extends through the developing vitelline envelope , till the extension tip contacts the oocyte membrane , as the vitelline envelope grows and perforation proceeds , the cytoplasmic extension becomes slim and long [14] . Finally , the micropylar cell degenerates , leaving a narrow canal called the ‘micropyle’ between the chorion and the egg [15 , 16] . Previous studies in other teleosts revealed potential drilling forces of the micropylar cell . The aggregation and elongation of microtubules and tonofilaments in the cytoplasmic bulge of the micropylar cell provides internal forces [11 , 14] , and two opposing rotations between the oocyte and the covering follicle cell layer are thought to provide the external force for the micropylar cell to bore through the chorion [13 , 17 , 18] . Although these studies described the morphological process of micropyle formation , little is known about the molecular mechanisms underlying formation of this essential structure . A key line of evidence comes from studies of a zebrafish maternal-effect mutant bucky ball ( buc ) , in which oocyte polarity fails to be established . This mutant has multiple micropyles in each egg , arising from the expanded animal identity in buc mutant oocytes [19 , 20] . Hippo signaling plays a variety of roles in development , regeneration , tissue homeostasis , and stress response [21 , 22] . The WW domain-containing transcription regulator protein 1 ( Wwtr1 ) is a transcriptional co-activator with a PDZ-binding domain ( Taz ) . Taz , together with Yes-associated protein ( Yap ) , are downstream effectors of Hippo signaling . As a transcriptional co-activator , Taz usually exerts its functions by binding to transcription factors , such as Teads and Smad2/3 , which modulates transcription of downstream genes [23] . As an oncoprotein , TAZ has been found up-regulated in many kinds of human cancers . TAZ also promotes epithelial-mesenchymal transition ( EMT ) , migration and invasion of cancer cells , where cell morphology is altered and cytoskeleton is inevitably rearranged [24] . Yap/Taz can regulate cytoskeleton dynamics . In medaka ( Oryzias latipes ) , Yap regulates cortical actomyosin activity and tissue tension by the downstream Rho GTPase activating protein ARHGAP , and mutants affecting the inhibitor of F-actin polymerization , hirame/yap , display reduced cortical actomyosin tension and a collapsed body shape [25] . Interestingly , similar to medaka hirame , the establishment of posterior body shape is disrupted in zebrafish yap1; taz double mutants [26 , 27] , which suggests that Yap1/Taz regulates cytoskeleton dynamics . In turn , Yap/Taz can be activated by environmental mechanical signals , for example matrix rigidity , which are usually transduced by the cytoskeleton [28 , 29] . Tumors affecting two female organs , breast and ovaries , have been used extensively to study TAZ functions [30 , 31] . However , to date , the role of TAZ in normal oogenesis and ovary differentiation has not been investigated . In a study of Taz function in zebrafish , we have unexpectedly found that Taz is required for the formation of micropyle during oogenesis . We show that taz transcripts are expressed maternally . When taz is knocked out , some homozygous taz mutants can survive to adulthood , and mutant females produce eggs with no micropyle . Our results suggest that Taz might regulate micropylar cell specification and morphogenesis during zebrafish oogenesis .
To study Taz function , we knocked out taz by targeting the first exon using CRISPR/Cas9 genome editing and recovered two mutant alleles , tazΔ10 and tazΔ1 , both of which produce mutant transcripts that encode truncated proteins with 148 and 145 amino acids , respectively ( Fig 1A , 1B and 1C ) . Importantly , no Taz protein was detected in tazΔ10/Δ10 mutant embryos ( Fig 1D ) , suggesting that the lesion in tazΔ10/Δ10 results in a null mutant . Consistent with a previous report [32] , tazΔ10/Δ10 mutant embryos displayed relatively normal morphology with exception of a smaller swim bladder than wild type and weak pericardial edema at 4 . 5 day post fertilization ( dpf ) ( Fig 1E and 1F ) . However , it did not lead to embryonic lethality as indicated by the expected incidence of homozygous mutants from intercrosses of heterozygotes ( ~25% at 7 dpf ) , and in accordance with Mendelian segregation ( Fig 1G ) . Some tazΔ10/Δ10 mutants could grow into adulthood , although the survival ratio was much lower than expected . Interestingly , while tazΔ10/Δ10 adult males were fertile ( Fig 2B and 2E ) , all embryos from mating of tazΔ10/Δ10 adult females were arrested at the one-cell stage regardless of the genotype of the male ( Fig 2C and 2E ) , and even though the females ovulated normally and produced eggs . We found similar phenotypes with the tazΔ1/Δ1 allele ( Fig 2D ) , and all subsequent studies reported in this work were done using tazΔ10/Δ10 mutants . Since tazΔ10/Δ10 adult females are infertile , this suggests that taz is indispensable for producing normal eggs . Therefore , we surveyed if taz was expressed in oocytes . In situ hybridization showed that taz transcripts were found in the cortex of oocytes and the attached follicle cells ( Fig 2F ) . Moreover , in one-cell stage embryos , where mRNAs are deposited in eggs by the mother , taz transcripts are abundant ( Fig 2H ) , which is consistent with transcriptomic datasets [33] ( http://www . ensembl . org/Danio_rerio/Location/View ? r=22%3A38049130-38114599 ) . Together , these data reveal that taz , a maternally expressed gene , is essential for fertilization . To determine the basis of the failure of tazΔ10/Δ10 eggs to progress beyond the one-cell stage , we examined the ovaries and oogenesis in mutant females . Compared with ovaries at the same stage in wild type females , the ovary of an 8-month old taz mutant female was grossly normal in the size , tissue composition and intraperitoneal position ( Fig 3A and 3B ) , and there were no apparent morphological defects in the color , size and shape of oocytes ( Fig 3C and 3D ) . Histological analysis showed that all stages of oocytes ( stage I to IV ) were present in taz mutant ovaries , and had no obviously difference from that in wild type controls ( Fig 3E and 3F ) , indicating that the oogenesis was largely normal in taz mutants . The establishment of animal-vegetal polarity in oocytes is a key event during oogenesis , and determines the formation of two major embryonic axes , the dorsal-ventral and left-right axis , in vertebrates [34] . Therefore , we examined if the failure of taz mutant oocytes to be fertilized was due to defects in animal-vegetal polarity . The Balbiani body ( Bb ) is the earliest vegetal structure in stage I oocytes , and can be marked by the expression of dazl transcripts [35] . In stage I oocytes of taz mutants , the Balbiani body appeared similarly as in wild type oocytes ( Fig 3G and 3H ) . Furthermore , while dazl transcripts were found located in the vegetal pole , cyclinB , an animal pole marker , was distributed on the opposite side of oocytes in both taz mutant and wild type ( Fig 3I and 3J ) , indicating that the animal-vegetal polarity was normally established in taz mutant oocyte . Similarly , expressions of other two polarity markers , pou2 and brl , were not altered in mutant oocytes ( S1 Fig ) . Taking together , we conclude that taz is not necessary for oogenesis or for the establishment of oocyte polarity in zebrafish . Since oogenesis seemed normal in taz mutant ovaries , next we checked if fertilization of mutant eggs was normal . In teleost eggs , the micropyle is a narrow canal for sperm entry through the chorion during fertilization . While all wild type eggs had a single , animal-pole localized micropyle ( Fig 4A ) , no micropyle was detected in taz mutant ( Fig 4B ) . Furthermore , a single obvious cytoplasmic projection from the plasma membrane to the micropyle was present in wild type eggs shortly after egg activation ( Fig 4C ) , whereas no protrusion was found in taz mutant eggs ( Fig 4D ) . These observations strongly suggest that the lack of micropyle in taz mutant eggs results in their not being fertilized as sperm likely cannot enter the egg . Once zebrafish eggs are activated , the second meiotic division is quickly completed , and the second polar body is extruded [36] , a hallmark of the completion of the meiosis . To examine if the failure of fertilization of taz mutant eggs is due to no sperm entry , we performed DAPI and Phalloidin staining in activated eggs . While the pronucleus ( from egg or sperm ) is only stained by DAPI , the polar body from egg , surrounded by Actin , is detected by both DAPI and Phalloidin . After fertilization , sperm DNA enters the egg , and two pronuclei , from the egg and sperm , and one polar body were found in wild type eggs ( Fig 4E and 4E’ ) . However , in taz mutant eggs , a polar body and only one pronucleus were observed ( Fig 4F and 4F’ ) , indicating that meiosis is complete but there is a lack of sperm entry . These data demonstrate that oocyte meiosis can be completed without Taz , and the failure of fertilization in taz mutant egg is due to lack of the micropyle . Our analysis suggests that oogenesis in taz mutant appears normal except for the lack of micropyle formation . In addition to oocytes , follicle cells are another group of cells that are essential for oogenesis to progress . In teleost eggs , follicle cells surround oocytes to provide nutrition . Some follicle cells specify into unique micropylar cells , which form one micropyle on each oocyte during stage III oogenesis in zebrafish [12 , 37] . To assess follicle cells during oogenesis , wild type and taz mutant ovaries were sectioned and stained with haematoxylin and eosin ( HE ) . Compared with wild type , in taz mutant ovaries , follicle cells surrounding oocytes of all stages had no obvious difference in size , shape or numbers ( Fig 3E and 3F ) . Interestingly , while follicle cells around oocytes showed basal levels of Taz expression , one particular cell was found highly enriched with Taz from late stage II to late stage III oogenesis ( Fig 5A , 5B and 5C ) . This cell became larger than other follicle cells , and displayed a unique morphology change from flattened to ‘nail’-like shape ( Fig 5A’ , 5A” , 5B’ , 5B” , 5C’ and 5C” ) . The micropylar cell depressed and eventually perforated the developing vitelline envelope ( Fig 5A’” , 5B’” and 5C’” ) . Referring to morphological criteria , this Taz-enriched cell is the micropylar cell . Notably , Taz was predominantly distributed in the nucleus of micropylar cells , and the levels gradually decreased with progression of micropylar cell development ( Fig 5A , 5B and 5C ) . The nuclear localization of Taz suggests that it might exert its function by transcriptional regulation of target genes . Moreover , high levels of Taz were found in the tip of cytoplasmic extension of the micropylar cell , especially in middle stage III oocytes ( Fig 5B and 5B” ) . In sectioned wild type ovaries , the high Taz expressing micropylar cell is located on the top of the animal pole of oocyte marked by cyclinB ( Fig 5D , 5D’ and 5D” ) . However , in taz mutant ovaries , neither the micropylar cell nor the invagination on the developing vitelline envelope was detected ( Fig 5E , 5E’ and 5E” ) . We also performed Taz immunostaining in whole mount oocytes , and detected a single high Taz expressing micropylar cell on the top of animal pole in wild type stage III oocytes , but not in taz mutants ( Fig 5F and 5G ) . These data suggest that Taz is required for the specification of micropylar cell , and the enrichment of Taz in micropylar cell agrees with an indispensable role for Taz in micropyle formation . Interestingly , in sections of wild type ovaries , the shape of the micropylar cell nucleus sometimes looked like two closely juxtaposed nuclei ( Fig 5A ) . To examine the micropylar cell membrane and nucleus in detail , we performed co-immunostaining with Taz and β-Catenin in whole oocytes , while DAPI was used to label DNA . During oogenesis between late stage II to late stage III , two DAPI signals in close proximity within one cell are identified in almost all micropylar cell nuclei ( Fig 6A–6C’” ) , being readily detected in late stage II/ early stage III oocytes , and gradually fading in late stage III oocytes . Co-labeling with an antibody towards Nup107 , a nuclear pore marker , also showed two lumps of DAPI signals surrounded by a continuous nuclear membrane in micropylar cells from late stage II to late stage III oogenesis ( Fig 6D–6F’” ) . However , we did not find clearly separated nuclei in all the oocytes ( n = 132 ) that we assessed . In many teleosts , formation of the micropyle is thought to require drilling of the vitelline envelope by the micropylar cell . During this process , the micropylar cell shape undergoes extensive changes [11 , 13 , 14] , and the cytoskeleton might participate in this process . To assess the possible role of Taz in regulating cytoskeletal changes during micropyle formation , we performed co-staining of Taz with F-actin or α-Tubulin in wild type oocytes . We found that Actin filaments were enriched at the leading edge of oocyte cortex and the leading tip of micropylar cell , towards the indentation ( Fig 7A ) . As oocytes mature , more Actin filaments were found deposited ( Fig 7B and 7C , S2 Fig ) . Tubulin was also enriched in the cytoplasm of the micropylar cell , and in the cytoplasmic extension into the vitelline envelope ( Fig 7D , 7E and 7F , S2 Fig ) . Considering the role of Yap1/Taz in regulating cytoskeleton in medaka and zebrafish [25 , 27] , the high expression of Taz and dynamic Actin and Tubulin in the micropylar cell suggests that Taz may regulate cytoskeletal arrangements during formation of a functional micropyle . Taken together , we have revealed a unique function of Taz in formation of the micropyle in zebrafish which is summarized in a model ( Fig 8 ) . In oocytes from late stage II to late stage III , the micropylar cell , sitting on the animal pole , becomes bigger and changes into a ‘nail’ shape . Taz is highly expressed in the micropylar cell . F-actin is deposited in the leading tip of the micropylar cell and the leading edge of oocyte cortex , and Tubulin is enriched in the micropylar cell cytoplasm and protrusion into the vitelline envelope . The dynamic cytoskeleton might facilitate perforation of the developing vitelline envelope . Without Taz , the micropylar cell is not specified , and no micropyle forms in taz mutant eggs .
The most interesting finding in this study is that mutations affecting Taz , a key effector of the Hippo signaling pathway lead to loss of a cell required for formation of the micropyle , the sperm entry port on eggs . Our findings identify the first molecular component in the establishment of this unique cell in zebrafish ovary . In taz mutant , loss function of Taz does not affect ovary and oocytes development , egg ovulation and the second meiosis of oocyte , but leads to failure of formation of the micropyle . Such a specific phenotype is due to the restricted high expression of Taz in the micropylar cell . The high expression of Taz in one particular follicle cell at the animal pole in mid-oogenesis identifies Taz as the first molecular marker for the micropylar cell . With the aid of high Taz expression , the micropylar cell is easy to be identified . Besides the known characteristics , such as the big size and unique shape [12] , most micropylar cells are found to have bilobed nuclei . These findings raise several interesting questions to be addressed in the future: Is DNA segregation incomplete in micropylar cells or are there two closely juxtaposed nuclei in micropylar cells ? What leads to this: is this owing to incomplete cell division , cell fusion or proliferation ? In preliminary experiments , we did not detect any pH3 signal , a maker for G2/M cell cycle phase , in the micropylar cell ( S3 Fig ) , suggesting that cell proliferation probably does not underlie the bilobed nuclei . We also observed that there is a gradual down-regulation of Taz in the developing micropylar cell , with high expression levels of Taz in micropylar cells during middle oogenesis and lower levels from late stage III onwards . One possible explanation is that the signals that maintain Taz expression might be decreased as micropylar cell development progresses . It is also possible that Taz is not required when the micropylar cell becomes mature and finally degenerates . Our study has demonstrated that Taz is required for micropylar cell specification from a follicle cell . At this stage , it is hard to distinguish if the high expression of Taz is a cause or consequence of micropylar cell specification . In a parallel study , Dingare et . al . demonstrate that Taz is required for micropylar formation in zebrafish , which agrees with our conclusion , and they also find that Taz is highly expressed in ectopic micropylar cells formed in buc mutant oocytes [38] . This suggests that once the micropylar cell is determined by other factors , it will express Taz . However , we cannot exclude the possibility that Taz is induced first . Identification of signals that induce Taz expression in follicle cells will help to address if Taz is a cause or consequence of micropylar cell specification . Besides upstream components in Hippo pathway which regulate Taz stability [21 , 23] , two aspects of oogenesis , which precede micropylar cell specification , need attention . One is the establishment of animal-vegetal polarity of the oocyte . It is widely accepted that follicle cells close to animal pole of oocyte contribute towards micropyle formation in many teleosts [14–16] , suggesting that i ) the animal pole determines the group of follicle cells competent for micropylar cell specification , and ii ) animal pole specific-mRNAs could be inducers of Taz . The second is the growth of oocyte . Yap/Taz are known to act as mechanosensors [28 , 29] . The volume expansion of oocytes may produce mechanical signals and activate Taz . These events , prior to micropylar cell specification , might induce Taz expression . Nonetheless , how a single follicle cell acquires micropylar cell fate is not clear . Inducible loss-of-function and overexpression of taz could address if Taz is a cause or consequence of micropylar cell specification . Lineage tracing in wild type and taz mutant ovaries , combined with single-cell gene expression profiling might also be informative [39–42] . Although we cannot identify if Taz is a cause or consequence of micropylar cell specification , our data reveal an essential role for Taz in this process . How does the micropylar cell exert its function by Taz expression ? The most dramatic behavior of the micropylar cell is the shape change from a follicular epithelium into a highly polarized cell with a prominent projection , a process that is overtly similar to EMT in cancer . Besides EMT , Taz , as an oncoprotein , also promotes migration and invasion of human cancer cells , where cell shape changes are prevalent [24] . The micropylar cell is thought to bore through the developing vitelline envelope to form a channel , a process during which the cell shape must change greatly . Both cancer and micropylar cells are dynamic in shape , and therefore , it is reasonable to speculate that Taz works in a similar way in both processes . Yap/Taz regulates the cytoskeleton [25 , 27] , and nuclear localization of Taz in the micropylar cell may transcriptionally regulate Actin and Tubulin to drive morphogenesis of the micropylar cell , although the direct target downstream genes are unknown yet . In support of this possibility , a previous study in medaka showed that bundles of microtubules and tonofilaments are formed and elongated in the protruding cytoplasm of the micropylar cell during its penetration of the developing vitelline envelope [13] . In addition to its expression in the nucleus , Taz is also expressed in the cytoplasm of the micropylar cell , and enriched in the leading tip of cytoplasmic extension , where F-actin is extremely abundant . It is worthy of investigating if cytoplasmic Taz regulates the cytoskeleton , and the mechanism of regulation . In experiments to examine if the cytoskeleton is required for micropylar cell maintenance , Latrunculin B or Blebbistain was used to transiently inhibit Actin polymerization and Myosin II ATPase activity , respectively . Both inhibitors don’t have obvious effects on the morphology of micropylar cells ( S4H” and S4I” Fig ) . However , dissociation of F-actin results in delocalization of Taz from nucleus to cytoplasm in the micropylar cell ( S4H” Fig ) , while perturbation of Myosin II does not ( S4I” Fig ) . These results are similar to observations in mammalian cell culture [43] , indicating the cytoskeleton is required for maintenance of nuclear localization of Taz . The first molecular evidence of regulation of micropyle formation comes from studies in a zebrafish mutant bucky ball , which have revealed that proper animal-vegetal polarity of the oocyte is essential for micropyle formation [19 , 20] . In zebrafish buc mutant oocytes , the vegetal Balbiani body never forms , leading to an expansion of animal pole-specific gene expression ( e . g . vg1 ) and multiple micropyles form in buc mutant eggs [19 , 20] . Previous studies also found that extra territories of vg1 transcripts coincide with the locations of ectopic micropylar cells in buc mutant oocytes [19] . By contrast , in taz mutant oocytes , animal-vegetal polarity is normal and yet , no micropyle forms . Therefore , the polarity of the oocyte alone is insufficient to determine micropyle formation , and additional mechanisms must govern micropyle cell fate . Our work identifies a new view of regulation during specification of this cell , and shows that follicle cells at the animal pole induce the formation of the micropyle in a Taz-dependent manner .
Zebrafish ( Danio rerio ) were raised and maintained in the fish facility in accordance with standard procedures [44] under approval from the Institutional Review Board of Southwest University ( Chongqing , China ) . ABtü strain and subsequently generated taz mutant lines ( tazΔ10/Δ10 and tazΔ1/Δ1 ) were used in this study . Embryos or oocytes were collected and staged as described [12 , 45] . Embryos ( or tail fin clips ) were lysed in the lysis buffer ( 10 mM Tris pH 8 . 2 , 50 mM KCl , 0 . 3% Tween-20 , 0 . 3% Nonidet P40 , 0 . 5 μg/μl Proteinase K ( Fermentas ) ) at 55°C for 14 hours , followed by enzyme inactivation at 94°C for 20 minutes . The target sequence of taz gRNA , 5’-GGAGTCTCCCGGGGCTCGG-3’ ( PAM site underlined ) , was located in exon 1 of zebrafish taz gene . Zebrafish Cas9 mRNA and the taz gRNA were synthesized respectively according to the descriptions [46 , 47] . After ZCas9 mRNA ( 300 pg ) and taz gRNA ( 50 pg ) co-injection into one-cell stage wild type embryos , the lysate of 10 embryos at 24 hour post fertilization ( hpf ) was used as template for PCR with primers taz fw ( 5’-AGACCTGGACACGGATCTGGA-3’ ) and taz rv ( 5’-CACTGTATGCACTCCACTAACTGGT-3’ ) . PCR products were sequenced to examine potential indels created in the taz gRNA target region . Embryos co-injected with functional taz gRNA and ZCas9 mRNA were raised to adults ( F0 ) . F0 fish were screened to identify founders with progeny harboring the indels in taz gene previously found . Offsprings of identified F0 were raised . Individual F1 adults was reconfirmed by PCR using genomic DNA from tail fin clips , and indel types in fish were determined by sequencing . To detect taz Δ10 genotype , primers were designed to amplify specific bands by PCR with a common primer , taz fw2 ( 5’-CGATCGGACGCAGGAGGAACAA-3’ ) , and two reverse primers , taz wt rv ( 5’-CGGGTGTGGGAGTGGAGTC-3’ ) and taz Δ10 rv ( 5’- CGGGTGTGGGAGTGGAGCT-3’ ) . For taz Δ1 genotyping , the above taz fw and taz rv primers were utilized to obtain PCR products for sequencing . For preparation of zebrafish protein samples , 5 dpf embryos were homogenized in cold PBS with protease inhibitors ( Roche ) using syringe ( 1 ml ) and needle ( size 23G ) . The deyolked body fragments were collected and heated in whole cell lysis buffer ( 20 mM NaF , 1 mM DTT , 1 mM EDTA , 0 . 1 mM Na3VO3 , 10% glycerol , 0 . 5% Nonidet P40 , 280 mM KCl , 20 mM Hepes pH7 . 9 ) at 100°C for 10 minutes . Lysate supernatant was used for western blot analysis according to the standard protocol [48] . In this study , primary antibodies , anti-Taz ( CST , 1:1000 ) and anti-β-Tubulin ( Thermo , 1:1000 ) were used , while anti-mouse-IgG-HRP ( Thermo , 1:5000 ) and anti-rabbit-IgG-HRP ( Thermo , 1:5000 ) worked as secondary antibodies . Adult females were euthanized by overdose tricaine treatment according to the guidelines of experimental animal welfare from ministry of science and technology of People’s Republic of China ( 2006 ) , and abdominal tissue are removed by sharp scissors . Images were taken under a stereo microscope . Dissected ovaries were fixed in 4% PFA under room temperature for 2 hours , followed by images acquisition . Wild type and mutant ovaries were dissected from 8 month old females , and fixed overnight in saturated picric acid at room temperature . Fixed tissues were embedded in paraffin and sections were collected at 5-μm thickness using a microtome ( Leica ) . Haematoxylin and eosin staining was performed according to standard protocol . Ovaries were dissected from adult abdomen and fixed in 4% PFA at room temperature for 2 hours . After overnight immersion in 30% sucrose in PBS at 4°C , the tissues were embedded in O . C . T . compound ( Sakura ) and frozen in ethanol at -80°C . Frozen tissues were sectioned at 10-μm thickness using a Cryotome ( Leica ) . Serial sections were used for in situ hybridization as described previously [49] . Antisense RNA probes cyclinB [37 , 50] and pou2 [37 , 50] , labeled by fluorescein and digoxigenin , respectively , were used for marking animal poles in oocytes , while digoxigenin labeled dazl [35 , 51] and brl [52 , 53] were used to indicate vegetal poles . Whole mount and section in situ hybridization were performed according to methods used in a previous report to examine gene expression patterns of taz [54] . A nonspecific protein-staining dye , 2% Coomassie Brilliant Blue R ( CB ) , was dissolved in DMSO . Prior to staining , the stock buffer was diluted in PBS ( 1:10 ) . Eggs were collected in 5 minutes after activation , stained for 3 minutes , and rinsed thoroughly in PBS [55] . Stained eggs were examined and photographed under a stereo microscope . For oocyte activation , Stage V oocytes were gently extruded from adult females , and activated by water . For in vitro fertilization , sperms were collected from adult males into Hank’s buffer and performed fertilization according to standard procedure [44] . For Immunohistochemistry , dissected ovaries were fixed in 4% PFA for 2 hours at room temperature , and oocytes were isolated in PBS by sharp forceps . For in vitro oocyte culture , dissected ovaries were gently dissociated by a Pasteur pipette in 90% L15 ( pH9 . 0 ) medium ( Gibco ) with 0 . 5% BSA ( Sigma ) . Isolated oocytes were cultured in 90% L15 ( pH9 . 0 ) medium with 0 . 5% BSA and 2ug/ml 17α-DHP ( Sigma ) at 28 . 5°C for 8 hours . Latrunculin B ( Santa Cruz ) or ( - ) -Blebbistain ( MedChem Express ) , dissolved in DMSO , were supplemented into culture medium to final concentrations at 7 . 6 μM and 600 μM to inhibit Actin polymerization and Myosin II ATPase activity respectively . The frozen sections of ovaries were prepared as mentioned above . Ovary sections and oocytes were performed for immunohistochemistry as described previously [49] . Anti-Taz ( CST; 1:200 ) , anti-β-Catenin ( Sigma; 1:200 ) , anti-Nup107 ( BioLegend; 1:400 ) and anti-α-Tubulin ( Sigma; 1:200 ) were used as primary antibodies , and subsequent visualization was achieved by the application of secondary antibodies Alexa Fluor 488 , Alexa Fluor 555 and Alexa Fluor 647 ( Life Technology; 1:400 ) . A solution of 4% BSA in PBS was used for blocking and diluting antibodies . FITC-Phalloidin ( Sigma , 1:200 ) was used to detect F-actin . In some experiments , the animal pole was first labeled by in situ hybridization using cyclinB probe , followed by immunohistochemistry according to standard procedure . Before covering with Vectashield ( Vector lab ) , DAPI ( Roche ) was employed to stain the nuclei . Images were acquired on a Zeiss LSM700 confocal microscope . Brightness of green fluorescence was slightly digitally enhanced to clearly show cytoplasmic expression of Taz on sectioned late stage III oocytes ( see Figs 5C , 7C’ and 7F’ ) . | In many fish , sperm enters eggs through a specialized channel called the ‘micropyle’ . The micropyle is formed by a special follicle cell , the ‘micropylar cell’ , which sits on the top of the developing egg during oogenesis , and forms the sperm entry canal . The underlying mechanisms of this process are unknown . We find that Taz , an effector of an important signaling pathway , the Hippo pathway , is specifically enriched in micropylar cells in zebrafish , and regulates formation of the micropyle . Loss of Taz function in females results in no micropylar cells , failure to form a micropyle on eggs , which are consequently , not fertilized . Our study identifies a new role for the Hippo/Taz pathway in cell fate specification in the ovary , and reveals a potential mechanism for forming the sperm entry port . Similar mechanisms might operate in other fish as well . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"fish",
"reproductive",
"system",
"vertebrates",
"animals",
"reproductive",
"physiology",
"germ",
"cells",
"animal",
"models",
"osteichthyes",
"developmental",
"biology",
"oocytes",
"model",
"organisms",
"experimental",
"organism",
"systems",
"embryos",
"cellular",
"structures",
"and",
"organelles",
"cytoskeleton",
"sperm",
"research",
"and",
"analysis",
"methods",
"embryology",
"animal",
"cells",
"animal",
"studies",
"fertilization",
"ovaries",
"zebrafish",
"eukaryota",
"cell",
"biology",
"ova",
"anatomy",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"oogenesis",
"organisms"
] | 2019 | The effector of Hippo signaling, Taz, is required for formation of the micropyle and fertilization in zebrafish |
Homeostatic synaptic plasticity is a negative-feedback mechanism for compensating excessive excitation or inhibition of neuronal activity . When neuronal activity is chronically suppressed , neurons increase synaptic strength across all affected synapses via synaptic scaling . One mechanism for this change is alteration of synaptic AMPA receptor ( AMPAR ) accumulation . Although decreased intracellular Ca2+ levels caused by chronic inhibition of neuronal activity are believed to be an important trigger of synaptic scaling , the mechanism of Ca2+-mediated AMPAR-dependent synaptic scaling is not yet understood . Here , we use dissociated mouse cortical neurons and employ Ca2+ imaging , electrophysiological , cell biological , and biochemical approaches to describe a novel mechanism in which homeostasis of Ca2+ signaling modulates activity deprivation-induced synaptic scaling by three steps: ( 1 ) suppression of neuronal activity decreases somatic Ca2+ signals; ( 2 ) reduced activity of calcineurin , a Ca2+-dependent serine/threonine phosphatase , increases synaptic expression of Ca2+-permeable AMPARs ( CPARs ) by stabilizing GluA1 phosphorylation; and ( 3 ) Ca2+ influx via CPARs restores CREB phosphorylation as a homeostatic response by Ca2+-induced Ca2+ release from the ER . Therefore , we suggest that synaptic scaling not only maintains neuronal stability by increasing postsynaptic strength but also maintains nuclear Ca2+ signaling by synaptic expression of CPARs and ER Ca2+ propagation .
Synaptic scaling , a form of homeostatic synaptic plasticity , is a negative feedback process that stabilizes neuronal activity in response to changes in synaptic strength by altering various aspects of neuronal function [1] . It has been implicated in neurodevelopment and in neurological disorders [2]–[5] . One of the mechanisms underlying synaptic scaling is the regulation of synaptic strength through control of delivery or retention of AMPARs at synapses [1] . During homeostatic adaptation , synaptic AMPARs are increased or reduced in response to activity deprivation or overexcitation , respectively , by altering AMPAR synaptic insertion and internalization [6] . Synaptic adaptation can be global and multiplicative , which is important for preserving the relative strength differences between synapses . Because each synapse strength is multiplied or divided by the same factor , each synaptic strength is increased or decreased in proportion to its initial strength [7] . Synaptic scaling is also induced by synapse-specific processes , providing local control of synaptic strength [8] . Numerous treatments that induce homeostatic regulation but differ in their experimental conditions have been reported . Nonetheless , the homeostatic plasticity mechanism is still not well understood . Here , we describe a novel mechanism in which activity deprivation induces synaptic scaling by a calcineurin-mediated process . AMPARs are the major excitatory postsynaptic glutamate receptor in the central nervous system and consist of four subunits ( GluA1–4 ) [9] . There are two general types of AMPARs formed through combination of these subunits , Ca2+-impermeable GluA2-containing and Ca2+-permeable , GluA2-lacking/GluA1-containing receptors [10] . Ca2+-permeable AMPARs ( CPARs ) are generally sensitive to polyamine block , although there is a third class of AMPARs that are Ca2+-permeable but insensitive to polyamines [11] . The GluA1 and GluA2 AMPAR subunits can assemble channels with markedly different electrophysiological and trafficking properties [10] , [11] and both GluA1 and GluA2 can contribute to homeostatic synaptic plasticity [1] , [12] , [13] . Phosphorylation of GluA1 within its intracellular carboxyl-terminal domain can regulate AMPAR membrane trafficking and channel open probability [14] . Phosphorylation of serine 845 in GluA1 [pGluA1 ( S845 ) ] is important for activity-dependent trafficking of GluA1-containing AMPARs , and cAMP-dependent protein kinase A ( PKA ) and cGMP-dependent protein kinase II ( cGKII ) can mediate this phosphorylation [14] , [15] . The Ca2+/calmodulin-dependent protein phosphatase , calcineurin , dephosphorylates pGluA1 ( S845 ) , which enables GluA1-containing AMPARs to be endocytosed from the plasma membrane during long-term depression [16] , [17] . Therefore , activity-dependent GluA1 phosphorylation can play critical roles in GluA1 synaptic trafficking and forming CPARs in synapses . The most studied experimental system for synaptic scaling is the inhibition of neuronal activity by TTX ( tetrodotoxin ) , which blocks sodium channels and thereby inhibits action potentials . TTX-dependent chronic inhibition of action potentials results in an increase in the strength of synaptic transmission as a compensatory process that can be measured by increases in AMPAR-mediated miniature excitatory postsynaptic currents ( mEPSCs ) [18] . Recent studies suggest that TTX reduces somatic Ca2+ influx and inhibits activation of Ca2+/calmodulin-dependent protein kinase IV ( CaMKIV ) , which promotes synaptic scaling [19] . CaMKs are important for Ca2+-dependent synaptic plasticity [20] , and inhibition of Ca2+ influx is sufficient to induce AMPAR-mediated synaptic scaling [21] , [22] . This suggests that reduction of CaMK activation and downstream signaling by activity deprivation-induced inhibition of Ca2+ influx can play a critical role in AMPAR-dependent homeostatic scaling , yet there is no complete molecular mechanism linking activity-dependent Ca2+ signals and homeostatic regulation of AMPARs . Here , we focus on the role of synaptic Ca2+ and calcineurin in synaptic scaling . We show that activity suppression reduces Ca2+ influx in neurons , which in turn decreases the activity of calcineurin . This stabilizes pGluA1 ( S845 ) , which increases synaptic CPARs . This increases synaptic strength as a compensatory response to activity deprivation and restores synapse-to-nucleus Ca2+ signaling via ER Ca2+ wave propagation . Thus , we conclude that synaptic scaling via calcineurin and CPARs provides a means to maintain not only synaptic activity but also Ca2+ signaling as a homeostatic response .
To confirm activity-dependent homeostatic scaling , we studied spontaneous synaptic transmission by measuring mEPSCs in DIV14–17 cultured mouse cortical neurons ( Figure 1a ) and found that treatment for 48 h with 2 µM TTX significantly increased average mEPSC amplitude ( no TTX , 19 . 68±0 . 99 pA and 48 TTX , 28 . 01±1 . 12 pA , p< . 0001 ) ( Figure 1b ) consistent with the previous finding [23] , whereas mEPSC frequency was not altered ( Figure 1c ) . Importantly , cumulative probability distributions of the mEPSC amplitude were uniformly increased by TTX treatment , and the increase in the amplitude with TTX treatment was multiplicative ( Figure 1e ) . There was a significant decrease in mEPSC decay time ( peak to 10% ) with TTX treatment ( no TTX , 2 . 66±0 . 15 ms and 48 TTX , 1 . 98±0 . 05 ms , p = . 0008 ) ( Figure 1d ) . Because CPARs show a shorter decay time [21] , [24] , we used 20 µM naspm ( 1-naphthyl acetyl spermine ) or 5 µM PhTX ( philanthotoxin-74 ) , blockers of CPARs , to determine if CPARs were responsible for the TTX-mediated increase of the amplitude ( Figure 1a ) . Consistent with previous findings [21] , [23] , [25] , naspm and PhTX treatment significantly reduced the TTX-induced increase in amplitude ( 48 TTX , 28 . 01±1 . 12 pA; 48 TTX+naspm , 21 . 31±0 . 44 pA , p = . 0002; and 48 TTX+PhTX , 18 . 50±0 . 58 pA , p< . 0001 ) ( Figure 1b ) , but frequency was not affected ( Figure 1c ) . Naspm and PhTX also significantly increased decay time ( 48 TTX , 1 . 98±0 . 05 ms; 48 TTX+naspm , 2 . 47±0 . 15 ms , p = . 0186; and 48 TTX+PhTX , 2 . 38±0 . 07 ms , p = . 0453 ) ( Figure 1d ) as found previously [21] . CPAR inhibitors had no effects on mEPSCs of neurons in the absence of TTX treatment , suggesting that CPARs made no contribution under the basal condition ( Figure 1 ) . Thus , TTX treatment induced CPAR-mediated multiplicative synaptic scaling . Because pGluA1 ( S845 ) is required not only for homeostatic scaling in the visual cortex [26] but also for maintaining CPARs on the synaptic membrane [27] , we measured the effects of TTX on pGluA1 ( S845 ) levels by purifying synaptosomes from TTX-treated neurons and measuring protein and phosphorylation levels of AMPAR subunits . TTX treatment significantly increased pGluA1 ( S845 ) ( p = . 024 ) , whereas total GluA1 and GluA2/3 levels were not changed ( Figure 2a ) . We further determined that surface GluA1 levels were increased ( p = . 0127 ) after TTX treatment , whereas surface GluA2/3 was not altered ( Figure 2b ) . We next analyzed mutant GluA1 ( GluA1 S845A , unable to be phosphorylated on serine 845 ) using GluA1 S845A knock-in mice [28] and found that TTX treatment was unable to induce synaptic scaling in neurons from the mutant mouse ( Figure 2c ) . This suggested that TTX treatment enhanced GluA1 surface trafficking by increasing pGluA1 ( S845 ) . This newly trafficked GluA1 could be in the form of CPARs that promote synaptic scaling . Increasing pGluA1 ( S845 ) can be achieved either by enhancing kinase activity or by decreasing phosphatase activity . A-kinase anchoring protein ( AKAP ) and SAP97 form a protein complex with GluA1 that tethers PKA and calcineurin , which regulate channel functions , respectively , through GluA1 phosphorylation and dephosphorylation [29] , [30] . Therefore , reduction of calcineurin activity is a candidate for mediating an increase of pGluA1 ( S845 ) in response to the TTX-induced reduction of Ca2+ influx . We found that calcineurin protein levels were significantly decreased ( p = . 0414 ) in synaptosomes following TTX treatment ( Figure 2a ) . To measure in vivo calcineurin activity directly , we used a fluorescence resonance energy transfer ( FRET ) -based calcineurin activity sensor that utilizes a calcineurin activity-dependent molecular switch based on the N-terminal regulatory domain of nuclear factor of activated T cells ( NFAT ) as a specific substrate , which was inserted between CFP and YFP [31] . Inhibition of calcineurin activity by 12 h treatment with 5 µM FK506 , which forms a drug-immunophilin complex that is a highly specific inhibitor for calcineurin [32] , significantly decreased FRET activity ( assayed by measuring the emission ratio ) as compared with that under the basal condition ( no TTX , 1 . 45±0 . 02 and FK506 , 1 . 06±0 . 01 , p< . 0001 ) , which confirmed that the reporter detected calcineurin activity ( Figure 3 ) . Calcineurin activity was significantly decreased after 24 h TTX treatment and further reduced after 48 h TTX treatment , whereas 12 h TTX had no effect on the emission ratio ( 12 TTX , 1 . 43±0 . 02; 24 TTX , 1 . 33±0 . 02 , p< . 0001; and 48 TTX , 1 . 20±0 . 01 , p< . 0001 ) ( Figure 3 ) . This suggested that chronic inhibition of neuronal activity decreased calcineurin activity in a time-dependent manner and lowered synaptic calcineurin levels . Calcineurin inhibition affects both mEPSC frequency and amplitude [33] , [34] and stabilizes pGluA1 ( S845 ) [35] , and reduction of cytoplasmic Ca2+ lowers calcineurin activity , followed by enhancement of GluA1-containing AMPAR-mediated transmission [36] . To determine whether inhibition of calcineurin was sufficient for inducing a pharmacologic form of synaptic scaling in the absence of TTX treatment , we next blocked calcineurin activity by 12 h treatment with 5 µM FK506 and measured mEPSCs ( Figure 4a ) . FK506 treatment significantly increased mEPSC amplitude compared with DMSO treatment ( DMSO , 20 . 38±1 . 08 pA and FK506 , 28 . 54±1 . 41 pA , p< . 0001 ) ( Figure 4b ) . Consistent with a previous study showing that inhibition of calcineurin increases mEPSC frequency [33] , [34] through calcineurin modulation of presynaptic activity [37] , we found increased mEPSC frequency in FK506-treated neurons ( DMSO , 4 . 21±0 . 37 Hz and FK506 , 9 . 88±0 . 27 Hz , p< . 0001 ) ( Figure 4c ) . Furthermore , the mEPSC decay time in FK506-treated neurons was significantly faster ( DMSO , 2 . 51±0 . 10 ms and FK506 , 1 . 96±0 . 10 ms , p = . 0047 ) , suggesting that CPARs mediated the scaling induced by FK506 ( Figure 4d ) . Moreover , cumulative probability distributions were uniformly shifted by FK506 , and the increase in the amplitude was multiplicative ( Figure 4e ) . We confirmed CPAR-mediated scaling in FK506-treated neurons by adding naspm or PhTX ( Figure 4a ) , which caused a significant reduction of mEPSC amplitude ( FK506 , 28 . 54±1 . 41 pA; FK506+naspm , 20 . 31±1 . 14 pA , p< . 0001; and FK506+PhTX , 19 . 33±0 . 76 pA , p< . 0001 ) , whereas no effect was observed following naspm or PhTX treatment of DMSO-treated neurons ( Figure 4b ) . There were no significant changes in mEPSC frequency after napsm or PhTX treatment of either DMSO or FK506-treated neurons ( Figure 4c ) . Moreover , the FK506-induced change in decay time was reversed by naspm and PhTX only for the FK506-treated neurons ( FK506 , 1 . 96±0 . 10 ms; FK506+naspm , 2 . 44±0 . 20 ms , p = . 0152; and FK506+PhTX , 2 . 70±0 . 15 ms , p = . 0003 ) ( Figure 4d ) . Similar to the effects of 48 h TTX treatment , FK506 treatment increased pGluA1 ( S845 ) ( p = . 0474 ) in synaptosomes , whereas total GluA2/3 and GluA1 levels were not altered ( Figure 5a ) . Surface GluA1 was significantly elevated with FK506 treatment ( p< . 0001 ) ( Figure 5b ) . Moreover , FK506 treatment significantly reduced calcineurin levels in synaptosomes ( p = . 0299 ) ( Figure 5a ) . These results indicated that inhibition of calcineurin by FK506 was sufficient to induce synaptic trafficking of CPARs by increasing pGluA1 ( S845 ) and that FK506 could produce a pharmacologic form of synaptic scaling without TTX-mediated activity deprivation . To test whether persistent calcineurin activity could block TTX-mediated synaptic scaling , we generated a constitutively active calcineurin mutant , which has Ca2+-independent , constitutive phosphatase activity , by deleting the calcineurin autoinhibitory domain ( CaN-ΔAI ) [38] . As expected , when we cotransfected HEK293 cells with GluA1 and CaN-ΔAI , pGluA1 ( S845 ) levels were significantly lower ( p = . 0198 ) than in cells transfected with GluA1 alone ( Ctrl ) ( Figure 6a ) . Although CaN-ΔAI decreased pGluA1 ( S845 ) , surface GluA1 levels remained unaffected in cultured neurons ( Figure S1a ) . When CaN-ΔAI was cotransfected with GFP into neurons and we measured mEPSCs after 48 h TTX treatment , we found that TTX was unable to induce synaptic scaling in the presence of CaN-ΔAI ( Figure 6b–e ) . However , TTX treatment of neurons expressing GFP alone induced a typical CPAR-mediated synaptic scaling ( Figure 6b–e ) as seen previously , with increased mEPSC amplitude ( no TTX , 12 . 98±0 . 47 pA and 48 TTX , 18 . 21±1 . 52 pA , p< . 0001 ) ( Figure 6c ) and decreased decay time ( no TTX , 4 . 37±0 . 31 ms and 48 TTX , 3 . 05±0 . 31 pA , p = . 0072 ) ( Figure 6e ) , whereas frequency of mEPSCs was not altered ( Figure 6d ) . This suggested that a gain-of-function calcineurin mutant could inhibit TTX-induced synaptic scaling . Ca2+ signals are thought to be important for synaptic scaling , which suggests that a reduction of Ca2+ influx may be a critical trigger for synaptic scaling [1] , [19] , [21] , [22] . Furthermore , lowering cytoplasmic Ca2+ levels has been reported to enhance GluA1-containing AMPAR-mediated transmission [36] . We investigated Ca2+ activity in cultured neurons transfected with GCaMP5 , a genetically encoded Ca2+ indicator [39] ( Figure 7a ) . We found active spontaneous Ca2+ transients in neurons without TTX treatment ( Figure 7b–c ) . To determine the effects of action potentials and mEPSC activity on Ca2+ transients , we first added TTX at the time of imaging and found that acute TTX treatment completely blocked Ca2+ activity ( p< . 0001 ) ( Figure 7a–c ) . Furthermore , naspm treatment of neurons in the absence of TTX treatment had no significant effect on Ca2+ transients ( Figure 7a–c ) , suggesting that action potentials play a critical role in generating the Ca2+ activity observed under these conditions , and that this activity is not dependent on CPARs . In contrast , following 48 h treatment with TTX , about 50% of the Ca2+ signal was restored ( p = . 0002 ) , and this restored activity observed in the presence of TTX was significantly reduced by naspm ( p = . 0079 ) ( Figure 7a–c ) . This suggested that TTX-induced scaling provided a mechanism for maintaining Ca2+ activity that was dependent in part upon the synaptic expression of CPARs . We next investigated effects of calcineurin inhibition on Ca2+ signals ( Figure 8a ) . Neurons with 12 h DMSO treatment displayed normal Ca2+ activity , and acute TTX treatment completely inhibited the activity ( p< . 0001 ) ( Figure 8a–c ) . Neurons treated for 12 h with FK506 showed active spontaneous Ca2+ transients comparable to those in neurons without TTX treatment ( Figure 8a–c ) . Conversely , when TTX was acutely added at the time of imaging to neurons that had been treated for 12 h with FK506 , TTX was unable to block the Ca2+ signals completely ( p = . 005 ) ( Figure 8a–c ) . To determine whether this Ca2+ signal activity was mediated by CPARs , naspm was added to neurons at the time of recording ( Figure 8a ) . Naspm significantly reduced the activity ( p = . 0069 ) , indicating it was from CPARs ( Figure 8a–c ) . Furthermore , we confirmed that CaN-ΔAI blocked synaptic scaling-mediated recovery of Ca2+ signals ( p< . 0001 ) ( Figure S1b ) , consistent with the finding that CaN-ΔAI inhibited TTX-induced synaptic scaling ( Figure 6c ) . This suggested that calcineurin activity is important for both synaptic scaling and Ca2+ homeostasis mediated by CPARs , which partially restored Ca2+ signaling . Taken together , these results demonstrate that CPAR/calcineurin-dependent synaptic scaling provides a mechanism for homeostasis of Ca2+ signals in part as a homeostatic response to activity deprivation-induced inhibition of Ca2+ activity . Both extracellular and intracellular sources of Ca2+ are used by neurons [40] . Although Ca2+ influx from extracellular sources is mediated by various Ca2+ channels including NMDA receptors ( NMDARs ) at synapses and voltage-gated Ca2+ channels in the plasma membrane , inositol 1 , 4 , 5-trisphosphate receptors ( IP3Rs ) and ryanodine receptors ( RyRs ) in the ER are responsible for intracellular Ca2+ release [40] . We first investigated which Ca2+ sources were responsible for GCaMP5-positive Ca2+ signals ( Figure 9a ) . To address this question , we blocked each Ca2+ channel and measured spontaneous Ca2+ signals without drug pretreatment ( Figure 9a–b ) . When we acutely treated neurons with 10 µM nifedipine , an L-type Ca2+ channel blocker , spontaneous Ca2+ signals were unaltered , but the NMDAR antagonist , 50 µM APV , significantly reduced Ca2+ activity ( p< . 0001 ) , suggesting that GCaMP5 detected Ca2+ signals including those from NMDARs but not from L-type Ca2+ channels ( Figure 9a–b ) . We next depleted Ca2+ from the ER by inhibiting sarco/endoplasmic reticulum Ca2+-ATPase using 1 µM thapsigargin and found that thapsigargin treatment completely inhibited Ca2+ activity ( p< . 0001 ) ( Figure 9a–b ) . Moreover , blocking both IP3Rs and RyRs by 50 µM 2APB and 25 µM dantrolene significantly lowered Ca2+ signals ( p< . 0001 ) , suggesting that GCaMP5 detected Ca2+ released from the ER , possibly dependent on the activity of NMDARs ( Figure 9a–b ) . This further suggested that GCaMP5-positive Ca2+ signals restored by synaptic scaling were mediated by ER Ca2+ release . NMDAR-mediated synaptic Ca2+ influx evokes Ca2+ signals in the nucleus via Ca2+ wave propagation through the ER [40] . This Ca2+ signaling is essential for synaptic plasticity and regulates gene expression through CREB in addition to local signaling in synapses [40] . Because an NMDAR antagonist blocks CREB activation [40] and CPARs also regulate ER Ca2+ release [41] , we hypothesized that CPARs replace the role of NMDARs in synapse-to-nucleus Ca2+ signaling via the ER Ca2+ release when neuronal activity is chronically suppressed by TTX . Consistent with previous findings [42] , [43] , we found that CREB activity ( assayed by measuring phosphorylation at serine 133 of CREB ) was reduced with 6 h treatment of 2 µM TTX ( p = . 0004 ) or 1 µM thapsigargin ( p = . 0019 ) , confirming that CREB activity was dependent on both neuronal activity and ER Ca2+ ( Figure 9c ) . However , after synaptic scaling was induced by 48 h treatment with TTX , CREB activity was significantly increased , suggesting that ER Ca2+ signals restored by synaptic scaling provided a means to maintain CREB phosphorylation in the nucleus ( Figure 9c ) . Treatment with 20 µM naspm for 6 h significantly reduced the CREB phosphorylation seen in neurons pretreated with TTX for 48 h ( p< . 0001 ) , suggesting CPARs were responsible for homeostasis of CREB phosphorylation ( Figure 9c ) . Taken together , this work shows that when neuronal activity is suppressed by TTX , synaptic scaling maintains basal CREB activity via synapse-to-nucleus Ca2+ signals by expression of CPARs at synapses and by ER Ca2+ waves .
We demonstrate a novel Ca2+ homeostasis-dependent mechanism of synaptic scaling mediated by calcineurin and CPARs . Based on our findings , we propose the following model . Under basal conditions , action potentials provide synaptic Ca2+ signals via NMDARs , followed by Ca2+-induced Ca2+ release from the ER , leading to nuclear Ca2+ signals that maintain CREB-mediated transcriptional activity . In addition , synaptic Ca2+ influx activates calcineurin , which removes GluA1 from the synaptic membrane by dephosphorylating pGluA1 ( S845 ) , providing a balance between GluA1 insertion by kinases and removal by phosphatases in synapses ( Figure 10 ) . However , under the condition of activity deprivation , NMDAR-mediated synaptic Ca2+ influx is inhibited , leading to inactivation of calcineurin . This induces synaptic expression of CPARs via stabilization of pGluA1 ( S845 ) , thereby enhancing synaptic strength and promoting synaptic Ca2+ influx via CPARs instead of NMDARs ( Figure 10 ) . This restores Ca2+ signals and CREB phosphorylation and activation . We thus suggest that synaptic scaling not only maintains neuronal activity by increasing CPAR-dependent postsynaptic strength but also maintains CREB activation by synapse-to-nucleus Ca2+ signaling . Although it has been shown that postsynaptic AMPARs play a critical role in homeostatic synaptic plasticity , there is no generally agreed mechanism for synaptic scaling , possibly due to the fact that multiple experimental conditions have been investigated [13] . Many studies conducted in several experimental models support a role for this plasticity mediated by GluA1-containing AMPARs . For example , various protocols have been used to inhibit neuronal activity and induce synaptic scaling in cultured neurons , such as inhibition of action potentials by TTX [23] , AMPARs by NBQX [21] , L-type Ca2+ channels by nifedipine [21] , or NMDARs and action potentials together by APV and TTX [22] . Regardless of inhibition protocols , each treatment induced CPAR-dependent synaptic scaling . Furthermore , visual deprivation in the cortex is sufficient for inducing CPAR-dependent homeostatic synaptic plasticity in vivo [26] . Nonetheless , the cellular mechanism by which neurons detect activity deprivation and what is the molecular readout of this signal that regulates postsynaptic AMPARs for synaptic scaling has not yet been identified . A recent study by Gainey et al . employing GluA2 knockdown reports that GluA2 is required for homeostatic synaptic plasticity [44] . It is possible that the increased expression of synaptic CPARs that occurs in the GluA2 knockdown prior to addition of TTX increases synaptic Ca2+ fluxes that prevent further CPAR synaptic trafficking required for synaptic scaling . In contrast , the GluA2 knockout exhibits normal synaptic scaling after chronic TTX treatment [45] . Significantly , in the knockout of GluA2 , GluA1 levels at synapses are lower than in the wild type [46] , a change which would generate significantly lower Ca2+ flux than the knockdown , which in turn would make synaptic scaling possible . Given these considerations , the experimental findings of the others are consistent with the current work . Ca2+ influx in response to synaptic stimulation or action potentials plays an important role in regulating various neuronal functions including releasing neurotransmitter , modulating ion channels , and promoting synaptic plasticity and gene expression [47] , [48] . Somatic Ca2+ levels are thought to be an important activity sensor in homeostatic synaptic plasticity [1] , [19] , [49] . Downstream effectors of Ca2+ signaling including CaMKs and adenylyl cyclases can be molecular readouts of the Ca2+ influx [13] . Because chronic neuronal inactivation reduces Ca2+ influx and downregulates adenylnly cyclases [50] , cAMP-dependent PKA activity is unlikely elevated by TTX to increase phosphorylation of S845 of GluA1 , although it needs further investigation . Calcineurin is the only Ca2+/calmodulin-activated phosphatase in the brain , and it is a major regulator of several key proteins mediating synaptic transmission and neuronal excitability in both pre- and postsynaptic areas [51] . Due to the fact that calcineurin inhibition promotes an increase in both mEPSC frequency and amplitude , it has been proposed to have a role in homeostatic synaptic plasticity [33] . Moreover , lowering basal Ca2+ levels has been shown to strengthen AMPAR-mediated transmission , which is dependent on GluA1 and calcineurin [36] . A computational modeling study predicts that calcineurin can be active at moderate Ca2+ concentrations , whereas the activity of PKA requires high Ca2+ levels [52] . It is thus possible that calcineurin can remain active in the short term , even after action potentials and synaptic Ca2+ influx are abolished by TTX . Consistent with this study , we found that calcineurin activity was not reduced immediately after TTX treatment , and the reduction was found after a 24 h treatment with TTX ( Figure 3 ) . This persistence of activity may explain why a significant length of time of application of TTX is required to express synaptic scaling . It has been shown that calcineurin inhibition increases pGluA1 ( S845 ) and selectively increases synaptic expression of CPARs [17] . Further investigation is required to determine how calcineurin inhibition selectively increases CPARs , given that it could potentially affect both GluA1 homomeric and GluA1/2 heteromeric AMPARs . We also showed that CaN-ΔAI was sufficient for reducing pGluA1 ( S845 ) levels , although surface GluA1 levels were not altered , which accounts for normal mEPSCs ( Figure 6 and Figure S1a ) . Although it is not clear how normal levels of surface GluA1 are maintained when S845 phosphorylation is decreased , this is not surprising because several lines of studies already show that ( 1 ) there is normal synaptic transmission in the hippocampus of CaN-ΔAI overexpressed transgenic mice [53]; ( 2 ) in GluA1 S845A mutant mice , surface GluA1 levels are not affected [28]; and ( 3 ) GluA1 S845A mice also display normal mEPSCs in the amygdala [54] . Taken together , phosphorylation of GluA1 to yield pGluA1 ( S845 ) may not be critical for maintaining basal synaptic transmission , but can be important for activity-dependent plasticity , such as long-term potentiation or synaptic scaling . It has been shown that lowering calcineurin activity with cyclosporin A , another calcineurin inhibitor , decreases not only enzymatic activity but also calcineurin protein levels [55] , consistent with our findings ( Figures 2a and 5a ) , suggesting that inactive calcineurin may be degraded . Neuronal activity regulates synaptic proteins and signaling through the ubiquitin-proteasome system , providing a mechanism that links activity and protein turnover [56] . Ca2+ entry is an important process regulated by neuronal activity that promotes a decrease of protein ubiquitination in synapses , which depends on calcineurin activity [57] . Calcineurin also can be ubiquitinated and undergo proteolysis in cardiomyocytes [58] . Thus , it is possible that chronic inhibition of neuronal activity decreases calcineurin activity by lowering Ca2+ influx , which promotes increased protein ubiquitination , including ubiquitination of calcineurin itself , followed by proteasome-mediated degradation , although this requires further investigation . During activity deprivation , synaptic Ca2+ influx is reduced , possibly followed by inhibition of downstream Ca2+ signaling [1] , [6] , [12] . Synaptic scaling may provide a mechanism to overcome these problems . GluA1-containing CPARs are an attractive candidate for restoration of Ca2+ activity during synaptic scaling because unlike GluA2-containing , Ca2+-impermeable AMPARs , they not only have larger single channel conductance but also are Ca2+-permeable [59] . Based on our findings , we suggest that during homeostatic synaptic scaling , CPARs are stabilized in synapses and conduct Ca2+ , which increases synaptic strength and also partially restores suppressed synaptic Ca2+ signals as shown by the finding that GCaMP5 predominantly detected Ca2+ release from the ER ( Figures 7–9 ) . In addition , cytosolic Ca2+ levels may reflect neuronal activity on a cell-wide basis , permitting Ca2+-dependent mechanisms to control all synapses , a feature of multiplicative homeostatic synaptic plasticity . Therefore , recruitment of CPARs may provide feedback regulation to maintain neuronal activity and Ca2+ signaling during synaptic scaling . CaMKs are important for Ca2+-dependent synaptic plasticity , and reduction of CaMKIV activation is sufficient for inducting synaptic scaling without TTX treatment [19] , [20] . CaMKIV-mediated activation of the CREB transcription factor is important for synaptic plasticity and learning [40] . Thus , it is possible that inhibition of CaMKIV activity reduces CREB activation and promotes synaptic scaling . Because the neuronal CREB transcription factor regulates various signaling pathways including those for learning , addiction , and pain [40] , homeostasis of basal CREB activity can be important for brain function and may be maintained by synaptic scaling . Thus , synaptic scaling can provide a mechanism for maintaining basal levels of CREB-mediated transcriptional activity via synaptic Ca2+ and CaMKIV when neuronal activity is suppressed . Our data support this idea by showing that ( 1 ) TTX inhibited somatic Ca2+ signals , ( 2 ) TTX treatment reduced CREB phosphorylation , and ( 3 ) synaptic scaling restored Ca2+ signaling and CREB activation . In summary , we conclude that synaptic scaling not only maintains neuronal stability by increasing CPAR-dependent postsynaptic strength but also maintains basal CREB transcriptional activity through nuclear Ca2+ signaling as a homeostatic response to suppression of neuronal activity .
Cortical primary neurons were prepared by a modification of the previously described method [60] . Neurons were isolated from embryonic day 17–18 C57Bl6 or GluA1 S845A mouse embryonic brain tissues . All animal studies were performed with an approved protocol from New York University Langone Medical Center's Institutional Animal Care and Use Committee . Neurons were plated on poly-L-lysine–coated 15 cm dishes for biochemical experiments , size 12 mm cover glasses for electrophysiology and FRET assay , or glass-bottom dishes for Ca2+ imaging . Cells were grown in Neurobasal medium with B27 and 0 . 5 mM Glutamax ( Life Technologies ) . For neuronal transfection , DIV4 neurons were transfected with Lipofectamine 2000 ( Life Technologies ) according to the manufacturer's protocol , and analysis was performed at DIV14–17 . Constitutively active calcineurin mutant ( CaN-ΔAI ) was generated according to the previous study [38] . A stop codon was introduced at lysine 399 of wild-type murine calcineurin alpha ( Addgene , 17871 ) to produce CaN-ΔAI that lacked the calmodulin-binding and autoinhibitory domains , leading to Ca2+-independent , constitutive phosphatase activity [38] . CaN-ΔAI was cotransfected with GluA1 into HEK293 cells using Lipofectamine 2000 ( Life Technologies ) to confirm expression and pGluA1 ( S845 ) levels by immunoblots . For surface GluA1 leveling , CaN-ΔAI was cotransfected with mCherry into neurons , and surface GluA1 was determined by incubation of a GluA1 antibody ( Calbiochem , PC246 ) under the nonpermeable condition . The Alexa Fluor-488 secondary antibody ( Molecular Probes , A-11008 ) was used to visualize surface GluA1 , and proximal dendrites ( <100 µm from the cell body ) were captured using Zeiss Axiovert 200 m . Images were analyzed by the ImageJ software . Miniature EPSCs were measured in cortical neurons cultured from C57Bl6 or GluA1 S845A embryos at DIV14–17 as described previously [60] . Neurons were voltage clamped with the whole cell ruptured path technique during the recording . The bath solution contained ( in mM ) 119 NaCl , 5 KCl , 2 . 5 CaCl2 , 1 . 5 MgCl2 , 30 glucose 20 HEPES ( Life Technologies ) , and 0 . 001 glycine ( Sigma ) , pH 7 . 4 . Patch electrodes ( 5–8 MΩ ) were filled with ( in mM ) 120 K-gluconate ( Sigma ) , 9 NaCl , 1 MgCl2 , 10 HEPES , 0 . 2 EGTA ( Sigma ) , 2 Mg-ATP ( Sigma ) , and 0 . 2 GTP ( Sigma ) . We added 1 µM TTX ( Tocris Biosciences ) and 10 µM bicuculline ( Tocris Biosciences ) to the bath to inhibit action potentials and miniature inhibitory postsynaptic currents , respectively . mEPSCs were recorded at −60 mV with a Warner amplifier ( PC-501A ) and filtered at 1 kHz . Recordings were digitized ( Digidata 1440 , Molecular Devices ) and analyzed using the Mini Analysis software ( Synaptosoft ) . The access resistance ( Ra<25 MΩ ) was monitored during recording to eliminate artifacts . Events whose amplitude was less than 7 . 5 pA were rejected . To induce synaptic scaling , neurons were pretreated with 2 µM TTX for 48 h or 5 µM FK506 or DMSO for 12 hrs . We added 20 µM naspm ( 1-naphthylacetyl spermine trihydrochloride , Tocris Biosciences ) or 5 µM philanthotoxin-74 ( Tocris Biosciences ) to suppress CPAR-mediated transmission in the bath solution . For the CaN-ΔAI experiment , GFP was cotransfected with CaN-ΔAI to visualize transfected neurons , and mEPSCs were analyzed at DIV14–17 . Synaptosomal fractions from DIV14 primary cortical neurons were prepared as described previously [60] , [61] . Surface biotinylation was performed according to the previous study [60] . Equal amounts of protein were loaded on 10% SDS-PAGE gel and transferred to the nitrocellulose membrane . Membranes were blotted with GluA1 ( Millipore , 1∶5 , 000 ) , GluA2/3 ( Millipore , 1∶500 ) , pGluA1 ( S845 ) ( Millipore , 1∶1 , 000 ) , calcineurin ( Millipore , 1∶1 , 000 or Santa Cruz Biotechnology , 1∶500 ) , actin ( Sigma , 1∶5 , 000 ) , pCREB ( Cell Signaling , 1∶500 ) , and CREB ( Cell Signaling , 1∶1 , 000 ) antibodies and developed with ECL ( Perkin Elmer ) . Synaptosomes were isolated from at least three independent cultures , and immunoblots were least duplicated for quantitative analysis . Neurons were transfected with the calcineurin activity biosensor , and FRET activity was measured at DIV14 according to a modification of the previously described method [31] . Neurons were pretreated with 2 µM TTX for 12 , 24 , or 48 h , or 5 µM FK506 for 12 h , and fixed with 4% paraformaldehyde . Images were captured by using Applied Precision PersonalDV live-cell imaging system in the Microscopy Core of New York University Langone Medical Center . The following formula was used to calculate the emission ratio:Pseudocolor images of the emission ratio were generated by using the ImageJ software , as previously reported [62] . DIV4 neurons were transfected with GCaMP5 ( Addgene , 31788 ) . Neurons were grown for 10–12 d after transfection in Neurobasal medium without phenol red and supplemented with B27 and 0 . 5 mM Glutamax . Glass-bottom dishes were mounted on a temperature-controlled stage on Zeiss Axiovert 200M and maintained at 37°C and 5% CO2 using a Zeiss stage incubator model S with CTI , digital temperature , and humidity controller . The imaging was captured for periods of 0 . 5 to 1 . 0 s depending on the intensity of the fluorescence signal using a 63× oil-immersion objective . One hundred images were obtained with a 1-s interval , and Ca2+ activity in the cell body ( excluding dendrites ) was analyzed using the ImageJ software . F0 was determined as the minimum value during the imaging . Total Ca2+ activity was obtained by combining 100 values of ΔF/F0 = ( Ft−F0 ) /F0 in each image , and values of ΔF/F0<0 . 3 were rejected due to bleaching . Neurons pretreated with 2 µM TTX for 6 or 48 h or 1 µM thapsigargin for 6 h were lysed with a nuclear preparation buffer A ( 10 mM Tris-HCl , pH 7 . 9 , 1 . 5 mM MgCl2 , 10 mM KCl , and 0 . 25% NP40 ) . Nuclear fraction was collected by centrifugation , resuspended in a nuclear preparation buffer B ( 20 mM Tris-HCl , pH 7 . 9 , 1 . 5 mM MgCl2 , 420 mM KCl , 0 . 2 mM EDTA , and 20% glycerol ) , and analyzed by immunoblots . Most statistical comparisons were analyzed with the GraphPad Prism6 software . Unpaired two-tailed Student's t tests were used in single comparisons . For multiple comparisons , we used one-way analysis of variance ( ANOVA ) followed by Fisher's Least Significant Difference ( LSD ) test to determine statistical significance . The Kolmogorov-Smirnov ( K-S ) test ( http://www . physics . csbsju . edu/stats/KS-test . html ) was used for comparisons of cumulative probabilities . Results were represented as mean ± s . e . m . , and a p value< . 05 was considered statistically significant . | Synaptic scaling is a form of homeostatic plasticity that normalizes the strength of synapses ( the structure that allows nerve cells to communicate ) and is triggered by chronic inhibition of neuronal activity . Although extensive studies have been conducted , the molecular mechanism of this synaptic adaptation is not understood . Using cultured cortical neurons , we show that chronic inhibition of neuronal activity reduces calcium influx into neurons , which , in turn , decreases the activity of the calcium-dependent phosphatase calcineurin . These changes lead to an increase in GluA1-containing , calcium-permeable AMPA receptors , which mediate communication at the synapse . Newly inserted calcium-permeable AMPA receptors restore calcium currents , which enhance synaptic strength and recover calcium signaling . We also show that inhibition or activation of calcineurin activity is sufficient to induce or block synaptic scaling , respectively , suggesting that calcineurin is an important mediator of homeostatic synaptic plasticity . Taken together , our findings show that synaptic scaling is a homeostatic process that not only enhances synaptic transmission but also maintains calcium signaling in neurons under activity deprivation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience",
"neuronal",
"plasticity",
"biology",
"and",
"life",
"sciences",
"cellular",
"neuroscience"
] | 2014 | Calcineurin Mediates Synaptic Scaling Via Synaptic Trafficking of Ca2+-Permeable AMPA Receptors |
Calcium/calmodulin-dependent protein kinase II ( CaMKII ) holoenzymes play a critical role in decoding Ca2+ signals in neurons . Understanding how this occurs has been the focus of numerous studies including many that use models . However , CaMKII is notoriously difficult to simulate in detail because of its multi-subunit nature , which causes a combinatorial explosion in the number of species that must be modeled . To study the Ca2+-calmodulin-CaMKII reaction network with detailed kinetics while including the effect of diffusion , we have customized an existing stochastic particle-based simulator , Smoldyn , to manage the problem of combinatorial explosion . With this new method , spatial and temporal aspects of the signaling network can be studied without compromising biochemical details . We used this new method to examine how calmodulin molecules , both partially loaded and fully loaded with Ca2+ , choose pathways to interact with and activate CaMKII under various Ca2+ input conditions . We found that the dependence of CaMKII phosphorylation on Ca2+ signal frequency is intrinsic to the network kinetics and the activation pattern can be modulated by the relative amount of Ca2+ to calmodulin and by the rate of Ca2+ diffusion . Depending on whether Ca2+ influx is saturating or not , calmodulin molecules could choose different routes within the network to activate CaMKII subunits , resulting in different frequency dependence patterns . In addition , the size of the holoenzyme produces a subtle effect on CaMKII activation . The more extended the subunits are organized , the easier for calmodulin molecules to access and activate the subunits . The findings suggest that particular intracellular environmental factors such as crowding and calmodulin availability can play an important role in decoding Ca2+ signals and can give rise to distinct CaMKII activation patterns in dendritic spines , Ca2+ channel nanodomains and cytoplasm .
Calcium/calmodulin-dependent protein kinase II ( CaMKII ) is an important enzyme widely distributed in the central nervous system and other tissues including cardiac muscle [1–3] . In intracellular Ca2+ signaling , the Ca2+ sensor protein calmodulin ( CaM ) binds to and activates CaMKII [4–6] . Importantly CaM binding to CaMKII produces an allosteric change in CaM that increases its binding affinities for Ca2+ [7] ( Fig 1 ) . In neurons , CaMKII molecules play an essential role in long-term potentiation ( LTP ) , our best model for the molecular basis of learning and memory [8] . In spines , activated CaMKII molecules interact with postsynaptic density proteins , facilitating actins to reorganize , leading to spine enlargement and upregulation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) receptor numbers [4] . CaMKII molecules can also phosphorylate AMPA receptors to regulate channel conductance [4 , 6] or form complexes with NMDA receptors [5] . In addition to synaptic functions , recent studies suggest that CaMKII in the soma can act as carriers to shuttle Ca2+-CaM into the nucleus [9 , 10] . Ca2+-CaM molecules are then unloaded in the nucleus , and participate in the calcium/calmodulin dependent protein kinase IV ( CaMKIV ) cascade to activate nuclear transcription factors . Therefore , CaMKII molecules play a key role in excitation-transcription coupling . The CaMKII molecule is a holoenzyme that consists of 12 subunits grouped in 2 rings [2 , 5 , 6 , 11] . Each subunit contains an association domain allowing the formation of multimers , a regulatory domain with a CaM binding site and phosphorylation sites , and a catalytic domain to act as a kinase [2 , 5 , 6 , 11] . Activities such as CaM binding and auto-phosphorylation result in various subunit states . For example , in the presence of Ca2+-CaM complexes , each subunit can bind a CaM molecule and expose its catalytic domain to phosphorylate the direct neighbor subunit at its Thr286 site ( for αCaMKII ) [12] . Once phosphorylated , the CaM unbinding rate decreases dramatically , leading to a prolonged activation of the subunit; we say , the CaM molecule is “trapped” by the CaMKII subunit [13] . Even when the CaM molecule unbinds , the CaMKII subunit stays activated , entering an autonomous state . Finally , Thr305/Thr306 can also be phosphorylated . There the threonine sites overlap with the CaM binding site and their phosphorylation blocks the binding of CaM molecules . In this case , the subunit becomes “capped” . Researchers have long been interested in modeling and simulating CaMKII [14–23] . However , the structural complexity and multi-state nature of CaMKII present a technical challenge . A major problem is combinatorial explosion [24] . One CaMKII subunit has only a moderate number of states to consider , but with 6 or 12 subunits on a holoenzyme , the number of combinations of states for a holoenzyme becomes extremely large . This is a common problem in systems biology since large proteins usually consist of multiple subunits . Software that adopts a rule-based approach such as BioNetGen [25] can be used to expand the network based on a set of given reaction rules . Nevertheless , for a CaMKII holoenzyme , using BioNetGen to generate the network is still computationally intensive . After expansion with BioNetGen , a 6-subunit holoenzyme with four states for each subunit has 700 unique species and 12 , 192 unique reactions ( See S2 File for detailed calculations ) . The size of the network and the problem of combinatorial explosion grow substantially when more detailed kinetics are considered . For example , each CaM has 2 lobes and in total 4 Ca2+-binding sites . This can introduce 5 , 9 , 16 or more binding states depending on assumptions made [7 , 19–21 , 23 , 26 , 27] . Here we follow the experimental work of Linse et al . [28] and assume that each lobe binds Ca2+ independently and within each lobe , Ca2+ binding is cooperative . This gives rise to 9 distinct binding states . In addition , it is known that Ca2+ binding kinetics to CaM is different whether CaM is free , bound to CaMKII or bound to phosphorylated CaMKII [29–32] . Therefore , we allow each CaM state to have distinct kinetics to interact with a CaMKII subunit . Also , each CaMKII subunit exhibits a distinct phosphorylation rate depending on the state of the bound CaM [21 , 29] . Consequentially , each subunit would potentially have 20 states . For simplicity , consider a 6-subunit CaMKII holoenzyme . Then for the holoenzyme , the total number of unique species alone reaches 10 , 668 , 140 ( computed with the necklace function [33] see S2 File ) . It would be extremely time-consuming and not practical for BioNetGen to generate this network . To overcome this problem , most previous modeling studies of CaMKII have simplified the Ca2+-CaM-CaMKII reaction network by either modeling CaMKII as monomers [18 , 21] or allowing only CaM fully-loaded with Ca2+ to interact with CaMKII subunits [14 , 16 , 17 , 22] . However , Pepke et al . [21] has shown that a reaction network without sufficient kinetic resolution has limited predictive power for intracellular signaling events . The intracellular environment presents another complication . Conventionally , biochemical reactions are modeled in a deterministic manner using Ordinary Differential Equations ( ODEs ) . The underlying assumptions are that reactions occur in a spatially homogenous environment , reactants are abundant and not subject to stochasticity , and molecules diffuse sufficiently fast . The majority of previous modeling studies belong to this category except for a few that are stochastic or hybrid models [15 , 18 , 20 , 27] . However , intracellular space is highly heterogeneous and compartmentalized [34 , 35] . Reactions are often restricted to a small space . Structures such as the cytoskeleton , scaffold proteins or endoplasmic reticulum often act as diffusion barriers to slow down molecules . In addition , most previous modeling studies focus on the dendritic spine , where NMDA receptor-channels are the main Ca2+ providers . In the present work , we focus on the soma where voltage-dependent Ca2+ channels ( CaV ) provide the Ca2+ influx . Specifically , we examined Ca2+-CaM-CaMKII network activity near L-type Ca2+ channels , which constitute the major Ca2+ source in the soma . This is an area less studied in previous models , but plays a critical role in excitation-transcription coupling [10 , 36] . To study this Ca2+-CaM-CaMKII signaling network and deal with the problem of combinatorial explosion , we modified the published and freely available simulator Smoldyn [37 , 38] . Smoldyn is a particle-based stochastic simulator and has been used for simulating reaction and diffusion processes in cells . It works well for relatively simple reaction networks but not for holoenzymes such as CaMKII . We added new data structures to Smoldyn to describe the reaction network in a compact way . CaMKII holoenzymes are modeled as a collection of subunits . Each subunit has a set of binding sites . Subunits react independently and diffuse collectively . Reactions are defined between binding sites . The reaction network is stored in a hash table to allow for lookup during simulations when reactants collide . Therefore , expanding and loading a complete network is not required . We tested and verified these modifications and then created a detailed Ca2+-CaM-CaMKII network to examine factors that affect the frequency dependence of CaMKII activation . We found that the total Ca2+ influx amount , as well as Ca2+ diffusion rate and CaM availability , can change the dependence of CaMKII phosphorylation on Ca2+ input frequencies . Meanwhile , driven by Ca2+ input with a given frequency , CaM species travel through an altered pathway along with the change of CaMKII phosphorylation pattern .
We first tested the modifications to Smoldyn used in this paper ( see Methods ) . The reaction and diffusion kinetics of Smoldyn have been thoroughly validated in the past [37] , but we still needed to validate that our modifications for molecular complex management were working properly . Our first test used a Ca2+-CaM network ( Fig 2 ) ( Model 1 ) . The 4 Ca2+ binding sites on CaM give rise to a total of 9 different binding states of a CaM molecule . For the reaction volume , we use a 500 nm × 500 nm × 500 nm cube with all sides being reflective . The size of this volume is what might be used to study a Ca2+ channel nanodomain ( the region up to 100nm from the channel pore ) or a small dendritic spine head . Initially , the cube contains 3000 Ca2+ ions and 700 apoCaM molecules , equivalent to 39 . 867 μm and 9 . 302 μm respectively . The concentrations are chosen to produce an observable amount of 4-Ca2+ bound CaM at the steady state . All molecules are initially uniformly distributed . We tested reactions using two different Ca2+ diffusion constants , 2 . 2 × 10−6 cm2 s−1 [39 , 40] and half of this 1 . 1 × 10−6 cm2 s−1 . A detailed description of the model and other testing models is in S1 File and S1 Table . We first characterized the reactions in the network to see if results of our stochastic model could reasonably be compared to results with standard ODE methods . A second order chemical reaction in the solvent phase consists of two steps: molecules encountering each other by diffusion followed by the molecules reacting with each other . If the encountering step takes a much longer time to occur than the reacting step , the reaction is diffusion-limited; otherwise , the reaction is activation-limited . Conventionally , an experimentally measured binding kinetic rate , kon , can be decomposed into an encounter rate , kenc , and an intrinsic activation rate , ka , using the following equation [41] , 1 k o n = 1 k e n c + 1 k a ( 1 ) For diffusion-limited reactions , ka ≫ kenc and kon ≈ kenc; for activation-limited reactions , kon ≈ ka as diffusion is sufficiently fast . Given the diffusion coefficients for Ca2+ and CaM and the parameter values for Ca2+ and CaM reaction kinetics in S1 Table , we calculated the k a k o n ratios and concluded that the reactions in the network belong to the activation-limited regime . This was also true if diffusion constants were reduced by half . Thus , this simple model resembles a well-mixed system and an ODE model will provide a good test standard . We coded a deterministic ODE system in COPASI [42] and compared results with our stochastic model using the same kinetics . We found that the stochastic model and the ODE COPASI model exhibited similar time courses for all CaM binding state species . Slowing down Ca2+ diffusion did not significantly alter the time courses for the parameter values used ( Fig 2C ) . As another verification , we tested the steady state fraction of phosphorylation of CaMKII holoenzymes when they are modeled as multi-subunit complexes ( Model 2 , in S1 File ) . Michalski and Loew [22] derived an expression for the steady state fraction of phosphorylated subunits as a function of the number of subunits in a holoenzyme . This expression assumes a closed system where CaMKII holoenzymes are exposed to a saturating amount of fully loaded CaM molecules and subunit phosphorylation is allowed from a neighbor that has CaM bound but not from one that is also phosphorylated . With these conditions , the steady state fraction of phosphorylated subunits is 1 2 for holoenzymes as dimers , 2 3 for trimers , and approaches 1−e−1 for large subunit numbers . We built a model using a closed system with 1 μm × 1 μm × 1 μm geometry . The model contained 6000 CaM bound CaMKII subunits . CaMKII holoenzymes all had either 2 , 3 , 6 or 12 subunits . Consistent with the Michalski and Loew formula assumptions , an already phosphorylated subunit could not act as a kinase to phosphorylate its neighbor . As expected , we obtained steady state phosphorylation levels that depended on the number of the subunits in a holoenzyme as per the Michalski and Loew formula ( S2 Fig ) . Finally , using the same closed system , we tested the complete network and compared results with those obtained from a simulation implemented with a spatial Gillespie algorithm [20] ( Model 3 , S1 File ) . The two methods showed comparable results ( S3 Fig ) . The results of all these tests suggest that our modifications of Smoldyn are working properly . To investigate how various CaM species contribute to the activation of CaMKII holoenzymes , we set up a prototype Ca2+-CaM-CaMKII network model . We were particularly interested in studying how network transitions involving partially loaded CaM species contribute to CaMKII activations , as this is not clearly understood . The prototype model comprises Ca2+-CaM-CaMKII interactions as shown in Fig 3A . In this network diagram , molecule species are represented as vertices and reactions are represented as edges . CaM molecules and CaMKII holoenzymes with 6-subunit rings were initially uniformly distributed in a 1 μm × 1 μm × 2 μm box . As an initial condition , the box contained 6020 CaM molecules ( 5 μm ) and 12036 CaMKII subunits ( 10 μm ) , within which 195 CaM molecules are bound to CaMKII . The initial state is at an equilibrium , which we carefully computed by running 5 simulation trials starting with the same numbers of CaM and CaMKII molecules . For simplicity , we did not consider resting intracellular Ca2+ as part of the steady state initial condition , as calculations showed its effect to be minor . Ca2+ influx was modeled as an entry from a single source at the top center of the box . A previously generated input file having a total of 5 Ca2+ bursts delivered at 5 Hz was used to provide Ca2+ influx during the simulation ( see Methods ) . We ran the simulation up to 2 . 2s , recorded the molecule numbers at every 1 ms ( Fig 3B ) and logged all reaction events . Using the events log , we counted the accumulated occurrences of each reaction type at every 10 ms , starting from the initial state until the Ca2+ bursts were finished . For a particular reaction , the number of occurrences during a time span is considered to be the net number of molecule state changes along the corresponding edge , i . e . , the number of unbinding events is subtracted from the number of binding events . A negative number means that unbinding occurs more often than binding within the given time span . We analyzed the edges of the Ca2+-CaM-CaMKII network ( Fig 3A ) . We decomposed the network to 2 layers . The back layer ( Layer 1 ) describes interactions between Ca2+ and the 9 states of CaM ( NxCy , x , y = 0 , 1 , 2 ) . The front layer ( Layer 2 ) is for reactions of Ca2+ with CaM attached to unphosphorylated CaMKII ( KNxCy ) . Starting in Layer 1 , there are up to 4 possible binding reactions for each CaM species to change state ( Fig 4A ) : binding a Ca2+ at a C site ( denoted as Cx∼ca ) , binding Ca2+ at an N site ( denoted as Nx∼ca ) , binding to an unphosphorylated CaMKII subunit ( denoted as K∼NxCy ) or binding to a phosphorylated CaMKII subunit ( denoted as Kp∼NxCy ) . Likewise , in Layer 2 , there are up to 3 possible binding reactions for each CaMKII bound CaM species to change state ( Fig 5A ) : binding a Ca2+ at a C site ( denoted as KCx∼ca ) , binding a Ca2+ at an N site ( denoted as KNx∼ca ) , and the CaMKII bound with the CaM becoming phosphorylated ( denoted as KNxCy∼p ) . For each CaM state , we counted the accumulated occurrences of the possible reaction types . Results are shown in Fig 4B . The plot reveals a preferred pathway for CaM and CaMKII state transitions as labeled in Fig 3A . Starting as apoCaM , a CaM molecule tends to bind a Ca2+ ion on the C1 site and then on the C2 site , entering the N0C2 state . CaM in the N0C2 state has a strong preference to enter Layer 2 by binding to a CaMKII subunit ( Fig 4B , the black line in panel N0C2 ) , likely followed by the phosphorylation of the bound CaMKII subunit ( Fig 5B , the black line in panel KN0C2 ) . Once bound to a CaMKII subunit , additional Ca2+ ions bind to CaM more quickly . In this scenario , it is rare for CaM to become fully bound with Ca2+ ions before binding with a CaMKII subunit . Nevertheless , phosphorylation of CaMKII subunits occurs most often when subunits are bound with fully loaded CaM , but still often occurs with CaM having C sites loaded , KN0C2 and KN1C2 , as shown in Fig 5B . We note that the way the preferred pathway is chosen at each vertex is a consequence of reaction affinities as given in S1 Table and these were chosen to be consistent with experimental studies and values in the previous modeling work [19–21] . For example , even though N sites bind Ca2+ faster , the C sites have higher affinity making Ca2+ binding to C sites preferred . To confirm the critical role of NxC2 ( x = 0 , 1 , 2 ) CaM in activation and phosphorylation of CaMKII , we set up two modified reaction schemes ( Fig 6A ) . In Scheme 1 , only CaMKII subunits bound with NxC2 are allowed to become phosphorylated . In Scheme 2 , phosphorylation is allowed only for subunits bound with N2Cx . Note that the phosphorylation rates in the two schemes are equivalent , i . e . , kon of reaction KN2C0∼p is the same as that of KN0C2∼p , and the kon of KN2C1∼p equals that of KN1C2∼p . Not surprisingly , the two schemes give rise to different phosphorylation levels as shown in Fig 6B . Scheme 1 performs slightly worse than the whole network , whereas Scheme 2 produces a much lower phosphorylation level . Therefore , edges of the network are not equally involved in CaMKII phosphorylation . A classic experiment by DeKoninck and Schulman [43] showed that in vitro , CaMKII holoenzyme activation is sensitive to the frequency of Ca2+-CaM pulses . A recent study [44] also showed that in vivo glutamate uncaging frequency affects CaMKII activation in dendritic spines . Numerous modeling studies have also reported a dependence of CaMKII activation on the frequency of Ca2+ signals [16 , 17 , 21 , 22] . To confirm this CaMKII activation pattern , we tested our 6-subunit CaMKII model using previously generated 10 Hz and 5 Hz Ca2+ influx files . The total number of Ca2+ ions entering was comparable in the two cases ( 40433 ions with 5 Hz vs . 40435 ions with 10 Hz ) . We examined the reaction occurrences over time and noticed that , compared to 5 Hz input , the 10 Hz input results in more rapid and more overall bindings between N0C2 CaM and CaMKII ( Fig 7A and 7B , also see S6 Fig ) , and consequently , more autophosphorylation . More bindings between N0C2 CaM and CaMKII is to be expected because higher frequency means more intensive Ca2+ input which drives CaM to travel through the preferred pathway ( Fig 3A ) . This leads to more N0C2 available to bind to CaMKII . More autophosphorylation is primarily due to increasing occurrences of KN2C2∼p reactions ( Fig 7C and 7D ) because the additional KN0C2 binds Ca2+ at higher affinity leading to state KN2C2 that has the highest rate of autophosphorylation . Therefore , the frequency effect is inherent in the binding properties among Ca2+ , CaM and CaMKII . Interestingly , the effect of frequency is not the same for all CaMKII CaM binding interactions . For example , the net bindings between N0C1 CaM and CaMKII increased with 10 Hz input while bindings between apoCaM and CaMKII decreased . To understand this note that with high frequency Ca2+ input , [Ca2+] is higher and more N0C1 forms . With more N0C1 available , there will be more binding of N0C1 with CaMKII . Hence the yellow line ( K∼N0C1 ) is higher in Fig 7B ( 10Hz ) than in Fig 7A ( 5Hz ) . In addition , there will be more Ca2+ binding to KN0C0 , forming KN0C1 , and this means that mass action kinetics will drive N0C0 to bind with CaMKII to replace the KN0C0 that transitioned to KN0C1 . However , more N0C1 formed from N0C0 with high frequency input means that there is less N0C0 available to drive the K∼N0C0 reaction . As a result , the green line ( K∼N0C0 ) is lower in Fig 7B ( 10Hz ) than in Fig 7A ( 5 Hz ) . However , the frequency effect can be reversed by providing a saturating amount of Ca2+ . In Fig 8 , we increased the Ca2+ channel number to allow more Ca2+ influx per action potential pulse . The total Ca2+ influx was again comparable for 5 Hz and 10 Hz input . As Ca2+ influx was increased , the network produced more phosphorylated CaMKII subunits for both input frequencies , but the difference in phosphorylation level between 5 Hz and 10 Hz input diminished . Eventually , the 10 Hz input became saturating and the network generated less phosphorylation with 10 Hz than with 5 Hz input ( Fig 8F ) . This reversal in frequency preference occurs at lower levels of Ca2+ input when available CaM is limited or Ca2+ diffusion is slowed ( Fig 9 ) . Notice that in the model Ca2+ can diffuse out of the reaction volume . As a result , a lower diffusion constant effectively means a higher level of Ca2+ . To demonstrate , we first compared simulations where the amount of CaM in the system was reduced from the default concentration of 5 μm to 2 . 5 μm . Limiting CaM reduces phosphorylation for a given Ca2+ input at both frequencies ( compare Fig 9A and 9B ) , but the difference in phosphorylation levels between the two frequency conditions is smaller . We see slightly more phosphorylation with 5 Hz input than 10 Hz input with the 3× Ca2+ input condition . With less CaM present , the Ca2+ influx has a better chance to saturate the available CaM allowing the frequency dependence to reverse at a lower influx level . We then compared simulations with default ( 2 . 2 × 10−6 cm2 s−1 ) and slowed ( 1 . 1 × 10−6 cm2 s−1 ) Ca2+ diffusion coefficients , noting that the slowed diffusion condition does not change the activation-limited regime of the network . Slowed diffusion results in a dramatic increase of CaMKII phosphorylation regardless of Ca2+ influx level and input frequency ( compare Fig 9A and 9C ) . Here the frequency preference for 10 Hz input also reverses at the 3× Ca2+ input condition . This occurs because slow Ca2+ diffusion allows the input to become sufficiently saturating at a lower level of Ca2+ influx and this leads to the frequency preference change . Finally , frequency dependence reverses dramatically with merely twice the baseline amount of Ca2+ influx when slow diffusion and limited CaM are combined ( Fig 9D–9F ) . To understand why frequency dependence changes , we examined what was happening in the full Ca2+-CaM-CaMKII network starting with Ca2+ binding to apoCaM and apoCaM availability . It is important to note that simulations with 10 Hz input and 5 Hz input had virtually the same total Ca2+ entering the volume . With 3× Ca2+ influx , 10 Hz bursts deplete apoCaM , but 5 Hz bursts do not ( Fig 10B ) . With 4× Ca2+ influx , it takes 4 bursts of influx to deplete apoCaM in the 5 Hz condition but only 3 with 10 Hz ( Fig 10C ) . When apoCaM molecules are depleted , Ca2+ must bind elsewhere in the network and this causes deviations from the preferred path as described below . One consequence is that the number of KN0C2 molecules formed is higher with 5 Hz input ( Fig 10E and 10F , red lines ) than with 10 Hz input ( Fig 10E and 10F , blue lines ) . This leads to more phosphorylation with 5 Hz than 10 Hz input for the 4× Ca2+ influx case because of the longer inter-burst interval , which allows more relief from apoCaM depletion . Thus Ca2+ can bind to apoCaM and subsequent reactions along the preferred path can occur . The frequency dependence reversal for reduced CaM availability and slowed Ca2+ diffusion can also be explained in terms of CaM saturation . For a given Ca2+ input apoCaM depletion will occur faster if there is less to begin with and also if Ca2+ diffusion is slowed , which will allow more chances for Ca2+ and CaM to interact before Ca2+ leaves the system . We found that there were deviations from the previously observed preferred pathway when saturating Ca2+ input is provided , particularly when CaM molecules are in the N0C2 state . In the presence of moderate Ca2+ influx ( Fig 11A and 11B ) CaM molecules follow the preferred pathway: the net bindings between N0C2 and CaMKII ( K∼N0C2 ) occur more often than the bindings of Ca2+ ions to the N1 site of N0C2 molecules ( N1∼ca ) . CaMKII binding with N0C2 dominates all types of CaMKII CaM interactions ( Fig 11D , 11E and 11G ) . However , when Ca2+ influx becomes saturating , bindings of Ca2+ ions on the N1 site of N0C2 molecules start to dominate over the bindings between N0C2 and CaMKII ( Fig 11C ) . CaM molecules tend to stay in Layer 1 until they get fully loaded with Ca2+ . As a result , CaMKII binding with N2C2 increases and becomes dominant over all other CaMKII CaM interactions ( Fig 11F , 11H and 11I ) . This gives rise to an altered preferred pathway ( Fig 12 solid green triangles ) . It is worth noting that the net bindings between CaMKII and N1C2 and N2C1 also rise as Ca2+ input becomes saturating ( Fig 11D–11I , blue lines and pink lines ) . Interestingly , when we examined the accumulated reaction occurrences with 5× Ca2+ influx at 10 Hz , we noticed that K∼N2C1 constitutes a second preferred pathway ( Fig 12 yellow triangles ) , which starts with N1∼ca reactions from apoCaM followed by N2∼ca , C1∼ca , and K∼N2C1 in Layer 1 , and KC2∼ca and KN2C2∼p in Layer 2 . In short , a saturating amount of Ca2+ forces CaM species to traverse states in a way that deviates from the preferred pathway observed with a moderate amount of Ca2+ . These deviations are driven by different amounts of reactants . To validate our conclusion about the pathway decision change , we used a reduced reaction network to capture the observed frequency preference reversal ( Fig 13 ) . The reduced network is derived from the initial steps in the whole Ca2+-CaM-CaMKII network . We used the original Smoldyn to simulate the simple network , to demonstrate that the reversal of frequency preference is not an artifact of our modified simulator but is inherent to the network itself . We used 5 pulses of instantaneous Ca2+ release as the input and varied the amount of Ca2+ influx per pulse . The CaMKII molecules are modeled as monomers with an arbitrary phosphorylation rate of 1 s−1 . As expected , the reversal of frequency preference can be qualitatively captured by the simple reaction scheme ( Fig 13A ) . The 10 Hz stimulus becomes saturating and fails to generate more phosphorylation than 5 Hz when Ca2+ influx reaches 50 , 000 ions per pulse ( Fig 13B ) . Recent work on CaMKII holoenzyme structure suggests that how subunits are organized can affect the activation of the holoenzyme . In particular , whether subunits are arranged in a compact or an extended way can change the accessibility of CaM to CaMKII . It is known that the structural arrangement is related to a linker region between a subunit’s kinase domain and the holoenzyme central hub . Bayer et al . [45] examined splice variants of β-CaMKII , which have identical kinase domains yet different linker lengths . These variants responded to Ca2+ oscillations differently , even though they showed no response difference to prolonged Ca2+-CaM input . In particular , for Ca2+ pulse input , the variants with longer linker length exhibited a higher autophosphorylation rate . Another study by Chao et al . [46] also indicated that the linker length affects the capability of a subunit’s kinase domain to undock from the central hub . Undocking helps the subunit to be released from an autoinhibited state . Thus a longer linker is expected to keep the kinase domain further away from the central hub , allowing a better chance for Ca2+-CaM to access the binding site . In our prototype model , each holoenzyme subunit has a distinct physical location and is separated by 8 nm from its neighbors . Studies suggest that a typical one-ring holoenzyme radius is 5 nm to 8 nm [46 , 47] . To examine the effect of a longer linker length on CaMKII activation , we varied the radius of a one-ring 6-subunit holoenzyme from 5 nm to 15 nm . Neighbor subunits are equally spaced at the same distance as the holoenzyme radius . For each radius condition , we ran 15 simulation trials using the default Ca2+ influx delivered at 5 Hz and 10 Hz respectively . As shown in Fig 14 , the effects of holoenzyme radius on phosphorylation level are related to the Ca2+ input frequencies . With 10 Hz pulses , phosphorylation increases steadily as the holoenzyme radius grows from 5 nm to 15 nm . This result suggests that linker length may affect Ca2+-CaM binding to CaMKII and subsequently affect phosphorylation levels , depending on the Ca2+ input conditions .
We used the modified Smoldyn simulator and reaction history information to obtain several insights into the Ca2+-CaM-CaMKII reaction network . First , under physiological conditions when Ca2+ influx is low to moderate , CaM molecules partially loaded with Ca2+ are important for CaMKII activation . In particular , reaction history shows that CaM molecules that have 2 Ca2+ ions attached on the C lobe ( in particular species N0C2 , but also N1C2 and N2C2 ) preferentially bind to CaMKII subunits before adding additional Ca2+ ions to the N lobe . This is consistent with the predominant pathway hypothesis suggested by Pepke et al . [21] as well as with experimental work by Shifman et al . [29] . Nevertheless , phosphorylation was found to occur primarily from CaMKII bound with CaM fully loaded with Ca2+ ( KN2C2 ) and higher frequencies of low to moderate Ca2+ input resulted in more CaMKII phosphorylation . Second , while CaMKII activation is known to be sensitive to the frequency of Ca2+ signals [43] , we found that the frequency dependence is reversed with strong Ca2+ signals , with more CaMKII subunit phosphorylation seen at 5 Hz input than at 10 Hz . Reaction history shows that this occurs because of a depletion of apoCaM , resulting in a change in the preferred pathway for CaMKII activation . Specifically , CaM with 2 Ca2+ ions on the C lobe now becomes more likely to bind Ca2+ on the N lobe than to bind with CaMKII . The change of pathway choice affects how the network is tuned to a particular Ca2+ input frequency . The fact that the strength of the Ca2+ signal matters is important because many experiments that study frequency dependence are done in conditions where CaM is saturated with Ca2+ and this is not the typical situation encountered by the cell . Third , we found that factors such as CaM availability and Ca2+ diffusion can also affect the frequency dependence of CaMKII activation by Ca2+ signals , also by changing the preferred pathway for CaMKII activation . A limited amount of CaM makes the given Ca2+ input more likely to saturate available CaM on both lobes before binding to CaMKII . Similarly , slow Ca2+ diffusion allows more extensive interactions between Ca2+ and CaM thus making it more likely for a given amount of Ca2+ input to become saturating . Experimental studies suggest that the number of freely diffusible CaM molecules is highly limited in vivo [48–50] and limited CaM further implies a regulatory role for the many endogenous CaM-binding proteins [51] . A limited amount of CaM or a slowed Ca2+ diffusion may permit enough Ca2+ to bind to available CaM and allow CaMKII activation to occur at a lower frequency or a lower strength Ca2+ signal . Fourth , it is known that intracellular crowding and spatial homogeneity can slow down molecule diffusion . For example , a recent biophysical study [52] suggests that the diffusion of Ca2+ ions can be reduced by ten times in a nanodomain around the Ca2+ channel mouth . We believe that such a restriction in Ca2+ diffusion may have substantial effects on CaMKII phosphorylation and the frequency dependence . For example , depending on the size of the nanodomain , slow Ca2+ diffusion in a nanodomain can potentially result in a localized Ca2+ signal with sufficient strength to activate CaMKII and downstream cascading proteins . Finally , we demonstrated that holoenzyme size is a possible means to affect the level of phosphorylation , depending on the Ca2+ influx . We increased the size of the holoenzyme by changing the distance between neighboring subunits and found that with 10 Hz pulses the corresponding phosphorylation levels of the network increased . This is consistent with the idea that the configuration of a holoenzyme , whether compact or extended , can affect the ability of CaM molecules to access CaMKII subunits [11] . The extension may allow a subunit to sample a volume that is both larger and further away from other subunits , increasing the possibility of a reaction and subsequently leading to more phosphorylated subunits . We did not implement volume exclusion for CaMKII holoenzymes . It is possible that volume exclusion could make the effects of holoenzyme size more apparent . Our model does not contain Thr305/Thr306 phosphorylation ( few subunits would have become phosphorylated there during the time period simulated ) and also lacks some newly discovered CaMKII structural feature mechanisms , which may lead to a more complicated activation pattern of CaMKII holoenzyme subunits . For example , it has been found that there exists a compact autoinhibition state , which occurs through dimerization of adjacent subunits from top and bottom rings [53] . Once Ca2+-CaM is bound to a dimerized subunit , the dimer disassembles and the two subunits swing away from the center of the holoenzyme . Another recent study indicated that phosphorylated CaMKII subunits can undergo subunit exchange to facilitate propagating activation triggered by Ca2+-CaM [54] . The significance of these additional features of CaMKII activation awaits future study . In addition we have not included other Ca2+ buffers or CaM-binding proteins in our models ( e . g . , calbindin , calcineurin , neurogranin , F-actin ) . Including additional reactions involving these molecules could affect Ca2+ concentration , CaM availability and effective Ca2+ diffusion , factors that we already analyze separately with the current simulations ( Figs 8–12 ) . One technical challenge for particle-based simulation is to handle diffusion-limited reactions , especially in the presence of highly concentrated molecules . One recent experimental study [26] estimated that the N sites of CaM act very fast to bind Ca2+ , much faster than previously cited for CaM-N lobe binding kinetics in experimental or modeling studies ( although see [55] for a critique of these estimates ) . If accurate , these fast binding kinetics would place these reactions in the diffusion-limited regime , rendering traditional mass action based methods inaccurate . However , for kinetics this fast , adequate simulation options are limited and not efficient . If using the original Smoldyn algorithm , the simulation time step would have to be considerably reduced to obtain the correct steady state [56]; alternatively , one might increase the geminate recombination probability [37] . Another software package using an enhanced Green’s Function Algorithm [57] can handle the high concentration diffusion-limited reactions accurately , but it takes an impractically long time to run a simulation . If diffusion is slowed considerably in local nanodomains such that Ca2+-CaM-CaMKII interactions become diffusion-limited , it will be necessary to develop different algorithms with improved efficiencies to handle these interactions accurately .
The modification is based on Smoldyn ( V2 . 37 ) . The reaction and diffusion kinetics of Smoldyn have been thoroughly validated in the past [37] . Since our modification has progressed independently , an additional check is necessary regarding compatibility or integration with updated versions of Smoldyn [38] and its future development . We expanded the molecule data structure in Smoldyn to include complexes , molecules and binding sites . A complex may contain multiple molecules and a molecule may contain multiple binding sites . Reactions are specified between binding sites . Each binding site has binary states . For example , bound is coded as 1 and unbound as 0; phosphorylated as 1 and unphosphorylated as 0 . Each molecule has a vector to store the states of binding sites . All reactions are stored in a hash table with reactants and their binding states as entry keys . A hash table is a data structure that stores association arrays and allows rapid lookup . In our case , the reaction network can be considered as associations between reactants , and therefore is ideal to be implemented using a hash table . CaM is an example of a molecule with multiple binding sites . The binding reactions involving the N and C lobes of CaM can be coded as in Fig 1B . A CaMKII holoenzyme is an example of a complex composed of two 6-subunit rings . Each subunit is a molecule containing binding sites for CaM and phosphorylation . Each ring has a radius of 8 nm and is separated from its direct neighbors at a fixed distance of 8 nm ( estimated from [47] ) . For simplicity , we usually modeled CaMKII holoenzymes as one ring of 6 subunits . A link to the sample reaction configuration files to be run with the modified Smoldyn is included in S1 File . A link to the modified Smoldyn source code is provided in S4 File . In the original version of Smoldyn , each reaction generates new molecules and reactant molecules are removed . In our case , since one molecule can have multiple binding sites and is potentially associated with multiple partners , entirely removing a molecule is not practical because other attached molecules would also be affected . In addition , removing and generating new molecules makes it difficult to track the reaction history of a molecule . Therefore , during reactions we do not remove molecules but merely change molecule binding states and positions . Molecules bound together physically overlap , synchronize their locations automatically and diffuse together . The diffusion coefficient is determined by the larger molecule . For example , when Ca2+ and CaM are bound , the attached Ca2+ molecule diffuses with the CaM diffusion rate; similarly , CaM bound to a CaMKII subunit will diffuse with the CaMKII holoenzyme . Macromolecules usually have multiple binding sites , and sometimes these sites compete for the same ligand . For example , CaM has 4 Ca2+ binding sites . Since the N and C sites act independently , the N1 and C1 sites compete for Ca2+ and an apoCaM N0C0 can become either N1C0 or N0C1 , resulting in a branching reaction scheme . Thus a decision process is needed to choose a reaction path when such a binding event occurs . To do this , consider the following two reactions Rxn1 has a forward rate constant kf1 in μm−1 s−1 and a backward rate constant kb1 in s−1 . Rxn2 has similar rate constants kf2 and kb2 . The two reactions can be viewed together as an equivalent rxn3 , which has overall kinetic rates kf3 and kb3 . According to the law of mass action , the reactions can be written as differential equations and we can obtain k f 3 = k f 1 + k f 2 ( 2 ) Smoldyn uses binding radii to implement second order reactions . If two molecules are spatially separated by a distance smaller than the corresponding binding radius , then the reaction proceeds . In Smoldyn , a special algorithm is used to calculate the binding radius , which depends on the kinetic rate constant , simulation time step and total diffusion rate of reactants . In the case of a branched binding scheme sharing common reactants , we first calculate a binding radius r3 based on kf3 . If the distance between a molecule pair is smaller than r3 , binding happens . To make a reaction choice , we generate a uniformly distributed random number from 0 to 1 . If the number falls in the range ( 0 , k f 1 k f 3 ] , then rxn1 is chosen; instead if the number falls in the range ( k f 1 k f 3 , 1 ] , we pick rxn2 . Following this approach , the network can be kept consistent with the prediction by the mass action law . We focus on the interactions among Ca2+ , CaM , and CaMKII . We first used a Ca2+-CaM network for testing to confirm that modifications to the simulator were working properly . Then we added CaMKII holoenzymes to study the reaction network in detail . We set up a box-shaped model to represent a portion of a cell body . The box has dimensions of 1 μm in width and length , as well as 2 μm in depth . The top surface of the box represents the cell membrane , reflective to all molecules . The four sides are also reflective ( effectively not different from using periodic boundaries S1 Fig ) . The bottom surface is partially absorbing to Ca2+ ions but reflective to CaM and CaMKII . This conservation of CaM molecules and CaMKII subunits guarantees a steady state initial condition . This partial absorption is a built-in feature in the original Smoldyn to resemble unbounded diffusion [58] . Voltage-gated Ca2+ channels ( presumably L-type ) are located on the top surface to provide Ca2+ influx . For simplicity , these channels are placed together at the center of the membrane . The channels open and close depending on a time-varying membrane voltage file generated from a NEURON model ( described below ) . CaMKII subunits are uniformly present at a concentration of 10 μm . They are also immobilized , presumably attached to actin [10] . Freely diffusible CaM molecules are uniformly distributed at a concentration of 5 μm . This is consistent with the notion that at the resting level , freely diffusible CaM molecules are considerably limited in number compared to their binding proteins [48 , 50 , 59] . S1 Table lists all the reactions with corresponding kinetic parameters involved in the network . Kinetic parameters are integrated from various sources as noted in S1 Table and are adjusted to satisfy microscopic reversibility . A model was constructed using NEURON [60] with a detailed morphology of a CA1 pyramidal cell and ion channel conductances to generate a voltage response at the soma to various stimulation conditions . Theta-burst stimulation ( 5 pulses at 100 Hz , repeated 5 times at 5 Hz or 10 Hz intervals ) was applied to synapses on spines taking into account a probability of release measured in experiments [61] . This stimulation activated AMPA and NMDA receptor-channels on dendritic spines causing depolarization in the dendritic tree , which propagated to the soma and initiated action potentials . Soma voltage profiles for 5 Hz and 10 Hz interval stimulation are shown in Fig 15A and 15C . This membrane voltage output from the NEURON model was used to determine Ca2+ influx through L-type Ca2+ channels in our reaction network model . Since the kinetics of L-type Ca2+ channels are relatively fast and their density is low , the membrane potential is little affected by their activity . Thus the NEURON model and the reaction network model can be safely decoupled . L-type Ca2+ channels are modeled stochastically . They open and close in response to the voltage input . The voltage-dependent opening and closing of these channels are modeled with the Hodgkin-Huxley formalism [62] . The rates of channel opening and closing are functions of membrane voltage and are calculated using variables ninf and τn , where ninf describes the steady-state voltage-dependent activation and τn is the time constant ( Fig 16A ) . Then the following set of equations are used to describe voltage-gated Ca2+ channel kinetics: r a t e o p e n ( V ) = n inf τ n ( 3 ) r a t e c l o s e ( V ) = 1 - n inf τ n ( 4 ) n inf = 1 1 + exp ( - V - V 1 / 2 s l o p e ) ( 5 ) τ n = 0 . 06 + 4 × 0 . 75 × 0 . 45 × 0 . 55 exp ( ( V - V 1 / 2 ) × 0 . 55 / s l o p e ) + exp ( - ( V - V 1 / 2 ) × 0 . 45 / s l o p e ) ( 6 ) where V1/2 and slope together describe the channel activation in response to voltage . In our model , V1/2 equals -15 mV and slope equals 8 mV . The rateopen and rateclose are used to calculate conditional probabilities to determine the state of a channel for the next time step in the following way P ( C | O ) = 1 - exp ( - r a t e c l o s e ( V ) d t ) ( 7 ) P ( O | C ) = 1 - exp ( - r a t e o p e n ( V ) d t ) ( 8 ) P ( O | O ) = 1 - P ( C | O ) ( 9 ) For each channel , at a given time , a probability is calculated based on the membrane voltage to decide whether a channel opens . If it opens , a varying number of Ca2+ ions enter . To calculate how many ions , we used the following equation [52] to obtain the unitary current for a single channel i c a = - g ( V - V s ) exp ( - ( V - V s ) R T / z F ) 1 - exp ( - ( V - V s ) R T / z F ) ( 10 ) where g is chosen as 5 pS and RT/zF equals 12 mV , and Vs is determined as described below . A current density is calculated using Goldman—Hodgkin—Katz current equation as follows , I = P V z 2 F 2 R T [ C a 2 + ] i - [ C a 2 + ] o exp ( - z V F / R T ) 1 - exp ( - z V F / R T ) ( 11 ) where extracellular [Ca2+]o equals 2 mM , intracellular [Ca2+]i equals 50 nm and a maximum membrane permeability to Ca2+ P is 0 . 241 × 10−3 cm s−1 . Since the membrane surface is 1 μm2 , the current density is converted to a total current Ighk for this area . If a total of N channels are present , in this membrane surface , the single channel current ica equals I g h k N . By fitting the total current Ighk with N × ica , we obtained an N of 16 and a Vs of −1 . 91 mV ( Fig 16B ) . From ica , the number of ions entering each open channel during one time step is calculated as i c a 2 e d t ( Fig 15B and 15D ) , where 2 is the valence of Ca2+ and e is the elementary charge . To guarantee a consistent amount of total Ca2+ for a given set of 5 Hz and 10 Hz voltage files , we generated 40 trials of Ca2+ influx files for each frequency and then selected the ones with equivalent total Ca2+ influx ( Fig 15E ) . | Ca2+ signals are commonly used by cells for various types of activities . In neurons , Ca2+ can regulate gene expression and dendritic spine enlargement , strengthen synaptic connectivity , and promote neural growth or even death . One important Ca2+ binding protein is calmodulin , which has a wide range of downstream effectors that relay and interpret Ca2+ signals . These downstream proteins have different calmodulin binding kinetics and can interact with calmodulin differently depending on Ca2+ signaling patterns and in particular , on signaling frequencies . In this study , we focus on one particular pathway that is thought to be important for learning and memory and involves calmodulin and the effector protein , Ca2+/calmodulin-dependent protein kinase II ( CaMKII ) . We have developed a novel computational method that avoids the problem of combinatorial explosion inherent with modeling this pathway , and the method allows us to study the pathway in more detail than conventional methods . Experiments have shown that CaMKII activity is sensitive to Ca2+ signal frequency and our models demonstrate how this frequency dependence relies on the amount of Ca2+ input , calmodulin availability and the Ca2+ diffusion rate . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"phosphorylation",
"single",
"channel",
"recording",
"action",
"potentials",
"medicine",
"and",
"health",
"sciences",
"membrane",
"potential",
"electrophysiology",
"reactants",
"neuroscience",
"network",
"analysis",
"membrane",
"electrophysiology",
"bioassays",
"and",
"physiological",
"analysis",
"research",
"and",
"analysis",
"methods",
"protein",
"kinase",
"signaling",
"cascade",
"computer",
"and",
"information",
"sciences",
"animal",
"cells",
"proteins",
"chemistry",
"electrophysiological",
"techniques",
"biochemistry",
"signal",
"transduction",
"cellular",
"neuroscience",
"cell",
"biology",
"post-translational",
"modification",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"chemical",
"reactions",
"cell",
"signaling",
"neurophysiology",
"signaling",
"cascades"
] | 2018 | Biophysical attributes that affect CaMKII activation deduced with a novel spatial stochastic simulation approach |
Hepatitis C virus ( HCV ) depends on liver-specific microRNA miR-122 for efficient viral RNA amplification in liver cells . This microRNA interacts with two different conserved sites at the very 5’ end of the viral RNA , enhancing miR-122 stability and promoting replication of the viral RNA . Treatment of HCV patients with oligonucleotides that sequester miR-122 resulted in profound loss of viral RNA in phase II clinical trials . However , some patients accumulated in their sera a viral RNA genome that contained a single cytidine to uridine mutation at the third nucleotide from the 5’ genomic end . It is shown here that this C3U variant indeed displayed higher rates of replication than that of wild-type HCV when miR-122 abundance is low in liver cells . However , when miR-122 abundance is high , binding of miR-122 to site 1 , most proximal to the 5’ end in the C3U variant RNA , is impaired without disrupting the binding of miR-122 to site 2 . As a result , C3U RNA displays a much lower rate of replication than wild-type mRNA when miR-122 abundance is high in the liver . This phenotype was accompanied by binding of a different set of cellular proteins to the 5’ end of the C3U RNA genome . In particular , binding of RNA helicase DDX6 was important for displaying the C3U RNA replication phenotype in liver cells . These findings suggest that sequestration of miR-122 leads to a resistance-associated mutation that has only been observed in treated patients so far , and raises the question about the function of the C3U variant in the peripheral blood .
Many cell- and virus-encoded microRNAs ( miRNAs ) regulate the expression of mRNAs by binding to the 3’ noncoding regions of target mRNAs . The binding is facilitated by an RNA-induced silencing complex ( RISC ) that mediates base-pair interactions between nucleotides two through seven in the microRNA ( seed sequences ) and their complementary sites in the target mRNA ( seed-match sequences ) . This targeting event inhibits the translation of the mRNA . In addition , deadenylation at the 3’ of the mRNA , followed by decapping and 5’ to 3’ degradation of the mRNA greatly increases its turnover [1 , 2] . The growth of hepatitis C virus ( HCV ) , a member of the flaviviridae , is dependent on the most abundant miRNA in the liver , miR-122 [3] . In the liver , miR-122 is known to be crucial for upregulation of cholesterol metabolism [4 , 5] . In the HCV genome , we discovered two binding sites for miR-122 at the 5’ proximal end of the viral RNA [3] . Occupancy of both sites by miR-122 is required for the maintenance of viral RNA abundance in infected liver cells [3 , 6–8] . Loss of HCV RNA abundance could be observed when HCV-infected cells were treated with modified oligonucleotides that have base-pair complementarity to miR-122 ( miR-122 anti-miRs ) [3] . HCV sequences termed site 1 and site 2 are seed-match sequences for miR-122 and are both absolutely conserved among all genotypes of HCV and present in all HCV gene sequences from patients deposited in gene banks . Deleterious effects of mutations in either miR-122 binding site 1 or site 2 on HCV RNA accumulation can be rescued by co-transfection of mimetic miR-122 duplexes that targeted the mutated HCV genomes [3 , 6] [7] . Thus , HCV subverts host miR-122 to increase its expression in liver cells . It is envisaged that HCV RNA genomes have evolved to bind the highly abundant liver-specific miR-122 [9] to guarantee persistence in the liver over many years . Mechanistically , miR-122 has been shown to protect the 5’ ppp-containing HCV genome from the action of 5’ RNA triphosphatase DUSP11 [10 , 11] and subsequent degradation by 5’ RNA exonucleases XRN1 [12] and XRN2 [13] . In addition , miR-122 has been shown to enhance translation initiation of the downstream located internal ribosome entry site ( IRES ) [14] [15] by a mechanism that involves in the proper folding of the IRES [16] . Further evidence suggests that miR-122 also participates in the switch of viral RNAs from the translation to the replication phase in the viral life cycle by displacing of RNA binding proteins that enhance viral mRNA translation [17] . Clinical applications of miR-122 anti-miRs first showed that sequestration of miR-122 in mice [4 , 5] and in non-human primates [18] lowered plasma and liver cholesterol abundance without any obvious adverse effects on liver function . Subsequently , Lanford and colleagues [19] tested the effects of sequestration of miR-122 after intravenous administration of unformulated locked nucleic acids ( LNAs ) -containing miR-122 anti-miRs in HCV-infected chimpanzees . Administration of LNAs caused a 500-fold reduction of viral titer in both serum and liver that persisted for several weeks . Encouraged by these results , independent studies evaluated the efficacy of two different miR-122 anti-miRs , miravirsen [20] and RG101 [21] , in patients with chronic HCV genotype 1 infections . Treated patients showed a 10–1000 fold reduction in viral serum titers . While the majority of the patients cleared the infection , several subjects in both studies experienced a virologic rebound several weeks after anti-miR treatment [21 , 22] . Sequence analysis of viral RNAs obtained from serum of several of those patients revealed a resistance-associated substitution of a uridine for a cytidine nucleotide 3 ( C3U ) [20 , 21] . The goal of the present study was to examine whether the C3U mutation truly confers resistance to miR-122 anti-miRs and to determine the mechanism of any such escape , to provide a basis for investigation whether these variants will compromise clinical treatments .
Sera from five out of six HCV patients whose viral titers rebounded following miR-122 anti-miR treatment contained HCV genomes with a C3U mutation . Two patients contained a C3U/C27U or C3U/G28A double mutation , respectively [21] . These findings suggested that the C3U RNA variant may replicate with higher efficiency than wild-type RNA in liver cells when miR-122 is sequestrated , and thus represent a drug-resistant variant . To test this hypothesis , in vitro-synthesized wild-type and C3U Gaussia luciferase-expressing H77S . 3/GLuc viral RNAs ( Fig 1A ) [23] were transfected into Huh7 . 5 cells that had previously been treated with non-122 targeting control anti-miR-106b-LNA ( locked nucleic acids ) , anti-miR-122-LNA miravirsen [22] , or anti-miR-122 RG1649 ( the active metabolite of RG101 ) [21] , and HCV RNA replication was monitored . Anti-miR-122 inhibitors miravirsen and RG101 were used , because both compounds were shown to lower HCV abundances in clinical trials [21 , 22] . Fig 1B shows effects of miR-122 LNAs and RG1649 RNA abundances of wild-type and C3U RNAs , as reflected by the accumulation of luciferase encoded in the viral genome . GLuc activity was normalized to GLuc activity measured in the presence of control miR-106b LNA . This was done , because the overall C3U RNA abundances were lower than WT in control-LNA transfected cells ( see data without normalization in S1 Fig ) . Wild-type HCV RNA accumulation was reduced relative to C3U HCV in miR-122 cells , which were treated with miR-122 LNA or RG1649 after 24 and 48 hours ( Fig 1B , S1 Fig ) . These findings argue that reduced abundance of miR-122 is less inhibitory to the growth of C3U HCV , thus rationalizing the emergence of the C3U genome in anti-miR-treated patients . To determine whether the C3U mutation acts by increasing the abundance of HCV RNA following miravirsen-mediated sequestration of miR-122 , the accumulation of newly synthesized viral RNA was measured by labeling with 5-ethynyl uridine ( 5-EU ) , an analog of uridine . Total RNA was isolated and conjugated to biotin in a copper-catalyzed reaction . Newly synthesized RNA was subsequently captured using magnetic streptavidin beads and quantitated by qRT-PCR using HCV-specific primers . While the abundance of wild-type RNA synthesis was reduced by ~80% in miravirsen-treated samples , only a 30% reduction was observed in C3U variant samples following miR-122 depletion compared to control miR-106b-LNA treatment ( Fig 1C ) . To determine whether the binding of miR-122 molecules to the C3U HCV genome plays any role at all in its replication , infectious RNAs were transfected into miR-122 knock-out Huh7 . 5 cells [24] that were pre-transfected with control duplex miR-106b or native miR-122 duplexes . Exogenously added control duplex failed to rescue the greatly reduced abundance of either wild-type or C3U RNA . Supplementation of miR-122 duplexes enhanced the amplification of both wild-type and mutant viral RNAs , albeit C3U to a significantly lesser extent than wild-type RNA ( Fig 1D ) . These results argue that C3U RNA requires at least a small amount of miR-122 . It is known that miR-122 has different affinities to site 1 and 2 in HCV RNA [25] . Thus , different affinities for miR-122 at site 1 or site 2 in both C3U and wild-type RNAs could explain the observed phenotype . To test this hypothesis , C3U-complementary duplex miR-122 molecules ( referred to as “mut-miR-122” ) were employed to monitor C3U RNA replication phenotypes in cells that expressed no wild-type version of miR-122 . Again , pre-treatment with miR-106b RNA mimetics did not rescue replication of wild-type or C3U viral RNAs ( Fig 1E ) . As was also expected , supplementation of mut-miR-122 failed to efficiently support wild-type viral RNA accumulation . However , supplementation of C3U with mut-miR-122 efficiently enhanced C3U RNA abundance . That both wild-type and mutant miR-122 can rescue the C3U genome to some extent , but neither as completely as native miR-122 rescues the wild-type genome , suggests that both wild-type and mutant miR-122 might bind to the C3U genome . To substantiate this finding further , miR-122 knock-out cells were pre-transfected with wild-type and mut-miR-122 duplexes alone or in combination and abundances of transfected infectious RNAs were monitored over time . Co-transfection of both wild-type and mut-miR-122 had minimal additive effect on the accumulation of wild-type HCV RNA compared to transfection of wild-type miR-122 alone ( Fig 1F ) . In sharp contrast , accumulation of the C3U variant was significantly enhanced in the presence of combined wild-type and mut-miR-122 mimetics compared to the presence of either alone ( Fig 1F ) . Analysis of viral infections by Northern blot analyses ( Fig 1G ) revealed the same phenotypes . These data show that a single C3U nucleotide substitution in the 5’ noncoding region of the HCV genomic RNA , where miR-122 occupies binding site 1 , results in increased resistance to miR-122 sequestration . Because HCV RNA accumulation in a C3U background could be effectively rescued only by a combination of native and mutant-miR-122 mimetics , sites 1 and 2 in C3U RNA are likely occupied by mutant and wild-type miR-122 , respectively , and occupancy of both is required for its optimal function . This situation is not likely to be obtained under any circumstance in liver cells , leading us to question whether the C3U mutation might bring about a large fitness cost to viruses that contain it . To investigate the impact of the C3U HCV mutation on viral fitness in liver cells when miR-122 abundance is normal , in vitro-synthesized wild-type and C3U H77 . S3 infectious RNAs [23] were electroporated into Huh7 . 5 liver cells [26] . At different times after electroporation , supernatants were collected and extracellular HCV titers were determined by fluorescent focus forming units ( FFU ) measurements after infection of Huh7 . 5 cells ( Fig 2A ) . In contrast to wild-type virus-infected cells , which reached a maximum of 103 FFU/ml , virus yield was ten-fold lower in cells infected with C3U HCV virus ( Fig 2A ) . Next , viral spread was examined by infecting naïve Huh7 . 5 cells with wild-type and mutant viruses . Supernatants were collected three days post-infection and quantified by FFU assays . Similarly , to the data shown in Fig 2A , viral production ( Fig 2B ) and RNA accumulation ( Fig 2C ) after these multiple cycles of infection were ten-fold lower in C3U HCV-infected compared to wild-type HCV-infected cells . Thus , the C3U mutation results in a significant reduction in mutant viral RNA and virion abundances at a step that does not involve viral entry . To measure the effects of the C3U mutation on viral mRNA translation and RNA replication , in vitro-synthesized wild-type and C3U Gaussia luciferase-expressing H77S . 3/GLuc viral RNAs ( Fig 1A ) were transfected into Huh7 . 5 cells and luciferase activities were examined at different times after transfection . Replication of the C3U viral RNA was ten-fold lower than that of wild-type RNA between 48 and 72 hours after transfection ( Fig 3A ) . Similar to the phenotype observed with luciferase activities , Northern blot analysis revealed a decrease of C3U RNA abundance compared to wild-type viral RNA at three days after RNA transfection ( Fig 3B ) . Both wild-type and mutant RNAs were sensitive to the HCV RNA polymerase NS5B inhibitor sofosbuvir ( Fig 3B ) , demonstrating that authentic viral RNAs were inspected in the Northern blots and that the C3U variant can be eliminated by sofosbuvir . The effect of the C3U mutation was not genotype-specific , because insertion of the C3U mutation into the J6/JFH1 RLuc genotype 2a infectious background also resulted in significant reduction in RNA replication ( S2 Fig ) . In addition , the effects of a G28A mutation on RNA abundances of transfected , chimeric C3-G28A and C3U-G28A were examined . This mutation , which is predicted to engage in base-pair interaction of nucleotide number one of miR-122 at site 2 , was detected in Huh7 cells during mir-122 sequestration [27–29] and in the blood of HCV-positive patients [29] . S3 Fig shows that the G28A mutation had no effect on the replication of HCV RNAs lacking a C3U mutation . This finding agrees with Israelow et al . [27] who reported that G28A virus replicates with similar efficiency than wild-type virus in the presence of miR-122 , but more efficiently than wild-type virus when miR-122 is limiting . However , chimeric C3U-G28A RNAs replicated with similar efficiency as C3U RNAs ( S3 Fig ) , arguing that the observed phenotype depends on the single C3U nucleotide change . Finally , the abundance of viral core protein was also reduced in C3U RNA-transfected cells at 72 hours compared to wild-type RNA ( Fig 3C ) , suggesting defects in protein synthesis , RNA replication or RNA stability in C3U HCV . To examine in detail whether reduced translation or replication contributed to low viral RNA abundances in C3U RNA-transfected cells , we studied the expression of chimeric RNAs , containing GDD-to-AAG mutations in the active site of viral RNA-dependent polymerases NS5B in both wild-type and C3U RNAs . Fig 3D shows that translation , RNA stability , or both of the C3U variant was similar to wild-type at all time points measured , arguing that the growth defect of C3U HCV is predominantly at the replication step . To examine effects of the C3U mutation on HCV translation by a different approach , polysomal mRNAs were analyzed from HCV RNA-transfected cells after separating cell lysates in sucrose gradients . The distribution of full-length HCV RNA in each individual fraction was analyzed by Northern blot analysis ( S4A Fig ) . HCV RNA was distributed in polysomal fractions 9 through 13 in both wild-type- and C3U-transfected samples ( Fig 4A and 4B ) . These data suggest that the observed reduced intracellular RNA abundance of C3U HCV is primarily due to a defect in RNA replication or stability . To investigate whether the significant reduction in C3U RNA abundance in the presence of miR-122 is a result of diminished RNA stability , Huh7 . 5 cells were transfected with HCV RNA for three days and subsequently treated with the nucleoside analog MK-0608 to block viral RNA synthesis . Total RNA was isolated at the indicated time points following drug treatment and the rate of HCV RNA decay was examined by Northern blot analysis . Data showed that wild-type HCV RNA was degraded at a slightly slower rate than C3U mutant HCV RNA ( Fig 4A ) . The approximate half-life of wild-type RNA is 3 . 2 hours compared to 2 . 6 hours for the C3U variant ( Fig 4A and 4B ) . Although modest , the reduction in RNA stability was statistically significant ( Fig 4C ) and could potentially impact the fitness of the C3U virus . Next , the effect of the C3U mutation on the rates of HCV RNA replication were evaluated by labeling cells with 5-EU three days post viral RNA transfection . Total RNA was isolated from cells pulsed for four and seven hours , captured and quantified as previously described . 5-EU-labeling for up to seven hours revealed a significant ~four-fold increase in the accumulation of newly-synthesized wild-type RNA compared to C3U variant RNA ( Fig 4D ) . This impaired rate of RNA synthesis coupled with a modest , but significant decrease in RNA stability are likely sufficient to explain the reduced fitness of the C3U variant in the liver . One explanation for the reduced abundance of C3U viral RNA during low abundance of miR-122 is that reduced binding of miR-122 at site 1 could render the RNA susceptible to attack by 5’ RNA triphosphatase DUSP11 [10] and subsequently to 5’ -3’ exonucleases . Therefore , we investigated whether the low abundance of HCV C3U RNA variant of type 1a could be rescued by reducing the abundance of XRN1 by siRNA-mediated gene silencing prior to HCV RNA transfection . The effect of XRN1 depletion on HCV replication was assessed by luciferase activity and Northern blot analyses of chimeric RNAs . Robust XRN1 depletion was observed in mock- and HCV-infected samples at 72 hours after RNA transfection ( Fig 5B , top panel ) . Depletion of XRN1 significantly stimulated the abundance of both wild-type and C3U HCV RNA at 48 and 72 hours after viral RNA transfection ( Fig 5A ) . Similarly , increased accumulation of wild-type and C3U mutant HCV RNA was detected by Northern blot analysis in siXRN1-treated samples ( Fig 5C ) . The effects of XRN1 depletion on wild-type HCV replication are consistent with previous observations using a similar HCV cell culture system [12] . Quantification indicated that wild-type and mutant viral RNA abundances significantly increased following XRN1 depletion by the same extent ( Fig 5D ) . These data suggest that both wild-type and mutant HCV RNA are similarly susceptible to XRN1 attack and that the defect in C3U RNA replication is not increased degradation due to lack of miR-122 binding at site 1 . To explore the possibility that differences in miR-122 occupancy in the C3U variant could alter its susceptibility to XRN1 attack further , we tested the stability of the replication defective ( GDD-to-AAG ) wild-type and mutant HCV RNA synthesized with and without a 5’ non-methylated guanosine cap analog after transfection into Huh7 . 5 cells . Viral RNAs containing a cap structure or a 5’ terminal ppp-N moiety in C3U HCV displayed similar stabilities to that of wild-type RNA across multiple infection time points ( Fig 5E ) . Data from both lines of investigation suggest that XRN1 is unlikely to explain the observed low C3U RNA abundance in Huh7 liver cells . The interaction between the 5’ noncoding region of HCV and miR-122 at sites 1 and 2 ( Fig 6A ) extends beyond the canonical base pairing between seed and seed-match sequences [6 , 7] . Mutational analysis has shown that base pairing between HCV and miR-122 at nucleotides 1–4 produces a 3’ overhang in miR-122 that shields the viral genome from subsequent exoribonuclease attack [7] . Also , previous observations indicate that the 5’ terminus of HCV forms a stable , trimolecular complex through interactions with the miR-122 at site 1 and 2 [25] . Adopting Mortimer’s and Doudna’s electrophoretic mobility shift assays ( EMSA ) approach [25] , we investigated whether the C3U substitution disrupted the formation of the HCV:miR-122 heterotrimeric complex in solution . First , to confirm that the 5’ terminus of HCV genotype 1a ( nucleotides 1–47 ) directly binds and forms an oligomeric complex with miR-122 , wild-type HCV RNA was incubated with increasing amounts of miR-122 and the resulting complexes were resolved using EMSA . Fig 6B shows that incubation of HCV RNA with different molar equivalents of miR-122 resulted in the gradual formation of a heterotrimeric complex that migrated more slowly than free HCV RNA ( Fig 6B , lane 0 ) . Incubation of HCV RNA with the neuron-specific miRNA , miR-124 , showed no complex formation , demonstrating that the interaction between HCV and miR-122 is specific . Next , we investigated effects of the C3U mutation on the formation of the heterotrimeric complexes . Fig 6C shows that the C3U-containing RNA required a much higher concentration of miR-122 to form any heterotrimeric complex at all . To examine whether mut-miR-122 could rescue the formation of heterotrimeric complexes , wild-type or C3U RNAs were incubated with mut-miR-122 and complexes resolved by EMSA . Incubation of wild-type HCV RNA with mut-miR-122 resulted in diminished formation of the trimeric complex ( Fig 6D ) . In contrast , formation of a trimolecular complex was observed after incubation of C3U RNA with mut-miR-122 ( Fig 6D ) . These result show that mut-miR-122 can bind to both site 1 and 2 in C3U RNA , but only to high-affinity site 2 in wild-type RNA . Binding of mut-miR-122 to its target site is mediated by its interaction with the HCV RNA that extends beyond the seed-seed match interactions . To confirm that C3U RNA and miR-122 interact at binding site 2 , the seed sequence of miRNA binding site 1 was mutated in both wild-type and C3U RNAs so that only site 2 was available for binding . Both RNAs did allow the formation of dimeric , but not trimeric complexes ( Fig 6E ) , arguing that miR-122 can bind to site 2 in C3U RNA independently of site 1 . These data suggest that a single C3U mutation outside the seed-match region of site 1 results in reduced binding of miR-122 to site 1 but allows binding of miR-122 to site 2 , culminating in the formation of weak , unstable trimeric complexes . Indeed , it has been shown that miR-122 binding suppresses the folding of the 5’ end of HCV RNA into more energetically favored structures [16] . How does the single C3U mutation contribute to enhanced RNA accumulation when miR-122 abundance is low ? It is possible that the C3U mutations introduces structural changes at the 5’ end of the viral positive strand or the 3’ end of the viral negative strand that contribute to the C3U variant phenotype . This is however unlikely , because of in silico prediction and mutational analyses indicated that the C3U in the viral positive or the complementary negative strand RNA is not part of a secondary structure [16 , 30–32] . Alternatively , the C3U could recruit a distinct set of proteins that aids in the amplification of the C3U RNA when miR-122 abundances are low . To examine this possibility , we analyzed the binding of proteins to wildtype and mutant RNA genomes in RNA-transfected cells . First , chimeric RNAs were generated in which the loop sequences in SL1 were altered to bind a fusion lambda-N-BirA* ligase protein ( Fig 7A ) [33] . As expected [31] , insertions at this position in SL1 were tolerated and gave rise to replicating RNAs ( WT+BoxB , C3U+BoxB ) after transfection of in vitro synthesized RNAs into cells . Western blot analyses showed that C3U+BoxB RNAs produced slightly less amount of viral proteins than WT+BoxB ( Fig 7B ) . Next , biotin was added to transfected cells and biotinylated proteins were isolated after addition of streptavidin beads [33] . Biotinylated protein were then identified by mass spectrometry . Fig 7C lists the ten proteins that bound specifically to the C3U genome . We were intrigued that DDX6 was biotinylated in C3U-infected cells , because DDX6 has been implicated in HCV RNA amplification [34–36] . Thus , DDX6 was depleted by CRISPR/Cas9 and effects on WT and C3U RNA abundances were monitored . Fig 7D shows that the kinetics of RNA replication of WT and C3U RNAs were the same in DDX6-depleted cells . This phenotype was reversed after add-back of DDX6 , arguing that DDX6 modulates C3U HCV amplification independently of mir-122 binding at site 1 . Investigation of the other nine proteins in the life cycle of HCV is under scrutiny . The pathways in which some of these proteins are predicted to operate suggest roles for some of these proteins in lipid metabolism and intracellular compartmentalization during viral infection . Specifically , SHC1 has been identified to interact with HCV receptor CD81 [37] and siRNA-mediated depletion of SHC1 inhibits HCV entry [38] . Proteome-wide interaction studies has revealed that GOLGA2 interacts with NS5A [39] . Thus , SHC1 and GOLGA2 may modulate entry/egress or replication of C3U RNAs , respectively .
Traditional antivirals have overwhelmingly focused on targeting virus-encoded proteins that are essential components of the viral life cycle using small molecule inhibitors . Due to the high mutation rates of RNA-dependent viral RNA polymerases , non-lethal mutations in the viral genome can result in drug-resistance phenotypes to these direct-acting antivirals . On the other hand , targeting the expression or function of host cellular factors that are vital for viral growth is predicted to result in a higher genetic barrier to resistance than direct-acting antivirals . The liver-expressed microRNA miR-122 was recently targeted in a miRNA-based anti-HCV therapeutic strategy . Pharmacological inhibition of miR-122 using modified anti-sense oligonucleotides ( anti-miRs ) significantly lowered the viral load in the serum of HCV-infected patients [20 , 21] . A C3U resistance-associated mutation in the miR-122 binding site 1 of the HCV 5’ noncoding region was observed in sera from patients that experienced virologic rebound several weeks following treatment with miR-122 anti-miRs . However , late rebounders did not display the C3U mutation . In follow-up studies with 18 patients , 6 of them displayed the C3U mutation at the time of rebound , and the mutation disappeared in 3 of the 6 patients before they underwent standard-of-care treatment [21] . This led us to investigate whether this single C3U substitution conferred true resistance to the inhibitory effects of anti-miRs and , if so , by what mechanism viral replication could occur at lowered abundance of miR-122 . While patient-derived virus did not grow in Huh7 cells , using a cell culture-adapted HCV genotype 1a infectious system we found that the C3U mutation , during sequestration of miR-122 , showed an increased ability to replicate the C3U RNA compared to wild-type RNA . However , this came at the expense of significantly diminished viral fitness in liver cells with normal abundance of miR-122 . Specifically , RNA stability and rates of RNA replication of the C3U RNA are impaired in cells that express normal amounts of miR-122 . Curiously , the C3U mutation has not been observed during selection experiments in Huh7 cells [20 , 27 , 29] and no naturally occurring C3U HCV genotypes have been deposited into Gene bank . Ono et al . observed a viral G28A mutation that arose in the serum and peripheral blood monocytes of type 2-infected patients [29] , however , the C3U variant has only been observed in patient serum after several weeks in anti-miR treatment , and only transiently . Whether the extrahepatic C3U genomes reflect growth of the C3U variant in the liver of re-bounding patients is not known , because liver biopsies are not available . However , the poor fitness of the C3U variants suggests that the C3U variant may contribute little to HCV-induced liver pathogenesis . This would be similar to the situation observed with sofosbuvir , i . e . during treatment of patients with this HCV polymerase inhibitor , selection is observed for variants whose fitness in the absence of the drug is so low that they do not persist [40] What is the mechanism by which the C3U variant can replicate in the presence of low amounts of miR-122 ? Using genetic and biochemical approaches , we discovered that miR-122 binds only at site 2 in the C3U RNA . Using EMSA , Mortimer and Doudna showed that miR-122 binds at miRNA binding site 2 with an affinity 50-fold greater than at site 1 [25] , explaining the continued binding of miR-122 at site 2 in C3U even when miR-122 abundance is low . Why can the C3U variant accumulate in extra-hepatic cells where miR-122 abundance is very low ? It is possible that novel inter- or intramolecular RNA-RNA interactions lead to stabilization of the viral genomic RNA that contains the C3U mutation in the absence of miR-122 . Novel RNA-protein interactions could also take place at the 5’ end of C3U RNAs . Indeed , cell-based biotinylation studies have revealed a set of proteins that specifically interact with the C3U genome . It will be important to identify which molecular interactions aid in the evolvement of the C3U genome , and whether the C3U variant engages with a similar set of proteins in extracellular reservoirs during miR-122 anti-miR treatment .
Huh7 . 5 Sec14L2 and Huh7 . 5 ΔmiR-122 cells ( generous gifts from Charles Rice , Rockefeller University , New York ) were maintained in DMEM supplemented with 10% FBS , 1% non-essential amino acids , and 2 mM L-glutamine ( Gibco ) . Plasmids H77S . 3 and H77S . 3/GLuc genotype 1a [23] ( generous gifts from Stan Lemon , University of North Carolina , North Carolina ) were transcribed using the T7 MEGAscript kit ( Ambion ) , according to the manufacturer’s instructions . Huh7 . 5 and Huh7 . 5 ΔmiR-122 cells , plated in 12-well dishes , were transfected with 1 μg of in vitro-transcribed ( IVT ) H77S . 3/GLuc RNA using the TransIT mRNA transfection kit ( Mirus Bio LLC ) according to the manufacturer’s protocol . After 6 hours of incubation at 37°C , supernatants were removed for GLuc assay and replaced with fresh media . Supernatants were subsequently collected at 24 hours intervals . Supernatants were stored at -20°C before luciferase assay . Infectious titers were determined by a fluorescent focus forming units ( FFU ) assay . Huh7 . 5 cells ( 3X104 ) were seeded in a 48-well plate and incubated overnight . Serial dilutions of virus stock were added to cells and incubated for six hours at 37°C . The diluted virus supernatant was removed and replaced with fresh medium . Media in each plate were exchanged daily . At day three post-infection , cells were washed once with PBS and fixed with cold methanol/acetone ( 1:1 ) . HCV infection was analyzed by using a mouse monoclonal antibody directed against HCV core ( Abcam ) at 1:300 dilution in 1% fish gelatin/PBS at room temperature for two hours and an AlexFluor488-conjugated goat anti-mouse antibody ( Invitrogen ) at 1:200 dilution at room temperature for 1 hour . The fluorescent focus forming units were counted using a fluorescence microscope . Antisense miR-122 locked nucleic acid ( LNA ) and RG101 have been previously described [18 , 21] . 3 , 000 Huh7 . 5 cells were seeded in quadruplicates in a 96-well plate one day prior to infection . The following day , cells were infected with wild-type and C3U virus at an MOI of 0 . 005 . Six hours post infection , media was aspirated and replaced with fresh media . Media was replaced with fresh media every day . At the indicated time post-infection , cells were lysed using the Power SYBR Green Cell-to-Ct kit ( Ambion ) , and RNAs were quantified on a Bio-rad CFX Connect quantitative-PCR ( qPCR ) machine and Ct values were normalized to internal control 18S ribosomal RNA expression values . The primer sequences for the HCV H77 . S3 genotype were: forward 5’- CCAACTGATCAACACCAACG -3’ and reverse 5’- AGCTGGTCAACCTCTCAGGA -3’ . The primer sequences for human 18S rRNA gene were: forward 5’- AGAAACGGCTACCACATCCA -3’ and reverse 5’-CACCAGACTTGCCCTCCA -3’ . Huh7 . 5 ΔmiR-122 cells were plated in 12-well dishes and transfected with annealed native miR-122 and mut-miR-122 duplexes ( 50nM ) alone or in combination using Dharmafect I ( Dharmacon ) following the manufacturer’s instruction . The following day , cells were transfected with 1 μg H77 GLuc IVT RNA as stated above . Twenty-four hours post H77 RNA transfection , cells were re-supplemented with 50nM of native or mut-miR-122 . Supernatants from transfected cells were collected at the indicated time points . The following oligonucleotides were used in this study: native miR-122 , 5’- UGGAGUGUGACAAUGGUGUUUGU-3’; mut-miR-122 , 5’-UGGAGUGUGACAAUAGUGUUUGU-3’; hsa-miR-106b , 5’-UAAAGUGCUGACAGUGCAGAU-3’ . Huh7 . 5 cells , plated in 60mm dishes , were transfected with 2 μg of H77 GLuc IVT RNA as stated above . Three days post-transfection , media was removed and replaced with media containing MK-0608 at a final concentration of 25 μM . RNA was collected at the indicated time points and HCV RNA was analyzed by Northern blot analysis . RNA half-lives were calculated from three independent experiments using GraphPad Prism . Fifty micrograms of IVT H77 WT GLuc-AAG and H77 C3U GLuc-AAG RNAs were capped using the ScriptGap m7G Capping System ( Cellscript C-SCC30610 ) according to the manufacturer’s protocol . RNA was extracted using the RNeasy Mini kit ( QIAGEN ) following the manufacturer’s instructions . Purified RNA was transfected into Huh7 . 5 cells and samples were collected at the indicated time points . Following RNA transfection , secreted GLuc activity was measured in 20 μl aliquots from supernatants using the Luciferase Assay System ( Promega ) , according to the manufacturer’s instructions . The luminescent readings were taken using Glomax 20/20 luminometer using a 10 second integration time . Virus production was done as previously described [41] Nucleotide substitutions to pH77S . 3 and pH77S . 3/GLuc were completed using the QuickChange Site Directed Mutagenesis Kit ( Agilent ) , according to the manufacturer’s protocol . For the C3U mutation , the following primers were utilized: 5’-ACGACTCACTATAGCTAGCCCCCTGATGGG-3’ and 5’- CCCATCAGGGGGCTAGCTATAGTGAGTCGT-3’ . To introduce the lethal mutation to the NS5B polymerase ( GDD>AAG ) , the following primer were used: 5’-CCATGCTCGTGTGTGCCGCCGGCTTAGTCGTTATCTG-3’ and 5’-CAGATAACGACTAAGCCGGCGGCACACACGAGCATGG-3’ . For XRN1-mediated depletion , the following RNAs were utilized: sense 5’-GAGGUGUUGUUUCGCAUUAUUdTdT-3’ and antisense 5’-AATAATGCGAAACAACACCTCdTdT-3’ . Sense and antisense strands were combined in 1X siRNA Buffer ( Dharmacon ) at a final concentration of 20 μM , denatured for 2 minutes at 98°C , and annealed for 1 hour at 37°C . As a negative control siRNA , the following oligonucleotides were used: sense 5′- GAUCAUACGUGCGAUCAGAdTdT-3’ and antisense 5’-UCUGAUCGCACGUAUGAUCdTdT-3’ . Huh7 . 5 cells were seeded overnight in 12 well plates . The following day , 50 nM of siRNA duplexes were transfected using Dharmafect I ( Dharmacon ) . Following overnight incubation at 37°C , cells were transfected with H77S . 3/GLuc , supernatants were collected at the indicated time points and total RNA was extracted 3 days post-transfection . Depletion of XRN1 was assessed by western blot analysis . Cells transfected for 3 days were washed with PBS once and lysed in RIPA buffer ( 50 mM Tris ( pH 8 . 0 ) , 150 mM NaCl , 0 . 5% sodium deoxycholate , 0 . 1% SDS , and 1% Triton X-100 ) in the presence of cOmplete™ , EDTA-free protease inhibitor cocktail ( Roche ) for 30 min on ice . Lysates were clarified to remove the non-soluble fraction by centrifugation at 14 , 000 rpm for 10 min at 4°C . Protein concentrations were measured by Bradford Assay and 50 μg of total protein lysate was mixed with 5X protein sample buffer containing reducing agent . Samples were separated on a 10% SDS-polyacrylamide gel , transferred to a PVDF membrane ( Millipore ) , and blocked with 5% non-fat milk in PBS-T . The following primary antibodies were used to probe the membranes: anti-Core ( C7-50 ) ( Abcam , ab2740 ) , anti-Actin ( Sigma ) , and anti-XRN1 ( Bethyl Lab A300-443A . ) Immunoblots were developed using Pierce ECL Western Blot Substrate ( Thermo Fisher ) following the manufacturer’s suggested instructions . Total RNA was extracted using the RNeasy Mini kit ( QIAGEN ) . For Northern blot analysis of HCV and actin RNA , 10 μg of total RNA in RNA loading buffer ( 32% formamide , 1X MOPS-EDTA-Sodium acetate ( MESA , Sigma ) , and 4 . 4% formaldehyde ) was denatured for 10 minutes at 65°C , separated in a 1% agarose gel containing 1X MESA and 3 . 7% formaldehyde , transferred and UV-crosslinked to a Zeta-probe membrane ( Bio-Rad ) overnight . The membrane was blocked and hybridized using ExpressHyb hybridization buffer ( Clontech ) and α-32P dATP-RadPrime DNA labeled probes . Nascent HCV RNA transcripts were quantified using the Click-iT Nascent RNA Capture Kit ( Thermo Fisher ) following the manufacturer’s instructions . Complementary DNA was synthesized using Superscript III reverse transcriptase ( Thermo Fisher ) following the manufacturer’s protocol . Newly synthesized HCV transcripts were quantified using Power SYBR Green PCR Master Mix ( Thermo Fisher ) . HCV transcript abundances were determined by comparisons to standard curves obtained from in vitro transcribed H77 . S3 RNA . The primer sequences for the HCV H77 . S3 genotype were: forward 5’- CGTGTGCTGCTCAATGTCTT -3’ and reverse 5’- AATGGCTGTCCAGAACTTGC -3’ . Newly C3U HCV synthesized RNA abundances were represented as fold change relative to EU labeling of wild-type HCV RNA . Wild-type and mutant HCV RNA oligonucleotides corresponding to H77S . 3 domain I ( nucleotides 1–47 ) were synthesized ( Stanford PAN facility ) . HCV domain I RNA ( 5 μ ) was mixed with 100 mM HEPES ( pH 7 . 5 ) , 100 mM KCl , and 5mM MgCl2 in a 5 μl reaction . Reactions were heated to 98°C for 3 min , cooled to 35°C for 5 min , and incubated with miR-122 oligos in molar ratios of 0 . 5 , 1 , 2 , 3 , and 4 at 37°C for 30 min . For anti-miR experiments , 1 μl of miR-122 antisense oligonucleotide was added to each reaction and incubated for an additional 10 minutes . Five μl of RNA loading dye ( 30% glycerol , 0 . 5X TBE and 6mM MgCl2 ) was added and samples were separated in a non-denaturing gel ( 12% 29:1 acrylamide:bisacrylamide , 0 . 5X TBE and 6mM MgCl2 ) at 4°C for 2 . 5 hours at 20 Amps . Gel was stained with SYBR Gold ( Invitrogen ) to visualize the HCV RNA-miR-122 interactions . Wildtype and C3U H77 . S3 IRES stem-loop I was mutated to the BoxB RNA motif sequence 5’-GCCCTGAAAAAGGGC-3’ [33] using the QuickChange Site Directed Mutagenesis Kit ( Agilent ) , according to the manufacturer’s protocol . Huh7 . 5 cells were then transfected with 10 μg wildtype , wildtype-BoxB , C3U , or C3U-BoxB in-vitro synthesized RNA using the TransIT mRNA transfection kit ( Mirus Bio LLC ) according to the manufacturer’s protocol . Six hours after incubation at 37°C , media was removed and replaced with fresh media . After 48 hour incubation , 4 μg of BirA* RaPID plasmid [33] was transfected into H77 . S3-transfected cells using Lipofectamine 2000 following the manufacturer’s instructions . Two days post-transfection , media was aspirated and replaced with 50μM biotin ( Sigma-Aldrich ) labeling media diluted with DMEM ( Thermo-Fisher Scientific ) , and incubated for 16 hours at 37°C . Media was then aspirated and cells were washed with cold 1X PBS . After addition of 0 . 6 ml of cell lysis buffer ( 0 . 5M NaCl , 50mM Tris-HCl , 0 . 2% SDS , 1mM DTT ) , lysates were scraped and transferred into individual Eppendorf tubes and placed on ice . Next , 52 μl of Triton-X-100 ( Sigma-Aldrich ) was mixed with the lysates , followed by addition of 0 . 65 ml of wash buffer 4 ( 50mM Tris-HCl ) . Samples were subsequently sonicated at intervals of 10 seconds until lysate was clear . In between sonication cycles , samples were cooled on ice for 10 seconds . Insoluble material was removed by sedimenting samples at 15 , 000 g at 4°C for 10 minutes . Unincorporated biotin was removed by centrifuging the cleared supernatant in Macrosep Advance Spin Filter 3K MWCO tubes ( VWR ) and centrifuged at 1 , 500 g at 4°C for 1 hour . Supernants were transferred to an Eppendorf tube and protein concentration was estimated using the Bradford assay . To retrieve biotin-labelled proteins , MyOne C1 Streptavidin beads ( Life Technologies ) were added overnight at 4°C in a rotator . The following day , tubes were placed on a magnetic stand and supernants aspirated . To each sample , 1 ml of wash buffer 1 ( 2% SDS ) was added and rotated for 5 minutes at room temp . This washing step was repeated two times . Next , supernatants were aspirated and 1 ml wash buffer 2 ( 0 . 1% Na-DOC , 1% Triton X-100 , 0 . 5M NaCl , 50mM HEPES pH 7 . 5 , 1mM EDTA ) was added and rotated for 5 minutes at room temperature . Magnetic beads were washed using 1 ml wash buffer 3 ( 0 . 5% Na-DOC , 250μM LiCl , 0 . 5% NP-40 , 10mM Tris-HCl , 1mM EDTA ) , followed by a wash with 1ml of buffer 4 ( 50mM Tris-HCl ) . All residual supernatant was removed and washed beads were directly submitted to the Stanford Mass Spectrometry facility for protein identification , and data analysis was performed as described by Ramanathan et al . [33] . Protein IDs can be seen in the attached Microsoft Excel file . Statistical analyses were performed with Prism 5 ( GraphPad ) . A two-tailed paired Student’s t-test was employed to assess significant differences between two groups . Error bars represent standard error of the mean . | With the advent of potent direct-acting antivirals ( DAA ) , hepatitis C virus ( HCV ) can now be eliminated from the majority of patients , using multidrug therapy with DAAs . However , such DAAs are not available for the treatment of most RNA virus infections . The main problem is the high error rate by which RNA-dependent RNA polymerases copy viral RNA genomes , allowing the selection of mutations that are resistant to DAAs . Thus , targeting host-encoded functions that are essential for growth of the virus but not for the host cell offer promising , novel approaches . HCV needs host-encoded microRNA miR-122 for its viral RNA replication in the liver , and depletion of miR-122 in HCV patients results in loss of viral RNA . This study shows that a single-nucleotide mutation in HCV allows viral RNA amplification when miR-122 abundances are low , concomitant with changes in its tropism . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"transfection",
"microbial",
"mutation",
"nucleic",
"acid",
"synthesis",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"hepacivirus",
"pathogens",
"microbiology",
"rna",
"stability",
"viruses",
"rna",
"viruses",
"rna",
"isolation",
"molecular",
"biology",
"techniques",
"rna",
"synthesis",
"chemical",
"synthesis",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"medical",
"microbiology",
"gene",
"expression",
"microbial",
"pathogens",
"hepatitis",
"c",
"virus",
"hepatitis",
"viruses",
"viral",
"replication",
"molecular",
"biology",
"biosynthetic",
"techniques",
"biochemistry",
"rna",
"biomolecular",
"isolation",
"nucleic",
"acids",
"flaviviruses",
"virology",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna",
"organisms"
] | 2019 | Impact of a patient-derived hepatitis C viral RNA genome with a mutated microRNA binding site |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.